560 lines
21 KiB
Python
560 lines
21 KiB
Python
# !/usr/bin/env python
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# -*- coding: UTF-8 -*-
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import datetime
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import logging
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import os
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import torch
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from PIL import Image
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import numpy as np
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import cv2
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import sys
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import pickle
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import torchaudio
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from huggingface_hub import hf_hub_download
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from moviepy import *
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import random
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from .src.utils.mp_utils import LMKExtractor
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from .src.utils.img_utils import pils_from_video
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from .src.utils.motion_utils import motion_sync
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from .src.utils.util import save_videos_grid, crop_and_pad,crop_and_pad_rectangle,center_crop
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from .echomimic_v2.src.utils.dwpose_util import draw_pose_select_v2
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from comfy.utils import common_upscale,ProgressBar
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import folder_paths
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weight_dtype = torch.float16
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cur_path = os.path.dirname(os.path.abspath(__file__))
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device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
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tensorrt_lite= os.path.join(folder_paths.get_input_directory(),"tensorrt_lite")
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def process_video_v2(ref_image_pil, uploaded_audio, width, height, length, seed,
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context_frames, context_overlap, cfg, steps, sample_rate, fps, pipe,
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save_video, pose_dir, audio_file_prefix,):
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origin_h = height
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origin_w = width
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if width!=height:
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max_l=max(width,height)
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width=max_l
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height = max_l
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ref_image_pil = nomarl_upscale(ref_image_pil, width, height)
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if pose_dir=="none":
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logging.warning("when use echo v2,need choice a pose dir,for get error using default pose !")
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pose_dir=os.path.join(cur_path,"echomimic_v2/assets/halfbody_demo/pose/01")
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if seed is not None and seed > -1:
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generator = torch.manual_seed(seed)
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else:
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generator = torch.manual_seed(random.randint(100, 1000000))
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#final_fps = fps
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start_idx = 0
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audio_clip = AudioFileClip(uploaded_audio)
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L = min(int(audio_clip.duration * fps),length,len(os.listdir(pose_dir))) # if above will cause error
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#L=min(length,L) #length is definitely
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print(f"***** infer length is {L}")
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pose_list = []
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for index in range(start_idx, start_idx + L):
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#tgt_musk = np.zeros((width, height, 3)).astype('uint8')
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tgt_musk_path = os.path.join(pose_dir, "{}.npy".format(index))
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detected_pose = np.load(tgt_musk_path, allow_pickle=True).tolist()
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imh_new, imw_new, rb, re, cb, ce = detected_pose['draw_pose_params']
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#print(imh_new, imw_new, rb, re, cb, ce)
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im = draw_pose_select_v2(detected_pose, imh_new, imw_new, ref_w=800)
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im = np.transpose(np.array(im), (1, 2, 0))
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new_H,new_W=im.shape[:2]
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tgt_musk = np.zeros((new_W, new_H, 3)).astype('uint8')
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tgt_musk[rb:re, cb:ce, :] = im
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tgt_musk = center_resize(tgt_musk, width, height)
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tgt_musk_pil = Image.fromarray(np.array(tgt_musk)).convert('RGB')
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pose_list.append(
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torch.Tensor(np.array(tgt_musk_pil)).to(dtype=weight_dtype, device=device).permute(2, 0, 1) / 255.0)
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poses_tensor = torch.stack(pose_list, dim=1).unsqueeze(0)
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print(f"poses_tensor:{poses_tensor.shape}")
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# audio_clip = AudioFileClip(audio_clip)
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#
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# audio_clip = audio_clip.set_duration(L / fps)
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video = pipe(
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ref_image_pil,
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uploaded_audio,
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poses_tensor[:, :, :L, ...],
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width,
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height,
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L,
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steps,
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cfg,
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generator=generator,
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audio_sample_rate=sample_rate,
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context_frames=context_frames,
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fps=fps,
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context_overlap=context_overlap,
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start_idx=start_idx,
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).videos
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final_length = min(video.shape[2],poses_tensor.shape[2], L)
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video_sig = video[:, :, :final_length, :, :]
