From 0e210d298b27a4cffac931e79a3d7f52fad4fa1d Mon Sep 17 00:00:00 2001 From: YangJX <7196390@qq.com> Date: Mon, 6 Jan 2025 14:25:42 +0800 Subject: [PATCH] Add files via upload add code --- LICENSE | 875 ++++++-- README.md | 74 +- Rope.bat | 3 + Rope.py | 5 + __init__.py | 958 +++++++++ data.json | 1 + logger.py | 36 + mklink | 0 rope/Coordinator.py | 175 ++ rope/Dicts.py | 413 ++++ rope/GUI.py | 1901 +++++++++++++++++ rope/GUIElements.py | 1247 +++++++++++ rope/Models.py | 1278 +++++++++++ rope/Styles.py | 286 +++ rope/VideoManager.py | 1478 +++++++++++++ rope/external/cliplib/__init__.py | 1 + .../cliplib/bpe_simple_vocab_16e6.txt.gz | Bin 0 -> 1356917 bytes rope/external/cliplib/clip.py | 245 +++ rope/external/cliplib/model.py | 436 ++++ rope/external/cliplib/simple_tokenizer.py | 132 ++ rope/external/clipseg.py | 538 +++++ rope/external/resnet.py | 109 + rope/media/OffState.png | Bin 0 -> 2145 bytes rope/media/OnState.png | Bin 0 -> 2063 bytes rope/media/add_marker_hover.png | Bin 0 -> 1780 bytes rope/media/add_marker_off.png | Bin 0 -> 1780 bytes rope/media/marker.png | Bin 0 -> 833 bytes rope/media/next_marker_hover.png | Bin 0 -> 2818 bytes rope/media/next_marker_off.png | Bin 0 -> 2854 bytes rope/media/play_hover.png | Bin 0 -> 2065 bytes rope/media/play_off.png | Bin 0 -> 1990 bytes rope/media/play_on.png | Bin 0 -> 1778 bytes rope/media/previous_marker_hover.png | Bin 0 -> 2732 bytes rope/media/previous_marker_off.png | Bin 0 -> 2798 bytes rope/media/rec_hover.png | Bin 0 -> 2295 bytes rope/media/rec_off.png | Bin 0 -> 2317 bytes rope/media/rec_on.png | Bin 0 -> 2195 bytes rope/media/remove_marker_hover.png | Bin 0 -> 1211 bytes rope/media/remove_marker_off.png | Bin 0 -> 1211 bytes rope/media/rope.png | Bin 0 -> 5586 bytes rope/media/save.png | Bin 0 -> 1993 bytes rope/media/splash.png | Bin 0 -> 1338466 bytes rope/media/stop_hover.png | Bin 0 -> 938 bytes rope/media/stop_off.png | Bin 0 -> 948 bytes rope/media/stop_on.png | Bin 0 -> 938 bytes rope/media/tl_beg_hover.png | Bin 0 -> 2745 bytes rope/media/tl_beg_off.png | Bin 0 -> 2751 bytes rope/media/tl_beg_on.png | Bin 0 -> 2573 bytes rope/media/tl_left_hover.png | Bin 0 -> 3443 bytes rope/media/tl_left_off.png | Bin 0 -> 3441 bytes rope/media/tl_left_on.png | Bin 0 -> 2237 bytes rope/media/tl_right_hover.png | Bin 0 -> 2907 bytes rope/media/tl_right_off.png | Bin 0 -> 2910 bytes rope/media/tl_right_on.png | Bin 0 -> 1935 bytes utils.py | 238 +++ videoCombine.py | 565 +++++ video_formats/16bit-png.json | 9 + video_formats/8bit-png.json | 7 + video_formats/ProRes.json | 10 + video_formats/av1-webm.json | 13 + video_formats/ffmpeg-gif.json | 8 + video_formats/gifski.json | 10 + video_formats/h264-mp4.json | 11 + video_formats/h265-mp4.json | 14 + video_formats/nvenc_h264-mp4.json | 12 + video_formats/nvenc_hevc-mp4.json | 13 + video_formats/webm.json | 12 + 67 files changed, 10910 insertions(+), 203 deletions(-) create mode 100644 Rope.bat create mode 100644 Rope.py create mode 100644 __init__.py create mode 100644 data.json create mode 100644 logger.py create mode 100644 mklink create mode 100644 rope/Coordinator.py create mode 100644 rope/Dicts.py create mode 100644 rope/GUI.py create mode 100644 rope/GUIElements.py create mode 100644 rope/Models.py create mode 100644 rope/Styles.py create mode 100644 rope/VideoManager.py create mode 100644 rope/external/cliplib/__init__.py create mode 100644 rope/external/cliplib/bpe_simple_vocab_16e6.txt.gz create mode 100644 rope/external/cliplib/clip.py create mode 100644 rope/external/cliplib/model.py create mode 100644 rope/external/cliplib/simple_tokenizer.py create mode 100644 rope/external/clipseg.py create mode 100644 rope/external/resnet.py create mode 100644 rope/media/OffState.png create mode 100644 rope/media/OnState.png create mode 100644 rope/media/add_marker_hover.png create mode 100644 rope/media/add_marker_off.png create mode 100644 rope/media/marker.png create mode 100644 rope/media/next_marker_hover.png create mode 100644 rope/media/next_marker_off.png create mode 100644 rope/media/play_hover.png create mode 100644 rope/media/play_off.png create mode 100644 rope/media/play_on.png create mode 100644 rope/media/previous_marker_hover.png create mode 100644 rope/media/previous_marker_off.png create mode 100644 rope/media/rec_hover.png create mode 100644 rope/media/rec_off.png create mode 100644 rope/media/rec_on.png create mode 100644 rope/media/remove_marker_hover.png create mode 100644 rope/media/remove_marker_off.png create mode 100644 rope/media/rope.png create mode 100644 rope/media/save.png create mode 100644 rope/media/splash.png create mode 100644 rope/media/stop_hover.png create mode 100644 rope/media/stop_off.png create mode 100644 rope/media/stop_on.png create mode 100644 rope/media/tl_beg_hover.png create mode 100644 rope/media/tl_beg_off.png create mode 100644 rope/media/tl_beg_on.png create mode 100644 rope/media/tl_left_hover.png create mode 100644 rope/media/tl_left_off.png create mode 100644 rope/media/tl_left_on.png create mode 100644 rope/media/tl_right_hover.png create mode 100644 rope/media/tl_right_off.png create mode 100644 rope/media/tl_right_on.png create mode 100644 utils.py create mode 100644 videoCombine.py create mode 100644 video_formats/16bit-png.json create mode 100644 video_formats/8bit-png.json create mode 100644 video_formats/ProRes.json create mode 100644 video_formats/av1-webm.json create mode 100644 video_formats/ffmpeg-gif.json create mode 100644 video_formats/gifski.json create mode 100644 video_formats/h264-mp4.json create mode 100644 video_formats/h265-mp4.json create mode 100644 video_formats/nvenc_h264-mp4.json create mode 100644 video_formats/nvenc_hevc-mp4.json create mode 100644 video_formats/webm.json diff --git a/LICENSE b/LICENSE index 261eeb9..3877ae0 100644 --- a/LICENSE +++ b/LICENSE @@ -1,201 +1,674 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md index b627110..51c3f0a 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,72 @@ -# ComfyUI_RopeWrapper -face swap Rope, wrap for ComfyUI +![image](https://github.com/Hillobar/Rope/assets/63615199/40f7397f-713c-4813-ac86-bab36f6bd5ba) + + +Rope implements the insightface inswapper_128 model with a helpful GUI. +### [Discord](https://discord.gg/EcdVAFJzqp) + +### [Donate](https://www.paypal.com/donate/?hosted_button_id=Y5SB9LSXFGRF2) + +### [Wiki with install instructions and usage](https://github.com/Hillobar/Rope/wiki) + +### [Demo Video (Rope-Ruby)](https://www.youtube.com/watch?v=4Y4U0TZ8cWY) + +### ${{\color{Goldenrod}{\textsf{Last Updated 2024-05-27}}}}$ ### +### ${{\color{Goldenrod}{\textsf{Welcome to Rope-Pearl!}}}}$ ### + +![Screenshot 2024-02-10 104718](https://github.com/Hillobar/Rope/assets/63615199/4b2ee574-c91e-4db2-ad66-5b775a049a6b) + +### Updates for Rope-Pearl-00: ### +### To update from Opal-03a, just need to replace the rope folder. +* (feature) Selectable model swapping output resolution - 128, 256, 512 +* (feature) Better selection of input images (ctrl and shift modifiers work mostly like windows behavior) +* (feature) Toggle between mean and median merging withou having to save to compare +* (feature) Added back keyboard controls (q, w, a, s, d, space) +* (feature) Gamma slider +* +![image](https://github.com/Hillobar/Rope/assets/63615199/9d89fded-addb-46fe-b2d7-bfe6f1a88188) + +### Performance: ### +Machine: 3090Ti (24GB), i5-13600K + + + +File: benchmark/target-1080p.mp4, 2048x1080, 269 frames, 25 fps, 10s + +Rendering time in seconds (5 threads): + +| Option | Crystal | Sapphire | Ruby | Opal | Pearl | +| --- | --- | --- | --- | --- | --- | +| Only Swap (128) | 7.3 | 7.5 | 4.4 | 4.3 | 4.4 | +| Swap (256) | --- | --- | --- | --- | 8.6 | +| Swap (512) | --- | --- | --- | --- | 28.6 | +| Swap+GFPGAN | 10.7 | 11.0 | 9.0 | 9.8 | 9.3 | +| Swap+Codeformer | 12.4 | 13.5 | 11.1 | 11.1 | 11.3 | +| Swap+one word CLIP | 10.4 | 11.2 | 9.1 | 9.3 | 9.3 | +| Swap+Occluder | 7.8 | 7.8 | 4.4 | 4.7 | 4.7 | +| Swap+MouthParser | 13.9 | 12.1 | 5.0 | 4.9 | 5.1 | + +### Disclaimer: ### +Rope is a personal project that I'm making available to the community as a thank you for all of the contributors ahead of me. +I've copied the disclaimer from [Swap-Mukham](https://github.com/harisreedhar/Swap-Mukham) here since it is well-written and applies 100% to this repo. + +I would like to emphasize that our swapping software is intended for responsible and ethical use only. I must stress that users are solely responsible for their actions when using our software. + +Intended Usage: This software is designed to assist users in creating realistic and entertaining content, such as movies, visual effects, virtual reality experiences, and other creative applications. I encourage users to explore these possibilities within the boundaries of legality, ethical considerations, and respect for others' privacy. + +Ethical Guidelines: Users are expected to adhere to a set of ethical guidelines when using our software. These guidelines include, but are not limited to: + +Not creating or sharing content that could harm, defame, or harass individuals. Obtaining proper consent and permissions from individuals featured in the content before using their likeness. Avoiding the use of this technology for deceptive purposes, including misinformation or malicious intent. Respecting and abiding by applicable laws, regulations, and copyright restrictions. + +Privacy and Consent: Users are responsible for ensuring that they have the necessary permissions and consents from individuals whose likeness they intend to use in their creations. We strongly discourage the creation of content without explicit consent, particularly if it involves non-consensual or private content. It is essential to respect the privacy and dignity of all individuals involved. + +Legal Considerations: Users must understand and comply with all relevant local, regional, and international laws pertaining to this technology. This includes laws related to privacy, defamation, intellectual property rights, and other relevant legislation. Users should consult legal professionals if they have any doubts regarding the legal implications of their creations. + +Liability and Responsibility: We, as the creators and providers of the deep fake software, cannot be held responsible for the actions or consequences resulting from the usage of our software. Users assume full liability and responsibility for any misuse, unintended effects, or abusive behavior associated with the content they create. + +By using this software, users acknowledge that they have read, understood, and agreed to abide by the above guidelines and disclaimers. We strongly encourage users to approach this technology with caution, integrity, and respect for the well-being and rights of others. + +Remember, technology should be used to empower and inspire, not to harm or deceive. Let's strive for ethical and responsible use of deep fake technology for the betterment of society. + + + + diff --git a/Rope.bat b/Rope.bat new file mode 100644 index 0000000..c6f5c57 --- /dev/null +++ b/Rope.bat @@ -0,0 +1,3 @@ +call venv\Scripts\activate.bat +python Rope.py +pause \ No newline at end of file diff --git a/Rope.py b/Rope.py new file mode 100644 index 0000000..59b8345 --- /dev/null +++ b/Rope.py @@ -0,0 +1,5 @@ +#!/usr/bin/env python3 + +from rope import Coordinator +if __name__ == "__main__": + Coordinator.run() \ No newline at end of file diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..075924b --- /dev/null +++ b/__init__.py @@ -0,0 +1,958 @@ +from .rope import Models as Models +from .rope import VideoManager as VM +import json +import torch +import numpy as np +import os +import folder_paths +from comfy.utils import ProgressBar +import pickle +import subprocess +#from .videoCombine import RopeVideoCombine +from .videoCombine import ffmpeg_process +import torchaudio +from .utils import ffmpeg_path +import mimetypes +import random +from tqdm import tqdm + +#####WEB_DIRECTORY = "./web" +def combine_audio_video(audio_path, video_path, output_path): + command = [ + ffmpeg_path, + '-i', video_path, + '-i', audio_path, + '-c:v', 'copy', + '-c:a', 'aac', + '-shortest', + output_path + ] + + subprocess.run(command, check=True) + return output_path + +def get_mime_type(file_path): + # 获取文件的 MIME 类型 + mime_type, _ = mimetypes.guess_type(file_path) + + # 如果无法猜测类型，返回默认类型 + if mime_type is None: + return 'application/octet-stream' + return mime_type + +class RopeWrapper_DetectNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + + "models": ("ROPE_MODEL", ), + #"vm": ("ROPE_VM", ), + "input_image": ("IMAGE", ), + #"source_face": ("IMAGE", ), + "SimilarityThreshold":("FLOAT", {"default": 70, "min": 0.0, "max": 100, "step": 1}), + "detection_threshold":("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + + } + } + + RETURN_TYPES = ("INT","DETECTRESULT","IMAGE",) + RETURN_NAMES = ("humanCount","DETECTRESULT","foundFaces",) + FUNCTION = "run" + CATEGORY = "RopeWrapper" + + def findCosineDistance(self, vector1, vector2): + cos_dist = 1.0 - np.dot(vector1, vector2)/(np.linalg.norm(vector1)*np.linalg.norm(vector2)) # 2..0 + return 100.0-cos_dist*50.0 + + def getEmbedding(self, img, models): + pad_scale = 0.2 + padded_width = int(img.size()[1]*(1.+pad_scale)) + padded_height = int(img.size()[0]*(1.+pad_scale)) + + padding = torch.zeros((padded_height, padded_width, 3), dtype=torch.uint8, device='cuda:0') + + width_start = int(img.size()[1]*pad_scale/2) + width_end = width_start+int(img.size()[1]) + height_start = int(img.size()[0]*pad_scale/2) + height_end = height_start+int(img.size()[0]) + + padding[height_start:height_end, width_start:width_end, :] = img + img = padding + + img = img.permute(2,0,1) + try: + kpss = models.run_detect(img, max_num=1)[0] # Just one face here + except IndexError: + return None + else: + face_emb, cropped = models.run_recognize(img, kpss) + return face_emb, img ,cropped + + def run(self, models,input_image,SimilarityThreshold,detection_threshold): + + # #print("Comfy UI Image shape:", input_image.shape) + # try: + # load_file = open("custom_nodes/Rope/saved_parameters.json", "r") + # except FileNotFoundError: + # print('No save file created yet!') + # else: + # # Load the file and save it to parameters + # vm.parameters = json.load(load_file) + # load_file.close() + + # output_Padding=[] + # output_cropped=[] + # source_face_emb=None + # source_faces=[] + # for face_img in source_face: + # tempImg = torch.round(face_img * 255) + # tempImg = tempImg.to(torch.uint8).to('cuda') + # source_face_emb, PaddingImg,_ = self.getEmbedding(tempImg, models) + # if source_face_emb is not None: + # source_faces.append(source_face_emb) + # print('source_face_emb got!!!') + + # PaddingImg = PaddingImg.permute(1,2,0) + # PaddingImg = PaddingImg.to(torch.float32)/255 + # output_Padding.append(PaddingImg) + # crop = cv2.cvtColor(croppedImg.cpu().numpy(), cv2.COLOR_BGR2RGB) + # crop = cv2.resize(crop, (85, 85)) + + # crop = crop.permute(1,2,0) + # crop = crop.to(torch.float32)/255 + # output_cropped.append(crop) + + + videoSwapInfo=[] + currentFrame=0 + found_faces = [] + #output = [] + pbar = ProgressBar(len(input_image)) + for img in input_image: + + img = img.permute(2,0,1) + img = torch.round(img * 255) + img = img.to(torch.uint8).to('cuda') + kpss = models.run_detect(img, 'Retinaface',max_num=50,score=detection_threshold) + + kps_emb_list = [] + for face_kps in kpss: + face_emb,cropped = models.run_recognize( img, face_kps) + kps_emb_list.append([face_kps, face_emb,cropped]) + + # print(f'frame: {currentFrame}, faces in this frame: {len(kpss)}, length of found_faces: {len(found_faces)}') + + FrameSwapInfo=[] + faceIndexInThisFrame=0 + if kps_emb_list: + for fface in kps_emb_list: + found = False + for found_face in found_faces: + sim = self.findCosineDistance(fface[1], found_face["Embedding"]) + # print((f'sim: {sim}')) + if sim > SimilarityThreshold: + found = True + FrameSwapInfo.append([found_face["index"], fface[0]]) + # print(f"swap face, index: {found_face['index']}, faceIndexInThisFrame:{faceIndexInThisFrame}") + break + if not found: + index=len(found_faces) + found_faces.append({"Face": fface[0], "Embedding": fface[1],"index":index,"Cropped":fface[2]}) + FrameSwapInfo.append([index,fface[0]]) + # print(f"add new face, index: {index}, faceIndexInThisFrame:{faceIndexInThisFrame}") + faceIndexInThisFrame+=1 + videoSwapInfo.append(FrameSwapInfo ) + + # for swapInfo in FrameSwapInfo: + # if swapInfo[0] < len(source_faces): + # source_emb = source_faces[swapInfo[0]] + # ## temp ## img = vm.swap_core(img, swapInfo[1], source_emb, vm.parameters, vm.control) + # print(f'use source[{swapInfo[0]}] swap in frame {currentFrame}') + #img = vm.swap_core(img, kps_emb_list[0][0], source_face_emb, vm.parameters, vm.control) + + #print("shape after swap_core:",img.shape) + ##shape after swap_core: torch.Size([3, 1706, 1279]) + + # img = img.permute(1,2,0) + #print("shape after permute:",img.shape) + ##shape after permute: torch.Size([1706, 1279, 3]) + # img = img.to(torch.uint8) + + # img = img.to(torch.float32)/255 + # output.append(img) + currentFrame+=1 + pbar.update(1) + + outputFoundFaces=[] + for face in found_faces: + #print(f'face index: {face["index"]}, face: {face["Face"]}') + faceImg = face["Cropped"].to(torch.float32)/255 + #print(f'faceImg shape: {faceImg.shape}') + outputFoundFaces.append(faceImg) + + #tensor_stacked = torch.stack(output) + #print("tensor_stacked shape:",tensor_stacked.shape) + ##tensor_stacked shape: torch.Size([1, 1706, 1279, 3]) + + currentHumanCount = len(found_faces) + detectResult={"faceCount":currentHumanCount,"swapInfo":videoSwapInfo} + # with open('detectResult.pkl', 'wb') as f: + # pickle.dump(detectResult, f) + #print('found_faces count:',currentHumanCount) + return ( currentHumanCount, detectResult, torch.stack(outputFoundFaces), ) + #torch.stack(output_cropped),torch.stack(output_Padding),) + +class RopeWrapper_LoadModels: + @classmethod + def INPUT_TYPES(s): + return { + "hidden": { + "unique_id": "UNIQUE_ID" + }, + } + + RETURN_TYPES = ("ROPE_MODEL","ROPE_VM") + RETURN_NAMES = ("ROPE_MODEL","ROPE_VM") + FUNCTION = "run" + CATEGORY = "RopeWrapper" + model=None + vm=None + def run(self, unique_id): + if self.model is None: + self.model = Models.Models() + self.model.setModelPath(os.path.dirname(os.path.realpath(__file__))+"/") + self.vm = VM.VideoManager(self.model) + + return ( self.model, self.vm ) + +class RopeWrapper_SwapNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "models": ("ROPE_MODEL", ), + "vm": ("ROPE_VM", ), + "input_image": ("IMAGE", ), + "source_face": ("IMAGE", ), + "detectResult": ("DETECTRESULT", ), + "combineVideo":("BOOLEAN", {"default": False}), + "frame_rate":("FLOAT", {"default": 30.0}), + "filenamePrefix": ("STRING", {"default": 'Rope_', "multiline": False}), + "saveOutput":("BOOLEAN", {"default": False}), + "outputFrameIndex":("INT", {"default": 0}), + }, + "optional": { + "audio": ("AUDIO",), + "ROPE_Options":("ROPE_OPTION",), + "source_target_matching": ("STRING", {"default": ' ', "multiline": True}), + }, + } + + RETURN_TYPES = ("IMAGE","STRING","STRING",) + RETURN_NAMES = ("SwappedImage","fileName","fileNameAndPath",) + FUNCTION = "run" + OUTPUT_NODE = True + CATEGORY = "RopeWrapper" + def getEmbedding(self, img, models): + pad_scale = 0.2 + padded_width = int(img.size()[1]*(1.+pad_scale)) + padded_height = int(img.size()[0]*(1.+pad_scale)) + + padding = torch.zeros((padded_height, padded_width, 3), dtype=torch.uint8, device='cuda:0') + + width_start = int(img.size()[1]*pad_scale/2) + width_end = width_start+int(img.size()[1]) + height_start = int(img.size()[0]*pad_scale/2) + height_end = height_start+int(img.size()[0]) + + padding[height_start:height_end, width_start:width_end, :] = img + img = padding + + img = img.permute(2,0,1) + try: + kpss = models.run_detect(img, max_num=1)[0] # Just one face here + except IndexError: + return None,None,None + else: + face_emb, cropped = models.run_recognize(img, kpss) + return face_emb, img ,cropped + + def parseString(self,str): + try: + if str is None: + return None + else: + if str == ' ': + return None + else: + lines = str.split(';') + return [ [int(x) for x in line.split(',')] for line in lines] + except: + return None + + def run(self, models,vm,input_image,source_face,detectResult,combineVideo,frame_rate,filenamePrefix,saveOutput,outputFrameIndex,audio=None,ROPE_Options=None,source_target_matching=None): + #tempFile =os.path.join( folder_paths.output_directory, "temp.mp4") + if saveOutput: + output_dir = folder_paths.get_output_directory() + else: + output_dir = folder_paths.get_temp_directory() + + ( + full_output_folder, + filename, + _, + subfolder, + _, + ) = folder_paths.get_save_image_path(filenamePrefix, output_dir) + + filename = filename + ''.join(random.choice("abcdefghijklmnopqrstupvxyz0123456789") for x in range(5)) + tempFile = os.path.join(full_output_folder, filename) + if ROPE_Options is None: + try: + load_file = open("custom_nodes/Rope/saved_parameters.json", "r") + except FileNotFoundError: + print('No save file created yet!') + else: + # Load the file and save it to parameters + vm.parameters = json.load(load_file) + load_file.close() + else: + vm.parameters=ROPE_Options + + #print('vm.parameters:',vm.parameters) + videoSwapInfo = detectResult["swapInfo"] + targetFaceCount=detectResult["faceCount"] + + if(source_target_matching is not None): + source2target=self.parseString(source_target_matching) + else: + source2target=None + #print('source_target_matching is NONE') + if source2target is None: + #print('source2target is NONE') + source2target=[] + for i in range(len(source_face)): + source2target.append([i]) + #print('source2target:',source2target) + source_face_emb=None + source_faces=[] + print("begin get embedding from source images") + sourceFaceID=0 + for face_img in source_face: + tempImg = torch.round(face_img * 255) + tempImg = tempImg.to(torch.uint8).to('cuda') + source_face_emb, _,_ = self.getEmbedding(tempImg, models) + if source_face_emb is not None: + source_faces.append(source_face_emb) + print('source_face_emb got:', sourceFaceID) + else: + source_faces.append(None) + print('source_face_emb got None', sourceFaceID) + sourceFaceID+=1 + + + target2source=[] + for i in range(targetFaceCount): + found=False + for j in range(len(source2target)): + for index in source2target[j]: + if index==i: + target2source.append(j) + found=True + break + if not found: + target2source.append(-1) + + #width = input_image.shape[2] + #height = input_image.shape[1] + num_frames = len(input_image) + pbar = ProgressBar(num_frames) + first_image = input_image[0] + #input_image = iter(input_image) + NeedPad=False + + if combineVideo: + if (first_image.shape[1] % 8) or (first_image.shape[0] % 8): + #output frames must be padded + to_pad = (-first_image.shape[1] % 8, + -first_image.shape[0] % 8) + padding = (to_pad[0]//2, to_pad[0] - to_pad[0]//2, + to_pad[1]//2, to_pad[1] - to_pad[1]//2) + padfunc = torch.nn.ReplicationPad2d(padding) + def pad(image): + #image = image.permute((2,0,1))#HWC to CHW + padded = padfunc(image.to(dtype=torch.float32)) + return padded + #return padded.permute((1,2,0)) + #images = map(pad, images) + new_dims = (-first_image.shape[1] % 8 + first_image.shape[1], + -first_image.shape[0] % 8 + first_image.shape[0]) + dimensions = f"{new_dims[0]}x{new_dims[1]}" + print("Output images were not of valid resolution and have had padding applied：",dimensions) + NeedPad=True + else: + dimensions = f"{first_image.shape[1]}x{first_image.shape[0]}" + + args=[ffmpeg_path, '-v', 'error', '-f', 'rawvideo', '-pix_fmt', 'rgb24', '-s', dimensions, '-r', str(frame_rate), '-i', '-', '-n', '-c:v', 'libx264', '-pix_fmt', 'yuv420p', '-crf', '19'] + video_format= {'main_pass': ['-n', '-c:v', 'libx264', '-pix_fmt', 'yuv420p', '-crf', '19'], 'audio_pass': ['-c:a', 'aac'], 'save_metadata': 'False', 'extension': 'mp4'} + env=os.environ.copy() + sp = ffmpeg_process(args,video_format,None,tempFile+".mp4",env) + sp.send(None) + + currentFrame=0 + output = [] + for img in tqdm(input_image,"Swapping faces"): + #for img in input_image: + + img = img.permute(2,0,1) + img = torch.round(img * 255) + img = img.to(torch.uint8).to('cuda') + #print("procedding frame:",currentFrame) + #print("shape before swap_core:",img.shape) + if currentFrame < len(videoSwapInfo): + FrameSwapInfo = videoSwapInfo[currentFrame] + for swapInfo in FrameSwapInfo: + targetIndex = swapInfo[0] + if target2source[targetIndex] != -1 and target2source[targetIndex] < len(source_faces): + source_emb = source_faces[target2source[targetIndex]] + if source_emb is not None: + img = vm.swap_core(img, swapInfo[1], source_emb, vm.parameters, vm.control) + + #print("shape after swap_core:",img.shape) + + if combineVideo: + if currentFrame == outputFrameIndex: + outFrame = img.permute(1,2,0) + outFrame = outFrame.to(torch.float32)/255 + outFrame=outFrame.cpu() + output.append(outFrame) + + if NeedPad: + img=pad(img) + img = img.permute(1,2,0) + img = img.to(torch.uint8) + img=img.cpu().numpy() + sp.send(np.ascontiguousarray(img)) + + else: + img = img.permute(1,2,0) + img = img.to(torch.float32)/255 + img=img.cpu() + #print("to png, img dim:",img.shape) + output.append(img) + + currentFrame+=1 + pbar.update(1) + + if combineVideo: + try: + sp.send(None) #第一次send None是告诉sp，图片已经全部输入完成 + sp.send(None) #第二次send None是结束进程。直到报StopIteration为止 + except StopIteration: + pass + ##生成视频后，用ffmpeg转成h264的例子 + ##https://discuss.streamlit.io/t/processing-video-with-opencv-and-write-it-to-disk-to-display-in-st-video/28891/2 + + output_file_full_path=tempFile+".mp4" + + if audio is not None : + ## 合并音频的代码，来自 comfyui-mix-labnodes + output_dir = folder_paths.get_output_directory() + # 判断是否是 Tensor 类型 + is_tensor = not isinstance(audio, dict) + # print('#判断是否是 Tensor 类型',is_tensor,audio) + if not is_tensor and 'waveform' in audio and 'sample_rate' in audio: + # {'waveform': tensor([], size=(1, 1, 0)), 'sample_rate': 44100} + is_tensor=True + + if "audio_path" in audio: + is_tensor=False + audio_file_path=audio["audio_path"] + + if is_tensor: + filename_prefix="audio_tmp" + audio_full_output_folder, audio_filename, counter, _, filename_prefix = folder_paths.get_save_image_path( + filename_prefix, + folder_paths.get_temp_directory()) + + filename_with_batch_num = audio_filename.replace("%batch_num%", str(1)) + audioFileName = f"{filename_with_batch_num}_{counter:05}_.wav" + + audio_file_path=os.path.join(audio_full_output_folder, audioFileName) + + torchaudio.save(audio_file_path, audio['waveform'].squeeze(0), audio["sample_rate"]) + + output_file_with_audio_path = tempFile+"audio.mp4" + filename=filename+"audio.mp4" + print("audio file :", audio_file_path) + print("video file :", output_file_full_path) + print("output file with audio :", output_file_with_audio_path) + combine_audio_video(audio_file_path,output_file_full_path,output_file_with_audio_path) + + # # # # # # # Create audio file if input was provided + # # # # # # output_file_with_audio_path = tempFile+"audio.mp4" + # # # # # # filename=filename+"audio.mp4" + # # # # # # video_format["audio_pass"] = ["-c:a", "aac"] + + # # # # # # # # FFmpeg command with audio re-encoding + # # # # # # # #TODO: expose audio quality options if format widgets makes it in + # # # # # # # #Reconsider forcing apad/shortest + # # # # # # # min_audio_dur = currentFrame / frame_rate + 1 + # # # # # # # mux_args = [ffmpeg_path, "-v", "error", "-n", "-i", tempFile+".mp4", + # # # # # # # "-i", "-", "-c:v", "copy"] \ + # # # # # # # + video_format["audio_pass"] \ + # # # # # # # + ["-af", "apad=whole_dur="+str(min_audio_dur), + # # # # # # # "-shortest", output_file_with_audio_path] + + # # # # # # # try: + # # # # # # # res = subprocess.run(mux_args, input=audio(), env=env, + # # # # # # # capture_output=True, check=True) + # # # # # # # except subprocess.CalledProcessError as e: + # # # # # # # raise Exception("An error occured in the ffmpeg subprocess:\n" \ + # # # # # # # + e.stderr.decode("utf-8")) + # # # # # # # if res.stderr: + # # # # # # # print(res.stderr.decode("utf-8"), end="", file=sys.stderr) + output_file_full_path = output_file_with_audio_path + else: + filename=filename+".mp4" + + if saveOutput: + previewOutputType="output" + else: + previewOutputType="temp" + + previews = [ + { + "filename": filename, + "subfolder": subfolder, + "type": previewOutputType, + "format": get_mime_type(filename), + "frame_rate": frame_rate, + } + ] + #[{'filename': 'AdvancedLivePortrait_00001.mp4', 'subfolder': '', 'type': 'temp', 'format': 'video/h264-mp4', 'frame_rate': 30.0}] + print("previews:",previews) + return {"ui":{"gifs": previews}, "result": (torch.stack(output), filename,output_file_full_path,)} + else: + return (torch.stack(output), "","",) + +class RopeWrapper_FaceRestore: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "models": ("ROPE_MODEL", ), + "vm": ("ROPE_VM", ), + "input_image": ("IMAGE", ), + "detection_threshold":("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "combineVideo":("BOOLEAN", {"default": False}), + "frame_rate":("FLOAT", {"default": 30.0}), + "filenamePrefix": ("STRING", {"default": 'RopeFaceRestore', "multiline": False}), + "saveOutput":("BOOLEAN", {"default": False}), + }, + "optional": { + "audio": ("AUDIO",), + "ROPE_Options":("ROPE_OPTION",), + }, + } + + RETURN_TYPES = ("IMAGE","STRING","STRING",) + RETURN_NAMES = ("SwappedImage","fileName","fileNameAndPath",) + FUNCTION = "run" + OUTPUT_NODE = True + CATEGORY = "RopeWrapper" + + def parseString(self,str): + try: + if str is None: + return None + else: + if str == ' ': + return None + else: + lines = str.split(';') + return [ [int(x) for x in line.split(',')] for line in lines] + except: + return None + + def run(self, models,vm,input_image,detection_threshold,combineVideo,frame_rate,filenamePrefix,saveOutput,audio=None,ROPE_Options=None): + #tempFile =os.path.join( folder_paths.output_directory, "temp.mp4") + if saveOutput: + output_dir = folder_paths.get_output_directory() + else: + output_dir = folder_paths.get_temp_directory() + + ( + full_output_folder, + filename, + _, + subfolder, + _, + ) = folder_paths.get_save_image_path(filenamePrefix, output_dir) + + filename = filename + ''.join(random.choice("abcdefghijklmnopqrstupvxyz0123456789") for x in range(5)) + tempFile = os.path.join(full_output_folder, filename) + if ROPE_Options is None: + try: + load_file = open("custom_nodes/Rope/saved_parameters.json", "r") + except FileNotFoundError: + print('No save file created yet!') + else: + # Load the file and save it to parameters + vm.parameters = json.load(load_file) + load_file.close() + else: + vm.parameters=ROPE_Options + + vm.parameters['RestorerSwitch'] = True + num_frames = len(input_image) + pbar = ProgressBar(num_frames) + first_image = input_image[0] + input_image = iter(input_image) + NeedPad=False + + if combineVideo: + if (first_image.shape[1] % 8) or (first_image.shape[0] % 8): + #output frames must be padded + to_pad = (-first_image.shape[1] % 8, + -first_image.shape[0] % 8) + padding = (to_pad[0]//2, to_pad[0] - to_pad[0]//2, + to_pad[1]//2, to_pad[1] - to_pad[1]//2) + padfunc = torch.nn.ReplicationPad2d(padding) + def pad(image): + #image = image.permute((2,0,1))#HWC to CHW + padded = padfunc(image.to(dtype=torch.float32)) + return padded + #return padded.permute((1,2,0)) + #images = map(pad, images) + new_dims = (-first_image.shape[1] % 8 + first_image.shape[1], + -first_image.shape[0] % 8 + first_image.shape[0]) + dimensions = f"{new_dims[0]}x{new_dims[1]}" + print("Output images were not of valid resolution and have had padding applied：",dimensions) + NeedPad=True + else: + dimensions = f"{first_image.shape[1]}x{first_image.shape[0]}" + + args=[ffmpeg_path, '-v', 'error', '-f', 'rawvideo', '-pix_fmt', 'rgb24', '-s', dimensions, '-r', str(frame_rate), '-i', '-', '-n', '-c:v', 'libx264', '-pix_fmt', 'yuv420p', '-crf', '19'] + video_format= {'main_pass': ['-n', '-c:v', 'libx264', '-pix_fmt', 'yuv420p', '-crf', '19'], 'audio_pass': ['-c:a', 'aac'], 'save_metadata': 'False', 'extension': 'mp4'} + env=os.environ.copy() + sp = ffmpeg_process(args,video_format,None,tempFile+".mp4",env) + sp.send(None) + + currentFrame=0 + output = [] + for img in tqdm( input_image, desc="faceRestore", total=num_frames): + #for img in input_image: + + img = img.permute(2,0,1) + img = torch.round(img * 255) + img = img.to(torch.uint8).to('cuda') + #print("procedding frame:",currentFrame) + kpss = models.run_detect(img, 'Retinaface',max_num=50,score=detection_threshold) + for kps in kpss: + img = vm.core_withoutSwap(img, kps, vm.parameters, vm.control) + + if combineVideo: + if NeedPad: + img=pad(img) + img = img.permute(1,2,0) + img = img.to(torch.uint8) + img=img.cpu().numpy() + sp.send(np.ascontiguousarray(img)) + + else: + img = img.permute(1,2,0) + img = img.to(torch.float32)/255 + img=img.cpu() + output.append(img) + + currentFrame+=1 + pbar.update(1) + + if combineVideo: + try: + print("exporting mp4...") + sp.send(None) #第一次send None是告诉sp，图片已经全部输入完成 + sp.send(None) #第二次send None是结束进程。直到报StopIteration为止 + except StopIteration: + pass + ##生成视频后，用ffmpeg转成h264的例子 + ##https://discuss.streamlit.io/t/processing-video-with-opencv-and-write-it-to-disk-to-display-in-st-video/28891/2 + + output_file_full_path=tempFile+".mp4" + + if audio is not None : + print("combine audio...") + ## 合并音频的代码，来自 comfyui-mix-labnodes + output_dir = folder_paths.get_output_directory() + # 判断是否是 Tensor 类型 + is_tensor = not isinstance(audio, dict) + # print('#判断是否是 Tensor 类型',is_tensor,audio) + if not is_tensor and 'waveform' in audio and 'sample_rate' in audio: + # {'waveform': tensor([], size=(1, 1, 0)), 'sample_rate': 44100} + is_tensor=True + + if "audio_path" in audio: + is_tensor=False + audio_file_path=audio["audio_path"] + + if is_tensor: + filename_prefix="audio_tmp" + audio_full_output_folder, audio_filename, counter, _, filename_prefix = folder_paths.get_save_image_path( + filename_prefix, + folder_paths.get_temp_directory()) + + filename_with_batch_num = audio_filename.replace("%batch_num%", str(1)) + audioFileName = f"{filename_with_batch_num}_{counter:05}_.wav" + + audio_file_path=os.path.join(audio_full_output_folder, audioFileName) + + torchaudio.save(audio_file_path, audio['waveform'].squeeze(0), audio["sample_rate"]) + + output_file_with_audio_path = tempFile+"audio.mp4" + filename=filename+"audio.mp4" + #print("audio file :", audio_file_path) + #print("video file :", output_file_full_path) + #print("output file with audio :", output_file_with_audio_path) + combine_audio_video(audio_file_path,output_file_full_path,output_file_with_audio_path) + + output_file_full_path = output_file_with_audio_path + else: + filename=filename+".mp4" + + if saveOutput: + previewOutputType="output" + else: + previewOutputType="temp" + + previews = [ + { + "filename": filename, + "subfolder": subfolder, + "type": "output" if saveOutput else "temp", + "format": "video/h264-mp4", + "frame_rate": frame_rate, + } + ] + #[{'filename': 'AdvancedLivePortrait_00001.mp4', 'subfolder': '', 'type': 'temp', 'format': 'video/h264-mp4', 'frame_rate': 30.0}] + print("previews:",previews) + return {"ui":{"video": previews}, "result": (None, filename,output_file_full_path,)} + else: + return (torch.stack(output), "","",) + +class RopeWrapper_OptionNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "RestorerSwitch": ("BOOLEAN",{"default":False} ), + "RestorerTypeTextSel": (['CF','GFPGAN', 'GPEN256', 'GPEN512'],), + "RestorerDetTypeTextSel": (['Blend','Original','Reference'],), + "RestorerSlider":("INT", {"default": 100, "min": 0, "max": 100, "step": 1}), + #ThresholdSlider + "OrientSwitch": ("BOOLEAN",{"default":False} ), + "OrientSlider":("INT", {"default": 180, "min": 0, "max": 270, "step": 90}), + "StrengthSwitch": ("BOOLEAN",{"default":False} ), + "StrengthSlider":("INT", {"default": 200, "min": 0, "max": 500, "step": 1}), + "BorderTopSlider":("INT", {"default": 10, "min": 0, "max": 64, "step": 1}), + "BorderSidesSlider":("INT", {"default": 10, "min": 0, "max": 64, "step": 1}), + "BorderBottomSlider":("INT", {"default": 10, "min": 0, "max": 64, "step": 1}), + "BorderBlurSlider":("INT", {"default": 10, "min": 0, "max": 64, "step": 1}), + "DiffSwitch": ("BOOLEAN",{"default":False} ), + "DiffSlider":("INT", {"default": 4, "min": 0.0, "max": 100, "step": 1}), + "OccluderSwitch": ("BOOLEAN",{"default":False} ), + "OccluderSlider":("INT", {"default": 0, "min": -100, "max": 100, "step": 1}), + "FaceParserSwitch": ("BOOLEAN",{"default":False} ), + "FaceParserSlider":("INT", {"default": 0, "min": -50, "max": 50, "step": 1}), + "MouthParserSlider":("INT", {"default": 0, "min": -50, "max": 50, "step": 1}), + "CLIPSwitch": ("BOOLEAN",{"default":False} ), + "CLIPTextEntry": ("STRING", {"default": " "}), + "CLIPSlider":("INT", {"default": 50, "min": 0, "max": 100, "step": 1}), + "BlendSlider":("INT", {"default": 5, "min": 0, "max": 100, "step": 1}), + "ColorSwitch": ("BOOLEAN",{"default":False} ), + "ColorRedSlider":("INT", {"default": 0, "min": -100, "max": 100, "step": 1}), + "ColorGreenSlider":("INT", {"default": 0, "min": -100, "max": 100, "step": 1}), + "ColorBlueSlider":("INT", {"default": 0, "min": -100, "max": 100, "step": 1}), + "ColorGammaSlider":("FLOAT", {"default": 0, "min": 0.0, "max": 2.0, "step": 0.02}), + "FaceAdjSwitch": ("BOOLEAN",{"default":False} ), + "KPSXSlider":("INT", {"default": 0, "min": -100, "max": 100, "step": 1}), + "KPSYSlider":("INT", {"default": 0, "min": -100, "max": 100, "step": 1}), + "KPSScaleSlider":("INT", {"default": 0, "min": -100, "max": 100, "step": 1}), + "SwapperTypeTextSel": (['128','256', '512'],), + }, + } + + RETURN_TYPES = ("ROPE_OPTION",) + FUNCTION = "run" + CATEGORY = "RopeWrapper" + def run(self, RestorerSwitch ,RestorerTypeTextSel ,RestorerDetTypeTextSel ,RestorerSlider ,OrientSwitch ,OrientSlider ,StrengthSwitch ,StrengthSlider ,BorderTopSlider ,BorderSidesSlider ,BorderBottomSlider ,BorderBlurSlider ,DiffSwitch ,DiffSlider ,OccluderSwitch ,OccluderSlider ,FaceParserSwitch ,FaceParserSlider ,MouthParserSlider ,CLIPSwitch ,CLIPTextEntry ,CLIPSlider ,BlendSlider ,ColorSwitch ,ColorRedSlider ,ColorGreenSlider ,ColorBlueSlider ,ColorGammaSlider ,FaceAdjSwitch ,KPSXSlider ,KPSYSlider ,KPSScaleSlider ,SwapperTypeTextSel): + option={ + "RestorerSwitch": False, + "RestorerTypeTextSel": "CF", + "RestorerDetTypeTextSel": "Blend", + "RestorerSlider": 100, + "ThresholdSlider": 50, + "OrientSwitch": False, + "OrientSlider": 180, + "StrengthSwitch": False, + "StrengthSlider": 200, + "BorderTopSlider": 10, + "BorderSidesSlider": 10, + "BorderBottomSlider": 10, + "BorderBlurSlider": 10, + "DiffSwitch": False, + "DiffSlider": 4, + "OccluderSwitch": False, + "OccluderSlider": 0, + "FaceParserSwitch": False, + "FaceParserSlider": 0, + "MouthParserSlider": 0, + "CLIPSwitch": False, + "CLIPTextEntry": "", + "CLIPSlider": 50, + "BlendSlider": 5, + "ColorSwitch": False, + "ColorRedSlider": 0, + "ColorGreenSlider": -4, + "ColorBlueSlider": 0, + "ColorGammaSlider": 1, + "FaceAdjSwitch": False, + "KPSXSlider": 0, + "KPSYSlider": 0, + "KPSScaleSlider": 0, + "FaceScaleSlider": 0, + "ThreadsSlider": 5, + "DetectTypeTextSel": "Retinaface", + "DetectScoreSlider": 50, + "RecordTypeTextSel": "FFMPEG", + "VideoQualSlider": 18, + "SwapperTypeTextSel": "128" + } + option["RestorerSwitch"]=RestorerSwitch + option["RestorerTypeTextSel"]=RestorerTypeTextSel + option["RestorerDetTypeTextSel"]=RestorerDetTypeTextSel + option["RestorerSlider"]=RestorerSlider + option["OrientSwitch"]=OrientSwitch + option["OrientSlider"]=OrientSlider + option["StrengthSwitch"]=StrengthSwitch + option["StrengthSlider"]=StrengthSlider + option["BorderTopSlider"]=BorderTopSlider + option["BorderSidesSlider"]=BorderSidesSlider + option["BorderBottomSlider"]=BorderBottomSlider + option["BorderBlurSlider"]=BorderBlurSlider + option["DiffSwitch"]=DiffSwitch + option["DiffSlider"]=DiffSlider + option["OccluderSwitch"]=OccluderSwitch + option["OccluderSlider"]=OccluderSlider + option["FaceParserSwitch"]=FaceParserSwitch + option["FaceParserSlider"]=FaceParserSlider + option["MouthParserSlider"]=MouthParserSlider + option["CLIPSwitch"]=CLIPSwitch + option["CLIPTextEntry"]=CLIPTextEntry + option["CLIPSlider"]=CLIPSlider + option["BlendSlider"]=BlendSlider + option["ColorSwitch"]=ColorSwitch + option["ColorRedSlider"]=ColorRedSlider + option["ColorGreenSlider"]=ColorGreenSlider + option["ColorBlueSlider"]=ColorBlueSlider + option["ColorGammaSlider"]=ColorGammaSlider + option["FaceAdjSwitch"]=FaceAdjSwitch + option["KPSXSlider"]=KPSXSlider + option["KPSYSlider"]=KPSYSlider + option["KPSScaleSlider"]=KPSScaleSlider + option["SwapperTypeTextSel"]=SwapperTypeTextSel + return (option,) + + +class RopeWrapper_SaveSwapInfo: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "fileName": ("STRING", {"default": 'TEMP_PKL', "multiline": False}), + "detectResult": ("DETECTRESULT", ), + }, + + } + RETURN_TYPES = () + FUNCTION = "run" + OUTPUT_NODE = True + CATEGORY = "RopeWrapper" + + def run(self, fileName, detectResult): + #print("os.path.realpath(__file__):",os.path.realpath(__file__)) + #base_path = os.path.dirname(os.path.realpath(__file__)) + models_dir = folder_paths.models_dir + ROPE_MODELS_PATH = os.path.join(models_dir, "rope") + if not os.path.exists(ROPE_MODELS_PATH): + os.makedirs(ROPE_MODELS_PATH) + counter = 1 + saveFile = os.path.join(ROPE_MODELS_PATH, fileName) + while os.path.exists(saveFile): + saveFile = os.path.join(ROPE_MODELS_PATH, fileName + '_' + str(counter)) + counter += 1 + print("Saving SwapInfo to file: ", saveFile) + #print(detectResult) + + with open(saveFile, 'wb') as f: + pickle.dump(detectResult, f) + return fileName + +class RopeWrapper_LoadSwapInfo: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "fileName": ("STRING", {"default": 'TEMP_PKL', "multiline": False}), + }, + } + RETURN_TYPES = ("DETECTRESULT",) + RETURN_NAMES = ("DETECTRESULT",) + FUNCTION = "run" + CATEGORY = "RopeWrapper" + def run(self, fileName): + models_dir = folder_paths.models_dir + ROPE_MODELS_PATH = os.path.join(models_dir, "rope") + saveFile = os.path.join(ROPE_MODELS_PATH, fileName) + #print("Loading SwapInfo from file: ", saveFile) + detectResult=None + try: + with open(saveFile, 'rb') as f: + detectResult = pickle.load(f) + #print("Loaded SwapInfo from file: ", detectResult) + return (detectResult,) + except Exception as e: + print("Error loading SwapInfo from file: ", e) + return (None,) + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + #"RopeVideoCombine":RopeVideoCombine, + "RopeWrapper_DetectNode":RopeWrapper_DetectNode, + "RopeWrapper_LoadModels":RopeWrapper_LoadModels, + "RopeWrapper_SwapNode":RopeWrapper_SwapNode, + #"RopeWrapper_SwapNodeTEST":RopeWrapper_SwapNodeTEST, + "RopeWrapper_OptionNode":RopeWrapper_OptionNode, + "RopeWrapper_SaveSwapInfo":RopeWrapper_SaveSwapInfo, + "RopeWrapper_LoadSwapInfo":RopeWrapper_LoadSwapInfo, + "RopeWrapper_FaceRestore":RopeWrapper_FaceRestore, + + +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + #"RopeVideoCombine":"RopeVideoCombine", + "RopeWrapper_DetectNode":"RopeWrapper_DetectNode", + "RopeWrapper_LoadModels":"RopeWrapper_LoadModels", + "RopeWrapper_SwapNode":"RopeWrapper_SwapNode", + #"RopeWrapper_SwapNodeTEST":"RopeWrapper_SwapNodeTEST", + "RopeWrapper_OptionNode":"RopeWrapper_OptionNode", + "RopeWrapper_SaveSwapInfo":"RopeWrapper_SaveSwapInfo", + "RopeWrapper_LoadSwapInfo":"RopeWrapper_LoadSwapInfo", + "RopeWrapper_FaceRestore":"RopeWrapper_FaceRestore", + +} diff --git a/data.json b/data.json new file mode 100644 index 0000000..c0437b6 --- /dev/null +++ b/data.json @@ -0,0 +1 @@ +{"source videos": "D:/RopeCrystal/InputV", "source faces": "D:/RopeCrystal/sourceFaces", "saved videos": "D:/RopeCrystal/output", "dock_win_geom": [1920, 1009, -8, -8]} \ No newline at end of file diff --git a/logger.py b/logger.py new file mode 100644 index 0000000..579b55a --- /dev/null +++ b/logger.py @@ -0,0 +1,36 @@ +import sys +import copy +import logging + + +class ColoredFormatter(logging.Formatter): + COLORS = { + "DEBUG": "\033[0;36m", # CYAN + "INFO": "\033[0;32m", # GREEN + "WARNING": "\033[0;33m", # YELLOW + "ERROR": "\033[0;31m", # RED + "CRITICAL": "\033[0;37;41m", # WHITE ON RED + "RESET": "\033[0m", # RESET COLOR + } + + def format(self, record): + colored_record = copy.copy(record) + levelname = colored_record.levelname + seq = self.COLORS.get(levelname, self.COLORS["RESET"]) + colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}" + return super().format(colored_record) + + +# Create a new logger +logger = logging.getLogger("VideoHelperSuite") +logger.propagate = False + +# Add handler if we don't have one. +if not logger.handlers: + handler = logging.StreamHandler(sys.stdout) + handler.setFormatter(ColoredFormatter("[%(name)s] - %(levelname)s - %(message)s")) + logger.addHandler(handler) + +# Configure logger +loglevel = logging.INFO +logger.setLevel(loglevel) diff --git a/mklink b/mklink new file mode 100644 index 0000000..e69de29 diff --git a/rope/Coordinator.py b/rope/Coordinator.py new file mode 100644 index 0000000..d77707b --- /dev/null +++ b/rope/Coordinator.py @@ -0,0 +1,175 @@ +# #!