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output_file = os.path.join(folder_paths.output_directory, f"{audio_file_prefix}_echo.mp4")
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print(f"**** final_length is : {final_length} ****")
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if origin_h!=origin_w:
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ouput_list = save_videos_grid(video_sig, output_file, n_rows=1, fps=fps, save_video=save_video,size=(origin_w,origin_h))
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else:
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ouput_list = save_videos_grid(video_sig, output_file, n_rows=1, fps=fps, save_video=save_video)
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if save_video:
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output_video_path = os.path.join(folder_paths.output_directory, f"{audio_file_prefix}_audio.mp4")
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video_clip = VideoFileClip(output_file)
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audio_clip = AudioFileClip(uploaded_audio)
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final_clip = video_clip.set_audio(audio_clip)
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final_clip.write_videofile(
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output_video_path,
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codec="libx264", audio_codec="aac")
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print(f"**** saving{output_file} at {output_video_path} ****")
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video_clip.reader.close()
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audio_clip.close()
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final_clip.reader.close()
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return ouput_list
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def process_video(face_img, uploaded_audio, width, height, length, seed, facemask_dilation_ratio,
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facecrop_dilation_ratio, context_frames, context_overlap, cfg, steps, sample_rate, fps, pipe,
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face_detector, save_video, pose_dir, video_images, audio_file_prefix,
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visualizer=None,):
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if seed is not None and seed > -1:
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generator = torch.manual_seed(seed)
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else:
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generator = torch.manual_seed(random.randint(100, 1000000))
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#### face musk prepare
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face_mask = np.zeros((face_img.shape[0], face_img.shape[1])).astype('uint8')
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det_bboxes, probs = face_detector.detect(face_img)
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select_bbox = select_face(det_bboxes, probs)
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if select_bbox is None:
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face_mask[:, :] = 255
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else:
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xyxy = select_bbox[:4].astype(float) # 面部处理出来是浮点数，无法实现整形
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xyxy = np.round(xyxy).astype("int")
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rb, re, cb, ce = xyxy[1], xyxy[3], xyxy[0], xyxy[2] #56 227 132 268
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r_pad = int((re - rb) * facemask_dilation_ratio) # ratio：0.1 遮罩膨胀系数 17*2
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c_pad = int((ce - cb) * facemask_dilation_ratio) # ratio：0.1 遮罩膨胀系数 14*2
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face_mask[rb - r_pad: re + r_pad, cb - c_pad: ce + c_pad] = 255
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#### face crop ####
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if facecrop_dilation_ratio<1.0:
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if facecrop_dilation_ratio==0:
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facecrop_dilation_ratio=1
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r_pad_crop = int((re - rb) * facecrop_dilation_ratio) # ratio 0.5 r_pad_crop：85,c_pad_crop:68
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c_pad_crop = int((ce - cb) * facecrop_dilation_ratio) # ratio 1.0 r_pad_crop：171,c_pad_crop:136
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crop_rect = [max(0, cb - c_pad_crop), max(0, rb - r_pad_crop), min(ce + c_pad_crop, face_img.shape[1]),
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min(re + r_pad_crop, face_img.shape[0])]
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if width == height:
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# 输出图片指定尺寸，如果是非方形，则会变形
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face_img_i, ori_face_rect_i = crop_and_pad(face_img, crop_rect)
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face_mask_m, ori_mask_rect_m = crop_and_pad(face_mask, crop_rect) # (0, 7, 384, 391)
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face_img = cv2.resize(face_img_i, (width, height))
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face_mask = cv2.resize(face_mask_m, (width, height))
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else:
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face_img,face_mask=crop_and_pad_rectangle(face_img,face_mask,crop_rect)
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face_img= cv2tensor(face_img).permute(0, 2, 3, 1)#[1, 3, 357, 245] =>[[1,357, 245,3]]
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face_mask = cv2tensor(face_mask).permute(0, 2, 3, 1)
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face_img=tensor_upscale(face_img, width, height)
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face_img=tensor2cv(face_img)
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face_mask = tensor_upscale(face_mask, width, height)
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face_mask=cv2.cvtColor(tensor2cv(face_mask), cv2.COLOR_BGR2GRAY)#二值化
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ret, face_mask = cv2.threshold(face_mask, 0, 255, cv2.THRESH_BINARY)
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else: #when ratio=1 no crop
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print("when facecrop_ratio=1.0,The maximum image size will be obtained, but there may be edge deformation.** 选择最大裁切为1.0时，边缘可能会出现形变！")
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ref_image_pil = Image.fromarray(face_img[:, :, [2, 1, 0]])
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if visualizer:
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if pose_dir == "none": # motion sync
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if isinstance(video_images,torch.Tensor):
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print("**** Use video pose drive video! ****")
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pose_dir_path,video_len = motion_sync_main(visualizer, width, height, video_images, face_img,facecrop_dilation_ratio,
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audio_file_prefix)
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else:
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raise ("**** You need link video_images for drive video ****")
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#pose_dir = os.path.join(cur_path, "assets", "test_pose_demo_pose") # default
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else:
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print("**** Use pkl drive video! ****")
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pose_dir_path = os.path.join(tensorrt_lite, pose_dir)
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files_and_directories = os.listdir(pose_dir_path)
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# 过滤出文件，排除子目录
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files = [f for f in files_and_directories if os.path.isfile(os.path.join(pose_dir_path, f))]
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video_len=len(files)
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if length>video_len:
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print(f"**** video length {video_len} is less than length,use {video_len} as {length} ****")
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length=video_len
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pose_list = []
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for index in range(len(os.listdir(pose_dir_path))):
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tgt_musk_path = os.path.join(pose_dir_path, f"{index}.pkl")
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with open(tgt_musk_path, "rb") as f:
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tgt_kpts = pickle.load(f)
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tgt_musk = visualizer.draw_landmarks((width, height), tgt_kpts,facecrop_dilation_ratio)
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tgt_musk_pil = Image.fromarray(np.array(tgt_musk).astype(np.uint8)).convert('RGB')
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pose_list.append(
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torch.Tensor(np.array(tgt_musk_pil)).to(dtype=weight_dtype, device="cuda").permute(2, 0, 1) / 255.0)
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face_mask_tensor = torch.stack(pose_list, dim=1).unsqueeze(0)
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else:
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print("**** Use audio drive video! ****")
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face_mask_tensor = torch.Tensor(face_mask).to(dtype=weight_dtype, device="cuda").unsqueeze(0).unsqueeze(
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0).unsqueeze(0) / 255.0
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video = pipe(
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ref_image_pil,
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uploaded_audio,
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face_mask_tensor,
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width,
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height,
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length,
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steps,
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cfg,
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generator=generator,
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audio_sample_rate=sample_rate,
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context_frames=context_frames,
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fps=fps,
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context_overlap=context_overlap
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).videos
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final_length = min(video.shape[2], face_mask_tensor.shape[2], length)
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output_file = os.path.join(folder_paths.output_directory, f"{audio_file_prefix}_echo.mp4")
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print(f"**** final_length is : {final_length} ****")
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ouput_list = save_videos_grid(video, output_file, n_rows=1, fps=fps, save_video=save_video)
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if save_video:
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output_video_path = os.path.join(folder_paths.output_directory, f"{audio_file_prefix}_audio.mp4")
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video_clip = VideoFileClip(output_file)
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audio_clip = AudioFileClip(uploaded_audio)
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final_clip = video_clip.set_audio(audio_clip)
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final_clip.write_videofile(
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output_video_path,
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codec="libx264", audio_codec="aac")
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print(f"**** saving{output_file} at {output_video_path} ****")
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video_clip.reader.close()
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audio_clip.close()
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final_clip.reader.close()
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return ouput_list
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def motion_sync_main(vis, width, height, video_images, face_img,facecrop_dilation_ratio, audio_file_prefix):
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lmk_extractor = LMKExtractor()
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ref_det = lmk_extractor(face_img)
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#driver_video = os.path.join(folder_paths.input_directory, driver_video)
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# if audio_form_video:
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# audio_path = os.path.join(folder_paths.input_directory, f"{audio_file_prefix}_audio.wav")
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# video_clip = VideoFileClip(driver_video)
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# audio_clip = video_clip.audio
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# audio_clip.write_audiofile(audio_path)
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# video_clip.close()
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# audio_clip.close()
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video_len,_,_,_=video_images.size()
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if video_len<25:
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raise "input video has not much frames for driver,change your input video!"
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else:
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tensor_list = list(torch.chunk(video_images, chunks=video_len))
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input_frames_cv2=[tensor2cv(tensor_upscale(i, width, height)) for i in tensor_list]
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# print(ref_det)
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sequence_driver_det = []
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if input_frames_cv2:
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try:
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print("**** Starting process video ****")
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for frame in input_frames_cv2:
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result = lmk_extractor(frame)
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assert result is not None, "bad video, face not detected"
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sequence_driver_det.append(result)
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except:
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print("face detection failed")
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else:
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raise "input video error,change your input video!"