/usr/bin/env python3 + +import time +import torch +from torchvision import transforms + +import rope.GUI as GUI +import rope.VideoManager as VM +import rope.Models as Models +from rope.external.clipseg import CLIPDensePredT + +resize_delay = 1 +mem_delay = 1 + +# @profile +def coordinator(): + global gui, vm, action, frame, r_frame, load_notice, resize_delay, mem_delay + # start = time.time() + + + if gui.get_action_length() > 0: + action.append(gui.get_action()) + if vm.get_action_length() > 0: + action.append(vm.get_action()) +################## + if vm.get_frame_length() > 0: + frame.append(vm.get_frame()) + + if len(frame) > 0: + gui.set_image(frame[0], False) + frame.pop(0) + #################### + if vm.get_requested_frame_length() > 0: + r_frame.append(vm.get_requested_frame()) + if len(r_frame) > 0: + gui.set_image(r_frame[0], True) + r_frame=[] + #################### + if len(action) > 0: + # print('Action:', action[0][0]) + # print('Value:', action[0][1]) + if action[0][0] == "load_target_video": + vm.load_target_video(action[0][1]) + action.pop(0) + elif action[0][0] == "load_target_image": + vm.load_target_image(action[0][1]) + action.pop(0) + elif action[0][0] == "play_video": + vm.play_video(action[0][1]) + action.pop(0) + elif action[0][0] == "get_requested_video_frame": + vm.get_requested_video_frame(action[0][1], marker=True) + action.pop(0) + elif action[0][0] == "get_requested_video_frame_without_markers": + vm.get_requested_video_frame(action[0][1], marker=False) + action.pop(0) + elif action[0][0] == "get_requested_image": + vm.get_requested_image() + action.pop(0) + # elif action[0][0] == "swap": + # vm.swap = action[0][1] + # action.pop(0) + elif action[0][0] == "target_faces": + vm.assign_found_faces(action[0][1]) + action.pop(0) + elif action [0][0] == "saved_video_path": + vm.saved_video_path = action[0][1] + action.pop(0) + elif action [0][0] == "vid_qual": + vm.vid_qual = int(action[0][1]) + action.pop(0) + elif action [0][0] == "set_stop": + vm.stop_marker = action[0][1] + action.pop(0) + elif action [0][0] == "perf_test": + vm.perf_test = action[0][1] + action.pop(0) + elif action [0][0] == 'ui_vars': + vm.ui_data = action[0][1] + action.pop(0) + elif action [0][0] == 'control': + vm.control = action[0][1] + action.pop(0) + elif action [0][0] == "parameters": + if action[0][1]["CLIPSwitch"]: + if not vm.clip_session: + vm.clip_session = load_clip_model() + + vm.parameters = action[0][1] + action.pop(0) + elif action [0][0] == "markers": + vm.markers = action[0][1] + action.pop(0) + + + elif action[0][0] == "function": + eval(action[0][1]) + action.pop(0) + elif action [0][0] == "clear_mem": + vm.clear_mem() + action.pop(0) + + + # From VM + elif action[0][0] == "stop_play": + gui.set_player_buttons_to_inactive() + action.pop(0) + + elif action[0][0] == "set_slider_length": + gui.set_video_slider_length(action[0][1]) + action.pop(0) + + + + else: + print("Action not found: "+action[0][0]+" "+str(action[0][1])) + action.pop(0) + + + + + if resize_delay > 100: + gui.check_for_video_resize() + resize_delay = 0 + else: + resize_delay +=1 + + if mem_delay > 1000: + gui.update_vram_indicator() + mem_delay = 0 + else: + mem_delay +=1 + + vm.process() + gui.after(1, coordinator) + # print(time.time() - start) + + + + + +def load_clip_model(): + # https://github.com/timojl/clipseg + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + clip_session = CLIPDensePredT(version='ViT-B/16', reduce_dim=64, complex_trans_conv=True) + # clip_session = CLIPDensePredTMasked(version='ViT-B/16', reduce_dim=64) + clip_session.eval(); + clip_session.load_state_dict(torch.load('./models/rd64-uni-refined.pth'), strict=False) + clip_session.to(device) + return clip_session + + + + +def run(): + global gui, vm, action, frame, r_frame, resize_delay, mem_delay + + models = Models.Models() + models.setModelPath("./") + gui = GUI.GUI(models) + vm = VM.VideoManager(models) + + + action = [] + frame = [] + r_frame = [] + + gui.initialize_gui() + + + coordinator() + + gui.mainloop() + + diff --git a/rope/Dicts.py b/rope/Dicts.py new file mode 100644 index 0000000..f158131 --- /dev/null +++ b/rope/Dicts.py @@ -0,0 +1,413 @@ +DEFAULT_DATA = { +# Buttons +'AddMarkerButtonDisplay': 'icon', +'AddMarkerButtonIconHover': './rope/media/add_marker_hover.png', +'AddMarkerButtonIconOff': './rope/media/add_marker_off.png', +'AddMarkerButtonIconOn': './rope/media/add_marker_off.png', +'AddMarkerButtonInfoText': 'ADD MARKER:\nAttaches a parameter marker to the current frame. Markers copy all parameter settings and apply them to all future frames, or until another marker is encountered.', +'AddMarkerButtonState': False, +'AudioDisplay': 'text', +'AudioInfoText': 'ENABLE REAL-TIME AUDIO:\nAdds audio from the input video during preview playback. If you are unable to maintain the input video frame rate, the audio will lag.', +'AudioState': False, +'AudioText': 'Enable Audio', +'AutoSwapState': False, +'ClearFacesDisplay': 'text', +'ClearFacesIcon': './rope/media/tarfacedel.png', +'ClearFacesIconHover': './rope/media/rec.png', +'ClearFacesIconOff': './rope/media/rec.png', +'ClearFacesIconOn': './rope/media/rec.png', +'ClearFacesInfoText': 'REMOVE FACES:\nRemove all currently found faces.', +'ClearFacesState': False, +'ClearFacesText': 'Clear Faces', +'ClearmemState': False, +'DefaultParamsButtonDisplay': 'text', +'DefaultParamsButtonInfoText': 'LOAD DEFAULT PARAMETERS:\nLoad the Rope default parameters for this column.', +'DefaultParamsButtonState': False, +'DefaultParamsButtonText': 'Load Defaults', +'DelEmbedDisplay': 'text', +'DelEmbedIconHover': './rope/media/rec.png', +'DelEmbedIconOff': './rope/media/rec.png', +'DelEmbedIconOn': './rope/media/rec.png', +'DelEmbedInfoText': 'DELETE EMBEDDING:\nDelete the currently selected embedding', +'DelEmbedState': False, +'DelEmbedText': 'Delete Emb', +'DelMarkerButtonDisplay': 'icon', +'DelMarkerButtonIconHover': './rope/media/remove_marker_hover.png', +'DelMarkerButtonIconOff': './rope/media/remove_marker_off.png', +'DelMarkerButtonIconOn': './rope/media/remove_marker_off.png', +'DelMarkerButtonInfoText': 'REMOVE MARKER:\nRemoves the parameter marker from the current frame.', +'DelMarkerButtonState': False, +'FindFacesDisplay': 'text', +'FindFacesIcon': './rope/media/tarface.png', +'FindFacesIconHover': './rope/media/rec.png', +'FindFacesIconOff': './rope/media/rec.png', +'FindFacesIconOn': './rope/media/rec.png', +'FindFacesInfoText': 'FIND FACES:\nFinds all new faces in the current frame.', +'FindFacesState': False, +'FindFacesText': 'Find Faces', +'ImgDockState': False, +'ImgVidMode': 'Videos', +'ImgVidState': False, +'LoadParamsButtonDisplay': 'text', +'LoadParamsButtonInfoText': 'LOAD SAVED PARAMETERS:\nLoads all parameters from this column if they have been previously saved. ', +'LoadParamsButtonState': False, +'LoadParamsButtonText': 'Load Params', +'LoadSFacesDisplay': 'both', +'LoadSFacesIcon': './rope/media/save.png', +'LoadSFacesIconHover': './rope/media/save.png', +'LoadSFacesIconOff': './rope/media/save.png', +'LoadSFacesIconOn': './rope/media/save.png', +'LoadSFacesInfoText': 'SELECT SOURCE FACES FOLDER:\nSelects and loads Source Faces from Folder. Make sure the folder only contains images.', +'LoadSFacesState': False, +'LoadSFacesText': 'Select Faces Folder', +'LoadTVideosDisplay': 'both', +'LoadTVideosIconHover': './rope/media/save.png', +'LoadTVideosIconOff': './rope/media/save.png', +'LoadTVideosIconOn': './rope/media/save.png', +'LoadTVideosInfoText': 'SELECT INPUT VIDEOS/IMAGES FOLDER:\nSelect and load media from folder.', +'LoadTVideosState': False, +'LoadTVideosText': 'Select Videos Folder', +'MaskViewDisplay': 'text', +'MaskViewInfoText': 'SHOW MASKS:\nDisplays the mask for a face side-by-side with the face. Useful for understanding the masking behaviors and results.', +'MaskViewState': False, +'MaskViewText': 'Show Mask', +'NextMarkerButtonDisplay': 'icon', +'NextMarkerButtonIconHover': './rope/media/next_marker_hover.png', +'NextMarkerButtonIconOff': './rope/media/next_marker_off.png', +'NextMarkerButtonIconOn': './rope/media/next_marker_off.png', +'NextMarkerButtonInfoText': 'NEXT MARKER:\nMove to the next marker.', +'NextMarkerButtonState': False, +'OutputFolderDisplay': 'both', +'OutputFolderIconHover': './rope/media/save.png', +'OutputFolderIconOff': './rope/media/save.png', +'OutputFolderIconOn': './rope/media/save.png', +'OutputFolderInfoText': 'SELECT SAVE FOLDER:\nSelect folder for saved videos and images.', +'OutputFolderState': False, +'OutputFolderText': 'Select Output Folder', +'PerfTestState': False, +'PlayDisplay': 'icon', +'PlayIconHover': './rope/media/play_hover.png', +'PlayIconOff': './rope/media/play_off.png', +'PlayIconOn': './rope/media/play_on.png', +'PlayInfoText': 'PLAY:\nPlays the video. Press again to stop playing', +'PlayState': False, +'PrevMarkerButtonDisplay': 'icon', +'PrevMarkerButtonIconHover': './rope/media/previous_marker_hover.png', +'PrevMarkerButtonIconOff': './rope/media/previous_marker_off.png', +'PrevMarkerButtonIconOn': './rope/media/previous_marker_off.png', +'PrevMarkerButtonInfoText': 'PREVIOUS MARKER:\nMove to the previous marker.', +'PrevMarkerButtonState': False, +'RecordDisplay': 'icon', +'RecordIconHover': './rope/media/rec_hover.png', +'RecordIconOff': './rope/media/rec_off.png', +'RecordIconOn': './rope/media/rec_on.png', +'RecordInfoText': 'RECORD:\nArms the PLAY button for recording. Press RECORD, then PLAY to record. Press PLAY again to stop recording.', +'RecordState': False, +'SaveImageState': False, +'SaveParamsButtonDisplay': 'text', +'SaveParamsButtonInfoText': 'SAVE PARAMETERS:\nSaves all parameters in this column.', +'SaveParamsButtonState': False, +'SaveParamsButtonText': 'Save Params', +'StartRopeDisplay': 'both', +'StartRopeIconHover': './rope/media/rope.png', +'StartRopeIconOff': './rope/media/rope.png', +'StartRopeIconOn': './rope/media/rope.png', +'StartRopeInfoText': 'STARTS ROPE:\nStarts up the Rope application.', +'StartRopeState': False, +'StartRopeText': 'Start Rope', +'SwapFacesDisplay': 'text', +'SwapFacesInfoText': 'SWAP:\nSwap assigned Source Faces and Target Faces.', +'SwapFacesState': False, +'SwapFacesText': 'Swap Faces', +'TLBeginningDisplay': 'icon', +'TLBeginningIconHover': './rope/media/tl_beg_hover.png', +'TLBeginningIconOff': './rope/media/tl_beg_off.png', +'TLBeginningIconOn': './rope/media/tl_beg_on.png', +'TLBeginningInfoText': 'TIMELINE START:\nMove the timeline handle to the first frame.', +'TLBeginningState': False, +'TLLeftDisplay': 'icon', +'TLLeftIconHover': './rope/media/tl_left_hover.png', +'TLLeftIconOff': './rope/media/tl_left_off.png', +'TLLeftIconOn': './rope/media/tl_left_on.png', +'TLLeftInfoText': 'TIMELEFT NUDGE LEFT:\nMove the timeline handle to the left 30 frames.', +'TLLeftState': False, +'TLRightDisplay': 'icon', +'TLRightIconHover': './rope/media/tl_right_hover.png', +'TLRightIconOff': './rope/media/tl_right_off.png', +'TLRightIconOn': './rope/media/tl_right_on.png', +'TLRightInfoText': 'TIMELEFT NUDGE RIGHT:\nMove the timeline handle to the RIGHT 30 frames.', +'TLRightState': False, + +'SaveImageButtonDisplay': 'text', +'SaveImageButtonInfoText': 'SAVE IMAGE:\nSaves the current image to your Output Folder.', +'SaveImageButtonState': False, +'SaveImageButtonText': 'Save Image', + +'AutoSwapButtonDisplay': 'text', +'AutoSwapButtonInfoText': 'AUTOSWAP:\nAutomatcially applies your currently selected Input Face to new images.', +'AutoSwapButtonState': False, +'AutoSwapButtonText': 'Auto Swap', + +'ClearVramButtonDisplay': 'text', +'ClearVramButtonInfoText': 'CLEAR VRAM:\nClears models from your VRAM.', +'ClearVramButtonState': False, +'ClearVramButtonText': 'Clear VRAM', + +'GetNewEmbButtonDisplay': 'text', +'GetNewEmbButtonInfoText': 'CLEAR VRAM:\nClears models from your VRAM.', +'GetNewEmbButtonState': False, +'GetNewEmbButtonText': 'Clear VRAM', + + +'StopMarkerButtonnDisplay': 'icon', +'StopMarkerButtonIconHover': './rope/media/previous_marker_hover.png', +'StopMarkerButtonIconOff': './rope/media/previous_marker_off.png', +'StopMarkerButtonIconOn': './rope/media/previous_marker_off.png', +'StopMarkerButtonInfoText': 'CLEAR VRAM:\nClears models from your VRAM.', +'StopMarkerButtonState': False, +'StopMarkerButtonText': 'Clear VRAM', + +#Switches +'ColorSwitchInfoText': 'RGB ADJUSTMENT:\nFine-tune the RGB color values of the swap.', +'ColorSwitchState': False, +'DiffSwitchInfoText': 'DIFFERENCER:\nAllow some of the original face to show in the swapped result when the difference between the two images is small. Can help bring back some texture to the swapped face', +'DiffSwitchState': False, +'FaceAdjSwitchInfoText': 'KPS and SCALE ADJUSTMENT:\nThis is an experimental feature to perform direct adjustments to the face landmarks found by the detector. There is also an option to adjust the scale of the swapped face.', +'FaceAdjSwitchState': False, +'FaceParserSwitchInfoText': 'BACKGROUND MASK:\nAllow the unprocessed background from the orginal image to show in the final swap.', +'FaceParserSwitchState': False, +'MouthParserSwitchInfoText': 'MOUTH MASK:\nAllow the mouth from the original face to show on the swapped face.', +'MouthParserSwitchState': False, +'OccluderSwitchInfoText': 'OCCLUSION MASK:\nAllow objects occluding the face to show up in the swapped image.', +'OccluderSwitchState': False, +'OrientSwitchInfoText': 'ORIENTATION:\nRotate the face detector to better detect faces at different angles', +'OrientSwitchState': False, +'RestorerSwitchInfoText': 'FACE RESTORER:\nRestore the swapped image by upscaling.', +'RestorerSwitchState': False, +'StrengthSwitchInfoText': 'SWAPPER STRENGTH:\nApply additional swapping iterations to increase the strength of the result, which may increase likeness', +'StrengthSwitchState': False, +'CLIPSwitchInfoText': 'TEXT MASKING:\nUse descriptions to identify objects that will be present in the final swapped image.', +'CLIPSwitchState': False, + +# Sliders +'BlendSliderAmount': 5, +'BlendSliderInc': 1, +'BlendSliderInfoText': 'BLEND:\nCombined masks blending distance. Is not applied to the border masks.', +'BlendSliderMax': 100, +'BlendSliderMin': 0, +'BorderBlurSliderAmount': 10, +'BorderBlurSliderInc': 1, +'BorderBlurSliderInfoText': 'BORDER MASK BLEND:\nBorder mask blending distance.', +'BorderBlurSliderMax': 64, +'BorderBlurSliderMin': 0, +'BorderBottomSliderAmount': 10, +'BorderBottomSliderInc': 1, +'BorderBottomSliderInfoText': 'BOTTOM BORDER DISTANCE:\nA rectangle with adjustable top, bottom, and sides that blends the swapped face rseult back into the original image.', +'BorderBottomSliderMax': 64, +'BorderBottomSliderMin': 0, +'BorderSidesSliderAmount': 10, +'BorderSidesSliderInc': 1, +'BorderSidesSliderInfoText': 'SIDES BORDER DISTANCE:\nA rectangle with adjustable top, bottom, and sides that blends the swapped face result back into the original image.', +'BorderSidesSliderMax': 64, +'BorderSidesSliderMin': 0, +'BorderTopSliderAmount': 10, +'BorderTopSliderInc': 1, +'BorderTopSliderInfoText': 'TOP BORDER DISTANCE:\nA rectangle with adjustable top, bottom, and sides that blends the swapped face result back into the original image.', +'BorderTopSliderMax': 64, +'BorderTopSliderMin': 0, +'ColorBlueSliderAmount': 0, +'ColorBlueSliderInc': 1, +'ColorBlueSliderInfoText': 'RGB BLUE ADJUSTMENT', +'ColorBlueSliderMax': 100, +'ColorBlueSliderMin': -100, +'ColorGreenSliderAmount': 0, +'ColorGreenSliderInc': 1, +'ColorGreenSliderInfoText': 'RGB GREEN ADJUSTMENT', +'ColorGreenSliderMax': 100, +'ColorGreenSliderMin': -100, +'ColorRedSliderAmount': 0, +'ColorRedSliderInc': 1, +'ColorRedSliderInfoText': 'RGB RED ADJUSTMENT', +'ColorRedSliderMax': 100, +'ColorRedSliderMin': -100, +'DetectScoreSliderAmount': 50, +'DetectScoreSliderInc': 1, +'DetectScoreSliderInfoText': 'DETECTION SCORE LIMIT:\nDetermines the minimum score required for a face to be detected. Higher values require higher quality faces. E.g., if faces are flickering when at extreme angles, raising this will limit swapping attempts.', +'DetectScoreSliderMax': 100, +'DetectScoreSliderMin': 1, +'DiffSliderAmount': 4, +'DiffSliderInc': 1, +'DiffSliderInfoText': 'DIFFERENCING AMOUNT:\nHigher values relaxes the similarity constraint.', +'DiffSliderMax': 100, +'DiffSliderMin': 0, +'FaceParserSliderAmount': 0, +'FaceParserSliderInc': 1, +'FaceParserSliderInfoText': 'BACKGROUND MASK AMOUNT:\nNegative/Positive values shrink and grow the mask.', +'FaceParserSliderMax': 50, +'FaceParserSliderMin': -50, +'FaceScaleSliderAmount': 0, +'FaceScaleSliderInc': 1, +'FaceScaleSliderInfoText': 'FACE SCALE AMOUNT', +'FaceScaleSliderMax': 20, +'FaceScaleSliderMin': -20, +'KPSScaleSliderAmount': 0, +'KPSScaleSliderInc': 1, +'KPSScaleSliderInfoText': 'KPS SCALE AMOUNT:\nGrows and shrinks the detection point distances.', +'KPSScaleSliderMax': 100, +'KPSScaleSliderMin': -100, +'KPSXSliderAmount': 0, +'KPSXSliderInc': 1, +'KPSXSliderInfoText': 'KPS X-DIRECTION AMOUNT:\nShifts the detection points left and right', +'KPSXSliderMax': 100, +'KPSXSliderMin': -100, +'KPSYSliderAmount': 0, +'KPSYSliderInc': 1, +'KPSYSliderInfoText': 'KPS Y-DIRECTION AMOUNT:\nShifts the detection points lup and down', +'KPSYSliderMax': 100, +'KPSYSliderMin': -100, +'MouthParserSliderAmount': 0, +'MouthParserSliderInc': 1, +'MouthParserSliderInfoText': 'MOUTH MASK AMOUNT:\nAdjust the size of the mask. Negative values only mask the inside of the mouth, including the tongue. Positive values also include lips', +'MouthParserSliderMax': 50, +'MouthParserSliderMin': -50, +'OccluderSliderAmount': 0, +'OccluderSliderInc': 1, +'OccluderSliderInfoText': 'OCCLUDER AMOUNT:\nGrows or shrinks the occluded region', +'OccluderSliderMax': 100, +'OccluderSliderMin': -100, +'OrientSliderAmount': 0, +'OrientSliderInc': 90, +'OrientSliderInfoText': 'ORIENTATION ANGLE:\nSet this to the angle of the input face angle to help with laying down/upside down/etc. Angles are read clockwise.', +'OrientSliderMax': 270, +'OrientSliderMin': 0, +'RestorerSliderAmount': 100, +'RestorerSliderInc': 5, +'RestorerSliderInfoText': 'RESTORER AMOUNT:\nBlends the Restored results back into the original swap.', +'RestorerSliderMax': 100, +'RestorerSliderMin': 0, +'StrengthSliderAmount': 100, +'StrengthSliderInc': 25, +'StrengthSliderInfoText': 'STRENGTH AMOUNT:\nIncrease up to 5x additional swaps (500%). 200% is generally a good result. Set to 0 to turn off swapping but allow the rest of the pipeline to apply to the original image.', +'StrengthSliderMax': 500, +'StrengthSliderMin': 0, +'ThreadsSliderAmount': 5, +'ThreadsSliderInc': 1, +'ThreadsSliderInfoText': 'EXECUTION THREADS:\nSet number of execution threads while playing and recording. Depends strongly on GPU VRAM. 5 threads for 24GB.', +'ThreadsSliderMax': 20, +'ThreadsSliderMin': 1, +'ThresholdSliderAmount': 55, +'ThresholdSliderInc': 1, +'ThresholdSliderInfoText': 'THRESHHOLD AMOUNT:\nRaise to reduce faces hopping around when swapping multiple people. A higher value is stricter.', +'ThresholdSliderMax': 100, +'ThresholdSliderMin': 0, +'VideoQualSliderAmount': 18, +'VideoQualSliderInc': 1, +'VideoQualSliderInfoText': 'VIDEO QUALITY:\nThe encoding quality of the recorded video. 0 is best, 50 is worst, 18 is mostly lossless. File size increases with a lower quality number.', +'VideoQualSliderMax': 50, +'VideoQualSliderMin': 0, + +'CLIPSliderAmount': 50, +'CLIPSliderInc': 1, +'CLIPSliderInfoText': 'TEXT MASKING STENGTH:\nIncrease to strengthen the effect.', +'CLIPSliderMax': 100, +'CLIPSliderMin': 0, + +'ColorGammaSliderAmount': 1, +'ColorGammaSliderInc': 0.02, +'ColorGammaSliderInfoText': 'GAMMA VALUE:\nChanges Gamma.', +'ColorGammaSliderMax': 2, +'ColorGammaSliderMin': 0, + + +# Text Selection +'DetectTypeTextSelInfoText': 'FACE DETECTION MODEL:\nSelect the face detection model. Mostly only subtle differences, but can significant differences when the face is at extreme angles or covered.', +'DetectTypeTextSelMode': 'Retinaface', +'DetectTypeTextSelModes': ['Retinaface', 'Yolov8', 'SCRDF'], +'PreviewModeTextSelInfoText': '', +'PreviewModeTextSelMode': 'Video', +'PreviewModeTextSelModes': ['Video', 'Image','Theater'], +'RecordTypeTextSelInfoText': 'VIDEO RECORDING LIBRARY:\nSelect the recording library used for video recording. FFMPEG uses the Video Quality slider to adjust the size and quality of the final video. OPENCV has no options but is faster and produces good results.', +'RecordTypeTextSelMode': 'FFMPEG', +'RecordTypeTextSelModes': ['FFMPEG', 'OPENCV'], +'RestorerDetTypeTextSelInfoText': 'ALIGNMENT:\nSelect how the face is aligned for the Restorer. Original preserves facial features and expressions, but can show some artifacts. Reference softens features. Blend is closer to Reference but is much faster.', +'RestorerDetTypeTextSelMode': 'Blend', +'RestorerDetTypeTextSelModes': ['Original', 'Blend', 'Reference'], +'RestorerTypeTextSelInfoText': 'RESTORER TYPE:\nSelect the Restorer type.\nSpeed: GPEN256>GFPGAN>CF>GPEN512', +'RestorerTypeTextSelMode': 'GFPGAN', +'RestorerTypeTextSelModes': ['GFPGAN', 'CF', 'GPEN256', 'GPEN512'], +'MergeTextSelInfoText': 'INPUT FACES MERGE MATH:\nWhen shift-clicking face for merging, determines how the embedding vectors are combined.', +'MergeTextSelMode': 'Mean', +'MergeTextSelModes': ['Mean', 'Median'], +'SwapperTypeTextSelInfoText': 'SWAPPER OUTPUT RESOLUTION:\nDetermines the resolution of the swapper output.', +'SwapperTypeTextSelMode': '128', +'SwapperTypeTextSelModes': ['128', '256', '512'], + + + +# Text Entry +'CLIPTextEntry': '', +'CLIPTextEntryInfoText': 'TEXT MASKING ENTRY:\nTo use, type a word(s) in the box separated by commas and press .', +} + +PARAM_VARS = { + + 'CLIPState': False, + 'CLIPMode': 0, + 'CLIPModes': ['CLIP'], + 'CLIPAmount': [50], + 'CLIPMin': 0, + 'CLIPMax': 100, + 'CLIPInc': 1, + 'CLIPUnit': '%', + 'CLIPIcon': './rope/media/CLIP.png', + 'CLIPMessage': 'CLIP - Text based occluder. Occluded objects are visible in the final image (occluded from the mask). [LB: on/off, MW: strength]', + 'CLIPFunction': False, + + "CLIPText": '', +} + +PARAMS = { + + 'ClearmemFunction': 'self.clear_mem()', + 'PerfTestFunction': 'self.toggle_perf_test()', + 'ImgVidFunction': 'self.toggle_vid_img()', + 'AutoSwapFunction': 'self.toggle_auto_swap()', + 'SaveImageFunction': 'self.save_image()', + + 'ClearmemIcon': './rope/media/clear_mem.png', + 'SaveImageIcon': './rope/media/save_disk.png', + 'PerfTestIcon': './rope/media/test.png', + 'RefDelIcon': './rope/media/construction.png', + 'TransformIcon': './rope/media/scale.png', + 'ThresholdIcon': './rope/media/thresh.png', + 'LoadSFacesIcon': './rope/media/save.png', + 'BorderIcon': './rope/media/maskup.png', + 'OccluderIcon': './rope/media/occluder.png', + 'ColorIcon': './rope/media/rgb.png', + 'StrengthIcon': './rope/media/strength.png', + 'OrientationIcon': './rope/media/orient.png', + 'DiffIcon': './rope/media/diff.png', + 'MouthParserIcon': './rope/media/parse.png', + 'AudioIcon': './rope/media/rgb.png', + 'VideoQualityIcon': './rope/media/tarface.png', + 'MaskViewIcon': './rope/media/maskblur.png', + 'BlurIcon': './rope/media/blur.png', + 'ToggleStopIcon': './rope/media/STOP.png', + 'DelEmbedIcon': './rope/media/delemb.png', + 'ImgVidIcon': './rope/media/imgvid.png', + + + + 'ImgVidMessage': 'IMAGE/VIDEO - Toggle between Image and Video folder view.', + 'ToggleStopMessage': 'STOP MARKER - Sets a frame that will stop the video playing/recording.', + 'AutoSwapMessage': 'AUTO SWAP - Automatically swaps the first person in an image to the selcted source faces [LB: Turn on/off]', + 'SaveImageMessage': 'SAVE IMAGE - Save image to output folder', + 'ClearmemMessage': 'CLEAR VRAM - Clears all models from VRAM [LB: Clear]', + 'PerfTestMessage': 'PERFORMANCE DATA - Displays timing data in the console for critical Rope functions. [LB: on/off]', + 'RefDelMessage': 'REFERENCE DELTA - Modify the reference points. Turn on mask preview to see adjustments. [LB: on/off, RB: translate x/y, and scale, MW: amount]' , + 'ThresholdMessage': 'THRESHOLD - Threshold for determining if Target Faces match faces in a frame. Lower is stricter. [LB: use amount/match all, MW: value]', + 'TransformMessage': 'SCALE - Adjust the scale of the face. Use with Background parser to blend into the image. [LB: on/off, MW: amount]', + 'PlayMessage': 'PLAY - Plays the video. Press again to stop playing', + + } + diff --git a/rope/GUI.py b/rope/GUI.py new file mode 100644 index 0000000..e21b8f8 --- /dev/null +++ b/rope/GUI.py @@ -0,0 +1,1901 @@ +import os +import cv2 +import tkinter as tk +from tkinter import filedialog, font +import numpy as np +from PIL import Image, ImageTk +import json +import time +import copy +import bisect +import torch +import torchvision + +torchvision.disable_beta_transforms_warning() +import mimetypes +import webbrowser +from random import random + + +import rope.GUIElements as GE +import rope.Styles as style + +from skimage import transform as trans +from torchvision.transforms import v2 + +import inspect #print(inspect.currentframe().f_back.f_code.co_name, 'resize_image') + +class GUI(tk.Tk): + def __init__(self, models): + super().__init__() + + self.models = models + self.title('Rope-Pearl-00') + self.target_media = [] + self.target_video_file = [] + self.action_q = [] + self.video_image = [] + self.video_loaded = False + self.image_loaded = False + self.image_file_name = [] + self.stop_marker = [] + self.stop_image = [] + self.stop_marker_icon = [] + self.window_last_change = [] + self.blank = tk.PhotoImage() + self.output_folder = [] + self.output_videos_text = [] + self.target_media_buttons = [] + self.input_videos_button = [] + self.input_videos_text = [] + self.target_media_canvas = [] + self.source_faces_buttons = [] + self.input_videos_button = [] + self.input_faces_text = [] + self.shift_i_len = 0 + self.source_faces_canvas = [] + self.video = [] + self.video_slider = [] + self.found_faces_canvas = [] + self.merged_embedding_name = [] + self.merged_embeddings_text = [] + self.me_name = [] + self.merged_faces_canvas = [] + self.parameters = {} + self.control = {} + self.widget = {} + self.static_widget = {} + self.layer = {} + + self.temp_emb = [] + + self.arcface_dst = np.array([[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366], [41.5493, 92.3655], [70.7299, 92.2041]], dtype=np.float32) + + + self.json_dict = { + "source videos": None, + "source faces": None, + "saved videos": None, + 'dock_win_geom': [1400, 800, self.winfo_screenwidth()/2-400, self.winfo_screenheight()/2-510], + } + + self.marker = { + 'frame': '0', + 'parameters': '', + 'icon_ref': '', + } + self.markers = [] + + self.target_face = { + "TKButton": [], + "ButtonState": "off", + "Image": [], + "Embedding": [], + "SourceFaceAssignments": [], + "EmbeddingNumber": 0, #used for adding additional found faces + 'AssignedEmbedding': [], #the currently assigned source embedding, including averaged ones + } + self.target_faces = [] + + self.source_face = { + "TKButton": [], + "ButtonState": "off", + "Image": [], + "Embedding": [] + } + self.source_faces = [] + + + +##### + def create_gui(self): + + # 1 x 3 Top level grid + self.grid_columnconfigure(0, weight=1) + self.grid_rowconfigure(0, weight=0) + self.grid_rowconfigure(1, weight=1) + self.grid_rowconfigure(2, weight=0) + + self.configure(style.frame_style_bg) + + # Top Frame + top_frame = tk.Frame(self, style.canvas_frame_label_1) + top_frame.grid(row=0, column=0, sticky='NEWS', padx=1, pady=1) + top_frame.grid_columnconfigure(0, weight=1) + top_frame.grid_columnconfigure(1, weight=0) + + # Middle Frame + middle_frame = tk.Frame( self, style.frame_style_bg) + middle_frame.grid(row=1, column=0, sticky='NEWS', padx=0, pady=0) + middle_frame.grid_rowconfigure(0, weight=1) + # Videos and Faces + middle_frame.grid_columnconfigure(0, weight=0) + # Preview + middle_frame.grid_columnconfigure(1, weight=1) + # Parameters + middle_frame.grid_columnconfigure(2, weight=0) + # Scrollbar + middle_frame.grid_columnconfigure(3, weight=0) + + # Bottom Frame + bottom_frame = tk.Frame( self, style.canvas_frame_label_1) + bottom_frame.grid(row=2, column=0, sticky='NEWS', padx=1, pady=1) + bottom_frame.grid_columnconfigure(0, minsize=100) + bottom_frame.grid_columnconfigure(1, weight=1) + bottom_frame.grid_columnconfigure(2, minsize=100) + +####### Top Frame + # Left + # Label + self.layer['topleft'] = tk.Frame(top_frame, style.canvas_frame_label_1, height = 42) + self.layer['topleft'].grid(row=0, column=0, sticky='NEWS', pady=0) + + # Buttons + self.widget['StartButton'] = GE.Button(self.layer['topleft'], 'StartRope', 1, self.load_all, None, 'control', 10, 9, width=200) + self.widget['OutputFolderButton'] = GE.Button(self.layer['topleft'], 'OutputFolder', 1, self.select_save_video_path, None, 'control', x=240, y=1, width=190) + self.output_videos_text = GE.Text(self.layer['topleft'], '', 1, 240, 20, 190, 20) + + # Right + self.layer['topright'] = tk.Frame(top_frame, style.canvas_frame_label_1, height=42, width=413) + self.layer['topright'].grid(row=0, column=1, sticky='NEWS', pady=0) + self.control['ClearVramButton'] = GE.Button(self.layer['topright'], 'ClearVramButton', 1, self.clear_mem, None, 'control', x=5, y=9, width=85, height=20) + self.static_widget['vram_indicator'] = GE.VRAM_Indicator(self.layer['topright'], 1, 300, 20, 100, 11) + +####### Middle Frame + + ### Videos and Faces + self.layer['InputVideoFrame'] = tk.Frame(middle_frame, style.canvas_frame_label_3) + self.layer['InputVideoFrame'].grid(row=0, column=0, sticky='NEWS', padx=1, pady=0) + # Buttons + self.layer['InputVideoFrame'].grid_rowconfigure(0, weight=0) + # Input Media Canvas + self.layer['InputVideoFrame'].grid_rowconfigure(1, weight=1) + + # Input Videos + self.layer['InputVideoFrame'].grid_columnconfigure(0, weight=0) + # Scrollbar + self.layer['InputVideoFrame'].grid_columnconfigure(1, weight=0) + # Input Faces Canvas + self.layer['InputVideoFrame'].grid_columnconfigure(0, weight=0) + # Scrollbar + self.layer['InputVideoFrame'].grid_columnconfigure(1, weight=0) + + # Input Videos + # Button Frame + frame = tk.Frame(self.layer['InputVideoFrame'], style.canvas_frame_label_2, height = 42) + frame.grid(row=0, column=0, columnspan = 2, sticky='NEWS', padx=0, pady=0) + + # Buttons + self.widget['VideoFolderButton'] = GE.Button(frame, 'LoadTVideos', 2, self.select_video_path, None, 'control', 10, 1, width=195) + self.input_videos_text = GE.Text(frame, '', 2, 10, 20, 190, 20) + + # Input Videos Canvas + self.target_media_canvas = tk.Canvas(self.layer['InputVideoFrame'], style.canvas_frame_label_3, height=100, width=195) + self.target_media_canvas.grid(row=1, column=0, sticky='NEWS', padx=10, pady=10) + self.target_media_canvas.bind("", self.target_videos_mouse_wheel) + self.target_media_canvas.create_text(8, 20, anchor='w', fill='grey25', font=("Arial italic", 20), text=" Input Videos") + + # Scroll Canvas + scroll_canvas = tk.Canvas(self.layer['InputVideoFrame'], style.canvas_frame_label_3, bd=0, ) + scroll_canvas.grid(row=1, column=1, sticky='NEWS', padx=0, pady=0) + scroll_canvas.grid_rowconfigure(0, weight=1) + scroll_canvas.grid_columnconfigure(0, weight=1) + + self.static_widget['input_videos_scrollbar'] = GE.Scrollbar_y(scroll_canvas, self.target_media_canvas) + + # Input Faces + # Button Frame + frame = tk.Frame(self.layer['InputVideoFrame'], style.canvas_frame_label_2, height = 42) + frame.grid(row=0, column=2, columnspan = 2, sticky='NEWS', padx=0, pady=0) + + # Buttons + self.widget['FacesFolderButton'] = GE.Button(frame, 'LoadSFaces', 2, self.select_faces_path, None, 'control', 10, 1, width=195) + self.input_faces_text = GE.Text(frame, '', 2, 10, 20, 190, 20) + + # Scroll Canvas + self.source_faces_canvas = tk.Canvas(self.layer['InputVideoFrame'], style.canvas_frame_label_3, height = 100, width=195) + self.source_faces_canvas.grid(row=1, column=2, sticky='NEWS', padx=10, pady=10) + self.source_faces_canvas.bind("", self.source_faces_mouse_wheel) + self.source_faces_canvas.create_text(8, 20, anchor='w', fill='grey25', font=("Arial italic", 20), text=" Input Faces") + + scroll_canvas = tk.Canvas(self.layer['InputVideoFrame'], style.canvas_frame_label_3, bd=0, ) + scroll_canvas.grid(row=1, column=3, sticky='NEWS', padx=0, pady=0) + scroll_canvas.grid_rowconfigure(0, weight=1) + scroll_canvas.grid_columnconfigure(0, weight=1) + + self.static_widget['input_faces_scrollbar'] = GE.Scrollbar_y(scroll_canvas, self.source_faces_canvas) + # GE.Separator_y(scroll_canvas, 14, 0) + GE.Separator_y(self.layer['InputVideoFrame'], 229, 0) + GE.Separator_x(self.layer['InputVideoFrame'], 0, 41) + + ### Preview + self.layer['preview_column'] = tk.Frame(middle_frame, style.canvas_bg) + self.layer['preview_column'].grid(row=0, column=1, sticky='NEWS', pady=0) + self.layer['preview_column'].grid_columnconfigure(0, weight=1) + # Preview Data + self.layer['preview_column'].grid_rowconfigure(0, weight=0) + # Preview Window + self.layer['preview_column'].grid_rowconfigure(1, weight=1) + # Timeline + self.layer['preview_column'].grid_rowconfigure(2, weight=0) + # MArkers + self.layer['preview_column'].grid_rowconfigure(3, weight=0) + # Controls + self.layer['preview_column'].grid_rowconfigure(4, weight=0) + # Found Faces + self.layer['preview_column'].grid_rowconfigure(5, weight=0) + # Merged Faces + self.layer['preview_column'].grid_rowconfigure(6, weight=0) + + # Preview Data + preview_data = tk.Frame(self.layer['preview_column'], style.canvas_frame_label_2, height = 24) + preview_data.grid(row=0, column=0, sticky='NEWS', pady=0) + preview_data.grid_columnconfigure(0, weight=1) + preview_data.grid_columnconfigure(1, weight=1) + preview_data.grid_columnconfigure(2, weight=1) + # preview_data.grid_columnconfigure(3, weight=1) + preview_data.grid_rowconfigure(0, weight=0) + + + + frame = tk.Frame(preview_data, style.canvas_frame_label_2, height = 24, width=100) + frame.grid(row=0, column=0) + self.widget['AudioButton'] = GE.Button(frame, 'Audio', 2, self.toggle_audio, None, 'control', x=0, y=0, width=100) + + frame = tk.Frame(preview_data, style.canvas_frame_label_2, height = 24, width=100) + frame.grid(row=0, column=1) + self.widget['MaskViewButton'] = GE.Button(frame, 'MaskView', 2, self.toggle_maskview, None, 'control', x=0, y=0, width=100) + + frame = tk.Frame(preview_data, style.canvas_frame_label_2, height = 24, width=200) + frame.grid(row=0, column=2) + self.widget['PreviewModeTextSel'] = GE.TextSelection(frame, 'PreviewModeTextSel', '', 2, self.set_view, True, 'control', width=200, height=20, x=0, y=0, text_percent=1) + + + # Preview Window + self.video = tk.Label(self.layer['preview_column'], bg='black') + self.video.grid(row=1, column=0, sticky='NEWS', padx=0, pady=0) + self.video.bind("", self.iterate_through_merged_embeddings) + self.video.bind("", lambda event: self.toggle_play_video()) + + # Videos + # Timeline + # Slider + self.layer['slider_frame'] = tk.Frame(self.layer['preview_column'], style.canvas_frame_label_2, height=20) + self.layer['slider_frame'].grid(row=2, column=0, sticky='NEWS', pady=0) + self.video_slider = GE.Timeline(self.layer['slider_frame'], self.widget, self.temp_toggle_swapper, self.add_action) + + # Markers + self.layer['markers_canvas'] = tk.Canvas(self.layer['preview_column'], style.canvas_frame_label_2, height = 20) + self.layer['markers_canvas'].grid(row=3, column=0, sticky='NEWS') + self.layer['markers_canvas'].bind('', lambda e:self.update_marker(e.width)) + + # self.create_ui_button('ToggleStop', marker_frame, 140, 2, width=36, height=36) + + # Controls + self.layer['preview_frame'] = tk.Frame(self.layer['preview_column'], style.canvas_bg, height = 40) + self.layer['preview_frame'].grid(row=4, column=0, sticky='NEWS') + self.layer['preview_frame'].grid_columnconfigure(0, weight=0) + self.layer['preview_frame'].grid_columnconfigure(1, weight=1) + self.layer['preview_frame'].grid_columnconfigure(2, weight=0) + self.layer['preview_frame'].grid_rowconfigure(0, weight=0) + self.layer['preview_frame'].grid_rowconfigure(1, weight=0) + + # Left Side + self.layer['play_controls_left'] = tk.Frame(self.layer['preview_frame'], style.canvas_frame_label_2, height=30, width=100 ) + self.layer['play_controls_left'].grid(row=0, column=0, sticky='NEWS', pady=0) + self.widget['SaveImageButton'] = GE.Button(self.layer['play_controls_left'], 'SaveImageButton', 2, self.save_image, None, 'control', x=10, y=5, width=100) + + # Center + cente_frame = tk.Frame(self.layer['preview_frame'], style.canvas_frame_label_2, height=30, ) + cente_frame.grid(row=0, column=1, sticky='NEWS', pady=0) + cente_frame.grid_columnconfigure(0, weight=0) + cente_frame.grid_rowconfigure(0, weight=0) + + play_control_frame = tk.Frame(cente_frame, style.canvas_frame_label_2, height=30, width=270 ) + play_control_frame.place(anchor="c", relx=.5, rely=.5) + + column = 0 + col_delta = 50 + self.widget['TLBegButton'] = GE.Button(play_control_frame, 'TLBeginning', 2, self.preview_control, 'q', 'control', x=column , y=2, width=20) + column += col_delta + self.widget['TLLeftButton'] = GE.Button(play_control_frame, 'TLLeft', 2, self.preview_control, 'a', 'control', x=column , y=2, width=20) + column += col_delta + self.widget['TLRecButton'] = GE.Button(play_control_frame, 'Record', 2, self.toggle_rec_video, None, 'control', x=column , y=2, width=20) + column += col_delta + self.widget['TLPlayButton'] = GE.Button(play_control_frame, 'Play', 2, self.toggle_play_video, None, 'control', x=column , y=2, width=20) + column += col_delta + self.widget['TLRightButton'] = GE.Button(play_control_frame, 'TLRight', 2, self.preview_control, 'd', 'control', x=column , y=2, width=20) + + # Right Side + right_playframe = tk.Frame(self.layer['preview_frame'], style.canvas_frame_label_2, height=30, width=100 ) + right_playframe.grid(row=0, column=2, sticky='NEWS', pady=0) + self.widget['AddMarkerButton'] = GE.Button(right_playframe, 'AddMarkerButton', 2, self.update_marker, 'add', 'control', x=0, y=5, width=20) + self.widget['DelMarkerButton'] = GE.Button(right_playframe, 'DelMarkerButton', 2, self.update_marker, 'delete', 'control', x=25, y=5, width=20) + self.widget['PrevMarkerButton'] = GE.Button(right_playframe, 'PrevMarkerButton', 2, self.update_marker, 'prev', 'control', x=50, y=5, width=20) + self.widget['NextMarkerButton'] = GE.Button(right_playframe, 'NextMarkerButton', 2, self.update_marker, 'next', 'control', x=75, y=5, width=20) + # self.widget['StopMarkerButton'] = GE.Button(right_playframe, 'StopMarkerButton', 2, self.update_marker, 'stop', 'control', x=100, y=5, width=20) + + # Images + self.layer['image_controls'] = tk.Frame(self.layer['preview_column'], style.canvas_frame_label_2, height=80) + self.layer['image_controls'].grid(row=2, column=0, rowspan=2, sticky='NEWS', pady=0) + self.widget['SaveImageButton'] = GE.Button(self.layer['image_controls'], 'SaveImageButton', 2, self.save_image, None, 'control', x=10, y=5, width=100) + self.widget['AutoSwapButton'] = GE.Button(self.layer['image_controls'], 'AutoSwapButton', 2, self.toggle_auto_swap, None, 'control', x=150, y=5, width=100) + + + self.layer['image_controls'].grid_forget() + + # FaceLab + self.layer['FaceLab_controls'] = tk.Frame(self.layer['preview_column'], style.canvas_frame_label_2, height=80) + self.layer['FaceLab_controls'].grid(row=2, column=0, rowspan=2, sticky='NEWS', pady=0) + + + + self.layer['FaceLab_controls'].grid_forget() + + + # Found Faces + ff_frame = tk.Frame(self.layer['preview_column'], style.canvas_frame_label_1) + ff_frame.grid(row=5, column=0, sticky='NEWS', pady=1) + ff_frame.grid_columnconfigure(0, weight=0) + ff_frame.grid_columnconfigure(1, weight=1) + ff_frame.grid_rowconfigure(0, weight=0) + + # Buttons + button_frame = tk.Frame(ff_frame, style.canvas_frame_label_2, height = 100, width = 112) + button_frame.grid( row = 0, column = 0, ) + + self.widget['FindFacesButton'] = GE.Button(button_frame, 'FindFaces', 2, self.find_faces, None, 'control', x=0, y=0, width=112, height=33) + self.widget['ClearFacesButton'] = GE.Button(button_frame, 'ClearFaces', 2, self.clear_faces, None, 'control', x=0, y=33, width=112, height=33) + self.widget['SwapFacesButton'] = GE.Button(button_frame, 'SwapFaces', 2, self.toggle_swapper, None, 'control', x=0, y=66, width=112, height=33) + + # Scroll Canvas + self.found_faces_canvas = tk.Canvas(ff_frame, style.canvas_frame_label_3, height = 100 ) + self.found_faces_canvas.grid( row = 0, column = 1, sticky='NEWS') + self.found_faces_canvas.bind("", self.target_faces_mouse_wheel) + self.found_faces_canvas.create_text(8, 45, anchor='w', fill='grey25', font=("Arial italic", 20), text=" Found Faces") + + self.static_widget['20'] = GE.Separator_y(ff_frame, 111, 0) + + + + # Merged Faces + mf_frame = tk.Frame(self.layer['preview_column'], style.canvas_frame_label_1) + mf_frame.grid(row=6, column=0, sticky='NEWS', pady=0) + mf_frame.grid_columnconfigure(0, minsize=10) + mf_frame.grid_columnconfigure(1, weight=1) + mf_frame.grid_rowconfigure(0, weight=0) + + # Buttons + button_frame = tk.Frame(mf_frame, style.canvas_frame_label_2, height = 100, width = 112) + button_frame.grid( row = 0, column = 0, ) + + self.widget['DelEmbedButton'] = GE.Button(button_frame, 'DelEmbed', 2, self.delete_merged_embedding, None, 'control', x=0, y=0, width=112, height=33) + + # Merged Embeddings Text + self.merged_embedding_name = tk.StringVar() + self.merged_embeddings_text = tk.Entry(button_frame, style.entry_2, textvariable=self.merged_embedding_name) + self.merged_embeddings_text.place(x=8, y=37, width = 96, height=20) + self.merged_embeddings_text.bind("", lambda event: self.save_selected_source_faces(self.merged_embedding_name)) + self.me_name = self.nametowidget(self.merged_embeddings_text) + + # Scroll Canvas + self.merged_faces_canvas = tk.Canvas(mf_frame, style.canvas_frame_label_3, height = 100) + self.merged_faces_canvas.grid( row = 0, column = 1, sticky='NEWS') + self.merged_faces_canvas.grid_rowconfigure(0, weight=1) + self.merged_faces_canvas.bind("", lambda event: self.merged_faces_canvas.xview_scroll(-int(event.delta/120.0), "units")) + self.merged_faces_canvas.create_text(8, 45, anchor='w', fill='grey25', font=("Arial italic", 20), text=" Merged Faces") + self.static_widget['21'] = GE.Separator_y(mf_frame, 111, 0) + + ### Parameters + width=398 + + self.layer['parameter_frame'] = tk.Frame(middle_frame, style.canvas_frame_label_3, bd=0, width=width) + self.layer['parameter_frame'].grid(row=0, column=2, sticky='NEWS', pady=0, padx=1) + + self.layer['parameter_frame'].grid_rowconfigure(0, weight=0) + self.layer['parameter_frame'].grid_rowconfigure(1, weight=1) + self.layer['parameter_frame'].grid_rowconfigure(2, weight=0) + self.layer['parameter_frame'].grid_columnconfigure(0, weight=0) + self.layer['parameter_frame'].grid_columnconfigure(1, weight=0) + + parameters_control_frame = tk.Frame(self.layer['parameter_frame'], style.canvas_frame_label_2, bd=0, width=width, height = 42) + parameters_control_frame.grid(row=0, column=0, columnspan=2, sticky='NEWS', pady=0, padx=0) + parameters_control_frame.grid_columnconfigure(0, weight=1) + parameters_control_frame.grid_columnconfigure(1, weight=1) + parameters_control_frame.grid_columnconfigure(2, weight=1) + parameters_control_frame.grid_rowconfigure(0, weight=0) + + + frame = tk.Frame(parameters_control_frame, style.canvas_frame_label_2, height = 42, width=100) + frame.grid(row=0, column=0) + self.widget['SaveParamsButton'] = GE.Button(frame, 'SaveParamsButton', 2, self.parameter_io, 'save', 'control', x=0 , y=8, width=100) + + frame = tk.Frame(parameters_control_frame, style.canvas_frame_label_2, height = 42, width=100) + frame.grid(row=0, column=1) + self.widget['LoadParamsButton'] = GE.Button(frame, 'LoadParamsButton', 2, self.parameter_io, 'load', 'control', x=0 , y=8, width=100) + + frame = tk.Frame(parameters_control_frame, style.canvas_frame_label_2, height = 42, width=100) + frame.grid(row=0, column=2) + self.widget['DefaultParamsButton'] = GE.Button(frame, 'DefaultParamsButton', 2, self.parameter_io, 'default', 'control', x=0 , y=8, width=100) + + + + self.layer['parameters_canvas'] = tk.Canvas(self.layer['parameter_frame'], style.canvas_frame_label_3, bd=0, width=width) + self.layer['parameters_canvas'].grid(row=1, column=0, sticky='NEWS', pady=0, padx=0) + + self.layer['parameters_frame'] = tk.Frame(self.layer['parameters_canvas'], style.canvas_frame_label_3, bd=0, width=width, height=1000) + self.layer['parameters_frame'].grid(row=0, column=0, sticky='NEWS', pady=0, padx=0) + + self.layer['parameters_canvas'].create_window(0, 0, window = self.layer['parameters_frame'], anchor='nw') + + self.layer['parameter_scroll_canvas'] = tk.Canvas(self.layer['parameter_frame'], style.canvas_frame_label_3, bd=0, ) + self.layer['parameter_scroll_canvas'].grid(row=1, column=1, sticky='NEWS', pady=0) + self.layer['parameter_scroll_canvas'].grid_rowconfigure(0, weight=1) + self.layer['parameter_scroll_canvas'].grid_columnconfigure(0, weight=1) + + self.static_widget['parameters_scrollbar'] = GE.Scrollbar_y(self.layer['parameter_scroll_canvas'], self.layer['parameters_canvas']) + + self.static_widget['30'] = GE.Separator_x(parameters_control_frame, 0, 41) + ### Layout ### + top_border_delta = 25 + bottom_border_delta = 5 + switch_delta = 25 + row_delta = 20 + row = 1 + column = 160 + + # Restore + self.widget['RestorerSwitch'] = GE.Switch2(self.layer['parameters_frame'], 'RestorerSwitch', 'Restorer', 3, self.update_data, 'parameter', 398, 20, 1, row) + row += switch_delta + self.widget['RestorerTypeTextSel'] = GE.TextSelection(self.layer['parameters_frame'], 'RestorerTypeTextSel', 'Restorer Type', 3, self.update_data, 'parameter', 'parameter', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['RestorerDetTypeTextSel'] = GE.TextSelection(self.layer['parameters_frame'], 'RestorerDetTypeTextSel', 'Detection Alignment', 3, self.update_data, 'parameter', 'parameter', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['RestorerSlider'] = GE.Slider2(self.layer['parameters_frame'], 'RestorerSlider', 'Blend', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += top_border_delta + self.static_widget['9'] = GE.Separator_x(self.layer['parameters_frame'], 0, row) + row += bottom_border_delta + + # Threshhold + self.widget['ThresholdSlider'] = GE.Slider2(self.layer['parameters_frame'], 'ThresholdSlider', 'Similarity Threshhold', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += top_border_delta + self.static_widget['3'] = GE.Separator_x(self.layer['parameters_frame'], 0, row) + row += bottom_border_delta + + # Orientation + self.widget['OrientSwitch'] = GE.Switch2(self.layer['parameters_frame'], 'OrientSwitch', 'Orientation', 3, self.update_data, 'parameter', 398, 20, 1, row) + row += switch_delta + self.widget['OrientSlider'] = GE.Slider2(self.layer['parameters_frame'], 'OrientSlider', 'Angle', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += top_border_delta + self.static_widget['2'] = GE.Separator_x(self.layer['parameters_frame'], 0, row) + row += bottom_border_delta + + # Strength + self.widget['StrengthSwitch'] = GE.Switch2(self.layer['parameters_frame'], 'StrengthSwitch', 'Strength', 3, self.update_data, 'parameter', 398, 20, 1, row) + row += switch_delta + self.widget['StrengthSlider'] = GE.Slider2(self.layer['parameters_frame'], 'StrengthSlider', 'Amount', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += top_border_delta + self.static_widget['5'] = GE.Separator_x(self.layer['parameters_frame'], 0, row) + row += bottom_border_delta + + # Border + self.widget['BorderTopSlider'] = GE.Slider2(self.layer['parameters_frame'], 'BorderTopSlider', 'Top Border Distance', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['BorderSidesSlider'] = GE.Slider2(self.layer['parameters_frame'], 'BorderSidesSlider', 'Sides Border Distance', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['BorderBottomSlider'] = GE.Slider2(self.layer['parameters_frame'], 'BorderBottomSlider', 'Bottom Border Distance', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['BorderBlurSlider'] = GE.Slider2(self.layer['parameters_frame'], 'BorderBlurSlider', 'Border Blend', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += top_border_delta + self.static_widget['7'] = GE.Separator_x(self.layer['parameters_frame'], 0, row) + row += bottom_border_delta + + # Diff + self.widget['DiffSwitch'] = GE.Switch2(self.layer['parameters_frame'], 'DiffSwitch', 'Differencing', 3, self.update_data, 'parameter', 398, 20, 1, row) + row += switch_delta + self.widget['DiffSlider'] = GE.Slider2(self.layer['parameters_frame'], 'DiffSlider', 'Amount', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += top_border_delta + self.static_widget['8'] = GE.Separator_x(self.layer['parameters_frame'], 0, row) + row += bottom_border_delta + + # Occluder + self.widget['OccluderSwitch'] = GE.Switch2(self.layer['parameters_frame'], 'OccluderSwitch', 'Occluder', 3, self.update_data, 'parameter', 398, 20, 1, row) + row += switch_delta + self.widget['OccluderSlider'] = GE.Slider2(self.layer['parameters_frame'], 'OccluderSlider', 'Size', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += top_border_delta + self.static_widget['10'] = GE.Separator_x(self.layer['parameters_frame'], 0, row) + row += bottom_border_delta + + # FaceParser - Face + self.widget['FaceParserSwitch'] = GE.Switch2(self.layer['parameters_frame'], 'FaceParserSwitch', 'Face Parser', 3, self.update_data, 'parameter', 398, 20, 1, row) + row += switch_delta + self.widget['FaceParserSlider'] = GE.Slider2(self.layer['parameters_frame'], 'FaceParserSlider', 'Background', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['MouthParserSlider'] = GE.Slider2(self.layer['parameters_frame'], 'MouthParserSlider', 'Mouth', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += top_border_delta + self.static_widget['12'] = GE.Separator_x(self.layer['parameters_frame'], 0, row) + row += bottom_border_delta + + # CLIP + self.widget['CLIPSwitch'] = GE.Switch2(self.layer['parameters_frame'], 'CLIPSwitch', 'Text-Based Masking', 3, self.update_data, 'parameter', 398, 20, 1, row) + row += switch_delta + self.widget['CLIPTextEntry'] = GE.Text_Entry(self.layer['parameters_frame'], 'CLIPTextEntry', 'Text-Based Masking', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['CLIPSlider'] = GE.Slider2(self.layer['parameters_frame'], 'CLIPSlider', 'Amount', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += top_border_delta + self.static_widget['12'] = GE.Separator_x(self.layer['parameters_frame'], 0, row) + row += bottom_border_delta + + # Blur + self.widget['BlendSlider'] = GE.Slider2(self.layer['parameters_frame'], 'BlendSlider', 'Overall Mask Blend', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += top_border_delta + self.static_widget['13'] = GE.Separator_x(self.layer['parameters_frame'], 0, row) + row += bottom_border_delta + + # Color Adjustments + self.widget['ColorSwitch'] = GE.Switch2(self.layer['parameters_frame'], 'ColorSwitch', 'Color Adjustments', 3, self.update_data, 'parameter', 398, 20, 1, row) + row += switch_delta + self.widget['ColorRedSlider'] = GE.Slider2(self.layer['parameters_frame'], 'ColorRedSlider', 'Red', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['ColorGreenSlider'] = GE.Slider2(self.layer['parameters_frame'], 'ColorGreenSlider', 'Green', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['ColorBlueSlider'] = GE.Slider2(self.layer['parameters_frame'], 'ColorBlueSlider', 'Blue', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['ColorGammaSlider'] = GE.Slider2(self.layer['parameters_frame'], 'ColorGammaSlider', 'Gamma', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += top_border_delta + self.static_widget['6'] = GE.Separator_x(self.layer['parameters_frame'], 0, row) + row += bottom_border_delta + + # KPS Adjustment and scaling + self.widget['FaceAdjSwitch'] = GE.Switch2(self.layer['parameters_frame'], 'FaceAdjSwitch', 'Input Face Adjustments', 3, self.update_data, 'parameter', 398, 20, 1, row) + row += switch_delta + self.widget['KPSXSlider'] = GE.Slider2(self.layer['parameters_frame'], 'KPSXSlider', 'KPS - X', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['KPSYSlider'] = GE.Slider2(self.layer['parameters_frame'], 'KPSYSlider', 'KPS - Y', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['KPSScaleSlider'] = GE.Slider2(self.layer['parameters_frame'], 'KPSScaleSlider', 'KPS - Scale', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['FaceScaleSlider'] = GE.Slider2(self.layer['parameters_frame'], 'FaceScaleSlider', 'Face Scale', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += top_border_delta + self.static_widget['4'] = GE.Separator_x(self.layer['parameters_frame'], 0, row) + row += bottom_border_delta + + # Cats and Dogs + self.widget['ThreadsSlider'] = GE.Slider2(self.layer['parameters_frame'], 'ThreadsSlider', 'Threads', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + + row += row_delta + self.widget['DetectTypeTextSel'] = GE.TextSelection(self.layer['parameters_frame'], 'DetectTypeTextSel', 'Detection Type', 3, self.update_data, 'parameter', 'parameter', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['DetectScoreSlider'] = GE.Slider2(self.layer['parameters_frame'], 'DetectScoreSlider', 'Detect Score', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['RecordTypeTextSel'] = GE.TextSelection(self.layer['parameters_frame'], 'RecordTypeTextSel', 'Record Type', 3, self.update_data, 'parameter', 'parameter', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['VideoQualSlider'] = GE.Slider2(self.layer['parameters_frame'], 'VideoQualSlider', 'FFMPEG Quality', 3, self.update_data, 'parameter', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['MergeTextSel'] = GE.TextSelection(self.layer['parameters_frame'], 'MergeTextSel', 'Merge Math', 3, self.select_input_faces, 'merge', '', 398, 20, 1, row, 0.62) + row += row_delta + self.widget['SwapperTypeTextSel'] = GE.TextSelection(self.layer['parameters_frame'], 'SwapperTypeTextSel', 'Swapper Resolution', 3, self.update_data, 'parameter', 'parameter', 398, 20, 1, row, 0.62) + + ### Other + self.layer['tooltip_frame'] = tk.Frame(self.layer['parameter_frame'], style.canvas_frame_label_3, height=80) + self.layer['tooltip_frame'].grid(row=2, column=0, columnspan=2, sticky='NEWS', padx=0, pady=0) + self.layer['tooltip_label'] = tk.Label(self.layer['tooltip_frame'], style.info_label, wraplength=width-10, image=self.blank, compound='left', height=80, width=width-10) + self.layer['tooltip_label'].place(x=5, y=5) + self.static_widget['13'] = GE.Separator_x(self.layer['tooltip_frame'], 0, 0) + ######### FaceLab + + self.layer['facelab_canvas'] = tk.Canvas(self.layer['parameter_frame'], style.canvas_frame_label_3, bd=0, width=width) + self.layer['facelab_canvas'].grid(row=1, column=0, sticky='NEWS', pady=0, padx=0) + # + self.layer['facelab_frame'] = tk.Frame(self.layer['facelab_canvas'], style.canvas_frame_label_3, bd=0, width=width, height=11000) + self.layer['facelab_frame'].grid(row=0, column=0, sticky='NEWS', pady=0, padx=0) + # + self.layer['facelab_canvas'].create_window(0, 0, window=self.layer['facelab_frame'], anchor='nw') + # + self.layer['facelab_scroll_canvas'] = tk.Canvas(self.layer['parameter_frame'], style.canvas_frame_label_3, bd=0, ) + self.layer['facelab_scroll_canvas'].grid(row=1, column=1, sticky='NEWS', pady=0) + self.layer['facelab_scroll_canvas'].grid_rowconfigure(0, weight=1) + self.layer['facelab_scroll_canvas'].grid_columnconfigure(0, weight=1) + + self.static_widget['facelab_scrollbar'] =GE.Scrollbar_y(self.layer['facelab_scroll_canvas'] , self.layer['facelab_canvas']) + # row_delta = 20 + # row = 1 + # for i in range(512): + # temp_str = 'emb_vec_'+str(i) + # self.widget[temp_str] = GE.Slider3(self.layer['facelab_frame'], 'emb_vec_'+str(i), str(i), 3, self.adjust_embedding, i, 398, 20, 1, row, 0.62) + # row += row_delta + + ### Layout ### + top_border_delta = 25 + bottom_border_delta = 5 + switch_delta = 25 + row_delta = 20 + row = 1 + column = 160 + ######### Options + + + + + self.status_left_label = tk.Label(bottom_frame, style.donate_1, cursor="hand2", text=" Questions/Help/Discussions (Discord)") + self.status_left_label.grid( row = 0, column = 0, sticky='NEWS') + self.status_left_label.bind("", lambda e: self.callback("https://discord.gg/EcdVAFJzqp")) + + + self.status_label = tk.Label(bottom_frame, style.donate_1, text="Rope Github") + self.status_label.grid( row = 0, column = 1, sticky='NEWS') + self.status_label.bind("", lambda e: self.callback("https://github.com/Hillobar/Rope")) + + self.donate_label = tk.Label(bottom_frame, style.donate_1, text="Enjoy Rope? Please Support! (Paypal) ", anchor='e') + self.donate_label.grid( row = 0, column = 2, sticky='NEWS') + self.donate_label.bind("", lambda e: self.callback("https://www.paypal.com/donate/?hosted_button_id=Y5SB9LSXFGRF2")) + + + + + + + # Update the parameters or controls dicts and get a new frame + def update_data(self, mode, name, use_markers=False): + # print(inspect.currentframe().f_back.f_code.co_name,) + if mode=='parameter': + self.parameters[name] = self.widget[name].get() + self.add_action('parameters', self.parameters) + + elif mode=='control': + self.control[name] = self.widget[name].get() + self.add_action('control', self.control) + + if use_markers: + self.add_action('get_requested_video_frame', self.video_slider.get()) + else: + self.add_action('get_requested_video_frame_without_markers', self.video_slider.get()) + + def callback(self, url): + webbrowser.open_new_tab(url) + + def target_faces_mouse_wheel(self, event): + self.found_faces_canvas.xview_scroll(1*int(event.delta/120.0), "units") + + + def source_faces_mouse_wheel(self, event): + self.source_faces_canvas.yview_scroll(-int(event.delta/120.0), "units") + + # Center of visible canvas as a percentage of the entire canvas + center = (self.source_faces_canvas.yview()[1]-self.source_faces_canvas.yview()[0])/2 + center = center+self.source_faces_canvas.yview()[0] + self.static_widget['input_faces_scrollbar'].set(center) + + + def target_videos_mouse_wheel(self, event): + self.target_media_canvas.yview_scroll(-int(event.delta/120.0), "units") + + # Center of visible canvas as a percentage of the entire canvas + center = (self.target_media_canvas.yview()[1]-self.target_media_canvas.yview()[0])/2 + center = center+self.target_media_canvas.yview()[0] + self.static_widget['input_videos_scrollbar'].set(center) + + + def parameters_mouse_wheel(self, event): + self.canvas.yview_scroll(1*int(event.delta/120.0), "units") + + + # focus_get() + def preview_control(self, event): + # print(event.char, event.keysym, event.keycode) + # print(type(event)) + if isinstance(event, str): + event = event + else: + event = event.char + + + if self.focus_get() != self.widget['CLIPTextEntry'] and self.focus_get() != self.merged_embeddings_text: + + + if self.video_loaded: + frame = self.video_slider.get() + video_length = self.video_slider.get_length() + if event == ' ': + self.toggle_play_video() + elif event == 'w': + frame += 1 + if frame > video_length: + frame = video_length + self.video_slider.set(frame) + self.add_action("get_requested_video_frame", frame) + elif event == 's': + frame -= 1 + if frame < 0: + frame = 0 + self.video_slider.set(frame) + self.add_action("get_requested_video_frame", frame) + elif event == 'd': + frame += 30 + if frame > video_length: + frame = video_length + self.video_slider.set(frame) + self.add_action("get_requested_video_frame", frame) + elif event == 'a': + frame -= 30 + if frame < 0: + frame = 0 + self.video_slider.set(frame) + self.add_action("get_requested_video_frame", frame) + elif event == 'q': + frame = 0 + self.video_slider.set(frame) + self.add_action("get_requested_video_frame", frame) + + + +# refactor - make sure files are closed + + def initialize_gui( self ): + json_object = {} + # check if data.json exists, if not then create it, else load it + try: + data_json_file = open("data.json", "r") + except: + with open("data.json", "w") as outfile: + json.dump(self.json_dict, outfile) + else: + json_object = json.load(data_json_file) + data_json_file.close() + + # Window position and size + try: + self.json_dict['dock_win_geom'] = json_object['dock_win_geom'] + except: + self.json_dict['dock_win_geom'] = self.json_dict['dock_win_geom'] + + # Initialize the window sizes and positions + self.geometry('%dx%d+%d+%d' % (self.json_dict['dock_win_geom'][0], self.json_dict['dock_win_geom'][1] , self.json_dict['dock_win_geom'][2], self.json_dict['dock_win_geom'][3])) + self.window_last_change = self.winfo_geometry() + + self.bind('', lambda event: self.preview_control(event)) + self.bind('', lambda event: self.preview_control(event)) + + self.resizable(width=True, height=True) + + # Build UI, update ui with default data + self.create_gui() + + self.video_image = cv2.cvtColor(cv2.imread('./rope/media/splash.png'), cv2.COLOR_BGR2RGB) + self.resize_image() + + # Create parameters and controls and and selctively fill with UI data + for key, value in self.widget.items(): + self.widget[key].add_info_frame(self.layer['tooltip_label']) + if self.widget[key].get_data_type()=='parameter': + self.parameters[key] = self.widget[key].get() + + + elif self.widget[key].get_data_type()=='control': + self.control[key] = self.widget[key].get() + + try: + self.json_dict["source videos"] = json_object["source videos"] + except KeyError: + self.widget['VideoFolderButton'].error_button() + else: + if self.json_dict["source videos"] == None: + self.widget['VideoFolderButton'].error_button() + else: + path = self.create_path_string(self.json_dict["source videos"], 28) + self.input_videos_text.configure(text=path) + + try: + self.json_dict["source faces"] = json_object["source faces"] + except KeyError: + self.widget['FacesFolderButton'].error_button() + else: + if self.json_dict["source faces"] == None: + self.widget['FacesFolderButton'].error_button() + else: + path = self.create_path_string(self.json_dict["source faces"], 28) + self.input_faces_text.configure(text=path) + + try: + self.json_dict["saved videos"] = json_object["saved videos"] + except KeyError: + self.widget['OutputFolderButton'].error_button() + else: + if self.json_dict["saved videos"] == None: + self.widget['OutputFolderButton'].error_button() + else: + path = self.create_path_string(self.json_dict["saved videos"], 28) + self.output_videos_text.configure(text=path) + self.add_action("saved_video_path", self.json_dict["saved videos"]) + + # Check for a user parameters file and load if present + try: + parameters_json_file = open("saved_parameters.json", "r") + except: + pass + else: + temp = json.load(parameters_json_file) + parameters_json_file.close() + for key, value in self.parameters.items(): + try: + self.parameters[key] = temp[key] + except KeyError: + pass + + # Update the UI + for key, value in self.parameters.items(): + self.widget[key].set(value, request_frame=False) + + self.add_action('parameters', self.parameters) + self.add_action('control', self.control) + + + self.widget['StartButton'].error_button() + self.set_view(False, '') + + + def create_path_string(self, path, text_len): + if len(path)>text_len: + last_folder = os.path.basename(os.path.normpath(path)) + last_folder_len = len(last_folder) + if last_folder_len>text_len: + path = path[:3]+'...'+path[-last_folder_len+6:] + else: + path = path[:text_len-last_folder_len]+'.../'+path[-last_folder_len:] + + return path + + def load_all(self): + if not self.json_dict["source videos"] or not self.json_dict["source faces"]: + print("Please set faces and videos folders first!") + return + + self.populate_target_videos() + self.load_input_faces() + self.widget['StartButton'].enable_button() + + + def select_video_path(self): + temp = self.json_dict["source videos"] + self.json_dict["source videos"] = filedialog.askdirectory(title="Select Target Videos Folder", initialdir=temp) + + path = self.create_path_string(self.json_dict["source videos"], 28) + self.input_videos_text.configure(text=path) + + with open("data.json", "w") as outfile: + json.dump(self.json_dict, outfile) + outfile.close() + self.widget['VideoFolderButton'].set(False, request_frame=False) + self.populate_target_videos() + + def select_save_video_path(self): + temp = self.json_dict["saved videos"] + self.json_dict["saved videos"] = filedialog.askdirectory(title="Select Save Video Folder", initialdir=temp) + + path = self.create_path_string(self.json_dict["saved videos"], 28) + self.output_videos_text.configure(text=path) + + with open("data.json", "w") as outfile: + json.dump(self.json_dict, outfile) + outfile.close() + self.widget['OutputFolderButton'].set(False, request_frame=False) + self.add_action("saved_video_path",self.json_dict["saved videos"]) + + def select_faces_path(self): + temp = self.json_dict["source faces"] + self.json_dict["source faces"] = filedialog.askdirectory(title="Select Source Faces Folder", initialdir=temp) + + path = self.create_path_string(self.json_dict["source faces"], 28) + self.input_faces_text.configure(text=path) + + with open("data.json", "w") as outfile: + json.dump(self.json_dict, outfile) + outfile.close() + self.widget['FacesFolderButton'].set(False, request_frame=False) + self.load_input_faces() + + def load_input_faces(self): + self.source_faces = [] + self.merged_faces_canvas.delete("all") + self.source_faces_canvas.delete("all") + + # First load merged embeddings + try: + temp0 = [] + with open("merged_embeddings.txt", "r") as embedfile: + temp = embedfile.read().splitlines() + + for i in range(0, len(temp), 513): + to = [temp[i][6:], np.array(temp[i+1:i+513], dtype='float32')] + temp0.append(to) + + for j in range(len(temp0)): + new_source_face = self.source_face.copy() + self.source_faces.append(new_source_face) + + self.source_faces[j]["ButtonState"] = False + self.source_faces[j]["Embedding"] = temp0[j][1] + self.source_faces[j]["TKButton"] = tk.Button(self.merged_faces_canvas, style.media_button_off_3, image=self.blank, text=temp0[j][0], height=14, width=84, compound='left') + + self.source_faces[j]["TKButton"].bind("", lambda event, arg=j: self.select_input_faces(event, arg)) + self.source_faces[j]["TKButton"].bind("", lambda event: self.merged_faces_canvas.xview_scroll(-int(event.delta/120.0), "units")) + + self.merged_faces_canvas.create_window((j//4)*92,8+(22*(j%4)), window = self.source_faces[j]["TKButton"],anchor='nw') + + self.merged_faces_canvas.configure(scrollregion = self.merged_faces_canvas.bbox("all")) + self.merged_faces_canvas.xview_moveto(0) + + except: + pass + + self.shift_i_len = len(self.source_faces) + + # Next Load images + directory = self.json_dict["source faces"] + filenames = [os.path.join(dirpath,f) for (dirpath, dirnames, filenames) in os.walk(directory) for f in filenames] + + # torch.cuda.memory._record_memory_history(True, trace_alloc_max_entries=100000, trace_alloc_record_context=True) + i=0 + for file in filenames: # Does not include full path + # Find all faces and ad to faces[] + # Guess File type based on extension + try: + file_type = mimetypes.guess_type(file)[0][:5] + except: + print('Unrecognized file type:', file) + else: + # Its an image + if file_type == 'image': + img = cv2.imread(file) + print('Loaded image:', img) + if img is not None: + img = torch.from_numpy(img.astype('uint8')).to('cuda') + + pad_scale = 0.2 + padded_width = int(img.size()[1]*(1.+pad_scale)) + padded_height = int(img.size()[0]*(1.+pad_scale)) + + padding = torch.zeros((padded_height, padded_width, 3), dtype=torch.uint8, device='cuda:0') + + width_start = int(img.size()[1]*pad_scale/2) + width_end = width_start+int(img.size()[1]) + height_start = int(img.size()[0]*pad_scale/2) + height_end = height_start+int(img.size()[0]) + + padding[height_start:height_end, width_start:width_end, :] = img + img = padding + + img = img.permute(2,0,1) + try: + kpss = self.models.run_detect(img, max_num=1)[0] # Just one face here + except IndexError: + print('Image cropped too close:', file) + else: + face_emb, cropped_image = self.models.run_recognize(img, kpss) + crop = cv2.cvtColor(cropped_image.cpu().numpy(), cv2.COLOR_BGR2RGB) + crop = cv2.resize(crop, (85, 85)) + + new_source_face = self.source_face.copy() + self.source_faces.append(new_source_face) + + self.source_faces[-1]["Image"] = ImageTk.PhotoImage(image=Image.fromarray(crop)) + self.source_faces[-1]["Embedding"] = face_emb + self.source_faces[-1]["TKButton"] = tk.Button(self.source_faces_canvas, style.media_button_off_3, image=self.source_faces[-1]["Image"], height=90, width=90) + self.source_faces[-1]["ButtonState"] = False + self.source_faces[-1]["file"] = file + + self.source_faces[-1]["TKButton"].bind("", lambda event, arg=len(self.source_faces)-1: self.select_input_faces(event, arg)) + self.source_faces[-1]["TKButton"].bind("", self.source_faces_mouse_wheel) + + self.source_faces_canvas.create_window((i % 2) * 100, (i // 2) * 100, window=self.source_faces[-1]["TKButton"], anchor='nw') + + self.static_widget['input_faces_scrollbar'].resize_scrollbar(None) + i = i + 1 + + else: + print('Bad file', file) + + + torch.cuda.empty_cache() + + def find_faces(self): + try: + img = torch.from_numpy(self.video_image).to('cuda') + img = img.permute(2,0,1) + kpss = self.models.run_detect(img, max_num=50) + + + ret = [] + for face_kps in kpss: + + # if kpss is not None: + # face_kps = kpss[i] + + + face_emb, cropped_img = self.models.run_recognize(img, face_kps) + ret.append([face_kps, face_emb, cropped_img]) + print('Face found') + print(cropped_img.shape) + + except Exception: + print(" No media selected") + + else: + # Find all faces and add to target_faces[] + if ret: + # Apply threshold tolerence + threshhold = self.parameters["ThresholdSlider"] + + # if self.parameters["ThresholdState"]: + # threshhold = 0.0 + + # Loop thgouh all faces in video frame + for face in ret: + found = False + + # Check if this face has already been found + for emb in self.target_faces: + if self.findCosineDistance(emb['Embedding'], face[1]) >= threshhold: + found = True + break + + # If we dont find any existing simularities, it means that this is a new face and should be added to our found faces + if not found: + crop = cv2.resize(face[2].cpu().numpy(), (82, 82)) + + new_target_face = self.target_face.copy() + self.target_faces.append(new_target_face) + last_index = len(self.target_faces)-1 + + self.target_faces[last_index]["TKButton"] = tk.Button(self.found_faces_canvas, style.media_button_off_3, height = 86, width = 86) + self.target_faces[last_index]["TKButton"].bind("", self.target_faces_mouse_wheel) + self.target_faces[last_index]["ButtonState"] = False + self.target_faces[last_index]["Image"] = ImageTk.PhotoImage(image=Image.fromarray(crop)) + self.target_faces[last_index]["Embedding"] = face[1] + self.target_faces[last_index]["EmbeddingNumber"] = 1 + + # Add image to button + self.target_faces[-1]["TKButton"].config( pady = 10, image = self.target_faces[last_index]["Image"], command=lambda k=last_index: self.toggle_found_faces_buttons_state(k)) + + # Add button to canvas + self.found_faces_canvas.create_window((last_index)*92, 8, window=self.target_faces[last_index]["TKButton"], anchor='nw') + + self.found_faces_canvas.configure(scrollregion = self.found_faces_canvas.bbox("all")) + + def clear_faces(self): + self.target_faces = [] + self.found_faces_canvas.delete("all") + + # toggle the target faces button and make assignments + def toggle_found_faces_buttons_state(self, button): + # Turn all Target faces off + for i in range(len(self.target_faces)): + self.target_faces[i]["ButtonState"] = False + self.target_faces[i]["TKButton"].config(style.media_button_off_3) + + # Set only the selected target face to on + self.target_faces[button]["ButtonState"] = True + self.target_faces[button]["TKButton"].config(style.media_button_on_3) + + # set all source face buttons to off + for i in range(len(self.source_faces)): + self.source_faces[i]["ButtonState"] = False + self.source_faces[i]["TKButton"].config(style.media_button_off_3) + + # turn back on the ones that are assigned to the curent target face + for i in range(len(self.target_faces[button]["SourceFaceAssignments"])): + self.source_faces[self.target_faces[button]["SourceFaceAssignments"][i]]["ButtonState"] = True + self.source_faces[self.target_faces[button]["SourceFaceAssignments"][i]]["TKButton"].config(style.media_button_on_3) + + def select_input_faces(self, event, button): + + try: + if event.state & 0x4 != 0: + modifier = 'ctrl' + elif event.state & 0x1 != 0: + modifier = 'shift' + else: + modifier = 'none' + except: + modifier = event + + + # If autoswap isnt on + # Clear all the highlights. Clear all states, excpet if a modifier is being used + # Start by turning off all the highlights on the input faces buttons + if modifier != 'auto': + for face in self.source_faces: + face["TKButton"].config(style.media_button_off_3) + + # and also clear the states if not selecting multiples + if modifier == 'none': + face["ButtonState"] = False + + # Toggle the state of the selected Input Face + if modifier != 'merge': + self.source_faces[button]["ButtonState"] = not self.source_faces[button]["ButtonState"] + + # if shift find any other input faces and activate the state of all faces in between + if modifier == 'shift': + for i in range(button-1, self.shift_i_len-1, -1): + if self.source_faces[i]["ButtonState"]: + for j in range(i, button, 1): + self.source_faces[j]["ButtonState"] = True + break + for i in range(button+1, len(self.source_faces), 1): + if self.source_faces[i]["ButtonState"]: + for j in range(button, i, 1): + self.source_faces[j]["ButtonState"] = True + break + + # Highlight all of input faces buttons that have a true state + for face in self.source_faces: + if face["ButtonState"]: + face["TKButton"].config(style.media_button_on_3) + + if self.widget['PreviewModeTextSel'].get() == 'FaceLab': + self.add_action("load_target_image", face["file"]) + self.image_loaded = True + + # Assign all active input faces to the active target face + for tface in self.target_faces: + if tface["ButtonState"]: + + # Clear all of the assignments + tface["SourceFaceAssignments"] = [] + + # Iterate through all Input faces + temp_holder = [] + for j in range(len(self.source_faces)): + + # If the source face is active + if self.source_faces[j]["ButtonState"]: + tface["SourceFaceAssignments"].append(j) + temp_holder.append(self.source_faces[j]['Embedding']) + + # do averaging + if temp_holder: + if self.widget['MergeTextSel'].get() == 'Median': + tface['AssignedEmbedding'] = np.median(temp_holder, 0) + elif self.widget['MergeTextSel'].get() == 'Mean': + tface['AssignedEmbedding'] = np.mean(temp_holder, 0) + + self.temp_emb = tface['AssignedEmbedding'] + + # for k in range(512): + # self.widget['emb_vec_' + str(k)].set(tface['AssignedEmbedding'][k], False) + break + + self.add_action("target_faces", self.target_faces) + self.add_action('get_requested_video_frame', self.video_slider.get()) + + # latent = torch.from_numpy(self.models.calc_swapper_latent(self.source_faces[button]['Embedding'])).float().to('cuda') + # face['ptrdata'] = self.models.run_swap_stg1(latent) + + + + def populate_target_videos(self): + # Recursively read all media files from directory + directory = self.json_dict["source videos"] + filenames = [os.path.join(dirpath,f) for (dirpath, dirnames, filenames) in os.walk(directory) for f in filenames] + + videos = [] + images = [] + self.target_media = [] + self.target_media_buttons = [] + self.target_media_canvas.delete("all") + + for file in filenames: # Does not include full path + # Guess File type based on extension + try: + file_type = mimetypes.guess_type(file)[0][:5] + except: + print('Unrecognized file type:', file) + else: + # Its an image + if file_type == 'image': + try: + image = cv2.imread(file) + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + except: + print('Trouble reading file:', file) + else: + ratio = float(image.shape[0]) / image.shape[1] + + new_height = 50 + new_width = int(new_height / ratio) + image = cv2.resize(image, (new_width, new_height)) + image[:new_height, :new_width, :] = image + images.append([image, file]) + + # Its a video + elif file_type == 'video': + try: + video = cv2.VideoCapture(file) + except: + print('Trouble reading file:', file) + else: + if video.isOpened(): + + # Grab a frame from the middle for a thumbnail + video.set(cv2.CAP_PROP_POS_FRAMES, int(video.get(cv2.CAP_PROP_FRAME_COUNT)/2)) + success, video_frame = video.read() + + if success: + video_frame = cv2.cvtColor(video_frame, cv2.COLOR_BGR2RGB) + ratio = float(video_frame.shape[0]) / video_frame.shape[1] + + new_height = 50 + new_width = int(new_height / ratio) + video_frame = cv2.resize(video_frame, (new_width, new_height)) + video_frame[:new_height, :new_width, :] = video_frame + + videos.append([video_frame, file]) + video.release() + + else: + print('Trouble reading file:', file) + else: + print('Trouble opening file:', file) + delx, dely = 100, 79 + if self.widget['PreviewModeTextSel'].get()== 'Image':#images + for i in range(len(images)): + self.target_media_buttons.append(tk.Button(self.target_media_canvas, style.media_button_off_3, height = 86, width = 86)) + + rgb_video = Image.fromarray(images[i][0]) + self.target_media.append(ImageTk.PhotoImage(image=rgb_video)) + self.target_media_buttons[i].config( image = self.target_media[i], command=lambda i=i: self.load_target(i, images[i][1], self.widget['PreviewModeTextSel'].get())) + self.target_media_buttons[i].bind("", self.target_videos_mouse_wheel) + self.target_media_canvas.create_window((i%2)*delx, (i//2)*dely, window = self.target_media_buttons[i], anchor='nw') + + self.target_media_canvas.configure(scrollregion = self.target_media_canvas.bbox("all")) + + elif self.widget['PreviewModeTextSel'].get()=='Video':#videos + + for i in range(len(videos)): + self.target_media_buttons.append(tk.Button(self.target_media_canvas, style.media_button_off_3, height = 65, width = 90)) + self.target_media.append(ImageTk.PhotoImage(image=Image.fromarray(videos[i][0]))) + + filename = os.path.basename(videos[i][1]) + if len(filename)>14: + filename = filename[:11]+'...' + + self.target_media_buttons[i].bind("", self.target_videos_mouse_wheel) + self.target_media_buttons[i].config(image = self.target_media[i], text=filename, compound='top', anchor='n',command=lambda i=i: self.load_target(i, videos[i][1], self.widget['PreviewModeTextSel'].get())) + self.target_media_canvas.create_window((i%2)*delx, (i//2)*dely, window = self.target_media_buttons[i], anchor='nw') + + self.static_widget['input_videos_scrollbar'].resize_scrollbar(None) + + def auto_swap(self): + # Reselect Target Image + # try: + self.find_faces() + self.target_faces[0]["ButtonState"] = True + self.target_faces[0]["TKButton"].config(style.media_button_on_3) + + # Reselect Source images + self.select_input_faces('auto', '') + self.toggle_swapper(True) + # except: + # pass + def toggle_auto_swap(self): + self.widget['AutoSwapButton'].toggle_button() + + + def load_target(self, button, media_file, media_type): + # Make sure the video stops playing + self.toggle_play_video('stop') + self.image_loaded = False + self.video_loaded = False + self.clear_faces() + + if media_type == 'Video': + self.video_slider.set(0) + self.add_action("load_target_video", media_file) + self.media_file_name = os.path.splitext(os.path.basename(media_file)) + self.video_loaded = True + + + elif media_type == 'Image': + self.add_action("load_target_image", media_file) + self.media_file_name = os.path.splitext(os.path.basename(media_file)) + self.image_loaded = True + + # # find faces + if self.widget['AutoSwapButton'].get(): + self.add_action('function', "gui.auto_swap()") + + + + + for i in range(len(self.target_media_buttons)): + self.target_media_buttons[i].config(style.media_button_off_3) + + self.target_media_buttons[button].config(style.media_button_on_3) + + # delete all markers + self.layer['markers_canvas'].delete('all') + self.markers = [] + self.stop_marker = [] + self.add_action("markers", self.markers) + + + + # @profile + def set_image(self, image, requested): + self.video_image = image[0] + frame = image[1] + + if not requested: + self.video_slider.set(frame) + self.parameter_update_from_marker(frame) + + self.resize_image() + + # @profile + def resize_image(self): + image = self.video_image + + if len(image) != 0: + + x1 = float(self.video.winfo_width()) + y1 = float(self.video.winfo_height()) + + + x2 = float(image.shape[1]) + y2 = float(image.shape[0]) + + + m1 = x1/y1 + m2 = x2/y2 + + if m2>m1: + x2 = x1 + y2 = x1/m2 + image = cv2.resize(image, (int(x2), int(y2))) + padding = int((y1-y2)/2.0) + image = cv2.copyMakeBorder( image, padding, padding, 0, 0, cv2.BORDER_CONSTANT) + else: + y2=y1 + x2=y2*m2 + image = cv2.resize(image, (int(x2), int(y2))) + padding=int((x1-x2)/2.0) + image = cv2.copyMakeBorder( image, 0, 0, padding, padding, cv2.BORDER_CONSTANT) + + image = Image.fromarray(image) + image = ImageTk.PhotoImage(image) + self.video.image = image + self.video.configure(image=self.video.image) + + + + + + def check_for_video_resize(self): + + # Read the geometry from the last time json was updated. json only updates once the window ahs stopped changing + win_geom = '%dx%d+%d+%d' % (self.json_dict['dock_win_geom'][0], self.json_dict['dock_win_geom'][1] , self.json_dict['dock_win_geom'][2], self.json_dict['dock_win_geom'][3]) + + # # window has started changing + if self.winfo_geometry() != win_geom: + # Resize image in video window + self.resize_image() + for k, v in self.widget.items(): + v.hide() + for k, v in self.static_widget.items(): + v.hide() + + + # Check if window has stopped changing + if self.winfo_geometry() != self.window_last_change: + self.window_last_change = self.winfo_geometry() + + # The window has stopped changing + else: + for k, v in self.widget.items(): + v.unhide() + for k, v in self.static_widget.items(): + v.unhide() + # Update json + str1 = self.winfo_geometry().split('x') + str2 = str1[1].split('+') + win_geom = [str1[0], str2[0], str2[1], str2[2]] + win_geom = [int(strings) for strings in win_geom] + self.json_dict['dock_win_geom'] = win_geom + with open("data.json", "w") as outfile: + json.dump(self.json_dict, outfile) + + + + + + def get_action(self): + action = self.action_q[0] + self.action_q.pop(0) + return action + + def get_action_length(self): + return len(self.action_q) + + + + def set_video_slider_length(self, video_length): + self.video_slider.set_length(video_length) + + + + def findCosineDistance(self, vector1, vector2): + cos_dist = 1 - np.dot(vector1, vector2)/(np.linalg.norm(vector1)*np.linalg.norm(vector2)) # 2..0 + return 100-cos_dist*50 + + + + + def toggle_play_video(self, set_value='toggle'): + if self.video_loaded: + + # Update button + if set_value == 'toggle': + self.widget['TLPlayButton'].toggle_button() + if set_value == 'stop': + self.widget['TLPlayButton'].disable_button() + if set_value == 'play': + self.widget['TLPlayButton'].enable_button() + + # If play + if self.widget['TLPlayButton'].get(): + if not self.video_loaded: + print("Please select video first!") + return + else: + # and record + if self.widget['TLRecButton'].get(): + if not self.json_dict["saved videos"]: + print("Set saved video folder first!") + self.add_action("play_video", "stop_from_gui") + + else: + self.add_action("play_video", "record") + + # only play + else: + self.add_action("play_video", "play") + + else: + self.add_action("play_video", "stop_from_gui") + + + def set_player_buttons_to_inactive(self): + self.widget['TLRecButton'].disable_button() + self.widget['TLPlayButton'].disable_button() + + + def toggle_swapper(self, toggle_value=-1): + # print(inspect.currentframe().f_back.f_code.co_name, 'toggle_swapper: '+'toggle_value='+str(toggle_value)) + + if toggle_value == -1: + self.widget['SwapFacesButton'].toggle_button() + + else: + if toggle_value: + self.widget['SwapFacesButton'].enable_button() + else: + self.widget['SwapFacesButton'].disable_button() + + if self.widget['PreviewModeTextSel'].get()=='Video' or self.widget['PreviewModeTextSel'].get()=='Theater': + self.update_data('control', 'SwapFacesButton', use_markers=True) + elif self.widget['PreviewModeTextSel'].get()=='Image': + self.update_data('control', 'SwapFacesButton', use_markers=False) + elif self.widget['PreviewModeTextSel'].get() == 'FaceLab': + self.update_data('control', 'SwapFacesButton', use_markers=False) + + + def temp_toggle_swapper(self, state): + if state=='off': + self.widget['SwapFacesButton'].temp_disable_button() + elif state=='on': + self.widget['SwapFacesButton'].temp_enable_button() + + self.update_data('control', 'SwapFacesButton', use_markers=True) + + def toggle_rec_video(self): + # Play button must be off to enable record button + if not self.widget['TLPlayButton'].get(): + self.widget['TLRecButton'].toggle_button() + + if self.widget['TLRecButton'].get(): + self.widget['TLRecButton'].enable_button() + + else: + self.widget['TLRecButton'].disable_button() + + + # this makes no sense + def add_action(self, action, parameter=None): # + # print(inspect.currentframe().f_back.f_code.co_name, '->add_action: '+action) + + if action != 'get_requested_video_frame' and action != 'get_requested_video_frame_without_markers': + self.action_q.append([action, parameter]) + + # Only do requests when the video is not playing - (moving the timeline or changing parameters) + elif self.video_loaded and not self.widget['TLPlayButton'].get(): + self.action_q.append([action, parameter]) + + elif self.image_loaded: + self.action_q.append([action, parameter]) + + def update_vram_indicator(self): + try: + used, total = self.models.get_gpu_memory() + except: + pass + else: + self.static_widget['vram_indicator'].set(used, total) + + + +# refactor and thread i/o + def save_selected_source_faces(self, text): + # get name from text field + text = text.get() + # get embeddings from all highlightebuttons + # iterate through the buttons + + temp_holder = [] + + for button in self.source_faces: + if button["ButtonState"]: + temp_holder.append(button['Embedding']) + + if temp_holder: + if self.widget['MergeTextSel'].get()=='Median': + ave_embedding = np.median(temp_holder,0) + elif self.widget['MergeTextSel'].get()=='Mean': + ave_embedding = np.mean(temp_holder,0) + + for tface in self.target_faces: + if tface["ButtonState"]: + ave_embedding = tface['AssignedEmbedding'] + + if text != "": + with open("merged_embeddings.txt", "a") as embedfile: + identifier = "Name: "+text + embedfile.write("%s\n" % identifier) + for number in ave_embedding: + embedfile.write("%s\n" % number) + else: + print('No embedding name specified') + else: + print('No Source Images selected') + + self.focus() + self.load_input_faces() + +# refactor and thread i/o + def delete_merged_embedding(self): #add multi select + + # get selected button + sel = [] + for j in range(len(self.source_faces)): + if self.source_faces[j]["ButtonState"]: + sel = j + break + + # check if it is a merged embedding + # if so, read txt embedding into list + temp0 = [] + if os.path.exists("merged_embeddings.txt"): + + with open("merged_embeddings.txt", "r") as embedfile: + temp = embedfile.read().splitlines() + + for i in range(0, len(temp), 513): + to = [temp[i], np.array(temp[i+1:i+513], dtype='float32')] + temp0.append(to) + + if j < len(temp0): + temp0.pop(j) + + with open("merged_embeddings.txt", "w") as embedfile: + for line in temp0: + embedfile.write("%s\n" % line[0]) + for i in range(512): + embedfile.write("%s\n" % line[1][i]) + + self.load_input_faces() + + def iterate_through_merged_embeddings(self, event): + if event.delta>0: + for i in range(len(self.source_faces)): + if self.source_faces[i]["ButtonState"] and i0: + self.select_input_faces('none', i-1) + break + + def set_view(self, load_target_videos,b): + # self.clear_faces() + # self.video_loaded = False + # self.image_loaded = False + if load_target_videos and self.widget['PreviewModeTextSel'].get() != 'Theater': + self.populate_target_videos() + + self.layer['slider_frame'].grid_forget() + self.layer['preview_frame'].grid_forget() + self.layer['markers_canvas'].grid_forget() + self.layer['image_controls'].grid_forget() + self.layer['FaceLab_controls'].grid_forget() + self.layer['InputVideoFrame'].grid_forget() + self.layer['parameter_frame'].grid_forget() + + self.layer['parameters_canvas'].grid_forget() + self.layer['parameter_scroll_canvas'].grid_forget() + + self.layer['facelab_canvas'].grid_forget() + self.layer['facelab_scroll_canvas'].grid_forget() + + if self.widget['PreviewModeTextSel'].get()=='Video': + self.image_loaded = False + self.layer['slider_frame'].grid(row=2, column=0, sticky='NEWS', pady=0) + self.layer['preview_frame'].grid(row=4, column=0, sticky='NEWS') + self.layer['markers_canvas'].grid(row=3, column=0, sticky='NEWS') + self.layer['parameter_frame'].grid(row=0, column=2, sticky='NEWS', pady=0, padx=1) + + self.layer['parameters_canvas'].grid(row=1, column=0, sticky='NEWS', pady=0, padx=0) + self.layer['parameter_scroll_canvas'].grid(row=1, column=1, sticky='NEWS', pady=0) + self.layer['InputVideoFrame'].grid(row=0, column=0, sticky='NEWS', padx=1, pady=0) + + + elif self.widget['PreviewModeTextSel'].get()=='Image': + self.video_loaded = False + self.layer['image_controls'].grid(row=2, column=0, rowspan=2, sticky='NEWS', pady=0) + + self.layer['parameters_canvas'].grid(row=1, column=0, sticky='NEWS', pady=0, padx=0) + self.layer['parameter_scroll_canvas'].grid(row=1, column=1, sticky='NEWS', pady=0) + self.layer['InputVideoFrame'].grid(row=0, column=0, sticky='NEWS', padx=1, pady=0) + self.layer['parameter_frame'].grid(row=0, column=2, sticky='NEWS', pady=0, padx=1) + + + elif self.widget['PreviewModeTextSel'].get() == 'FaceLab': + self.video_loaded = False + self.layer['FaceLab_controls'].grid(row=2, column=0, rowspan=2, sticky='NEWS', pady=0) + self.layer['facelab_canvas'].grid(row=1, column=0, sticky='NEWS', pady=0, padx=0) + self.layer['facelab_scroll_canvas'].grid(row=1, column=1, sticky='NEWS', pady=0) + self.layer['InputVideoFrame'].grid(row=0, column=0, sticky='NEWS', padx=1, pady=0) + self.layer['parameter_frame'].grid(row=0, column=2, sticky='NEWS', pady=0, padx=1) + + # # find the input image with the lowest value + # for face in self.source_faces: + # if face["ButtonState"]: + # self.image_loaded = True + # self.add_action("load_target_image", face["file"]) + # break + + + elif self.widget['PreviewModeTextSel'].get() == 'Theater': + self.image_loaded = False + self.layer['slider_frame'].grid(row=2, column=0, sticky='NEWS', pady=0) + self.layer['preview_frame'].grid(row=4, column=0, sticky='NEWS') + self.layer['markers_canvas'].grid(row=3, column=0, sticky='NEWS') + + + + + + + def update_marker(self, action): + + if action=='add': + # Delete existing marker at current frame and replace with new data + for i in range(len(self.markers)): + if self.markers[i]['frame'] == self.video_slider.get(): + self.layer['markers_canvas'].delete(self.markers[i]['icon_ref']) + self.markers.pop(i) + break + + width = self.layer['markers_canvas'].winfo_width()-20-40-20 + position = 20+int(width*self.video_slider.get()/self.video_slider.get_length()) + + temp_param = copy.deepcopy(self.parameters) + temp = { + 'frame': self.video_slider.get(), + 'parameters': temp_param, + 'icon_ref': self.layer['markers_canvas'].create_line(position,0, position, 15, fill='light goldenrod'), + } + + self.markers.append(temp) + def sort(e): + return e['frame'] + + self.markers.sort(key=sort) + self.add_action("markers", self.markers) + + # elif action=='stop': + # if self.stop_marker == self.video_slider.get(): + # self.stop_marker = [] + # self.add_action('set_stop', -1) + # self.video_slider_canvas.delete(self.stop_image) + # else: + # self.video_slider_canvas.delete(self.stop_image) + # self.stop_marker = self.video_slider.self.timeline_position + # self.add_action('set_stop', self.stop_marker) + # + # width = self.video_slider_canvas.winfo_width() - 30 + # position = 15 + int(width * self.video_slider.self.timeline_position / self.video_slider.configure('to')[4]) + # self.stop_image = self.video_slider_canvas.create_image(position, 30, image=self.stop_marker_icon) + + elif action=='delete': + for i in range(len(self.markers)): + if self.markers[i]['frame'] == self.video_slider.get(): + self.layer['markers_canvas'].delete(self.markers[i]['icon_ref']) + self.markers.pop(i) + break + + elif action=='prev': + + temp=[] + for i in range(len(self.markers)): + temp.append(self.markers[i]['frame']) + idx = bisect.bisect_left(temp, self.video_slider.get()) + + if idx > 0: + self.video_slider.set(self.markers[idx-1]['frame']) + + self.add_action('get_requested_video_frame', self.markers[idx-1]['frame']) + self.parameter_update_from_marker(self.markers[idx-1]['frame']) + + elif action=='next': + temp=[] + for i in range(len(self.markers)): + temp.append(self.markers[i]['frame']) + idx = bisect.bisect(temp, self.video_slider.get()) + + if idx < len(self.markers): + self.video_slider.set(self.markers[idx]['frame']) + + self.add_action('get_requested_video_frame', self.markers[idx]['frame']) + self.parameter_update_from_marker(self.markers[idx]['frame']) + + # resize canvas + else : + + self.layer['markers_canvas'].delete('all') + width = self.layer['markers_canvas'].winfo_width()-20-40-20 + + for marker in self.markers: + position = 20+int(width*marker['frame']/self.video_slider.get_length()) + marker['icon_ref'] = self.layer['markers_canvas'].create_line(position,0, position, 15, fill='light goldenrod') + + + + + def toggle_stop(self): + if self.stop_marker == self.video_slider.self.timeline_position: + self.stop_marker = [] + self.add_action('set_stop', -1) + self.video_slider_canvas.delete(self.stop_image) + else: + self.video_slider_canvas.delete(self.stop_image) + self.stop_marker = self.video_slider.self.timeline_position + self.add_action('set_stop', self.stop_marker) + + width = self.video_slider_canvas.winfo_width()-30 + position = 15+int(width*self.video_slider.self.timeline_position/self.video_slider.configure('to')[4]) + self.stop_image = self.video_slider_canvas.create_image(position, 30, image=self.stop_marker_icon) + + + def save_image(self): + filename = self.media_file_name[0]+"_"+str(time.time())[:10] + filename = os.path.join(self.json_dict["saved videos"], filename) + cv2.imwrite(filename+'.png', cv2.cvtColor(self.video_image, cv2.COLOR_BGR2RGB)) + print('Image saved as:', filename+'.png') + + def clear_mem(self): + self.widget['RestorerSwitch'].set(False) + self.widget['OccluderSwitch'].set(False) + self.widget['FaceParserSwitch'].set(False) + self.widget['CLIPSwitch'].set(False) + self.widget['SwapFacesButton'].set(False) + + self.models.delete_models() + torch.cuda.empty_cache() + + + +# Refactor this, doesn't seem very efficient + def parameter_update_from_marker(self, frame): + + # sync marker data + temp=[] + # create a separate list with the list of frame numbers with markers + for i in range(len(self.markers)): + temp.append(self.markers[i]['frame']) + # find the marker frame to the left of the current frame + idx = bisect.bisect(temp, frame) + # update UI with current marker state data + if idx>0: + # update paramter dict with marker entry + self.parameters = copy.deepcopy(self.markers[idx-1]['parameters']) + + # Update ui + for key, value in self.parameters.items(): + self.widget[key].set(self.parameters[key], request_frame=False) + + + # self.CLIP_text.delete(0, tk.END) + # self.CLIP_text.insert(0, self.parameters['CLIPText']) + + def toggle_audio(self): + self.widget['AudioButton'].toggle_button() + self.control['AudioButton'] = self.widget['AudioButton'].get() + self.add_action('control', self.control) + + def toggle_maskview(self): + self.widget['MaskViewButton'].toggle_button() + self.control['MaskViewButton'] = self.widget['MaskViewButton'].get() + self.add_action('control', self.control) + self.add_action('get_requested_video_frame', self.video_slider.get()) + + def parameter_io(self, task): + if task=='save': + with open("saved_parameters.json", "w") as save_file: + json.dump(self.parameters, save_file) + + elif task=='load': + try: + load_file = open("saved_parameters.json", "r") + except FileNotFoundError: + print('No save file created yet!') + else: + # Load the file and save it to parameters + self.parameters = json.load(load_file) + load_file.close() + + # Update the UI + for key, value in self.parameters.items(): + self.widget[key].set(value, request_frame=False) + + self.add_action('parameters', self.parameters) + self.add_action('control', self.control) + self.add_action('get_requested_video_frame', self.video_slider.get()) + + elif task=='default': + # Update the UI + for key, value in self.parameters.items(): + self.widget[key].load_default() + + self.add_action('parameters', self.parameters) + self.add_action('control', self.control) + self.add_action('get_requested_video_frame', self.video_slider.get()) + + + + def findCosineDistance2(self, vector1, vector2): + cos_dist = 1.0 - np.dot(vector1, vector2)/(np.linalg.norm(vector1)*np.linalg.norm(vector2)) # 2..0 + + print(np.dot(vector1, vector2)) + + return cos_dist \ No newline at end of file diff --git a/rope/GUIElements.py b/rope/GUIElements.py new file mode 100644 index 0000000..b0c9793 --- /dev/null +++ b/rope/GUIElements.py @@ -0,0 +1,1247 @@ +import tkinter as tk +from tkinter import font +from PIL import Image, ImageTk + +from rope.Dicts import DEFAULT_DATA +import rope.Styles as style + +#import inspect print(inspect.currentframe().f_back.f_code.co_name, 'resize_image') + +class Separator_x(): + def __init__(self, parent, x, y): + self.parent = parent + self.x = x + self.y = y + self.parent.update() + self.blank = tk.PhotoImage() + self.sep = tk.Label(self.parent, bg='#090909', image=self.blank, compound='c', border=0, width=self.parent.winfo_width(), height=1) + self.sep.place(x=self.x, y=self.y) + # self.parent.bind('', self.update_sep_after_window_resize) + + # def update_sep_after_window_resize(self, event): + # self.parent.update() + # self.sep.configure(width=self.parent.winfo_width()) + + def hide(self): + self.sep.place_forget() + + def unhide(self): + self.parent.update() + self.sep.place(x=self.x, y=self.y) + self.sep.configure(width=self.parent.winfo_width()) + + +class Separator_y(): + def __init__(self, parent, x, y): + self.parent = parent + self.x = x + self.y = y + self.parent.update() + self.blank = tk.PhotoImage() + self.sep = tk.Label(self.parent, bg='#090909', image=self.blank, compound='c', border=0, width=1, height=self.parent.winfo_height()) + self.sep.place(x=self.x, y=self.y) + # self.parent.bind('', self.update_sep_after_window_resize) + + # def update_sep_after_window_resize(self, event): + # self.parent.update() + # self.sep.configure(height=self.parent.winfo_height()) + + def hide(self): + self.sep.place_forget() + + def unhide(self): + self.parent.update() + self.sep.place(x=self.x, y=self.y) + self.sep.configure(height=self.parent.winfo_height()) + +class Text(): + def __init__(self, parent, text, style_level, x, y, width, height): + self.blank = tk.PhotoImage() + + if style_level == 1: + self.style = style.text_1 + elif style_level == 2: + self.style = style.text_2 + elif style_level == 3: + self.style = style.text_3 + + self.label = tk.Label(parent, self.style, image=self.blank, compound='c', text=text, anchor='w', width=width, height=height) + self.label.place(x=x, y=y) + + def configure(self, text): + self.label.configure(text=text) + +class Scrollbar_y(): + def __init__(self, parent, child): + + self.child = child + + self.trough_short_dim = 15 + self.trough_long_dim = [] + self.handle_short_dim = self.trough_short_dim*0.5 + + self.top_of_handle = [] + self.middle_of_handle = [] + self.bottom_of_handle = [] + + self.old_coord = 0 + + # Child data + self.child.bind('', self.resize_scrollbar) + + # Set the canvas + self.scrollbar_canvas = parent + self.scrollbar_canvas.configure(width=self.trough_short_dim) + self.scrollbar_canvas.bind("", self.scroll) + self.scrollbar_canvas.bind("", self.scroll) + self.scrollbar_canvas.bind("", self.scroll) + + # Draw handle + self.resize_scrollbar(None) + + def resize_scrollbar(self, event): # on window updates + self.child.update() + self.child.configure(scrollregion=self.child.bbox("all")) + + # Reconfigure data + self.trough_long_dim = self.child.winfo_height() + self.scrollbar_canvas.delete('all') + self.scrollbar_canvas.configure(height=self.trough_long_dim) + + # Redraw the scrollbar + x1 = (self.trough_short_dim-self.handle_short_dim)/2 + x2 = self.trough_short_dim-x1 + y1 = self.child.yview()[0]*self.trough_long_dim + y2 = self.child.yview()[1]*self.trough_long_dim + + self.middle_of_handle = self.scrollbar_canvas.create_rectangle(x1, y1, x2, y2, fill='grey25', outline='') + + def scroll(self, event): + delta = 0 + + # Get handle dimensions + handle_y1 = self.scrollbar_canvas.coords(self.middle_of_handle)[1] + handle_y2 = self.scrollbar_canvas.coords(self.middle_of_handle)[3] + handle_center = (handle_y2-handle_y1)/2 + handle_y1 + handle_length = handle_y2-handle_y1 + + if event.type == '38': # mousewheel + delta = -int(event.delta/20.0) + + elif event.type == '4': # l-button press + # If the mouse coord is within the handle dont jump the handle + if event.y > handle_y1 and event.yself.trough_long_dim: + delta = self.trough_long_dim-handle_y2 + + # update the scrollbar + self.scrollbar_canvas.move(self.middle_of_handle, 0, delta) + + # Get the new handle postition to calculate the change for the child + handle_y1 = self.scrollbar_canvas.coords(self.middle_of_handle)[1] + + # Move the child + self.child.yview_moveto(handle_y1/self.trough_long_dim) + + self.old_coord = event.y + + def set(self, value): + handle_y1 = self.scrollbar_canvas.coords(self.middle_of_handle)[1] + handle_y2 = self.scrollbar_canvas.coords(self.middle_of_handle)[3] + handle_center = (handle_y2-handle_y1)/2 + handle_y1 + + coord_del = self.scrollbar_canvas.winfo_height()*value-handle_center + self.old_coord = self.scrollbar_canvas.winfo_height()*value + + self.scrollbar_canvas.move(self.middle_of_handle, 0, coord_del) + + def hide(self): + pass + + def unhide(self): + pass + +class Timeline(): + def __init__(self, parent, widget, temp_toggle_swapper, add_action): + self.parent = parent + self.add_action = add_action + self.temp_toggle_swapper = temp_toggle_swapper + + self.frame_length = 0 + self.height = 20 + self.counter_width = 40 + + self.entry_string = tk.StringVar() + self.entry_string.set(0) + + self.last_position = 0 + + # Widget variables + self.max_ = 100#video_length + + self.handle = [] + self.slider_left = [] + self.slider_right = [] + + # Event trigget for window resize + self.parent.bind('', self.window_resize) + + # Add the Slider Canvas to the frame + self.slider = tk.Canvas(self.parent, style.timeline_canvas, height=self.height) + self.slider.place(x=0, y=0) + self.slider.bind('', lambda e: self.update_timeline_handle(e, True)) + self.slider.bind('', lambda e: self.update_timeline_handle(e, True)) + self.slider.bind('', lambda e: self.update_timeline_handle(e, True)) + self.slider.bind('', lambda e: self.update_timeline_handle(e, True)) + + # Add the Entry to the frame + self.entry_width = 40 + self.entry = tk.Entry(self.parent, style.entry_3, textvariable=self.entry_string) + self.entry.bind('', lambda event: self.entry_input(event)) + + def draw_timeline(self): + self.slider.delete('all') + + # Configure widths and placements + self.slider.configure(width=self.frame_length) + self.entry.place(x=self.parent.winfo_width()-self.counter_width, y=0) + + # Draw the slider + slider_pad = 20 + entry_pad = 20 + self.slider_left = slider_pad + self.slider_right = self.frame_length-entry_pad-self.entry_width + slider_center = (self.height)/2 + + line_loc = self.pos2coord(self.last_position) + + line_height = 8 + line_width = 1.5 + line_x1 = line_loc-line_width + line_y1 = slider_center -line_height + line_x2 = line_loc+line_width + line_y2 = slider_center +line_height + + + trough_x1 = self.slider_left + trough_y1 = slider_center-1 + trough_x2 = self.slider_right + trough_y2 = slider_center+1 + + self.slider.create_rectangle(trough_x1, trough_y1, trough_x2, trough_y2, fill='#43474D', outline='') + self.handle = self.slider.create_rectangle(line_x1, line_y1, line_x2, line_y2, fill='#FFFFFF', outline='') + + def coord2pos(self, coord): + return float(coord-self.slider_left)*self.max_/(self.slider_right-self.slider_left) + + def pos2coord(self, pos): + return float(float(pos)*(self.slider_right-self.slider_left)/self.max_ + self.slider_left) + + + def update_timeline_handle(self, event, also_update_entry=False): + requested = True + + if isinstance(event, float): + position = event + requested = False + else: + if event.type == '38': # mousewheel + position = self.last_position+int(event.delta/120.0) + + elif event.type == '4': # l-button press + x_coord = float(event.x) + position = self.coord2pos(x_coord) + + # Turn off swapping + self.temp_toggle_swapper('off') + self.add_action("play_video", "stop") + + elif event.type == '5': # l-button release + x_coord = float(event.x) + position = self.coord2pos(x_coord) + + # Turn on swapping, if it was already on and request new frame + self.temp_toggle_swapper('on') + + elif event.type == '6': # l-button drag + x_coord = float(event.x) + position = self.coord2pos(x_coord) + + # constrain mousewheel movement + if position < 0: position = 0 + elif position > self.max_: position = self.max_ + + # Find closest position increment + position = round(position) + + # moving sends many events, so only update when the next frame is reached + if position != self.last_position: + # Move handle to coordinate based on position + self.slider.move(self.handle, self.pos2coord(position) - self.pos2coord(self.last_position), 0) + + if requested: + self.add_action("get_requested_video_frame", position) + + # Save for next time + self.last_position = position + + if also_update_entry: + self.entry_string.set(str(position)) + + def entry_input(self, event): + # event.char + self.entry.update() + try: + input_num = float(self.entry_string.get()) + self.update_timeline_handle(input_num, False) + except: + return + + def set(self, value): + self.update_timeline_handle(float(value), also_update_entry=True) + + def get(self): + return int(self.last_position) + + + def set_length(self, value): + self.max_ = value + self.update_timeline_handle(float(self.last_position), also_update_entry=True) + + def get_length(self): + return int(self.max_) + + # Event when the window is resized + def window_resize(self, event): + self.parent.update() + self.frame_length = self.parent.winfo_width() + self.draw_timeline() + + + + +class Button(): + def __init__(self, parent, name, style_level, function, args, data_type, x, y, width=125, height=20): + self.default_data = DEFAULT_DATA + self.name = name + self.function = function + self.args = args + self.info = [] + self.state = [] + self.hold_state = [] + self.error = [] + self.data_type = data_type + + if style_level == 1: + self.button_style = style.button_1 + elif style_level == 2: + self.button_style = style.button_2 + elif style_level == 3: + self.button_style = style.button_3 + + + # Add Icon + if self.default_data[self.name+'Display'] == 'both': + img = Image.open(self.default_data[self.name+'IconOn']) + resized_image= img.resize((20,20), Image.ANTIALIAS) + self.icon_on = ImageTk.PhotoImage(resized_image) + img = Image.open(self.default_data[self.name+'IconOff']) + resized_image= img.resize((20,20), Image.ANTIALIAS) + self.icon_off = ImageTk.PhotoImage(resized_image) + img = Image.open(self.default_data[self.name+'IconHover']) + resized_image= img.resize((20,20), Image.ANTIALIAS) + self.icon_hover = ImageTk.PhotoImage(resized_image) + + text = ' '+self.default_data[self.name+'Text'] + + elif self.default_data[self.name+'Display'] == 'icon': + img = Image.open(self.default_data[self.name+'IconOn']) + resized_image= img.resize((20,20), Image.ANTIALIAS) + self.icon_on = ImageTk.PhotoImage(resized_image) + img = Image.open(self.default_data[self.name+'IconOff']) + resized_image= img.resize((20,20), Image.ANTIALIAS) + self.icon_off = ImageTk.PhotoImage(resized_image) + img = Image.open(self.default_data[self.name+'IconHover']) + resized_image= img.resize((20,20), Image.ANTIALIAS) + self.icon_hover = ImageTk.PhotoImage(resized_image) + + text = '' + + elif self.default_data[self.name+'Display'] == 'text': + self.icon_on = tk.PhotoImage() + self.icon_off = tk.PhotoImage() + self.icon_hover = tk.PhotoImage() + + text = ' '+self.default_data[self.name+'Text'] + + # Create Button and place + self.button = tk.Button(parent, self.button_style, compound='left', text=text, anchor='w') + self.button.configure(width=width, height=height) + self.button.place(x=x, y=y) + + self.button.bind("", lambda event: self.on_enter()) + self.button.bind("", lambda event: self.on_leave()) + + if self.function != None: + if self.args != None: + self.button.configure(command=lambda: self.function(self.args)) + else: + self.button.configure(command=lambda: self.function()) + + # Set inital state + self.button.configure(image=self.icon_on) + + if self.default_data[self.name+'State']: + self.enable_button() + + else: + self.disable_button() + + def add_info_frame(self, info): + self.info = info + + + def on_enter(self): + if self.info: + self.info.configure(text=self.default_data[self.name+'InfoText']) + + if not self.state and not self.error: + self.button.configure(image=self.icon_hover) + self.button.configure(fg='#B1B1B2') + + def on_leave(self): + if not self.state and not self.error: + + self.button.configure(image=self.icon_off) + self.button.configure(fg='#828282') + + def enable_button(self): + + self.button.configure(image=self.icon_on) + self.button.configure(fg='#FFFFFF') + self.state = True + self.error = False + + def disable_button(self): + + self.button.configure(image=self.icon_off) + self.button.configure(fg='#828282') + self.state = False + self.error = False + + def toggle_button(self): + self.state = not self.state + + if self.state: + self.button.configure(image=self.icon_on) + self.button.configure(fg='#FFFFFF') + else: + self.button.configure(image=self.icon_off) + self.button.configure(fg='#828282') + + def temp_disable_button(self): + self.hold_state = self.state + self.state = False + + def temp_enable_button(self): + self.state = self.hold_state + + def error_button(self): + + self.button.configure(image=self.icon_off) + self.button.configure(fg='light goldenrod') + self.state = False + self.error = True + + def get(self): + return self.state + + def set(self, value, request_frame=True): + if value: + self.enable_button() + + elif not value: + self.disable_button() + if request_frame: + if self.function != None: + if self.args != None: + self.function(self.args) + else: + self.function() + + def hide(self): + pass + + def unhide(self): + pass + + def get_data_type(self): + return self.data_type + + def load_default(self): + self.set(self.default_data[self.name+'State']) + +class TextSelection(): + def __init__(self, parent, name, display_text, style_level, function, argument, data_type, width, height, x, y, text_percent): + self.blank = tk.PhotoImage() + + self.default_data = DEFAULT_DATA + # Capture inputs as instance variables + self.parent = parent + self.name = name + self.function = function + self.argument = argument + self.data_type = data_type + self.width = width + self.height = height + self.style = [] + self.info = [] + + if style_level == 3: + self.frame_style = style.canvas_frame_label_3 + self.text_style = style.text_3 + self.sel_off_style = style.text_selection_off_3 + self.sel_on_style = style.text_selection_on_3 + + if style_level == 2: + self.frame_style = style.canvas_frame_label_2 + self.text_style = style.text_2 + self.sel_off_style = style.text_selection_off_2 + self.sel_on_style = style.text_selection_on_2 + + self.display_text = display_text+' ' + + self.textselect_label = {} + + # Initial data + self.selection = self.default_data[self.name+'Mode'] + + # Frame to hold everything + self.ts_frame = tk.Frame(self.parent, self.frame_style, width=self.width, height=self.height) + self.ts_frame.place(x=x, y=y) + self.ts_frame.bind("", lambda event: self.on_enter()) + + self.text_width = int(width*(1.0-text_percent)) + + # Create the text on the left + self.text_label = tk.Label(self.ts_frame, self.text_style, image=self.blank, compound='c', text=self.display_text, anchor='e', width=self.text_width, height=height) + self.text_label.place(x=0, y=0) + + # Loop through the parameter modes, create a label + # Gotta find the size of the buttons according to the font + self.font = tk.font.Font(family="Segoe UI", size=10, weight="normal") + x_spacing = self.text_width + 10 + + + for mode in self.default_data[self.name+'Modes']: + # Get size of text in pixels + m_len = self.font.measure(mode) + + # Create a label with the text + self.textselect_label[mode] = tk.Label(self.ts_frame, self.sel_off_style, text=mode, image=self.blank, compound='c', anchor='c', width=m_len, height=height) + self.textselect_label[mode].place(x=x_spacing, y=0) + self.textselect_label[mode].bind("", lambda event, mode=mode: self.select_ui_text_selection(mode)) + + # Initial value + if mode==self.selection: + self.textselect_label[mode].configure(self.sel_on_style) + + x_spacing = x_spacing + m_len+10 + + def select_ui_text_selection(self, selection, request_frame=True): + # Loop over all of the Modes + for mode in self.default_data[self.name+'Modes']: + + # If the Mode has been selected + if mode==selection: + # Set state to true + self.textselect_label[mode].configure(self.sel_on_style) + self.selection = mode + if request_frame: + self.function(self.argument, self.name) + + else: + self.textselect_label[mode].configure(self.sel_off_style) + + def add_info_frame(self, info): + self.info = info + + def on_enter(self): + if self.info: + self.info.configure(text=self.default_data[self.name+'InfoText']) + + def get(self): + return self.selection + + def set(self, value, request_frame=True): + self.select_ui_text_selection(value, request_frame) + + def hide(self): + pass + + def unhide(self): + pass + + def get_data_type(self): + return self.data_type + + def load_default(self): + self.set(self.default_data[self.name+'Mode']) + + +class Switch2(): + def __init__(self, parent, name, display_text, style_level, function, argument, width, height, x, y): + self.blank = tk.PhotoImage() + self.default_data = DEFAULT_DATA + # Capture inputs as instance variables + self.parent = parent + self.name = name + self.function = function + self.argument = argument + self.data_type = argument + self.width = width + self.height = height + self.x = x + self.y = y + self.style = [] + self.info = [] + + # Initial Value + self.state = self.default_data[name+'State'] + + if style_level == 3: + self.frame_style = style.canvas_frame_label_3 + self.text_style = style.text_3 + self.entry_style = style.entry_3 + + self.display_text = display_text + # Load Icons + self.img = Image.open(style.icon['IconOff']) + self.img = self.img.resize((40,40), Image.ANTIALIAS) + self.icon_off = ImageTk.PhotoImage(self.img) + + self.img = Image.open(style.icon['IconOn']) + self.img = self.img.resize((40,40), Image.ANTIALIAS) + self.icon_on = ImageTk.PhotoImage(self.img) + + # Frame to hold everything + self.switch_frame = tk.Frame(self.parent, self.frame_style, width=self.width, height=self.height) + self.switch_frame.place(x=self.x, y=self.y) + self.switch_frame.bind("", lambda event: self.on_enter()) + + + toggle_width = 40 + text_width = self.width-toggle_width + + # Toggle Switch + self.switch = tk.Label(self.switch_frame, style.parameter_switch_3, image=self.icon_off, width=toggle_width, height=self.height) + if self.state: + self.switch.configure(image=self.icon_on) + self.switch.place(x=0, y=2) + self.switch.bind("", lambda event: self.toggle_switch(event)) + + # Text + self.switch_text = tk.Label(self.switch_frame, style.parameter_switch_3, image=self.blank, compound='right', text=self.display_text, anchor='w', width=text_width, height=height-2) + self.switch_text.place(x=50, y=0) + + def toggle_switch(self, event, set_value=None, request_frame=True): + # flip state + if set_value==None: + self.state = not self.state + else: + self.state = set_value + + if self.state: + self.switch.configure(image=self.icon_on) + + else: + self.switch.configure(image=self.icon_off) + + if request_frame: + self.function(self.argument, self.name, use_markers=False) + + def add_info_frame(self, info): + self.info = info + + def on_enter(self): + if self.info: + self.info.configure(text=self.default_data[self.name+'InfoText']) + + + def hide(self): + self.switch_frame.place_forget() + self.switch.place_forget() + self.switch_text.place_forget() + + def unhide(self): + self.switch_frame.place(x=self.x, y=self.y) + self.switch.place(x=0, y=2) + self.switch_text.place(x=50, y=0) + + def set(self, value, request_frame=True): + self.toggle_switch(None, value, request_frame) + + def get(self): + return self.state + + def get_data_type(self): + return self.data_type + + def load_default(self): + self.set(self.default_data[self.name+'State']) + +class Slider2(): + def __init__(self, parent, name, display_text, style_level, function, argument, width, height, x, y, slider_percent): + + # self.constants = CONSTANTS + self.default_data = DEFAULT_DATA + self.blank = tk.PhotoImage() + + # Capture inputs as instance variables + self.parent = parent + self.name = name + self.function = function + self.data_type = argument + self.x = x + self.y = y + self.slider_percent = slider_percent + self.width = width + self.height = height + self.info = [] + + # Initial Value + self.amount = self.default_data[name+'Amount'] + + if style_level == 1: + self.frame_style = style.canvas_frame_label_1 + self.text_style = style.text_1 + self.entry_style = style.entry_3 + + elif style_level == 3: + self.frame_style = style.canvas_frame_label_3 + self.text_style = style.text_3 + self.entry_style = style.entry_3 + + # UI-controlled variables + self.entry_string = tk.StringVar() + self.entry_string.set(self.amount) + + # Widget variables + self.min_ = self.default_data[name+'Min'] + self.max_ = self.default_data[name+'Max'] + self.inc_ = self.default_data[name+'Inc'] + self.display_text = display_text+' ' + + # Set up spacing + # |----------------------|slider_pad|-slider-|entry_pad|-| + # |---1-slider_percent---|---slider_percent---| + # |--------------------width------------------| + + # Create a frame to hold it all + self.frame_x = x + self.frame_y = y + self.frame_width = width + self.frame_height = height + + self.frame = tk.Frame(self.parent, self.frame_style, width=self.frame_width, height=self.frame_height) + self.frame.place(x=self.frame_x, y=self.frame_y) + self.frame.bind("", lambda event: self.on_enter()) + + + # Add the slider Label text to the frame + self.txt_label_x = 0 + self.txt_label_y = 0 + self.txt_label_width = int(width*(1.0-slider_percent)) + + self.label = tk.Label(self.frame, self.text_style, image=self.blank, compound='c', text=self.display_text, anchor='e', width=self.txt_label_width, height=self.height) + self.label.place(x=self.txt_label_x, y=self.txt_label_y) + + # Add the Slider Canvas to the frame + self.slider_canvas_x = self.txt_label_width + self.slider_canvas_y = 0 + self.slider_canvas_width = width-self.txt_label_width + + self.slider = tk.Canvas(self.frame, self.frame_style, width=self.slider_canvas_width, height=self.height) + self.slider.place(x=self.slider_canvas_x, y=self.slider_canvas_y) + self.slider.bind('', lambda e: self.update_handle(e, True)) + self.slider.bind('', lambda e: self.update_handle(e, True)) + + # Add the Entry to the frame + self.entry_width = 60 + self.entry_x = self.frame_width-self.entry_width + self.entry_y = 0 + + self.entry = tk.Entry(self.frame, self.entry_style, textvariable=self.entry_string) + self.entry.place(x=self.entry_x, y=self.entry_y) + self.entry.bind('', lambda event: self.entry_input(event)) + + # Draw the slider + self.slider_pad = 20 + self.entry_pad = 20 + self.slider_left = self.slider_pad + self.slider_right = self.slider_canvas_width-self.entry_pad-self.entry_width + self.slider_center = (self.height+1)/2 + + self.oval_loc = self.pos2coord(self.amount) + self.oval_radius = 5 + self.oval_x1 = self.oval_loc-self.oval_radius + self.oval_y1 = self.slider_center-self.oval_radius + self.oval_x2 = self.oval_loc+self.oval_radius + self.oval_y2 = self.slider_center+self.oval_radius + + self.trough_x1 = self.slider_left + self.trough_y1 = self.slider_center-2 + self.trough_x2 = self.slider_right + self.trough_y2 = self.slider_center+2 + + self.slider.create_rectangle(self.trough_x1, self.trough_y1, self.trough_x2, self.trough_y2, fill='#1F1F1F', outline='') + self.handle = self.slider.create_oval(self.oval_x1, self.oval_y1, self.oval_x2, self.oval_y2, fill='#919191', outline='') + + def coord2pos(self, coord): + return float((coord-self.slider_left)*(self.max_-self.min_)/(self.slider_right-self.slider_left) + self.min_) + + def pos2coord(self, pos): + return float((float(pos)-self.min_)*(self.slider_right-self.slider_left)/(self.max_-self.min_) + self.slider_left) + + def update_handle(self, event, also_update_entry=False, request_frame=True): + if isinstance(event, float): + position = event + + elif event.type == '38': + position = self.amount+self.inc_*int(event.delta/120.0) + + elif event.type == '6': + x_coord = float(event.x) + position = self.coord2pos(x_coord) + + # constrain mousewheel movement + if position < self.min_: position = self.min_ + elif position > self.max_: position = self.max_ + + # Find closest position increment + position_inc = round((position-self.min_) / self.inc_) + position = (position_inc * self.inc_)+self.min_ + + # moving sends many events, so only update when the next frame is reached + if position != self.amount: + # Move handle to coordinate based on position + self.slider.move(self.handle, self.pos2coord(position) - self.pos2coord(self.amount), 0) + + # Save for next time + self.amount = position + + if also_update_entry: + self.entry_string.set(str(position)) + + if request_frame: + self.function(self.data_type, self.name, use_markers=False) + + # return True + # return False + + def add_info_frame(self, info): + self.info = info + + def on_enter(self): + if self.info: + self.info.configure(text=self.default_data[self.name+'InfoText']) + + def entry_input(self, event): + # event.char + self.entry.update() + try: + input_num = float(self.entry_string.get()) + self.update_handle(input_num, False) + except: + return + + def set(self, value, request_frame=True): + self.update_handle(float(value), True) + + def get(self): + return self.amount + + def hide(self): + self.frame.place_forget() + self.label.place_forget() + self.slider.place_forget() + self.entry.place_forget() + + def unhide(self): + self.frame.place(x=self.frame_x, y=self.frame_y) + self.label.place(x=self.txt_label_x, y=self.txt_label_y) + self.slider.place(x=self.slider_canvas_x, y=self.slider_canvas_y) + self.entry.place(x=self.entry_x, y=self.entry_y) + + # def save_to_file(self, filename, data): + # with open(filename, 'w') as outfile: + # json.dump(data, outfile) + + def get_data_type(self): + return self.data_type + + def load_default(self): + self.set(self.default_data[self.name+'Amount']) + + +class Slider3(): + def __init__(self, parent, name, display_text, style_level, function, argument, width, height, x, y, slider_percent): + + # self.constants = CONSTANTS + # self.default_data = DEFAULT_DATA + self.blank = tk.PhotoImage() + + # Capture inputs as instance variables + self.parent = parent + self.name = name + self.function = function + self.data_type = argument + self.x = x + self.y = y + self.slider_percent = slider_percent + self.width = width + self.height = height + self.info = [] + + # Initial Value + self.amount = 0 + + if style_level == 1: + self.frame_style = style.canvas_frame_label_1 + self.text_style = style.text_1 + self.entry_style = style.entry_3 + + elif style_level == 3: + self.frame_style = style.canvas_frame_label_3 + self.text_style = style.text_3 + self.entry_style = style.entry_3 + + # UI-controlled variables + self.entry_string = tk.StringVar() + self.entry_string.set(self.amount) + + # Widget variables + self.min_ = -2 + self.max_ = 2 + self.inc_ = 0.001 + self.display_text = display_text + ' ' + + # Set up spacing + # |----------------------|slider_pad|-slider-|entry_pad|-| + # |---1-slider_percent---|---slider_percent---| + # |--------------------width------------------| + + # Create a frame to hold it all + self.frame_x = x + self.frame_y = y + self.frame_width = width + self.frame_height = height + + self.frame = tk.Frame(self.parent, self.frame_style, width=self.frame_width, height=self.frame_height) + self.frame.place(x=self.frame_x, y=self.frame_y) + # self.frame.bind("", lambda event: self.on_enter()) + + + # Add the slider Label text to the frame + self.txt_label_x = 0 + self.txt_label_y = 0 + self.txt_label_width = int(width * (1.0 - slider_percent)) + + self.label = tk.Label(self.frame, self.text_style, image=self.blank, compound='c', text=self.display_text, anchor='e', width=self.txt_label_width, height=self.height) + self.label.place(x=self.txt_label_x, y=self.txt_label_y) + + # Add the Slider Canvas to the frame + self.slider_canvas_x = self.txt_label_width + self.slider_canvas_y = 0 + self.slider_canvas_width = width - self.txt_label_width + + self.slider = tk.Canvas(self.frame, self.frame_style, width=self.slider_canvas_width, height=self.height) + self.slider.place(x=self.slider_canvas_x, y=self.slider_canvas_y) + self.slider.bind('', lambda e: self.update_handle(e, True)) + self.slider.bind('', lambda e: self.update_handle(e, True)) + + # Add the Entry to the frame + self.entry_width = 60 + self.entry_x = self.frame_width - self.entry_width + self.entry_y = 0 + + self.entry = tk.Entry(self.frame, self.entry_style, textvariable=self.entry_string) + self.entry.place(x=self.entry_x, y=self.entry_y) + self.entry.bind('', lambda event: self.entry_input(event)) + + # Draw the slider + self.slider_pad = 20 + self.entry_pad = 20 + self.slider_left = self.slider_pad + self.slider_right = self.slider_canvas_width - self.entry_pad - self.entry_width + self.slider_center = (self.height + 1) / 2 + + self.oval_loc = self.pos2coord(self.amount) + self.oval_radius = 5 + self.oval_x1 = self.oval_loc - self.oval_radius + self.oval_y1 = self.slider_center - self.oval_radius + self.oval_x2 = self.oval_loc + self.oval_radius + self.oval_y2 = self.slider_center + self.oval_radius + + self.trough_x1 = self.slider_left + self.trough_y1 = self.slider_center - 2 + self.trough_x2 = self.slider_right + self.trough_y2 = self.slider_center + 2 + + self.slider.create_rectangle(self.trough_x1, self.trough_y1, self.trough_x2, self.trough_y2, fill='#1F1F1F', outline='') + self.handle = self.slider.create_oval(self.oval_x1, self.oval_y1, self.oval_x2, self.oval_y2, fill='#919191', outline='') + + def coord2pos(self, coord): + return float((coord - self.slider_left) * (self.max_ - self.min_) / (self.slider_right - self.slider_left) + self.min_) + + def pos2coord(self, pos): + return float((float(pos) - self.min_) * (self.slider_right - self.slider_left) / (self.max_ - self.min_) + self.slider_left) + + def update_handle(self, event, also_update_entry=False, request_frame=True): + if isinstance(event, float): + position = event + + elif event.type == '38': + position = self.amount + self.inc_ * int(event.delta / 120.0) + + elif event.type == '6': + x_coord = float(event.x) + position = self.coord2pos(x_coord) + + # constrain mousewheel movement + if position < self.min_: + position = self.min_ + elif position > self.max_: + position = self.max_ + + # Find closest position increment + position_inc = round((position - self.min_) / self.inc_) + position = (position_inc * self.inc_) + self.min_ + + # moving sends many events, so only update when the next frame is reached + if position != self.amount: + # Move handle to coordinate based on position + self.slider.move(self.handle, self.pos2coord(position) - self.pos2coord(self.amount), 0) + + # Save for next time + self.amount = position + + if also_update_entry: + self.entry_string.set(str(position)) + + if request_frame: + self.function(self.data_type) + + # return True + # return False + + def add_info_frame(self, info): + self.info = info + + # def on_enter(self): + # if self.info: + # self.info.configure(text=self.default_data[self.name + 'InfoText']) + + def entry_input(self, event): + # event.char + self.entry.update() + try: + input_num = float(self.entry_string.get()) + self.update_handle(input_num, False) + except: + return + + def set(self, value, request_frame=True): + self.update_handle(float(value), True, request_frame) + + def get(self): + return self.amount + + def hide(self): + self.frame.place_forget() + self.label.place_forget() + self.slider.place_forget() + self.entry.place_forget() + + def unhide(self): + self.frame.place(x=self.frame_x, y=self.frame_y) + self.label.place(x=self.txt_label_x, y=self.txt_label_y) + self.slider.place(x=self.slider_canvas_x, y=self.slider_canvas_y) + self.entry.place(x=self.entry_x, y=self.entry_y) + + # def save_to_file(self, filename, data): + # with open(filename, 'w') as outfile: + # json.dump(data, outfile) + + def get_data_type(self): + return self.data_type + + def load_default(self): + self.set(0) + +class Text_Entry(): + def __init__(self, parent, name, display_text, style_level, function, data_type, width, height, x, y, text_percent): + self.blank = tk.PhotoImage() + + self.default_data = DEFAULT_DATA + # Capture inputs as instance variables + self.parent = parent + self.name = name + self.function = function + self.data_type = data_type + self.width = width + self.height = height + self.style = [] + self.info = [] + + if style_level == 3: + self.frame_style = style.canvas_frame_label_3 + self.text_style = style.text_3 + self.sel_off_style = style.text_selection_off_3 + self.sel_on_style = style.text_selection_on_3 + + if style_level == 2: + self.frame_style = style.canvas_frame_label_2 + self.text_style = style.text_2 + self.sel_off_style = style.text_selection_off_2 + self.sel_on_style = style.text_selection_on_2 + + self.display_text = display_text+' ' + + + # Initial data + self.entry_text = tk.StringVar() + self.entry_text.set(self.default_data[self.name]) + + # Frame to hold everything + self.ts_frame = tk.Frame(self.parent, self.frame_style, width=self.width, height=self.height) + self.ts_frame.place(x=x, y=y) + self.ts_frame.bind("", lambda event: self.on_enter()) + + self.text_width = int(width*(1.0-text_percent)) + + # Create the text on the left + self.text_label = tk.Label(self.ts_frame, self.text_style, image=self.blank, compound='c', text=self.display_text, anchor='e', width=self.text_width, height=height) + self.text_label.place(x=0, y=0) + + + + self.entry = tk.Entry(self.ts_frame, style.entry_2, textvariable=self.entry_text) + self.entry.place(x=self.text_width+20, y=0, width = self.width-self.text_width-50, height=15) + self.entry.bind("", lambda event: self.send_text(self.entry_text.get())) + + def send_text(self, text): + self.function(self.data_type, self.name, use_markers=False) + + def add_info_frame(self, info): + self.info = info + + def on_enter(self): + if self.info: + self.info.configure(text=self.default_data[self.name+'InfoText']) + + def get(self): + return self.entry_text.get() + + def set(self, value, request_frame=True): + pass + # self.select_ui_text_selection(value, request_frame) + + def hide(self): + pass + + def unhide(self): + pass + + def get_data_type(self): + return self.data_type + + def load_default(self): + pass + # self.set(self.default_data[self.name+'Mode']) + +class VRAM_Indicator(): + def __init__(self, parent, style_level, width, height, x, y): + self.parent = parent + self.width = width + self.height = height + self.x = x + self.y = y + self.blank = tk.PhotoImage() + + self.used = 0 + self.total = 1 + + if style_level == 3: + self.frame_style = style.canvas_frame_label_3 + self.text_style = style.text_3 + self.sel_off_style = style.text_selection_off_3 + self.sel_on_style = style.text_selection_on_3 + + if style_level == 2: + self.frame_style = style.canvas_frame_label_2 + self.text_style = style.text_2 + self.sel_off_style = style.text_selection_off_2 + self.sel_on_style = style.text_selection_on_2 + + if style_level == 1: + self.frame_style = style.canvas_frame_label_1 + + self.frame = tk.Frame(self.parent, self.frame_style, width=self.width, height=self.height) + self.frame.place(x=self.x, y=self.y) + + self.label_name = tk.Label(self.frame, self.frame_style, image=self.blank, compound='c', fg='#b1b1b2', font=("Segoe UI", 9), width=50, text='VRAM', height=self.height) + self.label_name.place(x=0, y=0) + + + # self.label_value = tk.Label(self.frame, self.frame_style, bg='yellow', image=self.blank, compound='c', fg='#D0D0D0', font=("Segoe UI", 9), justify='right', width=100, text='VRAM', height=self.height) + # self.label_value.place(x=200, y=0) + + + self.canvas = tk.Canvas(self.frame, self.frame_style, highlightthickness =2, highlightbackground='#b1b1b2', width=self.width-60, height=self.height-4) + self.canvas.place(x=50, y=0) + + def update_display(self): + self.canvas.delete('all') + width = self.canvas.winfo_width() + + try: + ratio = self.used/self.total + except ZeroDivisionError: + ratio = 1 + + if ratio>0.9: + color = '#d10303' + else: + color = '#b1b1b2' + width = ratio*width + + self.canvas.create_rectangle(0, 0, width, self.height, fill=color) + + # text = str(self.used)+' / '+str(self.total)+' MB' + # self.label_value.configure(text=text) + + def set(self, used, total): + self.used = used + self.total = total + + self.update_display() + + def hide(self): + pass + + def unhide(self): + pass + \ No newline at end of file diff --git a/rope/Models.py b/rope/Models.py new file mode 100644 index 0000000..f384381 --- /dev/null +++ b/rope/Models.py @@ -0,0 +1,1278 @@ + +import cv2 +import numpy as np +from skimage import transform as trans +import torch +import torchvision +torchvision.disable_beta_transforms_warning() +from torchvision.transforms import v2 +from numpy.linalg import norm as l2norm +import onnxruntime +import onnx +from itertools import product as product +import subprocess as sp +onnxruntime.set_default_logger_severity(4) + +class Models(): + def __init__(self): + self.arcface_dst = np.array( [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366], [41.5493, 92.3655], [70.7299, 92.2041]], dtype=np.float32) + self.providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] + + self.retinaface_model = [] + self.yoloface_model = [] + self.scrdf_model = [] + self.resnet50_model, self.anchors = [], [] + + self.insight106_model = [] + + self.recognition_model = [] + self.swapper_model = [] + self.swapper_model_kps = [] + self.swapper_model_swap = [] + + self.emap = [] + self.GFPGAN_model = [] + self.GPEN_256_model = [] + self.GPEN_512_model = [] + self.codeformer_model = [] + + self.occluder_model = [] + self.faceparser_model = [] + + self.syncvec = torch.empty((1,1), dtype=torch.float32, device='cuda:0') + self.modelPath="./" + + def setModelPath(self, path): + self.modelPath = path + + def get_gpu_memory(self): + command = "nvidia-smi --query-gpu=memory.total --format=csv" + memory_total_info = sp.check_output(command.split()).decode('ascii').split('\n')[:-1][1:] + memory_total = [int(x.split()[0]) for i, x in enumerate(memory_total_info)] + + command = "nvidia-smi --query-gpu=memory.free --format=csv" + memory_free_info = sp.check_output(command.split()).decode('ascii').split('\n')[:-1][1:] + memory_free = [int(x.split()[0]) for i, x in enumerate(memory_free_info)] + + memory_used = memory_total[0] - memory_free[0] + + return memory_used, memory_total[0] + + def run_detect(self, img, detect_mode='Retinaface', max_num=1, score=0.5): + kpss = [] + + if detect_mode=='Retinaface': + if not self.retinaface_model: + print("now begin load det_10g.onnx") + self.retinaface_model = onnxruntime.InferenceSession(self.modelPath+'models/det_10g.onnx', providers=self.providers) + print("now loaded det_10g.onnx") + + + kpss = self.detect_retinaface(img, max_num=max_num, score=score) + + elif detect_mode=='SCRDF': + if not self.scrdf_model: + self.scrdf_model = onnxruntime.InferenceSession(self.modelPath+'models/scrfd_2.5g_bnkps.onnx', providers=self.providers) + + kpss = self.detect_scrdf(img, max_num=max_num, score=score) + + + elif detect_mode=='Yolov8': + if not self.yoloface_model: + self.yoloface_model = onnxruntime.InferenceSession(self.modelPath+'models/yoloface_8n.onnx', providers=self.providers) + self.insight106_model = onnxruntime.InferenceSession(self.modelPath+'models/2d106det.onnx', providers=self.providers) + # kpss = self.detect_yoloface2(img, max_num=max_num, score=score) + kpss = self.detect_retinaface(img, max_num=max_num, score=score) + + return kpss + + def run_align(self, img): + points = [] + + if not self.insight106_model: + self.insight106_model = onnxruntime.InferenceSession(self.modelPath+'models/2d106det.onnx', providers=self.providers) + + points = self.detect_insight106(img) + + def delete_models(self): + + self.retinaface_model = [] + self.yoloface_model = [] + self.scrdf_model = [] + self.resnet50_model = [] + self.insight106_model = [] + self.recognition_model = [] + self.swapper_model = [] + self.GFPGAN_model = [] + self.GPEN_256_model = [] + self.GPEN_512_model = [] + self.codeformer_model = [] + self.occluder_model = [] + self.faceparser_model = [] + + def run_recognize(self, img, kps): + if not self.recognition_model: + print("now begin load w600k_r50.onnx") + self.recognition_model = onnxruntime.InferenceSession(self.modelPath+'models/w600k_r50.onnx', providers=self.providers) + print("now loaded w600k_r50.onnx") + embedding, cropped_image = self.recognize(img, kps) + return embedding, cropped_image + + + def calc_swapper_latent(self, source_embedding): + if not self.swapper_model: + print("now begin load inswapper_128.onnx") + graph = onnx.load(self.modelPath+"models/inswapper_128.onnx").graph + self.emap = onnx.numpy_helper.to_array(graph.initializer[-1]) + print("now loaded inswapper_128.onnx") + + n_e = source_embedding / l2norm(source_embedding) + latent = n_e.reshape((1,-1)) + latent = np.dot(latent, self.emap) + latent /= np.linalg.norm(latent) + return latent + + def run_swapper(self, image, embedding, output): + if not self.swapper_model: + cuda_options = {"arena_extend_strategy": "kSameAsRequested", 'cudnn_conv_algo_search': 'DEFAULT'} + sess_options = onnxruntime.SessionOptions() + sess_options.enable_cpu_mem_arena = False + # self.swapper_model = onnxruntime.InferenceSession( self.modelPath+"models/inswapper_128_last_cubic.onnx", sess_options, providers=[('CUDAExecutionProvider', cuda_options), 'CPUExecutionProvider']) + self.swapper_model = onnxruntime.InferenceSession( self.modelPath+"models/inswapper_128.onnx", providers=self.providers) + + io_binding = self.swapper_model.io_binding() + io_binding.bind_input(name='target', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,128,128), buffer_ptr=image.data_ptr()) + io_binding.bind_input(name='source', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,512), buffer_ptr=embedding.data_ptr()) + io_binding.bind_output(name='output', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,128, 128), buffer_ptr=output.data_ptr()) + + self.syncvec.cpu() + self.swapper_model.run_with_iobinding(io_binding) + + def run_swap_stg1(self, embedding): + + # Load model + if not self.swapper_model_kps: + self.swapper_model_kps = onnxruntime.InferenceSession( self.modelPath+"models/inswapper_kps.onnx", providers=self.providers) + + # Wacky data structure + io_binding = self.swapper_model_kps.io_binding() + kps_1 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous() + kps_2 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous() + kps_3 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous() + kps_4 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous() + kps_5 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous() + kps_6 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous() + kps_7 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous() + kps_8 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous() + kps_9 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous() + kps_10 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous() + kps_11 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous() + kps_12 = torch.ones((1, 2048), dtype=torch.float16, device='cuda').contiguous() + + # Bind the data structures + io_binding.bind_input(name='source', device_type='cuda', device_id=0, element_type=np.float32, shape=(1, 512), buffer_ptr=embedding.data_ptr()) + io_binding.bind_output(name='1', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_1.data_ptr()) + io_binding.bind_output(name='2', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_2.data_ptr()) + io_binding.bind_output(name='3', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_3.data_ptr()) + io_binding.bind_output(name='4', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_4.data_ptr()) + io_binding.bind_output(name='5', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_5.data_ptr()) + io_binding.bind_output(name='6', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_6.data_ptr()) + io_binding.bind_output(name='7', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_7.data_ptr()) + io_binding.bind_output(name='8', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_8.data_ptr()) + io_binding.bind_output(name='9', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_9.data_ptr()) + io_binding.bind_output(name='10', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_10.data_ptr()) + io_binding.bind_output(name='11', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_11.data_ptr()) + io_binding.bind_output(name='12', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=kps_12.data_ptr()) + + self.syncvec.cpu() + self.swapper_model_kps.run_with_iobinding(io_binding) + + # List of pointers + holder = [] + holder.append(kps_1) + holder.append(kps_2) + holder.append(kps_3) + holder.append(kps_4) + holder.append(kps_5) + holder.append(kps_6) + holder.append(kps_7) + holder.append(kps_8) + holder.append(kps_9) + holder.append(kps_10) + holder.append(kps_11) + holder.append(kps_12) + + return holder + + + def run_swap_stg2(self, image, holder, output): + if not self.swapper_model_swap: + self.swapper_model_swap = onnxruntime.InferenceSession( self.modelPath+"models/inswapper_swap.onnx", providers=self.providers) + + io_binding = self.swapper_model_swap.io_binding() + io_binding.bind_input(name='target', device_type='cuda', device_id=0, element_type=np.float32, shape=(1, 3, 128, 128), buffer_ptr=image.data_ptr()) + io_binding.bind_input(name='onnx::Unsqueeze_170', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[0].data_ptr()) + io_binding.bind_input(name='onnx::Unsqueeze_224', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[1].data_ptr()) + io_binding.bind_input(name='onnx::Unsqueeze_278', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[2].data_ptr()) + io_binding.bind_input(name='onnx::Unsqueeze_332', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[3].data_ptr()) + io_binding.bind_input(name='onnx::Unsqueeze_386', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[4].data_ptr()) + io_binding.bind_input(name='onnx::Unsqueeze_440', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[5].data_ptr()) + io_binding.bind_input(name='onnx::Unsqueeze_494', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[6].data_ptr()) + io_binding.bind_input(name='onnx::Unsqueeze_548', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[7].data_ptr()) + io_binding.bind_input(name='onnx::Unsqueeze_602', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[8].data_ptr()) + io_binding.bind_input(name='onnx::Unsqueeze_656', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[9].data_ptr()) + io_binding.bind_input(name='onnx::Unsqueeze_710', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[10].data_ptr()) + io_binding.bind_input(name='onnx::Unsqueeze_764', device_type='cuda', device_id=0, element_type=np.float16, shape=(1, 2048), buffer_ptr=holder[11].data_ptr()) + io_binding.bind_output(name='output', device_type='cuda', device_id=0, element_type=np.float32, shape=(1, 3, 128, 128), buffer_ptr=output.data_ptr()) + + self.syncvec.cpu() + self.swapper_model_swap.run_with_iobinding(io_binding) + def run_GFPGAN(self, image, output): + if not self.GFPGAN_model: + # cuda_options = {"arena_extend_strategy": "kSameAsRequested", 'cudnn_conv_algo_search': 'DEFAULT'} + # sess_options = onnxruntime.SessionOptions() + # sess_options.enable_cpu_mem_arena = False + + # self.GFPGAN_model = onnxruntime.InferenceSession( self.modelPath+"models/GFPGANv1.4.onnx", sess_options, providers=[("CUDAExecutionProvider", cuda_options), 'CPUExecutionProvider']) + print("loading GFPGANv1.4.onnx") + self.GFPGAN_model = onnxruntime.InferenceSession( self.modelPath+"models/GFPGANv1.4.onnx", providers=self.providers) + print("GFPGANv1.4.onnx loaded") + + io_binding = self.GFPGAN_model.io_binding() + io_binding.bind_input(name='input', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,512,512), buffer_ptr=image.data_ptr()) + io_binding.bind_output(name='output', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,512,512), buffer_ptr=output.data_ptr()) + + self.syncvec.cpu() + self.GFPGAN_model.run_with_iobinding(io_binding) + + + def run_GPEN_512(self, image, output): + if not self.GPEN_512_model: + print("loading GPEN-BFR-512.onnx") + self.GPEN_512_model = onnxruntime.InferenceSession( self.modelPath+"models/GPEN-BFR-512.onnx", providers=self.providers) + print("GPEN-BFR-512.onnx loaded") + + io_binding = self.GPEN_512_model.io_binding() + io_binding.bind_input(name='input', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,512,512), buffer_ptr=image.data_ptr()) + io_binding.bind_output(name='output', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,512,512), buffer_ptr=output.data_ptr()) + + self.syncvec.cpu() + self.GPEN_512_model.run_with_iobinding(io_binding) + + def run_GPEN_256(self, image, output): + if not self.GPEN_256_model: + self.GPEN_256_model = onnxruntime.InferenceSession( self.modelPath+"models/GPEN-BFR-256.onnx", providers=self.providers) + + io_binding = self.GPEN_256_model.io_binding() + io_binding.bind_input(name='input', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,256,256), buffer_ptr=image.data_ptr()) + io_binding.bind_output(name='output', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,256,256), buffer_ptr=output.data_ptr()) + + self.syncvec.cpu() + self.GPEN_256_model.run_with_iobinding(io_binding) + + def run_codeformer(self, image, output): + if not self.codeformer_model: + print("loading codeformer_fp16.onnx") + self.codeformer_model = onnxruntime.InferenceSession( self.modelPath+"models/codeformer_fp16.onnx", providers=self.providers) + print("codeformer_fp16.onnx loaded") + + io_binding = self.codeformer_model.io_binding() + io_binding.bind_input(name='x', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,512,512), buffer_ptr=image.data_ptr()) + w = np.array([0.9], dtype=np.double) + io_binding.bind_cpu_input('w', w) + io_binding.bind_output(name='y', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,512,512), buffer_ptr=output.data_ptr()) + + self.syncvec.cpu() + self.codeformer_model.run_with_iobinding(io_binding) + + def run_occluder(self, image, output): + if not self.occluder_model: + print("loading occluder.onnx") + self.occluder_model = onnxruntime.InferenceSession(self.modelPath+"models/occluder.onnx", providers=self.providers) + print("occluder.onnx loaded") + + io_binding = self.occluder_model.io_binding() + io_binding.bind_input(name='img', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,256,256), buffer_ptr=image.data_ptr()) + io_binding.bind_output(name='output', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,1,256,256), buffer_ptr=output.data_ptr()) + + # torch.cuda.synchronize('cuda') + self.syncvec.cpu() + self.occluder_model.run_with_iobinding(io_binding) + + + def run_faceparser(self, image, output): + if not self.faceparser_model: + self.faceparser_model = onnxruntime.InferenceSession(self.modelPath+"models/faceparser_fp16.onnx", providers=self.providers) + + io_binding = self.faceparser_model.io_binding() + io_binding.bind_input(name='input', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,512,512), buffer_ptr=image.data_ptr()) + io_binding.bind_output(name='out', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,19,512,512), buffer_ptr=output.data_ptr()) + + # torch.cuda.synchronize('cuda') + self.syncvec.cpu() + self.faceparser_model.run_with_iobinding(io_binding) + + + def detect_retinaface(self, img, max_num, score): + + # Resize image to fit within the input_size + input_size = (640, 640) + im_ratio = torch.div(img.size()[1], img.size()[2]) + + # model_ratio = float(input_size[1]) / input_size[0] + model_ratio = 1.0 + if im_ratio>model_ratio: + new_height = input_size[1] + new_width = int(new_height / im_ratio) + else: + new_width = input_size[0] + new_height = int(new_width * im_ratio) + det_scale = torch.div(new_height, img.size()[1]) + + resize = v2.Resize((new_height, new_width), antialias=True) + img = resize(img) + img = img.permute(1,2,0) + + det_img = torch.zeros((input_size[1], input_size[0], 3), dtype=torch.float32, device='cuda:0') + det_img[:new_height,:new_width, :] = img + + # Switch to BGR and normalize + det_img = det_img[:, :, [2,1,0]] + det_img = torch.sub(det_img, 127.5) + det_img = torch.div(det_img, 128.0) + det_img = det_img.permute(2, 0, 1) #3,128,128 + + # Prepare data and find model parameters + det_img = torch.unsqueeze(det_img, 0).contiguous() + + io_binding = self.retinaface_model.io_binding() + io_binding.bind_input(name='input.1', device_type='cuda', device_id=0, element_type=np.float32, shape=det_img.size(), buffer_ptr=det_img.data_ptr()) + + io_binding.bind_output('448', 'cuda') + io_binding.bind_output('471', 'cuda') + io_binding.bind_output('494', 'cuda') + io_binding.bind_output('451', 'cuda') + io_binding.bind_output('474', 'cuda') + io_binding.bind_output('497', 'cuda') + io_binding.bind_output('454', 'cuda') + io_binding.bind_output('477', 'cuda') + io_binding.bind_output('500', 'cuda') + + + # Sync and run model + self.syncvec.cpu() + self.retinaface_model.run_with_iobinding(io_binding) + + + + net_outs = io_binding.copy_outputs_to_cpu() + + input_height = det_img.shape[2] + input_width = det_img.shape[3] + + fmc = 3 + center_cache = {} + scores_list = [] + bboxes_list = [] + kpss_list = [] + for idx, stride in enumerate([8, 16, 32]): + scores = net_outs[idx] + bbox_preds = net_outs[idx+fmc] + bbox_preds = bbox_preds * stride + + kps_preds = net_outs[idx+fmc*2] * stride + height = input_height // stride + width = input_width // stride + K = height * width + key = (height, width, stride) + if key in center_cache: + anchor_centers = center_cache[key] + else: + anchor_centers = np.stack(np.mgrid[:height, :width][::-1], axis=-1).astype(np.float32) + anchor_centers = (anchor_centers * stride).reshape( (-1, 2) ) + anchor_centers = np.stack([anchor_centers]*2, axis=1).reshape( (-1,2) ) + if len(center_cache)<100: + center_cache[key] = anchor_centers + + pos_inds = np.where(scores>=score)[0] + + x1 = anchor_centers[:, 0] - bbox_preds[:, 0] + y1 = anchor_centers[:, 1] - bbox_preds[:, 1] + x2 = anchor_centers[:, 0] + bbox_preds[:, 2] + y2 = anchor_centers[:, 1] + bbox_preds[:, 3] + + bboxes = np.stack([x1, y1, x2, y2], axis=-1) + + pos_scores = scores[pos_inds] + pos_bboxes = bboxes[pos_inds] + scores_list.append(pos_scores) + bboxes_list.append(pos_bboxes) + + preds = [] + for i in range(0, kps_preds.shape[1], 2): + px = anchor_centers[:, i%2] + kps_preds[:, i] + py = anchor_centers[:, i%2+1] + kps_preds[:, i+1] + + preds.append(px) + preds.append(py) + kpss = np.stack(preds, axis=-1) + #kpss = kps_preds + kpss = kpss.reshape( (kpss.shape[0], -1, 2) ) + pos_kpss = kpss[pos_inds] + kpss_list.append(pos_kpss) + + scores = np.vstack(scores_list) + scores_ravel = scores.ravel() + order = scores_ravel.argsort()[::-1] + + det_scale = det_scale.numpy()### + + bboxes = np.vstack(bboxes_list) / det_scale + + kpss = np.vstack(kpss_list) / det_scale + pre_det = np.hstack((bboxes, scores)).astype(np.float32, copy=False) + pre_det = pre_det[order, :] + + dets = pre_det + thresh = 0.4 + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scoresb = dets[:, 4] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + orderb = scoresb.argsort()[::-1] + + keep = [] + while orderb.size > 0: + i = orderb[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[orderb[1:]]) + yy1 = np.maximum(y1[i], y1[orderb[1:]]) + xx2 = np.minimum(x2[i], x2[orderb[1:]]) + yy2 = np.minimum(y2[i], y2[orderb[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + + inter = w * h + ovr = inter / (areas[i] + areas[orderb[1:]] - inter) + + inds = np.where(ovr <= thresh)[0] + orderb = orderb[inds + 1] + + det = pre_det[keep, :] + + kpss = kpss[order,:,:] + kpss = kpss[keep,:,:] + + if max_num > 0 and det.shape[0] > max_num: + area = (det[:, 2] - det[:, 0]) * (det[:, 3] - det[:, 1]) + det_img_center = det_img.shape[0] // 2, det_img.shape[1] // 2 + offsets = np.vstack([ + (det[:, 0] + det[:, 2]) / 2 - det_img_center[1], + (det[:, 1] + det[:, 3]) / 2 - det_img_center[0] + ]) + offset_dist_squared = np.sum(np.power(offsets, 2.0), 0) + + values = area - offset_dist_squared * 2.0 # some extra weight on the centering + bindex = np.argsort(values)[::-1] # some extra weight on the centering + bindex = bindex[0:max_num] + + if kpss is not None: + kpss = kpss[bindex, :] + + return kpss + + def detect_retinaface2(self, img, max_num, score): + + # Resize image to fit within the input_size + input_size = (640, 640) + im_ratio = torch.div(img.size()[1], img.size()[2]) + + # model_ratio = float(input_size[1]) / input_size[0] + model_ratio = 1.0 + if im_ratio > model_ratio: + new_height = input_size[1] + new_width = int(new_height / im_ratio) + else: + new_width = input_size[0] + new_height = int(new_width * im_ratio) + det_scale = torch.div(new_height, img.size()[1]) + + resize = v2.Resize((new_height, new_width), antialias=True) + img = resize(img) + img = img.permute(1, 2, 0) + + det_img = torch.zeros((input_size[1], input_size[0], 3), dtype=torch.float32, device='cuda:0') + det_img[:new_height, :new_width, :] = img + + # Switch to BGR and normalize + det_img = det_img[:, :, [2, 1, 0]] + det_img = torch.sub(det_img, 127.5) + det_img = torch.div(det_img, 128.0) + det_img = det_img.permute(2, 0, 1) # 3,128,128 + + # Prepare data and find model parameters + det_img = torch.unsqueeze(det_img, 0).contiguous() + + io_binding = self.retinaface_model.io_binding() + io_binding.bind_input(name='input.1', device_type='cuda', device_id=0, element_type=np.float32, shape=det_img.size(), buffer_ptr=det_img.data_ptr()) + + io_binding.bind_output('448', 'cuda') + io_binding.bind_output('471', 'cuda') + io_binding.bind_output('494', 'cuda') + io_binding.bind_output('451', 'cuda') + io_binding.bind_output('474', 'cuda') + io_binding.bind_output('497', 'cuda') + io_binding.bind_output('454', 'cuda') + io_binding.bind_output('477', 'cuda') + io_binding.bind_output('500', 'cuda') + + # Sync and run model + self.syncvec.cpu() + self.retinaface_model.run_with_iobinding(io_binding) + + net_outs = io_binding.copy_outputs_to_cpu() + + input_height = det_img.shape[2] + input_width = det_img.shape[3] + + fmc = 3 + center_cache = {} + scores_list = [] + bboxes_list = [] + kpss_list = [] + for idx, stride in enumerate([8, 16, 32]): + scores = net_outs[idx] + bbox_preds = net_outs[idx + fmc] + bbox_preds = bbox_preds * stride + + kps_preds = net_outs[idx + fmc * 2] * stride + height = input_height // stride + width = input_width // stride + K = height * width + key = (height, width, stride) + if key in center_cache: + anchor_centers = center_cache[key] + else: + anchor_centers = np.stack(np.mgrid[:height, :width][::-1], axis=-1).astype(np.float32) + anchor_centers = (anchor_centers * stride).reshape((-1, 2)) + anchor_centers = np.stack([anchor_centers] * 2, axis=1).reshape((-1, 2)) + if len(center_cache) < 100: + center_cache[key] = anchor_centers + + pos_inds = np.where(scores >= score)[0] + + x1 = anchor_centers[:, 0] - bbox_preds[:, 0] + y1 = anchor_centers[:, 1] - bbox_preds[:, 1] + x2 = anchor_centers[:, 0] + bbox_preds[:, 2] + y2 = anchor_centers[:, 1] + bbox_preds[:, 3] + + bboxes = np.stack([x1, y1, x2, y2], axis=-1) + + pos_scores = scores[pos_inds] + pos_bboxes = bboxes[pos_inds] + scores_list.append(pos_scores) + bboxes_list.append(pos_bboxes) + + preds = [] + for i in range(0, kps_preds.shape[1], 2): + px = anchor_centers[:, i % 2] + kps_preds[:, i] + py = anchor_centers[:, i % 2 + 1] + kps_preds[:, i + 1] + + preds.append(px) + preds.append(py) + kpss = np.stack(preds, axis=-1) + # kpss = kps_preds + kpss = kpss.reshape((kpss.shape[0], -1, 2)) + pos_kpss = kpss[pos_inds] + kpss_list.append(pos_kpss) + # result_boxes = cv2.dnn.NMSBoxes(bboxes_list, scores_list, 0.25, 0.45, 0.5) + # print(result_boxes) + scores = np.vstack(scores_list) + scores_ravel = scores.ravel() + order = scores_ravel.argsort()[::-1] + + det_scale = det_scale.numpy() ### + + bboxes = np.vstack(bboxes_list) / det_scale + + kpss = np.vstack(kpss_list) / det_scale + pre_det = np.hstack((bboxes, scores)).astype(np.float32, copy=False) + pre_det = pre_det[order, :] + + + + dets = pre_det + thresh = 0.4 + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scoresb = dets[:, 4] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + orderb = scoresb.argsort()[::-1] + + keep = [] + person_id = 0 + people = {} + + while orderb.size > 0: + # Add first box in list + i = orderb[0] + keep.append(i) + + people[person_id] = orderb[0] + + + # Find overlap of remaining boxes + xx1 = np.maximum(x1[i], x1[orderb[1:]]) + yy1 = np.maximum(y1[i], y1[orderb[1:]]) + xx2 = np.minimum(x2[i], x2[orderb[1:]]) + yy2 = np.minimum(y2[i], y2[orderb[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + + inter = w * h + + + ovr = inter / (areas[i] + areas[orderb[1:]] - inter) + + + inds0 = np.where(ovr > thresh)[0] + people[person_id] = np.hstack((people[person_id], orderb[inds0+1])).astype(np.int, copy=False) + + + # identify where there is no overlap ( 0 and det.shape[0] > max_num: + area = (det[:, 2] - det[:, 0]) * (det[:, 3] - det[:, 1]) + det_img_center = det_img.shape[0] // 2, det_img.shape[1] // 2 + offsets = np.vstack([ + (det[:, 0] + det[:, 2]) / 2 - det_img_center[1], + (det[:, 1] + det[:, 3]) / 2 - det_img_center[0] + ]) + offset_dist_squared = np.sum(np.power(offsets, 2.0), 0) + + values = area - offset_dist_squared * 2.0 # some extra weight on the centering + bindex = np.argsort(values)[::-1] # some extra weight on the centering + bindex = bindex[0:max_num] + + if kpss is not None: + kpss = kpss[bindex, :] + + # return kpss_ave + + return kpss_ave + def detect_scrdf(self, img, max_num, score): + # Resize image to fit within the input_size + input_size = (640, 640) + im_ratio = torch.div(img.size()[1], img.size()[2]) + + model_ratio = float(input_size[1]) / input_size[0] + if im_ratio>model_ratio: + new_height = input_size[1] + new_width = int(new_height / im_ratio) + else: + new_width = input_size[0] + new_height = int(new_width * im_ratio) + det_scale = torch.div(new_height, img.size()[1]) + + resize = v2.Resize((new_height, new_width), antialias=True) + img = resize(img) + img = img.permute(1,2,0) + + det_img = torch.zeros((input_size[1], input_size[0], 3), dtype=torch.float32, device='cuda:0') + det_img[:new_height,:new_width, :] = img + + # Switch to BGR and normalize + det_img = det_img[:, :, [2,1,0]] + det_img = torch.sub(det_img, 127.5) + det_img = torch.div(det_img, 128.0) + det_img = det_img.permute(2, 0, 1) #3,128,128 + + # Prepare data and find model parameters + det_img = torch.unsqueeze(det_img, 0).contiguous() + input_name = self.scrdf_model.get_inputs()[0].name + + outputs = self.scrdf_model.get_outputs() + output_names = [] + for o in outputs: + output_names.append(o.name) + + io_binding = self.scrdf_model.io_binding() + io_binding.bind_input(name=input_name, device_type='cuda', device_id=0, element_type=np.float32, shape=det_img.size(), buffer_ptr=det_img.data_ptr()) + + for i in range(len(output_names)): + io_binding.bind_output(output_names[i], 'cuda') + + # Sync and run model + syncvec = self.syncvec.cpu() + self.scrdf_model.run_with_iobinding(io_binding) + + net_outs = io_binding.copy_outputs_to_cpu() + + input_height = det_img.shape[2] + input_width = det_img.shape[3] + + fmc = 3 + center_cache = {} + scores_list = [] + bboxes_list = [] + kpss_list = [] + for idx, stride in enumerate([8, 16, 32]): + scores = net_outs[idx] + bbox_preds = net_outs[idx+fmc] + bbox_preds = bbox_preds * stride + + kps_preds = net_outs[idx+fmc*2] * stride + height = input_height // stride + width = input_width // stride + K = height * width + key = (height, width, stride) + if key in center_cache: + anchor_centers = center_cache[key] + else: + anchor_centers = np.stack(np.mgrid[:height, :width][::-1], axis=-1).astype(np.float32) + anchor_centers = (anchor_centers * stride).reshape( (-1, 2) ) + anchor_centers = np.stack([anchor_centers]*2, axis=1).reshape( (-1,2) ) + if len(center_cache)<100: + center_cache[key] = anchor_centers + + pos_inds = np.where(scores>=score)[0] + + x1 = anchor_centers[:, 0] - bbox_preds[:, 0] + y1 = anchor_centers[:, 1] - bbox_preds[:, 1] + x2 = anchor_centers[:, 0] + bbox_preds[:, 2] + y2 = anchor_centers[:, 1] + bbox_preds[:, 3] + + bboxes = np.stack([x1, y1, x2, y2], axis=-1) + + pos_scores = scores[pos_inds] + pos_bboxes = bboxes[pos_inds] + scores_list.append(pos_scores) + bboxes_list.append(pos_bboxes) + + preds = [] + for i in range(0, kps_preds.shape[1], 2): + px = anchor_centers[:, i%2] + kps_preds[:, i] + py = anchor_centers[:, i%2+1] + kps_preds[:, i+1] + + preds.append(px) + preds.append(py) + kpss = np.stack(preds, axis=-1) + #kpss = kps_preds + kpss = kpss.reshape( (kpss.shape[0], -1, 2) ) + pos_kpss = kpss[pos_inds] + kpss_list.append(pos_kpss) + + scores = np.vstack(scores_list) + scores_ravel = scores.ravel() + order = scores_ravel.argsort()[::-1] + + det_scale = det_scale.numpy()### + + bboxes = np.vstack(bboxes_list) / det_scale + + kpss = np.vstack(kpss_list) / det_scale + pre_det = np.hstack((bboxes, scores)).astype(np.float32, copy=False) + pre_det = pre_det[order, :] + + dets = pre_det + thresh = 0.4 + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scoresb = dets[:, 4] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + orderb = scoresb.argsort()[::-1] + + keep = [] + while orderb.size > 0: + i = orderb[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[orderb[1:]]) + yy1 = np.maximum(y1[i], y1[orderb[1:]]) + xx2 = np.minimum(x2[i], x2[orderb[1:]]) + yy2 = np.minimum(y2[i], y2[orderb[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + + inter = w * h + ovr = inter / (areas[i] + areas[orderb[1:]] - inter) + + inds = np.where(ovr <= thresh)[0] + orderb = orderb[inds + 1] + + det = pre_det[keep, :] + + kpss = kpss[order,:,:] + kpss = kpss[keep,:,:] + + if max_num > 0 and det.shape[0] > max_num: + area = (det[:, 2] - det[:, 0]) * (det[:, 3] - + det[:, 1]) + det_img_center = det_img.shape[0] // 2, det_img.shape[1] // 2 + offsets = np.vstack([ + (det[:, 0] + det[:, 2]) / 2 - det_img_center[1], + (det[:, 1] + det[:, 3]) / 2 - det_img_center[0] + ]) + offset_dist_squared = np.sum(np.power(offsets, 2.0), 0) + + values = area - offset_dist_squared * 2.0 # some extra weight on the centering + bindex = np.argsort(values)[::-1] # some extra weight on the centering + bindex = bindex[0:max_num] + + if kpss is not None: + kpss = kpss[bindex, :] + + return kpss + + def detect_yoloface(self, img, max_num, score): + + height = img.size(dim=1) + width = img.size(dim=2) + length = max((height, width)) + + image = torch.zeros((length, length, 3), dtype=torch.uint8, device='cuda') + img = img.permute(1,2,0) + + image[0:height, 0:width] = img + scale = length/640.0 + image = torch.div(image, 255.0) + + t640 = v2.Resize((640, 640), antialias=False) + image = image.permute(2, 0, 1) + image = t640(image) + + image = torch.unsqueeze(image, 0).contiguous() + + io_binding = self.yoloface_model.io_binding() + io_binding.bind_input(name='images', device_type='cuda', device_id=0, element_type=np.float32, shape=image.size(), buffer_ptr=image.data_ptr()) + io_binding.bind_output('output0', 'cuda') + + # Sync and run model + self.syncvec.cpu() + self.yoloface_model.run_with_iobinding(io_binding) + + net_outs = io_binding.copy_outputs_to_cpu() + + outputs = np.squeeze(net_outs).T + + bbox_raw, score_raw, kps_raw = np.split(outputs, [4, 5], axis=1) + + bbox_list = [] + score_list = [] + kps_list = [] + keep_indices = np.where(score_raw > score)[0] + + if keep_indices.any(): + bbox_raw, kps_raw, score_raw = bbox_raw[keep_indices], kps_raw[keep_indices], score_raw[keep_indices] + for bbox in bbox_raw: + bbox_list.append(np.array([(bbox[0]-bbox[2]/2), (bbox[1]-bbox[3]/2), (bbox[0]+bbox[2]/2), (bbox[1]+bbox[3]/2)])) + kps_raw = kps_raw * scale + + for kps in kps_raw: + indexes = np.arange(0, len(kps), 3) + temp_kps = [] + for index in indexes: + temp_kps.append([kps[index], kps[index + 1]]) + kps_list.append(np.array(temp_kps)) + score_list = score_raw.ravel().tolist() + + result_boxes = cv2.dnn.NMSBoxes(bbox_list, score_list, 0.25, 0.45, 0.5) + + result = [] + for r in result_boxes: + if r==max_num: + break + result.append(kps_list[r]) + + return np.array(result) + + def detect_yoloface2(self, image_in, max_num, score): + img = image_in.detach().clone() + + height = img.size(dim=1) + width = img.size(dim=2) + length = max((height, width)) + + image = torch.zeros((length, length, 3), dtype=torch.uint8, + device='cuda') + img = img.permute(1, 2, 0) + + image[0:height, 0:width] = img + scale = length / 640.0 + image = torch.div(image, 255.0) + + t640 = v2.Resize((640, 640), antialias=False) + image = image.permute(2, 0, 1) + image = t640(image) + + image = torch.unsqueeze(image, 0).contiguous() + + io_binding = self.yoloface_model.io_binding() + io_binding.bind_input(name='images', device_type='cuda', device_id=0, + element_type=np.float32, shape=image.size(), + buffer_ptr=image.data_ptr()) + io_binding.bind_output('output0', 'cuda') + + # Sync and run model + self.syncvec.cpu() + self.yoloface_model.run_with_iobinding(io_binding) + + net_outs = io_binding.copy_outputs_to_cpu() + + outputs = np.squeeze(net_outs).T + + bbox_raw, score_raw, kps_raw = np.split(outputs, [4, 5], axis=1) + + bbox_list = [] + score_list = [] + kps_list = [] + keep_indices = np.where(score_raw > score)[0] + + if keep_indices.any(): + bbox_raw, kps_raw, score_raw = bbox_raw[keep_indices], kps_raw[ + keep_indices], score_raw[keep_indices] + for bbox in bbox_raw: + bbox_list.append(np.array( + [(bbox[0] - bbox[2] / 2), (bbox[1] - bbox[3] / 2), + (bbox[0] + bbox[2] / 2), (bbox[1] + bbox[3] / 2)])) + kps_raw = kps_raw * scale + + for kps in kps_raw: + indexes = np.arange(0, len(kps), 3) + temp_kps = [] + for index in indexes: + temp_kps.append([kps[index], kps[index + 1]]) + kps_list.append(np.array(temp_kps)) + score_list = score_raw.ravel().tolist() + + result_boxes = cv2.dnn.NMSBoxes(bbox_list, score_list, 0.25, 0.45, 0.5) + + result = [] + for r in result_boxes: + if r == max_num: + break + bbox_list = bbox_list[r] + result.append(kps_list[r]) + bbox_list = bbox_list*scale + # print(bbox_list) + # print(bbox_list*scale) + + # img = image_in.detach().clone() + # test = image_in.permute(1, 2, 0) + # test = test.cpu().numpy() + # cv2.imwrite('1.jpg', test) + + # b_scale = 50 + # bbox_list[0] = bbox_list[0] - b_scale + # bbox_list[1] = bbox_list[1] - b_scale + # bbox_list[2] = bbox_list[2] + b_scale + # bbox_list[3] = bbox_list[3] + b_scale + + img = image_in.detach().clone() + + img = img[:, int(bbox_list[1]):int(bbox_list[3]), int(bbox_list[0]):int(bbox_list[2])] + # print(img.size()) + + + height = img.size(dim=1) + width = img.size(dim=2) + length = max((height, width)) + + image = torch.zeros((length, length, 3), dtype=torch.uint8, device='cuda') + img = img.permute(1,2,0) + + image[0:height, 0:width] = img + scale = length/192 + image = torch.div(image, 255.0) + + + t192 = v2.Resize((192, 192), antialias=False) + image = image.permute(2, 0, 1) + image = t192(image) + + test = image_in.detach().clone().permute(1, 2, 0) + test = test.cpu().numpy() + + input_mean = 0.0 + input_std = 1.0 + + self.lmk_dim = 2 + self.lmk_num = 106 + + bbox = bbox_list + w, h = (bbox[2] - bbox[0]), (bbox[3] - bbox[1]) + center = (bbox[2] + bbox[0]) / 2, (bbox[3] + bbox[1]) / 2 + rotate = 0 + _scale = 192 / (max(w, h) * 1.5) + # print('param:', img.shape, bbox, center, self.input_size, _scale, rotate) + aimg, M = self.transform(test, center, 192, _scale, rotate) + input_size = tuple(aimg.shape[0:2][::-1]) + # assert input_size==self.input_size + blob = cv2.dnn.blobFromImage(aimg, 1.0 / input_std, input_size, ( input_mean, input_mean, input_mean), swapRB=True) + pred = self.insight106_model.run(['fc1'], {'data': blob})[0][0] + if pred.shape[0] >= 3000: + pred = pred.reshape((-1, 3)) + else: + pred = pred.reshape((-1, 2)) + if self.lmk_num < pred.shape[0]: + pred = pred[self.lmk_num * -1:, :] + pred[:, 0:2] += 1 + pred[:, 0:2] *= 96 + if pred.shape[1] == 3: + pred[:, 2] *= (106) + + IM = cv2.invertAffineTransform(M) + pred = self.trans_points2d(pred, IM) + # face[self.taskname] = pred + # if self.require_pose: + # P = transform.estimate_affine_matrix_3d23d(self.mean_lmk, pred) + # s, R, t = transform.P2sRt(P) + # rx, ry, rz = transform.matrix2angle(R) + # pose = np.array([rx, ry, rz], dtype=np.float32) + # face['pose'] = pose # pitch, yaw, roll + # print(pred.shape) + # print(pred) + + for point in pred: + test[int(point[1])] [int(point[0])] [0] = 255 + test[int(point[1])] [int(point[0])] [1] = 255 + test[int(point[1])] [int(point[0])] [2] = 255 + cv2.imwrite('2.jpg', test) + + predd = [] + predd.append(pred[38]) + predd.append(pred[88]) + # predd.append(pred[86]) + # predd.append(pred[52]) + # predd.append(pred[61]) + + predd.append(kps_list[0][2]) + predd.append(kps_list[0][3]) + predd.append(kps_list[0][4]) + + # for point in predd: + # test[int(point[1])] [int(point[0])] [0] = 255 + # test[int(point[1])] [int(point[0])] [1] = 255 + # test[int(point[1])] [int(point[0])] [2] = 255 + # cv2.imwrite('2.jpg', test) + preddd=[] + preddd.append(predd) + return np.array(preddd) + def transform(self, data, center, output_size, scale, rotation): + scale_ratio = scale + rot = float(rotation) * np.pi / 180.0 + # translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio) + t1 = trans.SimilarityTransform(scale=scale_ratio) + cx = center[0] * scale_ratio + cy = center[1] * scale_ratio + t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy)) + t3 = trans.SimilarityTransform(rotation=rot) + t4 = trans.SimilarityTransform(translation=(output_size / 2, + output_size / 2)) + t = t1 + t2 + t3 + t4 + M = t.params[0:2] + cropped = cv2.warpAffine(data, + M, (output_size, output_size), + borderValue=0.0) + return cropped, M + + def trans_points2d(self, pts, M): + new_pts = np.zeros(shape=pts.shape, dtype=np.float32) + for i in range(pts.shape[0]): + pt = pts[i] + new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32) + new_pt = np.dot(M, new_pt) + # print('new_pt', new_pt.shape, new_pt) + new_pts[i] = new_pt[0:2] + + return new_pts + + # image = torch.unsqueeze(image, 0).contiguous() + # + # io_binding = self.insight106_model.io_binding() + # io_binding.bind_input(name='data', device_type='cuda', device_id=0, element_type=np.float32, shape=image.size(), buffer_ptr=image.data_ptr()) + # io_binding.bind_output('fc1', 'cuda') + # + # # Sync and run model + # self.syncvec.cpu() + # self.insight106_model.run_with_iobinding(io_binding) + # + # net_outs = io_binding.copy_outputs_to_cpu() + # print(net_outs) + # net_outs[0][0] = net_outs[0][0]+1. + # net_outs[0][0] = net_outs[0][0]/2. + # net_outs[0][0] = net_outs[0][0]*96 + # + # # net_outs[0] = net_outs[0]*scale + # # print(net_outs) + # test=test*255.0 + # for i in range(0, len(net_outs[0][0]), 2): + # test[int(net_outs[0][0][i+1])] [int(net_outs[0][0][i])] [0] = 255 + # test[int(net_outs[0][0][i+1])] [int(net_outs[0][0][i])] [1] = 255 + # test[int(net_outs[0][0][i+1])] [int(net_outs[0][0][i])] [2] = 255 + # cv2.imwrite('2.jpg', test) + # + # return np.array(result) + def recognize(self, img, face_kps): + # Find transform + dst = self.arcface_dst.copy() + dst[:, 0] += 8.0 + + tform = trans.SimilarityTransform() + tform.estimate(face_kps, dst) + + # Transform + img = v2.functional.affine(img, tform.rotation*57.2958, (tform.translation[0], tform.translation[1]) , tform.scale, 0, center = (0,0) ) + img = v2.functional.crop(img, 0,0, 128, 128) + + img = v2.Resize((112, 112), interpolation=v2.InterpolationMode.BILINEAR, antialias=False)(img) + + # Switch to BGR and normalize + img = img.permute(1,2,0) #112,112,3 + cropped_image = img + img = img[:, :, [2,1,0]] + img = torch.sub(img, 127.5) + img = torch.div(img, 127.5) + img = img.permute(2, 0, 1) #3,112,112 + + # Prepare data and find model parameters + img = torch.unsqueeze(img, 0).contiguous() + input_name = self.recognition_model.get_inputs()[0].name + + outputs = self.recognition_model.get_outputs() + output_names = [] + for o in outputs: + output_names.append(o.name) + + io_binding = self.recognition_model.io_binding() + io_binding.bind_input(name=input_name, device_type='cuda', device_id=0, element_type=np.float32, shape=img.size(), buffer_ptr=img.data_ptr()) + + for i in range(len(output_names)): + io_binding.bind_output(output_names[i], 'cuda') + + # Sync and run model + self.syncvec.cpu() + self.recognition_model.run_with_iobinding(io_binding) + + # Return embedding + return np.array(io_binding.copy_outputs_to_cpu()).flatten(), cropped_image + + def resnet50(self, image, score=.5): + if not self.resnet50_model: + self.resnet50_model = onnxruntime.InferenceSession(self.modelPath+"models/res50.onnx", providers=self.providers) + + feature_maps = [[64, 64], [32, 32], [16, 16]] + min_sizes = [[16, 32], [64, 128], [256, 512]] + steps = [8, 16, 32] + image_size = 512 + + for k, f in enumerate(feature_maps): + min_size_array = min_sizes[k] + for i, j in product(range(f[0]), range(f[1])): + for min_size in min_size_array: + s_kx = min_size / image_size + s_ky = min_size / image_size + dense_cx = [x * steps[k] / image_size for x in [j + 0.5]] + dense_cy = [y * steps[k] / image_size for y in [i + 0.5]] + for cy, cx in product(dense_cy, dense_cx): + self.anchors += [cx, cy, s_kx, s_ky] + + # image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR) + image = image.permute(1,2,0) + + # image = image - [104, 117, 123] + mean = torch.tensor([104, 117, 123], dtype=torch.float32, device='cuda') + image = torch.sub(image, mean) + + # image = image.transpose(2, 0, 1) + # image = np.float32(image[np.newaxis,:,:,:]) + image = image.permute(2,0,1) + image = torch.reshape(image, (1, 3, 512, 512)) + + + height, width = (512, 512) + tmp = [width, height, width, height, width, height, width, height, width, height] + scale1 = torch.tensor(tmp, dtype=torch.float32, device='cuda') + + # ort_inputs = {"input": image} + conf = torch.empty((1,10752,2), dtype=torch.float32, device='cuda').contiguous() + landmarks = torch.empty((1,10752,10), dtype=torch.float32, device='cuda').contiguous() + + io_binding = self.resnet50_model.io_binding() + io_binding.bind_input(name='input', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,3,512,512), buffer_ptr=image.data_ptr()) + io_binding.bind_output(name='conf', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,10752,2), buffer_ptr=conf.data_ptr()) + io_binding.bind_output(name='landmarks', device_type='cuda', device_id=0, element_type=np.float32, shape=(1,10752,10), buffer_ptr=landmarks.data_ptr()) + + # _, conf, landmarks = self.resnet_model.run(None, ort_inputs) + torch.cuda.synchronize('cuda') + self.resnet50_model.run_with_iobinding(io_binding) + + + # conf = torch.from_numpy(conf) + # scores = conf.squeeze(0).numpy()[:, 1] + scores = torch.squeeze(conf)[:, 1] + + # landmarks = torch.from_numpy(landmarks) + # landmarks = landmarks.to('cuda') + + priors = torch.tensor(self.anchors).view(-1, 4) + priors = priors.to('cuda') + + # pre = landmarks.squeeze(0) + pre = torch.squeeze(landmarks, 0) + + tmp = (priors[:, :2] + pre[:, :2] * 0.1 * priors[:, 2:], priors[:, :2] + pre[:, 2:4] * 0.1 * priors[:, 2:], priors[:, :2] + pre[:, 4:6] * 0.1 * priors[:, 2:], priors[:, :2] + pre[:, 6:8] * 0.1 * priors[:, 2:], priors[:, :2] + pre[:, 8:10] * 0.1 * priors[:, 2:]) + landmarks = torch.cat(tmp, dim=1) + # landmarks = landmarks * scale1 + landmarks = torch.mul(landmarks, scale1) + + landmarks = landmarks.cpu().numpy() + + # ignore low scores + inds = torch.where(scores>score)[0] + inds = inds.cpu().numpy() + scores = scores.cpu().numpy() + + landmarks, scores = landmarks[inds], scores[inds] + + # sort + order = scores.argsort()[::-1] + landmarks = landmarks[order][0] + + return np.array([[landmarks[i], landmarks[i + 1]] for i in range(0,10,2)]) \ No newline at end of file diff --git a/rope/Styles.py b/rope/Styles.py new file mode 100644 index 0000000..e973ba1 --- /dev/null +++ b/rope/Styles.py @@ -0,0 +1,286 @@ +canvas_frame_label_1 = { + 'bg': '#17181A', + 'bd': '0', + 'relief': 'flat', + 'highlightthickness': '0' + } + +canvas_frame_label_2 = { + 'bg': '#212126', + 'bd': '0', + 'relief': 'flat', + 'highlightthickness': '0' + } + +canvas_frame_label_3 = { + 'bg': '#28282e', + 'bd': '0', + 'relief': 'flat', + 'highlightthickness': '0' + } + +info_label = { + 'bg': '#28282e', + 'fg': '#FFFFFF', + 'bd': '5', + 'relief': 'flat', + 'highlightthickness': '0', + 'font': ("Segoe UI", 9), + 'anchor': 'nw', + 'justify': 'left', + } + +text_1 = { + 'bg': '#17181A', + 'fg': '#D0D0D0', + 'activebackground': '#212126', + 'activeforeground': 'white', + 'relief': 'flat', + 'border': '0', + 'font': ("Segoe UI", 9) + } +text_2 = { + 'bg': '#212126', + 'fg': '#D0D0D0', + 'activebackground': '#212126', + 'activeforeground': 'white', + 'relief': 'flat', + 'border': '0', + 'font': ("Segoe UI", 9) + } +text_3 = { + 'bg': '#28282e', + 'fg': '#D0D0D0', + 'activebackground': '#212126', + 'activeforeground': 'white', + 'relief': 'flat', + 'border': '0', + 'font': ("Segoe UI", 9) + } + + + +option_slider_style = { + 'bg': '#919191', #sliderbar color + 'activebackground': 'white', + 'highlightcolor': 'white', + 'highlightthickness': '0', + 'relief': 'flat', + 'sliderrelief': 'flat', + 'border': '0', + 'width': '3', + 'troughcolor': '#1F1F1F', + } + + +entry_3 = { + 'bg': '#1F1F1F', + 'fg': '#FFFFFF', + 'relief': 'flat', + 'border': '0', + 'width': '5', + 'justify': 'c', + 'font': ("Segoe UI", 9), + 'highlightthickness': '1', + 'highlightbackground': '#17181A', + } + +entry_2 = { + 'bg': '#1F1F1F', + 'fg': '#FFFFFF', + 'relief': 'flat', + 'border': '0', + 'highlightthickness': '1', + 'highlightbackground': '#17181A', + 'width': '5', + 'justify': 'l', + 'font': ("Segoe UI", 9) + } + +text_selection_off_3 = { + 'bg': '#28282e', + 'fg': '#7A7A7A', + 'activebackground': '#212126', + 'activeforeground': 'white', + 'relief': 'flat', + 'border': '0', + 'font': ("Segoe UI", 10) + } +text_selection_on_3 = { + 'bg': '#28282e', + 'fg': '#FFFFFF', + 'activebackground': '#212126', + 'activeforeground': 'white', + 'relief': 'flat', + 'border': '0', + 'font': ("Segoe UI", 10) + } +text_selection_off_2 = { + 'bg': '#212126', + 'fg': '#7A7A7A', + 'activebackground': '#212126', + 'activeforeground': 'white', + 'relief': 'flat', + 'border': '0', + 'font': ("Segoe UI", 10) + } +text_selection_on_2 = { + 'bg': '#212126', + 'fg': '#FFFFFF', + 'activebackground': '#212126', + 'activeforeground': 'white', + 'relief': 'flat', + 'border': '0', + 'font': ("Segoe UI", 10) + } + + +parameter_switch_3 = { + 'bg': '#28282e', + 'fg': '#FFFFFF', + 'activebackground': '#212126', + 'activeforeground': 'white', + 'relief': 'flat', + 'border': '0', + 'font': ("Segoe UI", 10) + } + + + + +canvas_bg = { + 'bg': '#090909', + 'relief': 'flat', + 'bd': '0', + 'highlightthickness': '0' + } + +icon = { + 'IconOn': './rope/media/OnState.png', + 'IconOff': './rope/media/OffState.png', + } + + +frame_style_bg = { + 'bg': '#090909', + 'relief': 'flat', + 'bd': '0' + } + +button_3 = { + 'bg': '#28282E', + 'fg': '#FFFFFF', + 'activebackground': '#28282E', + 'activeforeground': 'white', + 'relief': 'flat', + 'border': '0', + 'font': ("Segoe UI", 10) + } +button_2 = { + 'bg': '#212126', + 'fg': '#FFFFFF', + 'activebackground': '#212126', + 'activeforeground': 'white', + 'relief': 'flat', + 'border': '0', + 'font': ("Segoe UI", 10) + } +button_1 = { + 'bg': '#17181A', + 'fg': '#FFFFFF', + 'activebackground': '#17181A', + 'activeforeground': 'white', + 'relief': 'flat', + 'border': '0', + 'font': ("Segoe UI", 10) + } + +button_inactive = { + 'bg': '#17181A', + 'fg': '#FFFFFF', + 'activebackground': '#17181A', + 'activeforeground': 'white', + 'relief': 'flat', + 'border': '0', + 'font': ("Segoe UI", 10) + } + +button_active = { + 'bg': '#17181A', + 'fg': '#FFFFFF', + 'activebackground': '#17181A', + 'activeforeground': 'white', + 'relief': 'flat', + 'border': '0', + 'font': ("Segoe UI", 10) + } + + +media_button_off_3= { + 'bg': '#28282E', + 'fg': '#7A7A7A', + 'activebackground': '#212126', + 'activeforeground': 'white', + 'relief': 'flat', + 'border': '0', + 'font': ("Segoe UI", 8) + } + +media_button_on_3= { + 'bg': '#d10303', + 'fg': '#FFFFFF', + 'activebackground': '#212126', + 'activeforeground': 'white', + 'relief': 'flat', + 'border': '0', + 'font': ("Segoe UI", 8) + } + +ui_text_na_2 = { + 'bg': '#212126', + 'fg': '#7A7A7A', + 'activebackground': '#212126', + 'activeforeground': 'white', + 'relief': 'flat', + 'border': '0', + 'font': ("Segoe UI", 9) + } + +timeline_canvas = { + 'bg': '#212126', + 'relief': 'flat', + 'bd': '0', + 'highlightthickness': '0' + } + +donate_1 = { + 'bg': '#17181A', + 'fg': 'light goldenrod', + 'relief': 'flat', + 'border': '0', + 'font': ("Segoe UI Semibold", 10), + 'cursor': "hand2", + } + +# Panes +# 3:#28282E +# 2:#212126 +# 1:#17181A + +# preview background: #1A1A1A + +# Num Fields, slider bg: #1F1F1F +# slider ball: #919191 +# Borders:#090909 +# Text +# On/off:#FFFFFF +# labels: #D0D0D0 +# notActive: #7A7A7A +# active:#FFFFFF + +# highlighted button: #B1B1B2 +# Button off: #828282 + +# on: #FFFFFF +# hover: #b1b1b2 +# off: #828282 \ No newline at end of file diff --git a/rope/VideoManager.py b/rope/VideoManager.py new file mode 100644 index 0000000..f5740aa --- /dev/null +++ b/rope/VideoManager.py @@ -0,0 +1,1478 @@ +import os +import cv2 +#import tkinter as tk +from PIL import Image#, ImageTk +import threading +import time +import numpy as np +from skimage import transform as trans +import subprocess +from math import floor, ceil +import bisect +import onnxruntime +import torchvision +from torchvision.transforms.functional import normalize #update to v2 +import torch +from torchvision import transforms +torchvision.disable_beta_transforms_warning() +from torchvision.transforms import v2 +torch.set_grad_enabled(False) +onnxruntime.set_default_logger_severity(4) + +import inspect #print(inspect.currentframe().f_back.f_code.co_name, 'resize_image') + +device = 'cuda' + +lock=threading.Lock() + +class VideoManager(): + def __init__(self, models ): + self.models = models + # Model related + self.swapper_model = [] # insightface swapper model + # self.faceapp_model = [] # insight faceapp model + self.input_names = [] # names of the inswapper.onnx inputs + self.input_size = [] # size of the inswapper.onnx inputs + + self.output_names = [] # names of the inswapper.onnx outputs + self.arcface_dst = np.array( [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366], [41.5493, 92.3655], [70.7299, 92.2041]], dtype=np.float32) + + self.video_file = [] + + self.FFHQ_kps = np.array([[ 192.98138, 239.94708 ], [ 318.90277, 240.1936 ], [ 256.63416, 314.01935 ], [ 201.26117, 371.41043 ], [ 313.08905, 371.15118 ] ]) + + + + #Video related + self.capture = [] # cv2 video + self.is_video_loaded = False # flag for video loaded state + self.video_frame_total = None # length of currently loaded video + self.play = False # flag for the play button toggle + self.current_frame = 0 # the current frame of the video + self.create_video = False + self.output_video = [] + self.file_name = [] + + + # Play related + # self.set_read_threads = [] # Name of threaded function + self.frame_timer = 0.0 # used to set the framerate during playing + + # Queues + self.action_q = [] # queue for sending to the coordinator + self.frame_q = [] # queue for frames that are ready for coordinator + + self.r_frame_q = [] # queue for frames that are requested by the GUI + self.read_video_frame_q = [] + + # swapping related + # self.source_embedding = [] # array with indexed source embeddings + + self.found_faces = [] # array that maps the found faces to source faces + + self.parameters = [] + + + self.target_video = [] + + self.fps = 1.0 + self.temp_file = [] + + + self.clip_session = [] + + self.start_time = [] + self.record = False + self.output = [] + self.image = [] + + self.saved_video_path = [] + self.sp = [] + self.timer = [] + self.fps_average = [] + self.total_thread_time = 0.0 + + self.start_play_time = [] + self.start_play_frame = [] + + self.rec_thread = [] + self.markers = [] + self.is_image_loaded = False + self.stop_marker = -1 + self.perf_test = False + + self.control = [] + + + + + + self.process_q = { + "Thread": [], + "FrameNumber": [], + "ProcessedFrame": [], + "Status": 'clear', + "ThreadTime": [] + } + self.process_qs = [] + self.rec_q = { + "Thread": [], + "FrameNumber": [], + "Status": 'clear' + } + self.rec_qs = [] + + def assign_found_faces(self, found_faces): + self.found_faces = found_faces + + + def load_target_video( self, file ): + # If we already have a video loaded, release it + if self.capture: + self.capture.release() + + # Open file + self.video_file = file + self.capture = cv2.VideoCapture(file) + self.fps = self.capture.get(cv2.CAP_PROP_FPS) + + if not self.capture.isOpened(): + print("Cannot open file: ", file) + + else: + self.target_video = file + self.is_video_loaded = True + self.is_image_loaded = False + self.video_frame_total = int(self.capture.get(cv2.CAP_PROP_FRAME_COUNT)) + self.play = False + self.current_frame = 0 + self.frame_timer = time.time() + self.frame_q = [] + self.r_frame_q = [] + self.found_faces = [] + self.add_action("set_slider_length",self.video_frame_total-1) + + self.capture.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame) + success, image = self.capture.read() + + if success: + crop = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # RGB + temp = [crop, False] + self.r_frame_q.append(temp) + self.capture.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame) + + def load_target_image(self, file): + if self.capture: + self.capture.release() + self.is_video_loaded = False + self.play = False + self.frame_q = [] + self.r_frame_q = [] + self.found_faces = [] + self.image = cv2.imread(file) # BGR + self.image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB) # RGB + temp = [self.image, False] + self.frame_q.append(temp) + + self.is_image_loaded = True + + + ## Action queue + def add_action(self, action, param): + # print(inspect.currentframe().f_back.f_code.co_name, '->add_action: '+action) + temp = [action, param] + self.action_q.append(temp) + + def get_action_length(self): + return len(self.action_q) + + def get_action(self): + action = self.action_q[0] + self.action_q.pop(0) + return action + + ## Queues for the Coordinator + def get_frame(self): + frame = self.frame_q[0] + self.frame_q.pop(0) + return frame + + def get_frame_length(self): + return len(self.frame_q) + + def get_requested_frame(self): + frame = self.r_frame_q[0] + self.r_frame_q.pop(0) + return frame + + def get_requested_frame_length(self): + return len(self.r_frame_q) + + + def get_requested_video_frame(self, frame, marker=True): + temp = [] + if self.is_video_loaded: + + if self.play == True: + self.play_video("stop") + self.process_qs = [] + + self.current_frame = int(frame) + + self.capture.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame) + success, target_image = self.capture.read() #BGR + + if success: + target_image = cv2.cvtColor(target_image, cv2.COLOR_BGR2RGB) #RGB + if not self.control['SwapFacesButton']: + temp = [target_image, self.current_frame] #temp = RGB + else: + temp = [self.swap_video(target_image, self.current_frame, marker), self.current_frame] # temp = RGB + + self.r_frame_q.append(temp) + + elif self.is_image_loaded: + if not self.control['SwapFacesButton']: + temp = [self.image, self.current_frame] # image = RGB + + else: + temp = [self.swap_video(self.image, self.current_frame, False), self.current_frame] # image = RGB + + self.r_frame_q.append(temp) + + + def find_lowest_frame(self, queues): + min_frame=999999999 + index=-1 + + for idx, thread in enumerate(queues): + frame = thread['FrameNumber'] + if frame != []: + if frame < min_frame: + min_frame = frame + index=idx + return index, min_frame + + + def play_video(self, command): + # print(inspect.currentframe().f_back.f_code.co_name, '->play_video: ') + if command == "play": + # Initialization + self.play = True + self.fps_average = [] + self.process_qs = [] + self.capture.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame) + self.frame_timer = time.time() + + # Create reusable queue based on number of threads + for i in range(self.parameters['ThreadsSlider']): + new_process_q = self.process_q.copy() + self.process_qs.append(new_process_q) + + + # Start up audio if requested + if self.control['AudioButton']: + seek_time = (self.current_frame)/self.fps + args = ["ffplay", + '-vn', + '-ss', str(seek_time), + '-nodisp', + '-stats', + '-loglevel', 'quiet', + '-sync', 'audio', + self.video_file] + + + self.audio_sp = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) + + # Parse the console to find where the audio started + while True: + temp = self.audio_sp.stdout.read(69) + if temp[:7] != b' nan': + sought_time = float(temp[:7]) + self.current_frame = int(self.fps*sought_time) + + self.capture.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame) + + break + + +#' nan : 0.000 +#' 1.25 M-A: 0.000 fd= 0 aq= 12KB vq= 0KB sq= 0B f=0/0' + + + elif command == "stop": + self.play = False + self.add_action("stop_play", True) + + index, min_frame = self.find_lowest_frame(self.process_qs) + + if index != -1: + self.current_frame = min_frame-1 + + if self.control['AudioButton']: + self.audio_sp.terminate() + + torch.cuda.empty_cache() + + elif command=='stop_from_gui': + self.play = False + + # Find the lowest frame in the current render queue and set the current frame to the one before it + index, min_frame = self.find_lowest_frame(self.process_qs) + if index != -1: + self.current_frame = min_frame-1 + + if self.control['AudioButton']: + self.audio_sp.terminate() + + torch.cuda.empty_cache() + + elif command == "record": + self.record = True + self.play = True + self.total_thread_time = 0.0 + self.process_qs = [] + self.capture.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame) + + for i in range(self.parameters['ThreadsSlider']): + new_process_q = self.process_q.copy() + self.process_qs.append(new_process_q) + + # Initialize + self.timer = time.time() + frame_width = int(self.capture.get(3)) + frame_width = int(self.capture.get(3)) + frame_height = int(self.capture.get(4)) + + self.start_time = float(self.capture.get(cv2.CAP_PROP_POS_FRAMES) / float(self.fps)) + + self.file_name = os.path.splitext(os.path.basename(self.target_video)) + base_filename = self.file_name[0]+"_"+str(time.time())[:10] + self.output = os.path.join(self.saved_video_path, base_filename) + self.temp_file = self.output+"_temp"+self.file_name[1] + + if self.parameters['RecordTypeTextSel']=='FFMPEG': + args = ["ffmpeg", + '-hide_banner', + '-loglevel', 'error', + "-an", + "-r", str(self.fps), + "-i", "pipe:", + # '-g', '25', + "-vf", "format=yuvj420p", + "-c:v", "libx264", + "-crf", str(self.parameters['VideoQualSlider']), + "-r", str(self.fps), + "-s", str(frame_width)+"x"+str(frame_height), + self.temp_file] + + self.sp = subprocess.Popen(args, stdin=subprocess.PIPE) + + elif self.parameters['RecordTypeTextSel']=='OPENCV': + size = (frame_width, frame_height) + self.sp = cv2.VideoWriter(self.temp_file, cv2.VideoWriter_fourcc(*'mp4v') , self.fps, size) + + # @profile + def process(self): + process_qs_len = range(len(self.process_qs)) + + # Add threads to Queue + if self.play == True and self.is_video_loaded == True: + for item in self.process_qs: + if item['Status'] == 'clear' and self.current_frame < self.video_frame_total: + item['Thread'] = threading.Thread(target=self.thread_video_read, args = [self.current_frame]).start() + item['FrameNumber'] = self.current_frame + item['Status'] = 'started' + item['ThreadTime'] = time.time() + + self.current_frame += 1 + break + + else: + self.play = False + + # Always be emptying the queues + time_diff = time.time() - self.frame_timer + + if not self.record and time_diff >= 1.0/float(self.fps) and self.play: + + index, min_frame = self.find_lowest_frame(self.process_qs) + + if index != -1: + if self.process_qs[index]['Status'] == 'finished': + temp = [self.process_qs[index]['ProcessedFrame'], self.process_qs[index]['FrameNumber']] + self.frame_q.append(temp) + + # Report fps, other data + self.fps_average.append(1.0/time_diff) + if len(self.fps_average) >= floor(self.fps): + fps = round(np.average(self.fps_average), 2) + msg = "%s fps, %s process time" % (fps, round(self.process_qs[index]['ThreadTime'], 4)) + self.fps_average = [] + + if self.process_qs[index]['FrameNumber'] >= self.video_frame_total-1 or self.process_qs[index]['FrameNumber'] == self.stop_marker: + self.play_video('stop') + + self.process_qs[index]['Status'] = 'clear' + self.process_qs[index]['Thread'] = [] + self.process_qs[index]['FrameNumber'] = [] + self.process_qs[index]['ThreadTime'] = [] + self.frame_timer += 1.0/self.fps + + elif self.record: + + index, min_frame = self.find_lowest_frame(self.process_qs) + + if index != -1: + + # If the swapper thread has finished generating a frame + if self.process_qs[index]['Status'] == 'finished': + image = self.process_qs[index]['ProcessedFrame'] + + if self.parameters['RecordTypeTextSel']=='FFMPEG': + pil_image = Image.fromarray(image) + pil_image.save(self.sp.stdin, 'BMP') + + elif self.parameters['RecordTypeTextSel']=='OPENCV': + self.sp.write(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) + + temp = [image, self.process_qs[index]['FrameNumber']] + self.frame_q.append(temp) + + # Close video and process + if self.process_qs[index]['FrameNumber'] >= self.video_frame_total-1 or self.process_qs[index]['FrameNumber'] == self.stop_marker or self.play == False: + self.play_video("stop") + stop_time = float(self.capture.get(cv2.CAP_PROP_POS_FRAMES) / float(self.fps)) + if stop_time == 0: + stop_time = float(self.video_frame_total) / float(self.fps) + + if self.parameters['RecordTypeTextSel']=='FFMPEG': + self.sp.stdin.close() + self.sp.wait() + elif self.parameters['RecordTypeTextSel']=='OPENCV': + self.sp.release() + + orig_file = self.target_video + final_file = self.output+self.file_name[1] + print("adding audio...") + args = ["ffmpeg", + '-hide_banner', + '-loglevel', 'error', + "-i", self.temp_file, + "-ss", str(self.start_time), "-to", str(stop_time), "-i", orig_file, + "-c", "copy", # may be c:v + "-map", "0:v:0", "-map", "1:a:0?", + "-shortest", + final_file] + + four = subprocess.run(args) + os.remove(self.temp_file) + + timef= time.time() - self.timer + self.record = False + print('Video saved as:', final_file) + msg = "Total time: %s s." % (round(timef,1)) + print(msg) + + + self.total_thread_time = [] + self.process_qs[index]['Status'] = 'clear' + self.process_qs[index]['FrameNumber'] = [] + self.process_qs[index]['Thread'] = [] + self.frame_timer = time.time() + # @profile + def thread_video_read(self, frame_number): + with lock: + success, target_image = self.capture.read() + + if success: + target_image = cv2.cvtColor(target_image, cv2.COLOR_BGR2RGB) + if not self.control['SwapFacesButton']: + temp = [target_image, frame_number] + + else: + temp = [self.swap_video(target_image, frame_number, True), frame_number] + + for item in self.process_qs: + if item['FrameNumber'] == frame_number: + item['ProcessedFrame'] = temp[0] + item['Status'] = 'finished' + item['ThreadTime'] = time.time() - item['ThreadTime'] + break + + + + + # @profile + def swap_video(self, target_image, frame_number, use_markers): + # Grab a local copy of the parameters to prevent threading issues + parameters = self.parameters.copy() + control = self.control.copy() + + # Find out if the frame is in a marker zone and copy the parameters if true + if self.markers and use_markers: + temp=[] + for i in range(len(self.markers)): + temp.append(self.markers[i]['frame']) + idx = bisect.bisect(temp, frame_number) + + parameters = self.markers[idx-1]['parameters'].copy() + + # Load frame into VRAM + img = torch.from_numpy(target_image.astype('uint8')).to('cuda') #HxWxc + img = img.permute(2,0,1)#cxHxW + + #Scale up frame if it is smaller than 512 + img_x = img.size()[2] + img_y = img.size()[1] + + if img_x<512 and img_y<512: + # if x is smaller, set x to 512 + if img_x <= img_y: + tscale = v2.Resize((int(512*img_y/img_x), 512), antialias=True) + else: + tscale = v2.Resize((512, int(512*img_x/img_y)), antialias=True) + + img = tscale(img) + + elif img_x<512: + tscale = v2.Resize((int(512*img_y/img_x), 512), antialias=True) + img = tscale(img) + + elif img_y<512: + tscale = v2.Resize((512, int(512*img_x/img_y)), antialias=True) + img = tscale(img) + + # Rotate the frame + if parameters['OrientSwitch']: + img = v2.functional.rotate(img, angle=parameters['OrientSlider'], interpolation=v2.InterpolationMode.BILINEAR, expand=True) + + # Find all faces in frame and return a list of 5-pt kpss + kpss = self.func_w_test("detect", self.models.run_detect, img, parameters['DetectTypeTextSel'], max_num=20, score=parameters['DetectScoreSlider']/100.0) + # Get embeddings for all faces found in the fram + ret = [] + for face_kps in kpss: + face_emb, _ = self.func_w_test('recognize', self.models.run_recognize, img, face_kps) + ret.append([face_kps, face_emb]) + + if ret: + # Loop through target faces to see if they match our found face embeddings + for fface in ret: + for found_face in self.found_faces: + # sim between face in video and already found face + sim = self.findCosineDistance(fface[1], found_face["Embedding"]) + # if the face[i] in the frame matches afound face[j] AND the found face is active (not []) + if sim>=float(parameters["ThresholdSlider"]) and found_face["SourceFaceAssignments"]: + s_e = found_face["AssignedEmbedding"] + # s_e = found_face['ptrdata'] + img = self.func_w_test("swap_video", self.swap_core, img, fface[0], s_e, parameters, control) + # img = img.permute(2,0,1) + + img = img.permute(1,2,0) + if not control['MaskViewButton'] and parameters['OrientSwitch']: + img = img.permute(2,0,1) + img = transforms.functional.rotate(img, angle=-parameters['OrientSlider'], expand=True) + img = img.permute(1,2,0) + + else: + img = img.permute(1,2,0) + if parameters['OrientSwitch']: + img = img.permute(2,0,1) + img = v2.functional.rotate(img, angle=-parameters['OrientSlider'], interpolation=v2.InterpolationMode.BILINEAR, expand=True) + img = img.permute(1,2,0) + + if self.perf_test: + print('------------------------') + + # Unscale small videos + if img_x <512 or img_y < 512: + tscale = v2.Resize((img_y, img_x), antialias=True) + img = img.permute(2,0,1) + img = tscale(img) + img = img.permute(1,2,0) + + + img = img.cpu().numpy() + + # if ret: + # + # for kpoint in ret[0][0]: + # for i in range(-1, 1): + # for j in range(-1, 1): + # + # img[int(kpoint[1])+i][int(kpoint[0])+j][0] = 255 + # img[int(kpoint[1])+i][int(kpoint[0])+j][1] = 255 + # img[int(kpoint[1])+i][int(kpoint[0])+j][2] = 255 + + + return img.astype(np.uint8) + + def findCosineDistance(self, vector1, vector2): + cos_dist = 1.0 - np.dot(vector1, vector2)/(np.linalg.norm(vector1)*np.linalg.norm(vector2)) # 2..0 + + return 100.0-cos_dist*50.0 + + + + def func_w_test(self, name, func, *args, **argsv): + timing = time.time() + result = func(*args, **argsv) + if self.perf_test: + print(name, round(time.time()-timing, 5), 's') + return result + + # @profile + def swap_core(self, img, kps, s_e, parameters, control): # img = RGB + # 512 transforms + dst = self.arcface_dst * 4.0 + dst[:,0] += 32.0 + + # Change the ref points + if parameters['FaceAdjSwitch']: + dst[:,0] += parameters['KPSXSlider'] + dst[:,1] += parameters['KPSYSlider'] + dst[:,0] -= 255 + dst[:,0] *= (1+parameters['KPSScaleSlider']/100) + dst[:,0] += 255 + dst[:,1] -= 255 + dst[:,1] *= (1+parameters['KPSScaleSlider']/100) + dst[:,1] += 255 + + tform = trans.SimilarityTransform() + tform.estimate(kps, dst) + + # Scaling Transforms + t512 = v2.Resize((512, 512), interpolation=v2.InterpolationMode.BILINEAR, antialias=False) + t256 = v2.Resize((256, 256), interpolation=v2.InterpolationMode.BILINEAR, antialias=False) + t128 = v2.Resize((128, 128), interpolation=v2.InterpolationMode.BILINEAR, antialias=False) + + # Grab 512 face from image and create 256 and 128 copys + original_face_512 = v2.functional.affine(img, tform.rotation*57.2958, (tform.translation[0], tform.translation[1]) , tform.scale, 0, center = (0,0), interpolation=v2.InterpolationMode.BILINEAR ) + original_face_512 = v2.functional.crop(original_face_512, 0,0, 512, 512)# 3, 512, 512 + original_face_256 = t256(original_face_512) + original_face_128 = t128(original_face_256) + + latent = torch.from_numpy(self.models.calc_swapper_latent(s_e)).float().to('cuda') + + dim = 1 + if parameters['SwapperTypeTextSel'] == '128': + dim = 1 + input_face_affined = original_face_128 + elif parameters['SwapperTypeTextSel'] == '256': + dim = 2 + input_face_affined = original_face_256 + elif parameters['SwapperTypeTextSel'] == '512': + dim = 4 + input_face_affined = original_face_512 + + # Optional Scaling # change the thransform matrix + if parameters['FaceAdjSwitch']: + input_face_affined = v2.functional.affine(input_face_affined, 0, (0, 0), 1 + parameters['FaceScaleSlider'] / 100, 0, center=(dim*128-1, dim*128-1), interpolation=v2.InterpolationMode.BILINEAR) + + itex = 1 + if parameters['StrengthSwitch']: + itex = ceil(parameters['StrengthSlider'] / 100.) + + output_size = int(128 * dim) + output = torch.zeros((output_size, output_size, 3), dtype=torch.float32, device='cuda') + input_face_affined = input_face_affined.permute(1, 2, 0) + input_face_affined = torch.div(input_face_affined, 255.0) + + for k in range(itex): + for j in range(dim): + for i in range(dim): + input_face_disc = input_face_affined[j::dim,i::dim] + input_face_disc = input_face_disc.permute(2, 0, 1) + input_face_disc = torch.unsqueeze(input_face_disc, 0).contiguous() + + swapper_output = torch.empty((1,3,128,128), dtype=torch.float32, device='cuda').contiguous() + self.models.run_swapper(input_face_disc, latent, swapper_output) + + swapper_output = torch.squeeze(swapper_output) + swapper_output = swapper_output.permute(1, 2, 0) + + + output[j::dim, i::dim] = swapper_output.clone() + prev_face = input_face_affined.clone() + input_face_affined = output.clone() + output = torch.mul(output, 255) + output = torch.clamp(output, 0, 255) + + + output = output.permute(2, 0, 1) + + + swap = t512(output) + + if parameters['StrengthSwitch']: + if itex == 0: + swap = original_face_512.clone() + else: + alpha = np.mod(parameters['StrengthSlider'], 100)*0.01 + if alpha==0: + alpha=1 + + # Blend the images + prev_face = torch.mul(prev_face, 255) + prev_face = torch.clamp(prev_face, 0, 255) + prev_face = prev_face.permute(2, 0, 1) + prev_face = t512(prev_face) + swap = torch.mul(swap, alpha) + prev_face = torch.mul(prev_face, 1-alpha) + swap = torch.add(swap, prev_face) + + + + + # swap = torch.squeeze(swap) + # swap = torch.mul(swap, 255) + # swap = torch.clamp(swap, 0, 255) + # # swap_128 = swap + # swap = t256(swap) + # swap = t512(swap) + + + # Apply color corerctions + if parameters['ColorSwitch']: + # print(parameters['ColorGammaSlider']) + swap = torch.unsqueeze(swap,0) + swap = v2.functional.adjust_gamma(swap, parameters['ColorGammaSlider'], 1.0) + swap = torch.squeeze(swap) + swap = swap.permute(1, 2, 0).type(torch.float32) + + del_color = torch.tensor([parameters['ColorRedSlider'], parameters['ColorGreenSlider'], parameters['ColorBlueSlider']], device=device) + swap += del_color + swap = torch.clamp(swap, min=0., max=255.) + swap = swap.permute(2, 0, 1).type(torch.uint8) + + # Create border mask + border_mask = torch.ones((128, 128), dtype=torch.float32, device=device) + border_mask = torch.unsqueeze(border_mask,0) + + # if parameters['BorderState']: + top = parameters['BorderTopSlider'] + left = parameters['BorderSidesSlider'] + right = 128-parameters['BorderSidesSlider'] + bottom = 128-parameters['BorderBottomSlider'] + + border_mask[:, :top, :] = 0 + border_mask[:, bottom:, :] = 0 + border_mask[:, :, :left] = 0 + border_mask[:, :, right:] = 0 + + gauss = transforms.GaussianBlur(parameters['BorderBlurSlider']*2+1, (parameters['BorderBlurSlider']+1)*0.2) + border_mask = gauss(border_mask) + + # Create image mask + swap_mask = torch.ones((128, 128), dtype=torch.float32, device=device) + swap_mask = torch.unsqueeze(swap_mask,0) + + # Face Diffing + if parameters["DiffSwitch"]: + mask = self.apply_fake_diff(swap, original_face_512, parameters["DiffSlider"]) + # mask = t128(mask) + gauss = transforms.GaussianBlur(parameters['BlendSlider']*2+1, (parameters['BlendSlider']+1)*0.2) + mask = gauss(mask.type(torch.float32)) + swap = swap*mask + original_face_512*(1-mask) + + # Restorer + if parameters["RestorerSwitch"]: + swap = self.func_w_test('Restorer', self.apply_restorer, swap, parameters) + + + # Occluder + if parameters["OccluderSwitch"]: + mask = self.func_w_test('occluder', self.apply_occlusion , original_face_256, parameters["OccluderSlider"]) + mask = t128(mask) + swap_mask = torch.mul(swap_mask, mask) + + + if parameters["FaceParserSwitch"]: + mask = self.apply_face_parser(swap, parameters["FaceParserSlider"], parameters['MouthParserSlider']) + mask = t128(mask) + swap_mask = torch.mul(swap_mask, mask) + + # CLIPs + if parameters["CLIPSwitch"]: + with lock: + mask = self.func_w_test('CLIP', self.apply_CLIPs, original_face_512, parameters["CLIPTextEntry"], parameters["CLIPSlider"]) + mask = cv2.resize(mask, (128,128)) + mask = torch.from_numpy(mask).to('cuda') + swap_mask *= mask + + + # Add blur to swap_mask results + gauss = transforms.GaussianBlur(parameters['BlendSlider']*2+1, (parameters['BlendSlider']+1)*0.2) + swap_mask = gauss(swap_mask) + + + # Combine border and swap mask, scale, and apply to swap + swap_mask = torch.mul(swap_mask, border_mask) + swap_mask = t512(swap_mask) + swap = torch.mul(swap, swap_mask) + + #if not control['MaskViewButton']: + if True: + # Cslculate the area to be mergerd back to the original frame + IM512 = tform.inverse.params[0:2, :] + corners = np.array([[0,0], [0,511], [511, 0], [511, 511]]) + + x = (IM512[0][0]*corners[:,0] + IM512[0][1]*corners[:,1] + IM512[0][2]) + y = (IM512[1][0]*corners[:,0] + IM512[1][1]*corners[:,1] + IM512[1][2]) + + left = floor(np.min(x)) + if left<0: + left=0 + top = floor(np.min(y)) + if top<0: + top=0 + right = ceil(np.max(x)) + if right>img.shape[2]: + right=img.shape[2] + bottom = ceil(np.max(y)) + if bottom>img.shape[1]: + bottom=img.shape[1] + + # Untransform the swap + swap = v2.functional.pad(swap, (0,0,img.shape[2]-512, img.shape[1]-512)) + swap = v2.functional.affine(swap, tform.inverse.rotation*57.2958, (tform.inverse.translation[0], tform.inverse.translation[1]), tform.inverse.scale, 0,interpolation=v2.InterpolationMode.BILINEAR, center = (0,0) ) + swap = swap[0:3, top:bottom, left:right] + swap = swap.permute(1, 2, 0) + + # Untransform the swap mask + swap_mask = v2.functional.pad(swap_mask, (0,0,img.shape[2]-512, img.shape[1]-512)) + swap_mask = v2.functional.affine(swap_mask, tform.inverse.rotation*57.2958, (tform.inverse.translation[0], tform.inverse.translation[1]), tform.inverse.scale, 0, interpolation=v2.InterpolationMode.BILINEAR, center = (0,0) ) + swap_mask = swap_mask[0:1, top:bottom, left:right] + swap_mask = swap_mask.permute(1, 2, 0) + swap_mask = torch.sub(1, swap_mask) + + # Apply the mask to the original image areas + img_crop = img[0:3, top:bottom, left:right] + img_crop = img_crop.permute(1,2,0) + img_crop = torch.mul(swap_mask,img_crop) + + #Add the cropped areas and place them back into the original image + swap = torch.add(swap, img_crop) + swap = swap.type(torch.uint8) + swap = swap.permute(2,0,1) + img[0:3, top:bottom, left:right] = swap + + else: + # Invert swap mask + swap_mask = torch.sub(1, swap_mask) + + # Combine preswapped face with swap + original_face_512 = torch.mul(swap_mask, original_face_512) + original_face_512 = torch.add(swap, original_face_512) + original_face_512 = original_face_512.type(torch.uint8) + original_face_512 = original_face_512.permute(1, 2, 0) + + # Uninvert and create image from swap mask + swap_mask = torch.sub(1, swap_mask) + swap_mask = torch.cat((swap_mask,swap_mask,swap_mask),0) + swap_mask = swap_mask.permute(1, 2, 0) + + # Place them side by side + img = torch.hstack([original_face_512, swap_mask*255]) + img = img.permute(2,0,1) + + return img + + # @profile + def apply_occlusion(self, img, amount): + img = torch.div(img, 255) + img = torch.unsqueeze(img, 0) + outpred = torch.ones((256,256), dtype=torch.float32, device=device).contiguous() + + self.models.run_occluder(img, outpred) + + outpred = torch.squeeze(outpred) + outpred = (outpred > 0) + outpred = torch.unsqueeze(outpred, 0).type(torch.float32) + + if amount >0: + kernel = torch.ones((1,1,3,3), dtype=torch.float32, device=device) + + for i in range(int(amount)): + outpred = torch.nn.functional.conv2d(outpred, kernel, padding=(1, 1)) + outpred = torch.clamp(outpred, 0, 1) + + outpred = torch.squeeze(outpred) + + if amount <0: + outpred = torch.neg(outpred) + outpred = torch.add(outpred, 1) + kernel = torch.ones((1,1,3,3), dtype=torch.float32, device=device) + + for i in range(int(-amount)): + outpred = torch.nn.functional.conv2d(outpred, kernel, padding=(1, 1)) + outpred = torch.clamp(outpred, 0, 1) + + outpred = torch.squeeze(outpred) + outpred = torch.neg(outpred) + outpred = torch.add(outpred, 1) + + outpred = torch.reshape(outpred, (1, 256, 256)) + return outpred + + + def apply_CLIPs(self, img, CLIPText, CLIPAmount): + clip_mask = np.ones((352, 352)) + img = img.permute(1,2,0) + img = img.cpu().numpy() + # img = img.to(torch.float) + # img = img.permute(1,2,0) + transform = transforms.Compose([transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + transforms.Resize((352, 352))]) + CLIPimg = transform(img).unsqueeze(0) + + if CLIPText != "": + prompts = CLIPText.split(',') + + with torch.no_grad(): + preds = self.clip_session(CLIPimg.repeat(len(prompts),1,1,1), prompts)[0] + # preds = self.clip_session(CLIPimg, maskimg, True)[0] + + clip_mask = 1 - torch.sigmoid(preds[0][0]) + for i in range(len(prompts)-1): + clip_mask *= 1-torch.sigmoid(preds[i+1][0]) + clip_mask = clip_mask.data.cpu().numpy() + + thresh = CLIPAmount/100.0 + clip_mask[clip_mask>thresh] = 1.0 + clip_mask[clip_mask<=thresh] = 0.0 + return clip_mask + + # @profile + def apply_face_parser(self, img, FaceAmount, MouthAmount): + + # atts = [1 'skin', 2 'l_brow', 3 'r_brow', 4 'l_eye', 5 'r_eye', 6 'eye_g', 7 'l_ear', 8 'r_ear', 9 'ear_r', 10 'nose', 11 'mouth', 12 'u_lip', 13 'l_lip', 14 'neck', 15 'neck_l', 16 'cloth', 17 'hair', 18 'hat'] + + outpred = torch.ones((512,512), dtype=torch.float32, device='cuda').contiguous() + + + img = torch.div(img, 255) + img = v2.functional.normalize(img, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) + img = torch.reshape(img, (1, 3, 512, 512)) + outpred = torch.empty((1,19,512,512), dtype=torch.float32, device='cuda').contiguous() + + self.models.run_faceparser(img, outpred) + + outpred = torch.squeeze(outpred) + outpred = torch.argmax(outpred, 0) + + # Mouth Parse + if MouthAmount <0: + mouth_idxs = torch.tensor([11], device='cuda') + iters = int(-MouthAmount) + + mouth_parse = torch.isin(outpred, mouth_idxs) + mouth_parse = torch.clamp(~mouth_parse, 0, 1).type(torch.float32) + mouth_parse = torch.reshape(mouth_parse, (1, 1, 512, 512)) + mouth_parse = torch.neg(mouth_parse) + mouth_parse = torch.add(mouth_parse, 1) + + kernel = torch.ones((1, 1, 3, 3), dtype=torch.float32, + device='cuda') + + for i in range(iters): + mouth_parse = torch.nn.functional.conv2d(mouth_parse, kernel, + padding=(1, 1)) + mouth_parse = torch.clamp(mouth_parse, 0, 1) + + mouth_parse = torch.squeeze(mouth_parse) + mouth_parse = torch.neg(mouth_parse) + mouth_parse = torch.add(mouth_parse, 1) + mouth_parse = torch.reshape(mouth_parse, (1, 512, 512)) + + elif MouthAmount >0: + mouth_idxs = torch.tensor([11,12,13], device='cuda') + iters = int(MouthAmount) + + mouth_parse = torch.isin(outpred, mouth_idxs) + mouth_parse = torch.clamp(~mouth_parse, 0, 1).type(torch.float32) + mouth_parse = torch.reshape(mouth_parse, (1,1,512,512)) + mouth_parse = torch.neg(mouth_parse) + mouth_parse = torch.add(mouth_parse, 1) + + kernel = torch.ones((1,1,3,3), dtype=torch.float32, device='cuda') + + for i in range(iters): + mouth_parse = torch.nn.functional.conv2d(mouth_parse, kernel, padding=(1, 1)) + mouth_parse = torch.clamp(mouth_parse, 0, 1) + + mouth_parse = torch.squeeze(mouth_parse) + mouth_parse = torch.neg(mouth_parse) + mouth_parse = torch.add(mouth_parse, 1) + mouth_parse = torch.reshape(mouth_parse, (1, 512, 512)) + + else: + mouth_parse = torch.ones((1, 512, 512), dtype=torch.float32, device='cuda') + + # BG Parse + bg_idxs = torch.tensor([0, 14, 15, 16, 17, 18], device=device) + bg_parse = torch.isin(outpred, bg_idxs) + bg_parse = torch.clamp(~bg_parse, 0, 1).type(torch.float32) + bg_parse = torch.reshape(bg_parse, (1, 1, 512, 512)) + + if FaceAmount > 0: + kernel = torch.ones((1, 1, 3, 3), dtype=torch.float32, device=device) + + for i in range(int(FaceAmount)): + bg_parse = torch.nn.functional.conv2d(bg_parse, kernel, padding=(1, 1)) + bg_parse = torch.clamp(bg_parse, 0, 1) + + bg_parse = torch.squeeze(bg_parse) + + elif FaceAmount < 0: + bg_parse = torch.neg(bg_parse) + bg_parse = torch.add(bg_parse, 1) + + kernel = torch.ones((1, 1, 3, 3), dtype=torch.float32, device=device) + + for i in range(int(-FaceAmount)): + bg_parse = torch.nn.functional.conv2d(bg_parse, kernel, padding=(1, 1)) + bg_parse = torch.clamp(bg_parse, 0, 1) + + bg_parse = torch.squeeze(bg_parse) + bg_parse = torch.neg(bg_parse) + bg_parse = torch.add(bg_parse, 1) + bg_parse = torch.reshape(bg_parse, (1, 512, 512)) + else: + bg_parse = torch.ones((1,512,512), dtype=torch.float32, device='cuda') + + out_parse = torch.mul(bg_parse, mouth_parse) + + return out_parse + + def apply_bg_face_parser(self, img, FaceParserAmount): + + # atts = [1 'skin', 2 'l_brow', 3 'r_brow', 4 'l_eye', 5 'r_eye', 6 'eye_g', 7 'l_ear', 8 'r_ear', 9 'ear_r', 10 'nose', 11 'mouth', 12 'u_lip', 13 'l_lip', 14 'neck', 15 'neck_l', 16 'cloth', 17 'hair', 18 'hat'] + # out = np.ones((512, 512), dtype=np.float32) + + outpred = torch.ones((512,512), dtype=torch.float32, device='cuda').contiguous() + + # turn mouth parser off at 0 so someone can just use the mouth parser + if FaceParserAmount != 0: + img = torch.div(img, 255) + img = v2.functional.normalize(img, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) + img = torch.reshape(img, (1, 3, 512, 512)) + outpred = torch.empty((1,19,512,512), dtype=torch.float32, device=device).contiguous() + + self.models.run_faceparser(img, outpred) + + outpred = torch.squeeze(outpred) + outpred = torch.argmax(outpred, 0) + + test = torch.tensor([ 0, 14, 15, 16, 17, 18], device=device) + outpred = torch.isin(outpred, test) + outpred = torch.clamp(~outpred, 0, 1).type(torch.float32) + outpred = torch.reshape(outpred, (1,1,512,512)) + + if FaceParserAmount >0: + kernel = torch.ones((1,1,3,3), dtype=torch.float32, device=device) + + for i in range(int(FaceParserAmount)): + outpred = torch.nn.functional.conv2d(outpred, kernel, padding=(1, 1)) + outpred = torch.clamp(outpred, 0, 1) + + outpred = torch.squeeze(outpred) + + if FaceParserAmount <0: + outpred = torch.neg(outpred) + outpred = torch.add(outpred, 1) + + kernel = torch.ones((1,1,3,3), dtype=torch.float32, device=device) + + for i in range(int(-FaceParserAmount)): + outpred = torch.nn.functional.conv2d(outpred, kernel, padding=(1, 1)) + outpred = torch.clamp(outpred, 0, 1) + + outpred = torch.squeeze(outpred) + outpred = torch.neg(outpred) + outpred = torch.add(outpred, 1) + + outpred = torch.reshape(outpred, (1, 512, 512)) + + return outpred + + + + def apply_restorer(self, swapped_face_upscaled, parameters): + temp = swapped_face_upscaled + t512 = v2.Resize((512, 512), antialias=False) + t256 = v2.Resize((256, 256), antialias=False) + + # If using a separate detection mode + if parameters['RestorerDetTypeTextSel'] == 'Blend' or parameters['RestorerDetTypeTextSel'] == 'Reference': + if parameters['RestorerDetTypeTextSel'] == 'Blend': + # Set up Transformation + dst = self.arcface_dst * 4.0 + dst[:,0] += 32.0 + + elif parameters['RestorerDetTypeTextSel'] == 'Reference': + try: + dst = self.models.resnet50(swapped_face_upscaled, score=parameters['DetectScoreSlider']/100.0) + except: + return swapped_face_upscaled + + tform = trans.SimilarityTransform() + tform.estimate(dst, self.FFHQ_kps) + + # Transform, scale, and normalize + temp = v2.functional.affine(swapped_face_upscaled, tform.rotation*57.2958, (tform.translation[0], tform.translation[1]) , tform.scale, 0, center = (0,0) ) + temp = v2.functional.crop(temp, 0,0, 512, 512) + + temp = torch.div(temp, 255) + temp = v2.functional.normalize(temp, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=False) + if parameters['RestorerTypeTextSel'] == 'GPEN256': + temp = t256(temp) + temp = torch.unsqueeze(temp, 0).contiguous() + + # Bindings + outpred = torch.empty((1,3,512,512), dtype=torch.float32, device=device).contiguous() + + if parameters['RestorerTypeTextSel'] == 'GFPGAN': + self.models.run_GFPGAN(temp, outpred) + + elif parameters['RestorerTypeTextSel'] == 'CF': + self.models.run_codeformer(temp, outpred) + + elif parameters['RestorerTypeTextSel'] == 'GPEN256': + outpred = torch.empty((1,3,256,256), dtype=torch.float32, device=device).contiguous() + self.models.run_GPEN_256(temp, outpred) + + elif parameters['RestorerTypeTextSel'] == 'GPEN512': + self.models.run_GPEN_512(temp, outpred) + + + # Format back to cxHxW @ 255 + outpred = torch.squeeze(outpred) + outpred = torch.clamp(outpred, -1, 1) + outpred = torch.add(outpred, 1) + outpred = torch.div(outpred, 2) + outpred = torch.mul(outpred, 255) + if parameters['RestorerTypeTextSel'] == 'GPEN256': + outpred = t512(outpred) + + # Invert Transform + if parameters['RestorerDetTypeTextSel'] == 'Blend' or parameters['RestorerDetTypeTextSel'] == 'Reference': + outpred = v2.functional.affine(outpred, tform.inverse.rotation*57.2958, (tform.inverse.translation[0], tform.inverse.translation[1]), tform.inverse.scale, 0, interpolation=v2.InterpolationMode.BILINEAR, center = (0,0) ) + + # Blend + alpha = float(parameters["RestorerSlider"])/100.0 + outpred = torch.add(torch.mul(outpred, alpha), torch.mul(swapped_face_upscaled, 1-alpha)) + + return outpred + + def apply_fake_diff(self, swapped_face, original_face, DiffAmount): + swapped_face = swapped_face.permute(1,2,0) + original_face = original_face.permute(1,2,0) + + diff = swapped_face-original_face + diff = torch.abs(diff) + + # Find the diffrence between the swap and original, per channel + fthresh = DiffAmount*2.55 + + # Bimodal + diff[diff=fthresh] = 1 + + # If any of the channels exceeded the threshhold, them add them to the mask + diff = torch.sum(diff, dim=2) + diff = torch.unsqueeze(diff, 2) + diff[diff>0] = 1 + + diff = diff.permute(2,0,1) + + return diff + + + + def clear_mem(self): + del self.swapper_model + del self.GFPGAN_model + del self.occluder_model + del self.face_parsing_model + del self.codeformer_model + del self.GPEN_256_model + del self.GPEN_512_model + del self.resnet_model + del self.detection_model + del self.recognition_model + + self.swapper_model = [] + self.GFPGAN_model = [] + self.occluder_model = [] + self.face_parsing_model = [] + self.codeformer_model = [] + self.GPEN_256_model = [] + self.GPEN_512_model = [] + self.resnet_model = [] + self.detection_model = [] + self.recognition_model = [] + + # test = swap.permute(1, 2, 0) + # test = test.cpu().numpy() + # cv2.imwrite('2.jpg', test) + + ##个性化需求，不做换脸，只做其他修复 + def core_withoutSwap(self, img, kps, parameters, control): # img = RGB + # 512 transforms + dst = self.arcface_dst * 4.0 + dst[:,0] += 32.0 + + # Change the ref points + if parameters['FaceAdjSwitch']: + dst[:,0] += parameters['KPSXSlider'] + dst[:,1] += parameters['KPSYSlider'] + dst[:,0] -= 255 + dst[:,0] *= (1+parameters['KPSScaleSlider']/100) + dst[:,0] += 255 + dst[:,1] -= 255 + dst[:,1] *= (1+parameters['KPSScaleSlider']/100) + dst[:,1] += 255 + + tform = trans.SimilarityTransform() + tform.estimate(kps, dst) + + # Scaling Transforms + t512 = v2.Resize((512, 512), interpolation=v2.InterpolationMode.BILINEAR, antialias=False) + t256 = v2.Resize((256, 256), interpolation=v2.InterpolationMode.BILINEAR, antialias=False) + t128 = v2.Resize((128, 128), interpolation=v2.InterpolationMode.BILINEAR, antialias=False) + + # Grab 512 face from image and create 256 and 128 copys + original_face_512 = v2.functional.affine(img, tform.rotation*57.2958, (tform.translation[0], tform.translation[1]) , tform.scale, 0, center = (0,0), interpolation=v2.InterpolationMode.BILINEAR ) + original_face_512 = v2.functional.crop(original_face_512, 0,0, 512, 512)# 3, 512, 512 + # # # # original_face_256 = t256(original_face_512) + # # # # original_face_128 = t128(original_face_256) + + # # # # #latent = torch.from_numpy(self.models.calc_swapper_latent(s_e)).float().to('cuda') + + # # # # dim = 1 + # # # # if parameters['SwapperTypeTextSel'] == '128': + # # # # dim = 1 + # # # # input_face_affined = original_face_128 + # # # # elif parameters['SwapperTypeTextSel'] == '256': + # # # # dim = 2 + # # # # input_face_affined = original_face_256 + # # # # elif parameters['SwapperTypeTextSel'] == '512': + # # # # dim = 4 + # # # # input_face_affined = original_face_512 + + # # # # # Optional Scaling # change the thransform matrix + # # # # if parameters['FaceAdjSwitch']: + # # # # input_face_affined = v2.functional.affine(input_face_affined, 0, (0, 0), 1 + parameters['FaceScaleSlider'] / 100, 0, center=(dim*128-1, dim*128-1), interpolation=v2.InterpolationMode.BILINEAR) + + # # # # itex = 1 + # # # # if parameters['StrengthSwitch']: + # # # # itex = ceil(parameters['StrengthSlider'] / 100.) + + # # # # output_size = int(128 * dim) + # # # # output = torch.zeros((output_size, output_size, 3), dtype=torch.float32, device='cuda') + # # # # input_face_affined = input_face_affined.permute(1, 2, 0) + # # # # input_face_affined = torch.div(input_face_affined, 255.0) + + # # # # for k in range(itex): + # # # # for j in range(dim): + # # # # for i in range(dim): + # # # # input_face_disc = input_face_affined[j::dim,i::dim] + # # # # input_face_disc = input_face_disc.permute(2, 0, 1) + # # # # input_face_disc = torch.unsqueeze(input_face_disc, 0).contiguous() + + # # # # swapper_output = torch.empty((1,3,128,128), dtype=torch.float32, device='cuda').contiguous() + # # # # self.models.run_swapper(input_face_disc, latent, swapper_output) + + # # # # swapper_output = torch.squeeze(swapper_output) + # # # # swapper_output = swapper_output.permute(1, 2, 0) + + + # # # # output[j::dim, i::dim] = swapper_output.clone() + # # # # prev_face = input_face_affined.clone() + # # # # input_face_affined = output.clone() + # # # # output = torch.mul(output, 255) + # # # # output = torch.clamp(output, 0, 255) + + + # # # # output = output.permute(2, 0, 1) + + + # # # # swap = t512(output) + + # # # # if parameters['StrengthSwitch']: + # # # # if itex == 0: + # # # # swap = original_face_512.clone() + # # # # else: + # # # # alpha = np.mod(parameters['StrengthSlider'], 100)*0.01 + # # # # if alpha==0: + # # # # alpha=1 + + # # # # # Blend the images + # # # # prev_face = torch.mul(prev_face, 255) + # # # # prev_face = torch.clamp(prev_face, 0, 255) + # # # # prev_face = prev_face.permute(2, 0, 1) + # # # # prev_face = t512(prev_face) + # # # # swap = torch.mul(swap, alpha) + # # # # prev_face = torch.mul(prev_face, 1-alpha) + # # # # swap = torch.add(swap, prev_face) + + + + + # # # # # swap = torch.squeeze(swap) + # # # # # swap = torch.mul(swap, 255) + # # # # # swap = torch.clamp(swap, 0, 255) + # # # # # # swap_128 = swap + # # # # # swap = t256(swap) + # # # # # swap = t512(swap) + swap = original_face_512 + + # Apply color corerctions + if parameters['ColorSwitch']: + # print(parameters['ColorGammaSlider']) + swap = torch.unsqueeze(swap,0) + swap = v2.functional.adjust_gamma(swap, parameters['ColorGammaSlider'], 1.0) + swap = torch.squeeze(swap) + swap = swap.permute(1, 2, 0).type(torch.float32) + + del_color = torch.tensor([parameters['ColorRedSlider'], parameters['ColorGreenSlider'], parameters['ColorBlueSlider']], device=device) + swap += del_color + swap = torch.clamp(swap, min=0., max=255.) + swap = swap.permute(2, 0, 1).type(torch.uint8) + + # Create border mask + border_mask = torch.ones((128, 128), dtype=torch.float32, device=device) + border_mask = torch.unsqueeze(border_mask,0) + + # if parameters['BorderState']: + top = parameters['BorderTopSlider'] + left = parameters['BorderSidesSlider'] + right = 128-parameters['BorderSidesSlider'] + bottom = 128-parameters['BorderBottomSlider'] + + border_mask[:, :top, :] = 0 + border_mask[:, bottom:, :] = 0 + border_mask[:, :, :left] = 0 + border_mask[:, :, right:] = 0 + + gauss = transforms.GaussianBlur(parameters['BorderBlurSlider']*2+1, (parameters['BorderBlurSlider']+1)*0.2) + border_mask = gauss(border_mask) + + # Create image mask + swap_mask = torch.ones((128, 128), dtype=torch.float32, device=device) + swap_mask = torch.unsqueeze(swap_mask,0) + + # Face Diffing + if parameters["DiffSwitch"]: + mask = self.apply_fake_diff(swap, original_face_512, parameters["DiffSlider"]) + # mask = t128(mask) + gauss = transforms.GaussianBlur(parameters['BlendSlider']*2+1, (parameters['BlendSlider']+1)*0.2) + mask = gauss(mask.type(torch.float32)) + swap = swap*mask + original_face_512*(1-mask) + + # Restorer + if parameters["RestorerSwitch"]: + swap = self.func_w_test('Restorer', self.apply_restorer, swap, parameters) + + + # Occluder + if parameters["OccluderSwitch"]: + mask = self.func_w_test('occluder', self.apply_occlusion , original_face_256, parameters["OccluderSlider"]) + mask = t128(mask) + swap_mask = torch.mul(swap_mask, mask) + + + if parameters["FaceParserSwitch"]: + mask = self.apply_face_parser(swap, parameters["FaceParserSlider"], parameters['MouthParserSlider']) + mask = t128(mask) + swap_mask = torch.mul(swap_mask, mask) + + # CLIPs + if parameters["CLIPSwitch"]: + with lock: + mask = self.func_w_test('CLIP', self.apply_CLIPs, original_face_512, parameters["CLIPTextEntry"], parameters["CLIPSlider"]) + mask = cv2.resize(mask, (128,128)) + mask = torch.from_numpy(mask).to('cuda') + swap_mask *= mask + + + # Add blur to swap_mask results + gauss = transforms.GaussianBlur(parameters['BlendSlider']*2+1, (parameters['BlendSlider']+1)*0.2) + swap_mask = gauss(swap_mask) + + + # Combine border and swap mask, scale, and apply to swap + swap_mask = torch.mul(swap_mask, border_mask) + swap_mask = t512(swap_mask) + swap = torch.mul(swap, swap_mask) + + #if not control['MaskViewButton']: + if True: + # Cslculate the area to be mergerd back to the original frame + IM512 = tform.inverse.params[0:2, :] + corners = np.array([[0,0], [0,511], [511, 0], [511, 511]]) + + x = (IM512[0][0]*corners[:,0] + IM512[0][1]*corners[:,1] + IM512[0][2]) + y = (IM512[1][0]*corners[:,0] + IM512[1][1]*corners[:,1] + IM512[1][2]) + + left = floor(np.min(x)) + if left<0: + left=0 + top = floor(np.min(y)) + if top<0: + top=0 + right = ceil(np.max(x)) + if right>img.shape[2]: + right=img.shape[2] + bottom = ceil(np.max(y)) + if bottom>img.shape[1]: + bottom=img.shape[1] + + # Untransform the swap + swap = v2.functional.pad(swap, (0,0,img.shape[2]-512, img.shape[1]-512)) + swap = v2.functional.affine(swap, tform.inverse.rotation*57.2958, (tform.inverse.translation[0], tform.inverse.translation[1]), tform.inverse.scale, 0,interpolation=v2.InterpolationMode.BILINEAR, center = (0,0) ) + swap = swap[0:3, top:bottom, left:right] + swap = swap.permute(1, 2, 0) + + # Untransform the swap mask + swap_mask = v2.functional.pad(swap_mask, (0,0,img.shape[2]-512, img.shape[1]-512)) + swap_mask = v2.functional.affine(swap_mask, tform.inverse.rotation*57.2958, (tform.inverse.translation[0], tform.inverse.translation[1]), tform.inverse.scale, 0, interpolation=v2.InterpolationMode.BILINEAR, center = (0,0) ) + swap_mask = swap_mask[0:1, top:bottom, left:right] + swap_mask = swap_mask.permute(1, 2, 0) + swap_mask = torch.sub(1, swap_mask) + + # Apply the mask to the original image areas + img_crop = img[0:3, top:bottom, left:right] + img_crop = img_crop.permute(1,2,0) + img_crop = torch.mul(swap_mask,img_crop) + + #Add the cropped areas and place them back into the original image + swap = torch.add(swap, img_crop) + swap = swap.type(torch.uint8) + swap = swap.permute(2,0,1) + img[0:3, top:bottom, left:right] = swap + + else: + # Invert swap mask + swap_mask = torch.sub(1, swap_mask) + + # Combine preswapped face with swap + original_face_512 = torch.mul(swap_mask, original_face_512) + original_face_512 = torch.add(swap, original_face_512) + original_face_512 = original_face_512.type(torch.uint8) + original_face_512 = original_face_512.permute(1, 2, 0) + + # Uninvert and create image from swap mask + swap_mask = torch.sub(1, swap_mask) + swap_mask = torch.cat((swap_mask,swap_mask,swap_mask),0) + swap_mask = swap_mask.permute(1, 2, 0) + + # Place them side by side + img = torch.hstack([original_face_512, swap_mask*255]) + img = img.permute(2,0,1) + + return img + \ No newline at end of file diff --git a/rope/external/cliplib/__init__.py b/rope/external/cliplib/__init__.py new file mode 100644 index 0000000..9f5c16b --- /dev/null +++ b/rope/external/cliplib/__init__.py @@ -0,0 +1 @@ +from .clip import * diff --git a/rope/external/cliplib/bpe_simple_vocab_16e6.txt.gz b/rope/external/cliplib/bpe_simple_vocab_16e6.txt.gz new file mode 100644 index 0000000000000000000000000000000000000000..7b5088a527f720063f044eb928eee315f63b2fc0 GIT binary patch literal 1356917 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+from tqdm import tqdm + +from .model import build_model +from .simple_tokenizer import SimpleTokenizer as _Tokenizer + +try: + from torchvision.transforms import InterpolationMode + BICUBIC = InterpolationMode.BICUBIC +except ImportError: + BICUBIC = Image.BICUBIC + + +if packaging.version.parse(torch.__version__) < packaging.version.parse("1.7.1"): + warnings.warn("PyTorch version 1.7.1 or higher is recommended") + + +__all__ = ["available_models", "load", "tokenize"] +_tokenizer = _Tokenizer() + +_MODELS = { + "RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt", + "RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt", + "RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt", + "RN50x16": "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt", + "RN50x64": "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt", + "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt", + "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt", + "ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt", + "ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt", +} + + +def _download(url: str, root: str): + os.makedirs(root, exist_ok=True) + filename = os.path.basename(url) + + expected_sha256 = url.split("/")[-2] + download_target = os.path.join(root, filename) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256: + return download_target + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256: + raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match") + + return download_target + + +def _convert_image_to_rgb(image): + return image.convert("RGB") + + +def _transform(n_px): + return Compose([ + Resize(n_px, interpolation=BICUBIC), + CenterCrop(n_px), + _convert_image_to_rgb, + ToTensor(), + Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), + ]) + + +def available_models() -> List[str]: + """Returns the names of available CLIP models""" + return list(_MODELS.keys()) + + +def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit: bool = False, download_root: str = None): + """Load a CLIP model + + Parameters + ---------- + name : str + A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict + + device : Union[str, torch.device] + The device to put the loaded model + + jit : bool + Whether to load the optimized JIT model or more hackable non-JIT model (default). + + download_root: str + path to download the model files; by default, it uses "~/.cache/clip" + + Returns + ------- + model : torch.nn.Module + The CLIP model + + preprocess : Callable[[PIL.Image], torch.Tensor] + A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input + """ + if name in _MODELS: + model_path = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip")) + elif os.path.isfile(name): + model_path = name + else: + raise RuntimeError(f"Model {name} not found; available models = {available_models()}") + + with open(model_path, 'rb') as opened_file: + try: + # loading JIT archive + model = torch.jit.load(opened_file, map_location=device if jit else "cpu").eval() + state_dict = None + except RuntimeError: + # loading saved state dict + if jit: + warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead") + jit = False + state_dict = torch.load(opened_file, map_location="cpu") + + if not jit: + model = build_model(state_dict or model.state_dict()).to(device) + if str(device) == "cpu": + model.float() + return model, _transform(model.visual.input_resolution) + + # patch the device names + device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]) + device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1] + + def _node_get(node: torch._C.Node, key: str): + """Gets attributes of a node which is polymorphic over return type. + + From https://github.com/pytorch/pytorch/pull/82628 + """ + sel = node.kindOf(key) + return getattr(node, sel)(key) + + def patch_device(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("prim::Constant"): + if "value" in node.attributeNames() and str(_node_get(node, "value")).startswith("cuda"): + node.copyAttributes(device_node) + + model.apply(patch_device) + patch_device(model.encode_image) + patch_device(model.encode_text) + + # patch dtype to float32 on CPU + if str(device) == "cpu": + float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[]) + float_input = list(float_holder.graph.findNode("aten::to").inputs())[1] + float_node = float_input.node() + + def patch_float(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("aten::to"): + inputs = list(node.inputs()) + for i in [1, 2]: # dtype can be the second or third argument to aten::to() + if _node_get(inputs[i].node(), "value") == 5: + inputs[i].node().copyAttributes(float_node) + + model.apply(patch_float) + patch_float(model.encode_image) + patch_float(model.encode_text) + + model.float() + + return model, _transform(model.input_resolution.item()) + + +def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> Union[torch.IntTensor, torch.LongTensor]: + """ + Returns the tokenized representation of given input string(s) + + Parameters + ---------- + texts : Union[str, List[str]] + An input string or a list of input strings to tokenize + + context_length : int + The context length to use; all CLIP models use 77 as the context length + + truncate: bool + Whether to truncate the text in case its encoding is longer than the context length + + Returns + ------- + A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]. + We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long. + """ + if isinstance(texts, str): + texts = [texts] + + sot_token = _tokenizer.encoder["<|startoftext|>"] + eot_token = _tokenizer.encoder["<|endoftext|>"] + all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts] + if packaging.version.parse(torch.__version__) < packaging.version.parse("1.8.0"): + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + else: + result = torch.zeros(len(all_tokens), context_length, dtype=torch.int) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + if truncate: + tokens = tokens[:context_length] + tokens[-1] = eot_token + else: + raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}") + result[i, :len(tokens)] = torch.tensor(tokens) + + return result diff --git a/rope/external/cliplib/model.py b/rope/external/cliplib/model.py new file mode 100644 index 0000000..5eb19b5 --- /dev/null +++ b/rope/external/cliplib/model.py @@ -0,0 +1,436 @@ +from collections import OrderedDict +from typing import Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1): + super().__init__() + + # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 + self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.relu1 = nn.ReLU(inplace=True) + + self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.relu2 = nn.ReLU(inplace=True) + + self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() + + self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.relu3 = nn.ReLU(inplace=True) + + self.downsample = None + self.stride = stride + + if stride > 1 or inplanes != planes * Bottleneck.expansion: + # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 + self.downsample = nn.Sequential(OrderedDict([ + ("-1", nn.AvgPool2d(stride)), + ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), + ("1", nn.BatchNorm2d(planes * self.expansion)) + ])) + + def forward(self, x: torch.Tensor): + identity = x + + out = self.relu1(self.bn1(self.conv1(x))) + out = self.relu2(self.bn2(self.conv2(out))) + out = self.avgpool(out) + out = self.bn3(self.conv3(out)) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu3(out) + return out + + +class AttentionPool2d(nn.Module): + def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): + super().__init__() + self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5) + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) + self.num_heads = num_heads + + def forward(self, x): + x = x.flatten(start_dim=2).permute(2, 0, 1) # NCHW -> (HW)NC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC + x, _ = F.multi_head_attention_forward( + query=x[:1], key=x, value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0, + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False + ) + return x.squeeze(0) + + +class ModifiedResNet(nn.Module): + """ + A ResNet class that is similar to torchvision's but contains the following changes: + - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. + - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 + - The final pooling layer is a QKV attention instead of an average pool + """ + + def __init__(self, layers, output_dim, heads, input_resolution=224, width=64): + super().__init__() + self.output_dim = output_dim + self.input_resolution = input_resolution + + # the 3-layer stem + self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(width // 2) + self.relu1 = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(width // 2) + self.relu2 = nn.ReLU(inplace=True) + self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(width) + self.relu3 = nn.ReLU(inplace=True) + self.avgpool = nn.AvgPool2d(2) + + # residual layers + self._inplanes = width # this is a *mutable* variable used during construction + self.layer1 = self._make_layer(width, layers[0]) + self.layer2 = self._make_layer(width * 2, layers[1], stride=2) + self.layer3 = self._make_layer(width * 4, layers[2], stride=2) + self.layer4 = self._make_layer(width * 8, layers[3], stride=2) + + embed_dim = width * 32 # the ResNet feature dimension + self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim) + + def _make_layer(self, planes, blocks, stride=1): + layers = [Bottleneck(self._inplanes, planes, stride)] + + self._inplanes = planes * Bottleneck.expansion + for _ in range(1, blocks): + layers.append(Bottleneck(self._inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + def stem(x): + x = self.relu1(self.bn1(self.conv1(x))) + x = self.relu2(self.bn2(self.conv2(x))) + x = self.relu3(self.bn3(self.conv3(x))) + x = self.avgpool(x) + return x + + x = x.type(self.conv1.weight.dtype) + x = stem(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.attnpool(x) + + return x + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +class QuickGELU(nn.Module): + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class ResidualAttentionBlock(nn.Module): + def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): + super().__init__() + + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, d_model * 4)), + ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model)) + ])) + self.ln_2 = LayerNorm(d_model) + self.attn_mask = attn_mask + + def attention(self, x: torch.Tensor): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] + + def forward(self, x: torch.Tensor): + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class Transformer(nn.Module): + def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None): + super().__init__() + self.width = width + self.layers = layers + self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)]) + + def forward(self, x: torch.Tensor): + return self.resblocks(x) + + +class VisionTransformer(nn.Module): + def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int): + super().__init__() + self.input_resolution = input_resolution + self.output_dim = output_dim + self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) + + scale = width ** -0.5 + self.class_embedding = nn.Parameter(scale * torch.randn(width)) + self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width)) + self.ln_pre = LayerNorm(width) + + self.transformer = Transformer(width, layers, heads) + + self.ln_post = LayerNorm(width) + self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) + + def forward(self, x: torch.Tensor): + x = self.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width] + x = x + self.positional_embedding.to(x.dtype) + x = self.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + + x = self.ln_post(x[:, 0, :]) + + if self.proj is not None: + x = x @ self.proj + + return x + + +class CLIP(nn.Module): + def __init__(self, + embed_dim: int, + # vision + image_resolution: int, + vision_layers: Union[Tuple[int, int, int, int], int], + vision_width: int, + vision_patch_size: int, + # text + context_length: int, + vocab_size: int, + transformer_width: int, + transformer_heads: int, + transformer_layers: int + ): + super().__init__() + + self.context_length = context_length + + if isinstance(vision_layers, (tuple, list)): + vision_heads = vision_width * 32 // 64 + self.visual = ModifiedResNet( + layers=vision_layers, + output_dim=embed_dim, + heads=vision_heads, + input_resolution=image_resolution, + width=vision_width + ) + else: + vision_heads = vision_width // 64 + self.visual = VisionTransformer( + input_resolution=image_resolution, + patch_size=vision_patch_size, + width=vision_width, + layers=vision_layers, + heads=vision_heads, + output_dim=embed_dim + ) + + self.transformer = Transformer( + width=transformer_width, + layers=transformer_layers, + heads=transformer_heads, + attn_mask=self.build_attention_mask() + ) + + self.vocab_size = vocab_size + self.token_embedding = nn.Embedding(vocab_size, transformer_width) + self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width)) + self.ln_final = LayerNorm(transformer_width) + + self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim)) + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + self.initialize_parameters() + + def initialize_parameters(self): + nn.init.normal_(self.token_embedding.weight, std=0.02) + nn.init.normal_(self.positional_embedding, std=0.01) + + if isinstance(self.visual, ModifiedResNet): + if self.visual.attnpool is not None: + std = self.visual.attnpool.c_proj.in_features ** -0.5 + nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std) + nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std) + nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std) + nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std) + + for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]: + for name, param in resnet_block.named_parameters(): + if name.endswith("bn3.weight"): + nn.init.zeros_(param) + + proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width ** -0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the vision tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + @property + def dtype(self): + return self.visual.conv1.weight.dtype + + def encode_image(self, image): + return self.visual(image.type(self.dtype)) + + def encode_text(self, text): + x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.type(self.dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x).type(self.dtype) + + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + + return x + + def forward(self, image, text): + image_features = self.encode_image(image) + text_features = self.encode_text(text) + + # normalized features + image_features = image_features / image_features.norm(dim=1, keepdim=True) + text_features = text_features / text_features.norm(dim=1, keepdim=True) + + # cosine similarity as logits + logit_scale = self.logit_scale.exp() + logits_per_image = logit_scale * image_features @ text_features.t() + logits_per_text = logits_per_image.t() + + # shape = [global_batch_size, global_batch_size] + return logits_per_image, logits_per_text + + +def convert_weights(model: nn.Module): + """Convert applicable model parameters to fp16""" + + def _convert_weights_to_fp16(l): + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): + l.weight.data = l.weight.data.half() + if l.bias is not None: + l.bias.data = l.bias.data.half() + + if isinstance(l, nn.MultiheadAttention): + for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: + tensor = getattr(l, attr) + if tensor is not None: + tensor.data = tensor.data.half() + + for name in ["text_projection", "proj"]: + if hasattr(l, name): + attr = getattr(l, name) + if attr is not None: + attr.data = attr.data.half() + + model.apply(_convert_weights_to_fp16) + + +def build_model(state_dict: dict): + vit = "visual.proj" in state_dict + + if vit: + vision_width = state_dict["visual.conv1.weight"].shape[0] + vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) + vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] + grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) + image_resolution = vision_patch_size * grid_size + else: + counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] + vision_layers = tuple(counts) + vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] + output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) + vision_patch_size = None + assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] + image_resolution = output_width * 32 + + embed_dim = state_dict["text_projection"].shape[1] + context_length = state_dict["positional_embedding"].shape[0] + vocab_size = state_dict["token_embedding.weight"].shape[0] + transformer_width = state_dict["ln_final.weight"].shape[0] + transformer_heads = transformer_width // 64 + transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith("transformer.resblocks"))) + + model = CLIP( + embed_dim, + image_resolution, vision_layers, vision_width, vision_patch_size, + context_length, vocab_size, transformer_width, transformer_heads, transformer_layers + ) + + for key in ["input_resolution", "context_length", "vocab_size"]: + if key in state_dict: + del state_dict[key] + + convert_weights(model) + model.load_state_dict(state_dict) + return model.eval() diff --git a/rope/external/cliplib/simple_tokenizer.py b/rope/external/cliplib/simple_tokenizer.py new file mode 100644 index 0000000..ec202a9 --- /dev/null +++ b/rope/external/cliplib/simple_tokenizer.py @@ -0,0 +1,132 @@ +import gzip +import html +import os +from functools import lru_cache + +import ftfy +import regex as re + + +@lru_cache() +def default_bpe(): + return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") + + +@lru_cache() +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a signficant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8+n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +class SimpleTokenizer(object): + def __init__(self, bpe_path: str = default_bpe()): + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') + merges = merges[1:49152-256-2+1] + merges = [tuple(merge.split()) for merge in merges] + vocab = list(bytes_to_unicode().values()) + vocab = vocab + [v+'' for v in vocab] + for merge in merges: + vocab.append(''.join(merge)) + vocab.extend(['<|startoftext|>', '<|endoftext|>']) + self.encoder = dict(zip(vocab, range(len(vocab)))) + self.decoder = {v: k for k, v in self.encoder.items()} + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'} + self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token[:-1]) + ( token[-1] + '',) + pairs = get_pairs(word) + + if not pairs: + return token+'' + + while True: + bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except: + new_word.extend(word[i:]) + break + + if word[i] == first and i < len(word)-1 and word[i+1] == second: + new_word.append(first+second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = ' '.join(word) + self.cache[token] = word + return word + + def encode(self, text): + bpe_tokens = [] + text = whitespace_clean(basic_clean(text)).lower() + for token in re.findall(self.pat, text): + token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) + bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) + return bpe_tokens + + def decode(self, tokens): + text = ''.join([self.decoder[token] for token in tokens]) + text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ') + return text diff --git a/rope/external/clipseg.py b/rope/external/clipseg.py new file mode 100644 index 0000000..ca47a65 --- /dev/null +++ b/rope/external/clipseg.py @@ -0,0 +1,538 @@ +import math +from os.path import basename, dirname, join, isfile +import torch +from torch import nn +from torch.nn import functional as nnf +from torch.nn.modules.activation import ReLU + + +def get_prompt_list(prompt): + if prompt == 'plain': + return ['{}'] + elif prompt == 'fixed': + return ['a photo of a {}.'] + elif prompt == 'shuffle': + return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.'] + elif prompt == 'shuffle+': + return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.', + 'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.', + 'a bad photo of a {}.', 'a photo of the {}.'] + else: + raise ValueError('Invalid value for prompt') + + +def forward_multihead_attention(x, b, with_aff=False, attn_mask=None): + """ + Simplified version of multihead attention (taken from torch source code but without tons of if clauses). + The mlp and layer norm come from CLIP. + x: input. + b: multihead attention module. + """ + + x_ = b.ln_1(x) + q, k, v = nnf.linear(x_, b.attn.in_proj_weight, b.attn.in_proj_bias).chunk(3, dim=-1) + tgt_len, bsz, embed_dim = q.size() + + head_dim = embed_dim // b.attn.num_heads + scaling = float(head_dim) ** -0.5 + + q = q.contiguous().view(tgt_len, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1) + k = k.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1) + v = v.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1) + + q = q * scaling + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) # n_heads * batch_size, tokens^2, tokens^2 + if attn_mask is not None: + + + attn_mask_type, attn_mask = attn_mask + n_heads = attn_output_weights.size(0) // attn_mask.size(0) + attn_mask = attn_mask.repeat(n_heads, 1) + + if attn_mask_type == 'cls_token': + # the mask only affects similarities compared to the readout-token. + attn_output_weights[:, 0, 1:] = attn_output_weights[:, 0, 1:] * attn_mask[None,...] + # attn_output_weights[:, 0, 0] = 0*attn_output_weights[:, 0, 0] + + if attn_mask_type == 'all': + # print(attn_output_weights.shape, attn_mask[:, None].shape) + attn_output_weights[:, 1:, 1:] = attn_output_weights[:, 1:, 1:] * attn_mask[:, None] + + + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + + attn_output = torch.bmm(attn_output_weights, v) + attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) + attn_output = b.attn.out_proj(attn_output) + + x = x + attn_output + x = x + b.mlp(b.ln_2(x)) + + if with_aff: + return x, attn_output_weights + else: + return x + + +class CLIPDenseBase(nn.Module): + + def __init__(self, version, reduce_cond, reduce_dim, prompt, n_tokens): + super().__init__() + + from rope.external.cliplib import clip + + # prec = torch.FloatTensor + self.clip_model, _ = clip.load(version, device='cpu', jit=False) + self.model = self.clip_model.visual + + # if not None, scale conv weights such that we obtain n_tokens. + self.n_tokens = n_tokens + + for p in self.clip_model.parameters(): + p.requires_grad_(False) + + # conditional + if reduce_cond is not None: + self.reduce_cond = nn.Linear(512, reduce_cond) + for p in self.reduce_cond.parameters(): + p.requires_grad_(False) + else: + self.reduce_cond = None + + self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim) + self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim) + + self.reduce = nn.Linear(768, reduce_dim) + + self.prompt_list = get_prompt_list(prompt) + + # precomputed prompts + import pickle + if isfile('precomputed_prompt_vectors.pickle'): + precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb')) + self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()} + else: + self.precomputed_prompts = dict() + + def rescaled_pos_emb(self, new_size): + assert len(new_size) == 2 + + a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape) + b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T + return torch.cat([self.model.positional_embedding[:1], b]) + + def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None): + + + with torch.no_grad(): + + inp_size = x_inp.shape[2:] + + if self.n_tokens is not None: + stride2 = x_inp.shape[2] // self.n_tokens + conv_weight2 = nnf.interpolate(self.model.conv1.weight, (stride2, stride2), mode='bilinear', align_corners=True) + x = nnf.conv2d(x_inp, conv_weight2, bias=self.model.conv1.bias, stride=stride2, dilation=self.model.conv1.dilation) + else: + x = self.model.conv1(x_inp) # shape = [*, width, grid, grid] + + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + + x = torch.cat([self.model.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width] + + standard_n_tokens = 50 if self.model.conv1.kernel_size[0] == 32 else 197 + + if x.shape[1] != standard_n_tokens: + new_shape = int(math.sqrt(x.shape[1]-1)) + x = x + self.rescaled_pos_emb((new_shape, new_shape)).to(x.dtype)[None,:,:] + else: + x = x + self.model.positional_embedding.to(x.dtype) + + x = self.model.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + + activations, affinities = [], [] + for i, res_block in enumerate(self.model.transformer.resblocks): + + if mask is not None: + mask_layer, mask_type, mask_tensor = mask + if mask_layer == i or mask_layer == 'all': + # import ipdb; ipdb.set_trace() + size = int(math.sqrt(x.shape[0] - 1)) + + attn_mask = (mask_type, nnf.interpolate(mask_tensor.unsqueeze(1).float(), (size, size)).view(mask_tensor.shape[0], size * size)) + + else: + attn_mask = None + else: + attn_mask = None + + x, aff_per_head = forward_multihead_attention(x, res_block, with_aff=True, attn_mask=attn_mask) + + if i in extract_layers: + affinities += [aff_per_head] + + #if self.n_tokens is not None: + # activations += [nnf.interpolate(x, inp_size, mode='bilinear', align_corners=True)] + #else: + activations += [x] + + if len(extract_layers) > 0 and i == max(extract_layers) and skip: + print('early skip') + break + + x = x.permute(1, 0, 2) # LND -> NLD + x = self.model.ln_post(x[:, 0, :]) + + if self.model.proj is not None: + x = x @ self.model.proj + + return x, activations, affinities + + def sample_prompts(self, words, prompt_list=None): + + prompt_list = prompt_list if prompt_list is not None else self.prompt_list + + prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True) + prompts = [prompt_list[i] for i in prompt_indices] + return [promt.format(w) for promt, w in zip(prompts, words)] + + def get_cond_vec(self, conditional, batch_size): + # compute conditional from a single string + if conditional is not None and type(conditional) == str: + cond = self.compute_conditional(conditional) + cond = cond.repeat(batch_size, 1) + + # compute conditional from string list/tuple + elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str: + assert len(conditional) == batch_size + cond = self.compute_conditional(conditional) + + # use conditional directly + elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2: + cond = conditional + + # compute conditional from image + elif conditional is not None and type(conditional) == torch.Tensor: + with torch.no_grad(): + cond, _, _ = self.visual_forward(conditional) + else: + raise ValueError('invalid conditional') + return cond + + def compute_conditional(self, conditional): + from rope.external.cliplib import clip + + dev = next(self.parameters()).device + + if type(conditional) in {list, tuple}: + text_tokens = clip.tokenize(conditional).to(dev) + cond = self.clip_model.encode_text(text_tokens) + else: + if conditional in self.precomputed_prompts: + cond = self.precomputed_prompts[conditional].float().to(dev) + else: + text_tokens = clip.tokenize([conditional]).to(dev) + cond = self.clip_model.encode_text(text_tokens)[0] + + if self.shift_vector is not None: + return cond + self.shift_vector + else: + return cond + + +def clip_load_untrained(version): + assert version == 'ViT-B/16' + from clip.model import CLIP + from clip.clip import _MODELS, _download + model = torch.jit.load(_download(_MODELS['ViT-B/16'])).eval() + state_dict = model.state_dict() + + vision_width = state_dict["visual.conv1.weight"].shape[0] + vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) + vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] + grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) + image_resolution = vision_patch_size * grid_size + embed_dim = state_dict["text_projection"].shape[1] + context_length = state_dict["positional_embedding"].shape[0] + vocab_size = state_dict["token_embedding.weight"].shape[0] + transformer_width = state_dict["ln_final.weight"].shape[0] + transformer_heads = transformer_width // 64 + transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks"))) + + return CLIP(embed_dim, image_resolution, vision_layers, vision_width, vision_patch_size, + context_length, vocab_size, transformer_width, transformer_heads, transformer_layers) + + +class CLIPDensePredT(CLIPDenseBase): + + def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed', + extra_blocks=0, reduce_cond=None, fix_shift=False, + learn_trans_conv_only=False, limit_to_clip_only=False, upsample=False, + add_calibration=False, rev_activations=False, trans_conv=None, n_tokens=None, complex_trans_conv=False): + + super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens) + # device = 'cpu' + + self.extract_layers = extract_layers + self.cond_layer = cond_layer + self.limit_to_clip_only = limit_to_clip_only + self.process_cond = None + self.rev_activations = rev_activations + + depth = len(extract_layers) + + if add_calibration: + self.calibration_conds = 1 + + self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None + + self.add_activation1 = True + + self.version = version + + self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version] + + if fix_shift: + # self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'clip_text_shift_vector.pth')), requires_grad=False) + self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'shift_text_to_vis.pth')), requires_grad=False) + # self.shift_vector = nn.Parameter(-1*torch.load(join(dirname(basename(__file__)), 'shift2.pth')), requires_grad=False) + else: + self.shift_vector = None + + if trans_conv is None: + trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version] + else: + # explicitly define transposed conv kernel size + trans_conv_ks = (trans_conv, trans_conv) + + if not complex_trans_conv: + self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks) + else: + assert trans_conv_ks[0] == trans_conv_ks[1] + + tp_kernels = (trans_conv_ks[0] // 4, trans_conv_ks[0] // 4) + + self.trans_conv = nn.Sequential( + nn.Conv2d(reduce_dim, reduce_dim, kernel_size=3, padding=1), + nn.ReLU(), + nn.ConvTranspose2d(reduce_dim, reduce_dim // 2, kernel_size=tp_kernels[0], stride=tp_kernels[0]), + nn.ReLU(), + nn.ConvTranspose2d(reduce_dim // 2, 1, kernel_size=tp_kernels[1], stride=tp_kernels[1]), + ) + +# self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks) + + assert len(self.extract_layers) == depth + + self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)]) + self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))]) + self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)]) + + # refinement and trans conv + + if learn_trans_conv_only: + for p in self.parameters(): + p.requires_grad_(False) + + for p in self.trans_conv.parameters(): + p.requires_grad_(True) + + self.prompt_list = get_prompt_list(prompt) + + + def forward(self, inp_image, conditional=None, return_features=False, mask=None): + + assert type(return_features) == bool + + inp_image = inp_image.to(self.model.positional_embedding.device) + + if mask is not None: + raise ValueError('mask not supported') + + # x_inp = normalize(inp_image) + x_inp = inp_image + + bs, dev = inp_image.shape[0], x_inp.device + + cond = self.get_cond_vec(conditional, bs) + + visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers)) + + activation1 = activations[0] + activations = activations[1:] + + _activations = activations[::-1] if not self.rev_activations else activations + + a = None + for i, (activation, block, reduce) in enumerate(zip(_activations, self.blocks, self.reduces)): + + if a is not None: + a = reduce(activation) + a + else: + a = reduce(activation) + + if i == self.cond_layer: + if self.reduce_cond is not None: + cond = self.reduce_cond(cond) + + a = self.film_mul(cond) * a + self.film_add(cond) + + a = block(a) + + for block in self.extra_blocks: + a = a + block(a) + + a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens + + size = int(math.sqrt(a.shape[2])) + + a = a.view(bs, a.shape[1], size, size) + + a = self.trans_conv(a) + + if self.n_tokens is not None: + a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear', align_corners=True) + + if self.upsample_proj is not None: + a = self.upsample_proj(a) + a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear') + + if return_features: + return a, visual_q, cond, [activation1] + activations + else: + return a, + + + +class CLIPDensePredTMasked(CLIPDensePredT): + + def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, + prompt='fixed', extra_blocks=0, reduce_cond=None, fix_shift=False, learn_trans_conv_only=False, + refine=None, limit_to_clip_only=False, upsample=False, add_calibration=False, n_tokens=None): + + super().__init__(version=version, extract_layers=extract_layers, cond_layer=cond_layer, reduce_dim=reduce_dim, + n_heads=n_heads, prompt=prompt, extra_blocks=extra_blocks, reduce_cond=reduce_cond, + fix_shift=fix_shift, learn_trans_conv_only=learn_trans_conv_only, + limit_to_clip_only=limit_to_clip_only, upsample=upsample, add_calibration=add_calibration, + n_tokens=n_tokens) + + def visual_forward_masked(self, img_s, seg_s): + return super().visual_forward(img_s, mask=('all', 'cls_token', seg_s)) + + def forward(self, img_q, cond_or_img_s, seg_s=None, return_features=False): + + if seg_s is None: + cond = cond_or_img_s + else: + img_s = cond_or_img_s + + with torch.no_grad(): + cond, _, _ = self.visual_forward_masked(img_s, seg_s) + + return super().forward(img_q, cond, return_features=return_features) + + + +class CLIPDenseBaseline(CLIPDenseBase): + + def __init__(self, version='ViT-B/32', cond_layer=0, + extract_layer=9, reduce_dim=128, reduce2_dim=None, prompt='fixed', + reduce_cond=None, limit_to_clip_only=False, n_tokens=None): + + super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens) + device = 'cpu' + + # self.cond_layer = cond_layer + self.extract_layer = extract_layer + self.limit_to_clip_only = limit_to_clip_only + self.shift_vector = None + + self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version] + + assert reduce2_dim is not None + + self.reduce2 = nn.Sequential( + nn.Linear(reduce_dim, reduce2_dim), + nn.ReLU(), + nn.Linear(reduce2_dim, reduce_dim) + ) + + trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version] + self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks) + + + def forward(self, inp_image, conditional=None, return_features=False): + + inp_image = inp_image.to(self.model.positional_embedding.device) + + # x_inp = normalize(inp_image) + x_inp = inp_image + + bs, dev = inp_image.shape[0], x_inp.device + + cond = self.get_cond_vec(conditional, bs) + + visual_q, activations, affinities = self.visual_forward(x_inp, extract_layers=[self.extract_layer]) + + a = activations[0] + a = self.reduce(a) + a = self.film_mul(cond) * a + self.film_add(cond) + + if self.reduce2 is not None: + a = self.reduce2(a) + + # the original model would execute a transformer block here + + a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens + + size = int(math.sqrt(a.shape[2])) + + a = a.view(bs, a.shape[1], size, size) + a = self.trans_conv(a) + + if return_features: + return a, visual_q, cond, activations + else: + return a, + + +class CLIPSegMultiLabel(nn.Module): + + def __init__(self, model) -> None: + super().__init__() + + from third_party.JoEm.data_loader import get_seen_idx, get_unseen_idx, VOC + + self.pascal_classes = VOC + + from models.clipseg import CLIPDensePredT + from general_utils import load_model + # self.clipseg = load_model('rd64-vit16-neg0.2-phrasecut', strict=False) + self.clipseg = load_model(model, strict=False) + + self.clipseg.eval() + + def forward(self, x): + + bs = x.shape[0] + out = torch.ones(21, bs, 352, 352).to(x.device) * -10 + + for class_id, class_name in enumerate(self.pascal_classes): + + fac = 3 if class_name == 'background' else 1 + + with torch.no_grad(): + pred = torch.sigmoid(self.clipseg(x, class_name)[0][:,0]) * fac + + out[class_id] += pred + + + out = out.permute(1, 0, 2, 3) + + return out + + # construct output tensor + diff --git a/rope/external/resnet.py b/rope/external/resnet.py new file mode 100644 index 0000000..64969da --- /dev/null +++ b/rope/external/resnet.py @@ -0,0 +1,109 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.model_zoo as modelzoo + +# from modules.bn import InPlaceABNSync as BatchNorm2d + +resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth' + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + def __init__(self, in_chan, out_chan, stride=1): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(in_chan, out_chan, stride) + self.bn1 = nn.BatchNorm2d(out_chan) + self.conv2 = conv3x3(out_chan, out_chan) + self.bn2 = nn.BatchNorm2d(out_chan) + self.relu = nn.ReLU(inplace=True) + self.downsample = None + if in_chan != out_chan or stride != 1: + self.downsample = nn.Sequential( + nn.Conv2d(in_chan, out_chan, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(out_chan), + ) + + def forward(self, x): + residual = self.conv1(x) + residual = F.relu(self.bn1(residual)) + residual = self.conv2(residual) + residual = self.bn2(residual) + + shortcut = x + if self.downsample is not None: + shortcut = self.downsample(x) + + out = shortcut + residual + out = self.relu(out) + return out + + +def create_layer_basic(in_chan, out_chan, bnum, stride=1): + layers = [BasicBlock(in_chan, out_chan, stride=stride)] + for i in range(bnum-1): + layers.append(BasicBlock(out_chan, out_chan, stride=1)) + return nn.Sequential(*layers) + + +class Resnet18(nn.Module): + def __init__(self): + super(Resnet18, self).__init__() + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1) + self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2) + self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2) + self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2) + self.init_weight() + + def forward(self, x): + x = self.conv1(x) + x = F.relu(self.bn1(x)) + x = self.maxpool(x) + + x = self.layer1(x) + feat8 = self.layer2(x) # 1/8 + feat16 = self.layer3(feat8) # 1/16 + feat32 = self.layer4(feat16) # 1/32 + return feat8, feat16, feat32 + + def init_weight(self): + state_dict = modelzoo.load_url(resnet18_url) + self_state_dict = self.state_dict() + for k, v in state_dict.items(): + if 'fc' in k: continue + self_state_dict.update({k: v}) + self.load_state_dict(self_state_dict) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv2d)): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +if __name__ == "__main__": + net = Resnet18() + x = torch.randn(16, 3, 224, 224) + out = net(x) + print(out[0].size()) + print(out[1].size()) + print(out[2].size()) + net.get_params() diff --git a/rope/media/OffState.png b/rope/media/OffState.png new file mode 100644 index 0000000000000000000000000000000000000000..c28ee59f44f48c5da23d6758a2311ff201abd6f4 GIT binary patch literal 2145 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int(copyright_year) + return score + + +if "VHS_FORCE_FFMPEG_PATH" in os.environ: + ffmpeg_path = os.environ.get("VHS_FORCE_FFMPEG_PATH") +else: + ffmpeg_paths = [] + try: + from imageio_ffmpeg import get_ffmpeg_exe + imageio_ffmpeg_path = get_ffmpeg_exe() + ffmpeg_paths.append(imageio_ffmpeg_path) + except: + if "VHS_USE_IMAGEIO_FFMPEG" in os.environ: + raise + logger.warn("Failed to import imageio_ffmpeg") + if "VHS_USE_IMAGEIO_FFMPEG" in os.environ: + ffmpeg_path = imageio_ffmpeg_path + else: + system_ffmpeg = shutil.which("ffmpeg") + if system_ffmpeg is not None: + ffmpeg_paths.append(system_ffmpeg) + if os.path.isfile("ffmpeg"): + ffmpeg_paths.append(os.path.abspath("ffmpeg")) + if os.path.isfile("ffmpeg.exe"): + ffmpeg_paths.append(os.path.abspath("ffmpeg.exe")) + if len(ffmpeg_paths) == 0: + logger.error("No valid ffmpeg found.") + ffmpeg_path = None + elif len(ffmpeg_paths) == 1: + #Evaluation of suitability isn't required, can take sole option + #to reduce startup time + ffmpeg_path = ffmpeg_paths[0] + else: + ffmpeg_path = max(ffmpeg_paths, key=ffmpeg_suitability) +gifski_path = os.environ.get("VHS_GIFSKI", None) +if gifski_path is None: + gifski_path = os.environ.get("JOV_GIFSKI", None) + if gifski_path is None: + gifski_path = shutil.which("gifski") + +def is_safe_path(path): + if "VHS_STRICT_PATHS" not in os.environ: + return True + basedir = os.path.abspath('.') + try: + common_path = os.path.commonpath([basedir, path]) + except: + #Different drive on windows + return False + return common_path == basedir + +def get_sorted_dir_files_from_directory(directory: str, skip_first_images: int=0, select_every_nth: int=1, extensions: Iterable=None): + directory = strip_path(directory) + dir_files = os.listdir(directory) + dir_files = sorted(dir_files) + dir_files = [os.path.join(directory, x) for x in dir_files] + dir_files = list(filter(lambda filepath: os.path.isfile(filepath), dir_files)) + # filter by extension, if needed + if extensions is not None: + extensions = list(extensions) + new_dir_files = [] + for filepath in dir_files: + ext = "." + filepath.split(".")[-1] + if ext.lower() in extensions: + new_dir_files.append(filepath) + dir_files = new_dir_files + # start at skip_first_images + dir_files = dir_files[skip_first_images:] + dir_files = dir_files[0::select_every_nth] + return dir_files + + +# modified from https://stackoverflow.com/questions/22058048/hashing-a-file-in-python +def calculate_file_hash(filename: str, hash_every_n: int = 1): + #Larger video files were taking >.5 seconds to hash even when cached, + #so instead the modified time from the filesystem is used as a hash + h = hashlib.sha256() + h.update(filename.encode()) + h.update(str(os.path.getmtime(filename)).encode()) + return h.hexdigest() + +prompt_queue = server.PromptServer.instance.prompt_queue +def requeue_workflow_unchecked(): + """Requeues the current workflow without checking for multiple requeues""" + currently_running = prompt_queue.currently_running + (_, _, prompt, extra_data, outputs_to_execute) = next(iter(currently_running.values())) + + #Ensure batch_managers are marked stale + prompt = prompt.copy() + for uid in prompt: + if prompt[uid]['class_type'] == 'VHS_BatchManager': + prompt[uid]['inputs']['requeue'] = prompt[uid]['inputs'].get('requeue',0)+1 + + #execution.py has guards for concurrency, but server doesn't. + #TODO: Check that this won't be an issue + number = -server.PromptServer.instance.number + server.PromptServer.instance.number += 1 + prompt_id = str(server.uuid.uuid4()) + prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute)) + +requeue_guard = [None, 0, 0, {}] +def requeue_workflow(requeue_required=(-1,True)): + assert(len(prompt_queue.currently_running) == 1) + global requeue_guard + (run_number, _, prompt, _, _) = next(iter(prompt_queue.currently_running.values())) + if requeue_guard[0] != run_number: + #Calculate a count of how many outputs are managed by a batch manager + managed_outputs=0 + for bm_uid in prompt: + if prompt[bm_uid]['class_type'] == 'VHS_BatchManager': + for output_uid in prompt: + if prompt[output_uid]['class_type'] in ["RopeVideoCombine"]: + for inp in prompt[output_uid]['inputs'].values(): + if inp == [bm_uid, 0]: + managed_outputs+=1 + requeue_guard = [run_number, 0, managed_outputs, {}] + requeue_guard[1] = requeue_guard[1]+1 + requeue_guard[3][requeue_required[0]] = requeue_required[1] + if requeue_guard[1] == requeue_guard[2] and max(requeue_guard[3].values()): + requeue_workflow_unchecked() + +def get_audio(file, start_time=0, duration=0): + args = [ffmpeg_path, "-v", "error", "-i", file] + if start_time > 0: + args += ["-ss", str(start_time)] + if duration > 0: + args += ["-t", str(duration)] + try: + res = subprocess.run(args + ["-f", "wav", "-"], + stdout=subprocess.PIPE, check=True).stdout + except subprocess.CalledProcessError as e: + logger.warning(f"Failed to extract audio from: {file}") + return False + return res + + +def lazy_eval(func): + class Cache: + def __init__(self, func): + self.res = None + self.func = func + def get(self): + if self.res is None: + self.res = self.func() + return self.res + cache = Cache(func) + return lambda : cache.get() + + +def is_url(url): + return url.split("://")[0] in ["http", "https"] + +def validate_sequence(path): + #Check if path is a valid ffmpeg sequence that points to at least one file + (path, file) = os.path.split(path) + if not os.path.isdir(path): + return False + match = re.search('%0?\\d+d', file) + if not match: + return False + seq = match.group() + if seq == '%d': + seq = '\\\\d+' + else: + seq = '\\\\d{%s}' % seq[1:-1] + file_matcher = re.compile(re.sub('%0?\\d+d', seq, file)) + for file in os.listdir(path): + if file_matcher.fullmatch(file): + return True + return False + +def strip_path(path): + #This leaves whitespace inside quotes and only a single " + #thus ' ""test"' -> '"test' + #consider path.strip(string.whitespace+"\"") + #or weightier re.fullmatch("[\\s\"]*(.+?)[\\s\"]*", path).group(1) + path = path.strip() + if path.startswith("\""): + path = path[1:] + if path.endswith("\""): + path = path[:-1] + return path +def hash_path(path): + if path is None: + return "input" + if is_url(path): + return "url" + return calculate_file_hash(strip_path(path)) + + +def validate_path(path, allow_none=False, allow_url=True): + if path is None: + return allow_none + if is_url(path): + #Probably not feasible to check if url resolves here + if not allow_url: + return "URLs are unsupported for this path" + return is_safe_path(path) + if not os.path.isfile(strip_path(path)): + return "Invalid file path: {}".format(path) + return is_safe_path(path) diff --git a/videoCombine.py b/videoCombine.py new file mode 100644 index 0000000..c4a0803 --- /dev/null +++ b/videoCombine.py @@ -0,0 +1,565 @@ +## 这个文件来自，comfyui-videoHelperSuite，除了加print之外没有改动 +## all credits go to the original author +import os +import sys +import json +import subprocess +import numpy as np +import re +import datetime +from typing import List +import torch +from PIL import Image, ExifTags +from PIL.PngImagePlugin import PngInfo +from pathlib import Path +from string import Template +import itertools + +import folder_paths +from .utils import ffmpeg_path, get_audio, hash_path, validate_path, requeue_workflow, gifski_path, calculate_file_hash, strip_path +from comfy.utils import ProgressBar + +folder_paths.folder_names_and_paths["VHS_video_formats"] = ( + [ + os.path.join(os.path.dirname(os.path.abspath(__file__)), "video_formats"), + ], + [".json"] +) +audio_extensions = ['mp3', 'mp4', 'wav', 'ogg'] + +def gen_format_widgets(video_format): + for k in video_format: + if k.endswith("_pass"): + for i in range(len(video_format[k])): + if isinstance(video_format[k][i], list): + item = [video_format[k][i]] + yield item + video_format[k][i] = item[0] + else: + if isinstance(video_format[k], list): + item = [video_format[k]] + yield item + video_format[k] = item[0] + +def get_video_formats(): + formats = [] + for format_name in folder_paths.get_filename_list("VHS_video_formats"): + format_name = format_name[:-5] + video_format_path = folder_paths.get_full_path("VHS_video_formats", format_name + ".json") + with open(video_format_path, 'r') as stream: + video_format = json.load(stream) + if "gifski_pass" in video_format and gifski_path is None: + #Skip format + continue + widgets = [w[0] for w in gen_format_widgets(video_format)] + if (len(widgets) > 0): + formats.append(["video/" + format_name, widgets]) + else: + formats.append("video/" + format_name) + return formats + +def get_format_widget_defaults(format_name): + video_format_path = folder_paths.get_full_path("VHS_video_formats", format_name + ".json") + with open(video_format_path, 'r') as stream: + video_format = json.load(stream) + results = {} + for w in gen_format_widgets(video_format): + if len(w[0]) > 2 and 'default' in w[0][2]: + default = w[0][2]['default'] + else: + if type(w[0][1]) is list: + default = w[0][1][0] + else: + #NOTE: This doesn't respect max/min, but should be good enough as a fallback to a fallback to a fallback + default = {"BOOLEAN": False, "INT": 0, "FLOAT": 0, "STRING": ""}[w[0][1]] + results[w[0][0]] = default + return results + + +def apply_format_widgets(format_name, kwargs): + video_format_path = folder_paths.get_full_path("VHS_video_formats", format_name + ".json") + with open(video_format_path, 'r') as stream: + video_format = json.load(stream) + for w in gen_format_widgets(video_format): + assert(w[0][0] in kwargs) + if len(w[0]) > 3: + w[0] = Template(w[0][3]).substitute(val=kwargs[w[0][0]]) + else: + w[0] = str(kwargs[w[0][0]]) + return video_format + +def tensor_to_int(tensor, bits): + #TODO: investigate benefit of rounding by adding 0.5 before clip/cast + tensor = tensor.cpu().numpy() * (2**bits-1) + return np.clip(tensor, 0, (2**bits-1)) +def tensor_to_shorts(tensor): + return tensor_to_int(tensor, 16).astype(np.uint16) +def tensor_to_bytes(tensor): + return tensor_to_int(tensor, 8).astype(np.uint8) + +def ffmpeg_process(args, video_format, video_metadata, file_path, env): + # print("in ffmpeg_process") + # print("args:", args) + # print("video_format:",video_format) + # print("video_metadata:",video_metadata) + # print("file_path:",file_path) + # print("env:",env) + + + res = None + frame_data = yield + total_frames_output = 0 + if video_format.get('save_metadata', 'False') != 'False': + os.makedirs(folder_paths.get_temp_directory(), exist_ok=True) + metadata = json.dumps(video_metadata) + metadata_path = os.path.join(folder_paths.get_temp_directory(), "metadata.txt") + #metadata from file should escape = ; # \ and newline + metadata = metadata.replace("\\","\\\\") + metadata = metadata.replace(";","\\;") + metadata = metadata.replace("#","\\#") + metadata = metadata.replace("=","\\=") + metadata = metadata.replace("\n","\\\n") + metadata = "comment=" + metadata + with open(metadata_path, "w") as f: + f.write(";FFMETADATA1\n") + f.write(metadata) + m_args = args[:1] + ["-i", metadata_path] + args[1:] + ["-metadata", "creation_time=now"] + with subprocess.Popen(m_args + [file_path], stderr=subprocess.PIPE, + stdin=subprocess.PIPE, env=env) as proc: + try: + while frame_data is not None: + proc.stdin.write(frame_data) + #TODO: skip flush for increased speed + frame_data = yield + total_frames_output+=1 + proc.stdin.flush() + proc.stdin.close() + res = proc.stderr.read() + except BrokenPipeError as e: + err = proc.stderr.read() + #Check if output file exists. If it does, the re-execution + #will also fail. This obscures the cause of the error + #and seems to never occur concurrent to the metadata issue + if os.path.exists(file_path): + raise Exception("An error occurred in the ffmpeg subprocess:\n" \ + + err.decode("utf-8")) + #Res was not set + print(err.decode("utf-8"), end="", file=sys.stderr) + print("An error occurred when saving with metadata") + if res != b'': + with subprocess.Popen(args + [file_path], stderr=subprocess.PIPE, + stdin=subprocess.PIPE, env=env) as proc: + try: + while frame_data is not None: + proc.stdin.write(frame_data) + frame_data = yield + total_frames_output+=1 + proc.stdin.flush() + proc.stdin.close() + res = proc.stderr.read() + except BrokenPipeError as e: + res = proc.stderr.read() + raise Exception("An error occurred in the ffmpeg subprocess:\n" \ + + res.decode("utf-8")) + yield total_frames_output + if len(res) > 0: + print(res.decode("utf-8"), end="", file=sys.stderr) + +def gifski_process(args, video_format, file_path, env): + frame_data = yield + with subprocess.Popen(args + video_format['main_pass'] + ['-f', 'yuv4mpegpipe', '-'], + stderr=subprocess.PIPE, stdin=subprocess.PIPE, + stdout=subprocess.PIPE, env=env) as procff: + with subprocess.Popen([gifski_path] + video_format['gifski_pass'] + + ['-q', '-o', file_path, '-'], stderr=subprocess.PIPE, + stdin=procff.stdout, stdout=subprocess.PIPE, + env=env) as procgs: + try: + while frame_data is not None: + procff.stdin.write(frame_data) + frame_data = yield + procff.stdin.flush() + procff.stdin.close() + resff = procff.stderr.read() + resgs = procgs.stderr.read() + outgs = procgs.stdout.read() + except BrokenPipeError as e: + procff.stdin.close() + resff = procff.stderr.read() + resgs = procgs.stderr.read() + raise Exception("An error occurred while creating gifski output\n" \ + + "Make sure you are using gifski --version >=1.32.0\nffmpeg: " \ + + resff.decode("utf-8") + '\ngifski: ' + resgs.decode("utf-8")) + if len(resff) > 0: + print(resff.decode("utf-8"), end="", file=sys.stderr) + if len(resgs) > 0: + print(resgs.decode("utf-8"), end="", file=sys.stderr) + #should always be empty as the quiet flag is passed + if len(outgs) > 0: + print(outgs.decode("utf-8")) + +def to_pingpong(inp): + if not hasattr(inp, "__getitem__"): + inp = list(inp) + yield from inp + for i in range(len(inp)-2,0,-1): + yield inp[i] + +class RopeVideoCombine: + @classmethod + def INPUT_TYPES(s): + ffmpeg_formats = get_video_formats() + return { + "required": { + "frame_rate": ( + "FLOAT", + {"default": 8, "min": 1, "step": 1}, + ), + "loop_count": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}), + "filename_prefix": ("STRING", {"default": "RopeHahahaha"}), + "format": (["image/gif", "image/webp"] + ffmpeg_formats,), + "pingpong": ("BOOLEAN", {"default": False}), + "save_output": ("BOOLEAN", {"default": True}), + }, + "optional": { + "images": ("IMAGE",), + "audio": ("VHS_AUDIO",), + "meta_batch": ("VHS_BatchManager",), + "vae": ("VAE",), + "latents": ("LATENT",), + }, + "hidden": { + "prompt": "PROMPT", + "extra_pnginfo": "EXTRA_PNGINFO", + "unique_id": "UNIQUE_ID" + }, + } + + RETURN_TYPES = ("VHS_FILENAMES",) + RETURN_NAMES = ("Filenames",) + OUTPUT_NODE = True + CATEGORY = "RopeWrapper" + FUNCTION = "combine_video" + + def combine_video( + self, + frame_rate: int, + loop_count: int, + images=None, + latents=None, + filename_prefix="RopeHahahaha", + format="image/gif", + pingpong=False, + save_output=True, + prompt=None, + extra_pnginfo=None, + audio=None, + unique_id=None, + manual_format_widgets=None, + meta_batch=None, + vae=None + ): + if latents is not None: + images = latents + if images is None: + return ((save_output, []),) + if vae is not None: + if isinstance(images, dict): + images = images['samples'] + else: + vae = None + + if isinstance(images, torch.Tensor) and images.size(0) == 0: + return ((save_output, []),) + num_frames = len(images) + pbar = ProgressBar(num_frames) + if vae is not None: + downscale_ratio = getattr(vae, "downscale_ratio", 8) + width = images.size(3)*downscale_ratio + height = images.size(2)*downscale_ratio + frames_per_batch = (1920 * 1080 * 16) // (width * height) or 1 + #Python 3.12 adds an itertools.batched, but it's easily replicated for legacy support + def batched(it, n): + while batch := tuple(itertools.islice(it, n)): + yield batch + def batched_encode(images, vae, frames_per_batch): + for batch in batched(iter(images), frames_per_batch): + image_batch = torch.from_numpy(np.array(batch)) + yield from vae.decode(image_batch) + images = batched_encode(images, vae, frames_per_batch) + first_image = next(images) + #repush first_image + images = itertools.chain([first_image], images) + else: + first_image = images[0] + images = iter(images) + # get output information + output_dir = ( + folder_paths.get_output_directory() + if save_output + else folder_paths.get_temp_directory() + ) + ( + full_output_folder, + filename, + _, + subfolder, + _, + ) = folder_paths.get_save_image_path(filename_prefix, output_dir) + output_files = [] + + metadata = PngInfo() + video_metadata = {} + if prompt is not None: + metadata.add_text("prompt", json.dumps(prompt)) + video_metadata["prompt"] = prompt + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add_text(x, json.dumps(extra_pnginfo[x])) + video_metadata[x] = extra_pnginfo[x] + metadata.add_text("CreationTime", datetime.datetime.now().isoformat(" ")[:19]) + + if meta_batch is not None and unique_id in meta_batch.outputs: + (counter, output_process) = meta_batch.outputs[unique_id] + else: + # comfy counter workaround + max_counter = 0 + + # Loop through the existing files + matcher = re.compile(f"{re.escape(filename)}_(\\d+)\\D*\\..+", re.IGNORECASE) + for existing_file in os.listdir(full_output_folder): + # Check if the file matches the expected format + match = matcher.fullmatch(existing_file) + if match: + # Extract the numeric portion of the filename + file_counter = int(match.group(1)) + # Update the maximum counter value if necessary + if file_counter > max_counter: + max_counter = file_counter + + # Increment the counter by 1 to get the next available value + counter = max_counter + 1 + output_process = None + + # save first frame as png to keep metadata + file = f"{filename}_{counter:05}.png" + file_path = os.path.join(full_output_folder, file) + Image.fromarray(tensor_to_bytes(first_image)).save( + file_path, + pnginfo=metadata, + compress_level=4, + ) + output_files.append(file_path) + + format_type, format_ext = format.split("/") + if format_type == "image": + if meta_batch is not None: + raise Exception("Pillow('image/') formats are not compatible with batched output") + image_kwargs = {} + if format_ext == "gif": + image_kwargs['disposal'] = 2 + if format_ext == "webp": + #Save timestamp information + exif = Image.Exif() + exif[ExifTags.IFD.Exif] = {36867: datetime.datetime.now().isoformat(" ")[:19]} + image_kwargs['exif'] = exif + file = f"{filename}_{counter:05}.{format_ext}" + file_path = os.path.join(full_output_folder, file) + if pingpong: + images = to_pingpong(images) + frames = map(lambda x : Image.fromarray(tensor_to_bytes(x)), images) + # Use pillow directly to save an animated image + next(frames).save( + file_path, + format=format_ext.upper(), + save_all=True, + append_images=frames, + duration=round(1000 / frame_rate), + loop=loop_count, + compress_level=4, + **image_kwargs + ) + output_files.append(file_path) + else: + # Use ffmpeg to save a video + if ffmpeg_path is None: + raise ProcessLookupError(f"ffmpeg is required for video outputs and could not be found.\nIn order to use video outputs, you must either:\n- Install imageio-ffmpeg with pip,\n- Place a ffmpeg executable in {os.path.abspath('')}, or\n- Install ffmpeg and add it to the system path.") + + #Acquire additional format_widget values + kwargs = None + if manual_format_widgets is None: + if prompt is not None: + kwargs = prompt[unique_id]['inputs'] + else: + manual_format_widgets = {} + if kwargs is None: + kwargs = get_format_widget_defaults(format_ext) + missing = {} + for k in kwargs.keys(): + if k in manual_format_widgets: + kwargs[k] = manual_format_widgets[k] + else: + missing[k] = kwargs[k] + if len(missing) > 0: + print("Extra format values were not provided, the following defaults will be used: " + str(kwargs) + "\nThis is likely due to usage of ComfyUI-to-python. These values can be manually set by supplying a manual_format_widgets argument") + + video_format = apply_format_widgets(format_ext, kwargs) + has_alpha = first_image.shape[-1] == 4 + dim_alignment = video_format.get("dim_alignment", 8) + if (first_image.shape[1] % dim_alignment) or (first_image.shape[0] % dim_alignment): + #output frames must be padded + to_pad = (-first_image.shape[1] % dim_alignment, + -first_image.shape[0] % dim_alignment) + padding = (to_pad[0]//2, to_pad[0] - to_pad[0]//2, + to_pad[1]//2, to_pad[1] - to_pad[1]//2) + padfunc = torch.nn.ReplicationPad2d(padding) + def pad(image): + image = image.permute((2,0,1))#HWC to CHW + padded = padfunc(image.to(dtype=torch.float32)) + return padded.permute((1,2,0)) + images = map(pad, images) + new_dims = (-first_image.shape[1] % dim_alignment + first_image.shape[1], + -first_image.shape[0] % dim_alignment + first_image.shape[0]) + dimensions = f"{new_dims[0]}x{new_dims[1]}" + print("Output images were not of valid resolution and have had padding applied") + else: + dimensions = f"{first_image.shape[1]}x{first_image.shape[0]}" + if loop_count > 0: + loop_args = ["-vf", "loop=loop=" + str(loop_count)+":size=" + str(num_frames)] + else: + loop_args = [] + if pingpong: + if meta_batch is not None: + print("pingpong is incompatible with batched output") + images = to_pingpong(images) + if video_format.get('input_color_depth', '8bit') == '16bit': + images = map(tensor_to_shorts, images) + if has_alpha: + i_pix_fmt = 'rgba64' + else: + i_pix_fmt = 'rgb48' + else: + images = map(tensor_to_bytes, images) + if has_alpha: + i_pix_fmt = 'rgba' + else: + i_pix_fmt = 'rgb24' + file = f"{filename}_{counter:05}.{video_format['extension']}" + file_path = os.path.join(full_output_folder, file) + bitrate_arg = [] + bitrate = video_format.get('bitrate') + if bitrate is not None: + bitrate_arg = ["-b:v", str(bitrate) + "M" if video_format.get('megabit') == 'True' else str(bitrate) + "K"] + args = [ffmpeg_path, "-v", "error", "-f", "rawvideo", "-pix_fmt", i_pix_fmt, + "-s", dimensions, "-r", str(frame_rate), "-i", "-"] \ + + loop_args + + images = map(lambda x: x.tobytes(), images) + env=os.environ.copy() + if "environment" in video_format: + env.update(video_format["environment"]) + + if "pre_pass" in video_format: + if meta_batch is not None: + #Performing a prepass requires keeping access to all frames. + #Potential solutions include keeping just output frames in + #memory or using 3 passes with intermediate file, but + #very long gifs probably shouldn't be encouraged + raise Exception("Formats which require a pre_pass are incompatible with Batch Manager.") + images = [b''.join(images)] + os.makedirs(folder_paths.get_temp_directory(), exist_ok=True) + pre_pass_args = args[:13] + video_format['pre_pass'] + try: + subprocess.run(pre_pass_args, input=images[0], env=env, + capture_output=True, check=True) + except subprocess.CalledProcessError as e: + raise Exception("An error occurred in the ffmpeg prepass:\n" \ + + e.stderr.decode("utf-8")) + if "inputs_main_pass" in video_format: + args = args[:13] + video_format['inputs_main_pass'] + args[13:] + + if output_process is None: + if 'gifski_pass' in video_format: + output_process = gifski_process(args, video_format, file_path, env) + else: + args += video_format['main_pass'] + bitrate_arg + output_process = ffmpeg_process(args, video_format, video_metadata, file_path, env) + #Proceed to first yield + output_process.send(None) + if meta_batch is not None: + meta_batch.outputs[unique_id] = (counter, output_process) + + for image in images: + pbar.update(1) + output_process.send(image) + if meta_batch is not None: + requeue_workflow((meta_batch.unique_id, not meta_batch.has_closed_inputs)) + if meta_batch is None or meta_batch.has_closed_inputs: + #Close pipe and wait for termination. + try: + total_frames_output = output_process.send(None) + output_process.send(None) + except StopIteration: + pass + if meta_batch is not None: + meta_batch.outputs.pop(unique_id) + if len(meta_batch.outputs) == 0: + meta_batch.reset() + else: + #batch is unfinished + #TODO: Check if empty output breaks other custom nodes + return {"ui": {"unfinished_batch": [True]}, "result": ((save_output, []),)} + + output_files.append(file_path) + + if audio is not None and audio() is not False: + # Create audio file if input was provided + output_file_with_audio = f"{filename}_{counter:05}-audio.{video_format['extension']}" + output_file_with_audio_path = os.path.join(full_output_folder, output_file_with_audio) + if "audio_pass" not in video_format: + logger.warn("Selected video format does not have explicit audio support") + video_format["audio_pass"] = ["-c:a", "libopus"] + + + # FFmpeg command with audio re-encoding + #TODO: expose audio quality options if format widgets makes it in + #Reconsider forcing apad/shortest + min_audio_dur = total_frames_output / frame_rate + 1 + mux_args = [ffmpeg_path, "-v", "error", "-n", "-i", file_path, + "-i", "-", "-c:v", "copy"] \ + + video_format["audio_pass"] \ + + ["-af", "apad=whole_dur="+str(min_audio_dur), + "-shortest", output_file_with_audio_path] + + try: + res = subprocess.run(mux_args, input=audio(), env=env, + capture_output=True, check=True) + except subprocess.CalledProcessError as e: + raise Exception("An error occured in the ffmpeg subprocess:\n" \ + + e.stderr.decode("utf-8")) + if res.stderr: + print(res.stderr.decode("utf-8"), end="", file=sys.stderr) + output_files.append(output_file_with_audio_path) + #Return this file with audio to the webui. + #It will be muted unless opened or saved with right click + file = output_file_with_audio + + previews = [ + { + "filename": file, + "subfolder": subfolder, + "type": "output" if save_output else "temp", + "format": format, + "frame_rate": frame_rate, + } + ] + if num_frames == 1 and 'png' in format and '%03d' in file: + previews[0]['format'] = 'image/png' + previews[0]['filename'] = file.replace('%03d', '001') + #print(previews) + #print("save_output:",save_output) + #print("output_files:",output_files) + return {"ui": {"gifs": previews}, "result": ((save_output, output_files),)} + @classmethod + def VALIDATE_INPUTS(self, format, **kwargs): + return True diff --git a/video_formats/16bit-png.json b/video_formats/16bit-png.json new file mode 100644 index 0000000..04da16a --- /dev/null +++ b/video_formats/16bit-png.json @@ -0,0 +1,9 @@ +{ + "main_pass": + [ + "-n", + "-pix_fmt", "rgba64" + ], + "input_color_depth": "16bit", + "extension": "%03d.png" +} diff --git a/video_formats/8bit-png.json b/video_formats/8bit-png.json new file mode 100644 index 0000000..b4144fe --- /dev/null +++ b/video_formats/8bit-png.json @@ -0,0 +1,7 @@ +{ + "main_pass": + [ + "-n" + ], + "extension": "%03d.png" +} diff --git a/video_formats/ProRes.json b/video_formats/ProRes.json new file mode 100644 index 0000000..a3896e2 --- /dev/null +++ b/video_formats/ProRes.json @@ -0,0 +1,10 @@ +{ + "main_pass": + [ + "-n", "-c:v", "prores_ks", + "-profile:v","3", + "-pix_fmt", "yuv422p10" + ], + "audio_pass": ["-c:a", "pcm_s16le"], + "extension": "mov" +} diff --git a/video_formats/av1-webm.json b/video_formats/av1-webm.json new file mode 100644 index 0000000..81c325b --- /dev/null +++ b/video_formats/av1-webm.json @@ -0,0 +1,13 @@ +{ + "main_pass": + [ + "-n", "-c:v", "libsvtav1", + "-pix_fmt", ["pix_fmt", ["yuv420p10le", "yuv420p"]], + "-crf", ["crf","INT", {"default": 23, "min": 0, "max": 100, "step": 1}] + ], + "audio_pass": ["-c:a", "libopus"], + "input_color_depth": ["input_color_depth", ["8bit", "16bit"]], + "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}], + "extension": "webm", + "environment": {"SVT_LOG": "1"} +} diff --git a/video_formats/ffmpeg-gif.json b/video_formats/ffmpeg-gif.json new file mode 100644 index 0000000..5483256 --- /dev/null +++ b/video_formats/ffmpeg-gif.json @@ -0,0 +1,8 @@ +{ + "main_pass": + [ + "-n", + "-filter_complex", ["dither", ["bayer", "heckbert", "floyd_steinberg", "sierra2", "sierra2_4a", "sierra3", "burkes", "atkinson", "none"], {"default": "sierra2_4a"}, "[0:v] split [a][b]; [a] palettegen=reserve_transparent=on:transparency_color=ffffff [p]; [b][p] paletteuse=dither=$val"] + ], + "extension": "gif" +} diff --git a/video_formats/gifski.json b/video_formats/gifski.json new file mode 100644 index 0000000..87f116c --- /dev/null +++ b/video_formats/gifski.json @@ -0,0 +1,10 @@ +{ + "main_pass": + [ + "-pix_fmt", "yuv420p" + ], + "extension": "gif", + "gifski_pass": [ + "-Q", ["quality","INT", {"default": 90, "min": 1, "max": 100, "step": 1}] + ] +} diff --git a/video_formats/h264-mp4.json b/video_formats/h264-mp4.json new file mode 100644 index 0000000..6c23193 --- /dev/null +++ b/video_formats/h264-mp4.json @@ -0,0 +1,11 @@ +{ + "main_pass": + [ + "-n", "-c:v", "libx264", + "-pix_fmt", ["pix_fmt", ["yuv420p", "yuv420p10le"]], + "-crf", ["crf","INT", {"default": 19, "min": 0, "max": 100, "step": 1}] + ], + "audio_pass": ["-c:a", "aac"], + "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}], + "extension": "mp4" +} diff --git a/video_formats/h265-mp4.json b/video_formats/h265-mp4.json new file mode 100644 index 0000000..a624371 --- /dev/null +++ b/video_formats/h265-mp4.json @@ -0,0 +1,14 @@ +{ + "main_pass": + [ + "-n", "-c:v", "libx265", + "-vtag", "hvc1", + "-pix_fmt", ["pix_fmt", ["yuv420p10le", "yuv420p"]], + "-crf", ["crf","INT", {"default": 22, "min": 0, "max": 100, "step": 1}], + "-preset", "medium", + "-x265-params", "log-level=quiet" + ], + "audio_pass": ["-c:a", "aac"], + "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}], + "extension": "mp4" +} diff --git a/video_formats/nvenc_h264-mp4.json b/video_formats/nvenc_h264-mp4.json new file mode 100644 index 0000000..56f2350 --- /dev/null +++ b/video_formats/nvenc_h264-mp4.json @@ -0,0 +1,12 @@ +{ + "main_pass": + [ + "-n", "-c:v", "h264_nvenc", + "-pix_fmt", ["pix_fmt", ["yuv420p", "yuv420p10le"]] + ], + "audio_pass": ["-c:a", "aac"], + "bitrate": ["bitrate","INT", {"default": 10, "min": 1, "max": 999, "step": 1 }], + "megabit": ["megabit","BOOLEAN", {"default": true}], + "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}], + "extension": "mp4" +} diff --git a/video_formats/nvenc_hevc-mp4.json b/video_formats/nvenc_hevc-mp4.json new file mode 100644 index 0000000..259657b --- /dev/null +++ b/video_formats/nvenc_hevc-mp4.json @@ -0,0 +1,13 @@ +{ + "main_pass": + [ + "-n", "-c:v", "hevc_nvenc", + "-vtag", "hvc1", + "-pix_fmt", ["pix_fmt", ["yuv420p", "yuv420p10le"]] + ], + "audio_pass": ["-c:a", "aac"], + "bitrate": ["bitrate","INT", {"default": 10, "min": 1, "max": 999, "step": 1 }], + "megabit": ["megabit","BOOLEAN", {"default": true}], + "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}], + "extension": "mp4" +} diff --git a/video_formats/webm.json b/video_formats/webm.json new file mode 100644 index 0000000..ecfe3a4 --- /dev/null +++ b/video_formats/webm.json @@ -0,0 +1,12 @@ +{ + "main_pass": + [ + "-n", + "-pix_fmt", "yuv420p", + "-crf", ["crf","INT", {"default": 20, "min": 0, "max": 100, "step": 1}], + "-b:v", "0" + ], + "audio_pass": ["-c:a", "libvorbis"], + "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}], + "extension": "webm" +}

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