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print("**** motion sync lenght " f"{len(sequence_driver_det)} ****")
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if vis:
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if facecrop_dilation_ratio==0:
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facecrop_dilation_ratio=1
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pose_frames_driver = [vis.draw_landmarks((width, height), i["lmks"],facecrop_dilation_ratio, normed=True) for i in sequence_driver_det]
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poses_add_driver = [(i * 0.5 + j * 0.5).clip(0, 255).astype(np.uint8) for i, j in
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zip(input_frames_cv2, pose_frames_driver)]
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#print(f"**** poses_add_driver is done in len : {len(poses_add_driver)} ****")
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save_dir = os.path.join(tensorrt_lite, audio_file_prefix)
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if not os.path.exists(save_dir):
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os.makedirs(save_dir)
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sequence_det_ms = motion_sync(sequence_driver_det, ref_det)
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for i in range(len(sequence_det_ms)):
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with open('{}/{}.pkl'.format(save_dir, i), 'wb') as file:
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pickle.dump(sequence_det_ms[i], file)
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print(f"**** motion_sync {save_dir} is done ****")
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else: #即便有文件夹，还是重新生成，避免出错
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timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
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save_dir=os.path.join(tensorrt_lite,f"{audio_file_prefix}_{timestamp}")
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os.makedirs(save_dir)
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sequence_det_ms = motion_sync(sequence_driver_det, ref_det)
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for i in range(len(sequence_det_ms)):
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with open('{}/{}.pkl'.format(save_dir, i), 'wb') as file:
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pickle.dump(sequence_det_ms[i], file)
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print(f"**** motion_sync {save_dir} is done ****")
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return save_dir,video_len
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def select_face(det_bboxes, probs):
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## max face from faces that the prob is above 0.8
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## box: xyxy
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if det_bboxes is None or probs is None:
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return None
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filtered_bboxes = []
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for bbox_i in range(len(det_bboxes)):
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if probs[bbox_i] > 0.8:
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filtered_bboxes.append(det_bboxes[bbox_i])
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if len(filtered_bboxes) == 0:
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return None
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sorted_bboxes = sorted(filtered_bboxes, key=lambda x: (x[3] - x[1]) * (x[2] - x[0]), reverse=True)
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return sorted_bboxes[0]
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def find_directories(base_path):
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directories = []
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for root, dirs, files in os.walk(base_path):
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for name in dirs:
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directories.append(name)
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return directories
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def download_weights(file_dir,repo_id,subfolder="",pt_name=""):
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if subfolder:
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file_path = os.path.join(file_dir,subfolder, pt_name)
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sub_dir=os.path.join(file_dir,subfolder)
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if not os.path.exists(sub_dir):
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os.makedirs(sub_dir)
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if not os.path.exists(file_path):
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pt_path = hf_hub_download(
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repo_id=repo_id,
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subfolder=subfolder,
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filename=pt_name,
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local_dir = file_dir,
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)
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else:
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pt_path=get_instance_path(file_path)
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return pt_path
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else:
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file_path = os.path.join(file_dir, pt_name)
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if not os.path.exists(file_dir):
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os.makedirs(file_dir)
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if not os.path.exists(file_path):
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pt_path = hf_hub_download(
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repo_id=repo_id,
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filename=pt_name,
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local_dir=file_dir,
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)
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else:
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pt_path=get_instance_path(file_path)
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return pt_path
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def pil2narry(img):
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img = torch.from_numpy(np.array(img).astype(np.float32) / 255.0).unsqueeze(0)
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return img
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def narry_list(list_in):
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for i in range(len(list_in)):
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value = list_in[i]
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modified_value = pil2narry(value)
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list_in[i] = modified_value
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return list_in
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def get_video_img(tensor):
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if tensor == None:
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return None
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outputs = []
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for x in tensor:
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x = tensor_to_pil(x)
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outputs.append(x)
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yield outputs
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def instance_path(path, repo):
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if repo == "":
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if path == "none":
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repo = "none"
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else:
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model_path = get_local_path(folder_paths.base_path, path)
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repo = get_instance_path(model_path)
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return repo
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def gen_img_form_video(tensor):
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pil = []
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for x in tensor:
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pil[x] = tensor_to_pil(x)
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yield pil
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def phi_list(list_in):
|
||
for i in range(len(list_in)):
|
||
value = list_in[i]
|
||
list_in[i] = value
|
||
return list_in
|
||
|
||
def tensor_to_pil(tensor):
|
||
image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
|
||
image = Image.fromarray(image_np, mode='RGB')
|
||
return image
|
||
|
||
def nomarl_upscale(img_tensor, width, height):
|
||
samples = img_tensor.movedim(-1, 1)
|
||
img = common_upscale(samples, width, height, "nearest-exact", "center")
|
||
samples = img.movedim(1, -1)
|
||
img_pil = tensor_to_pil(samples)
|
||
return img_pil
|
||
|
||
def tensor_upscale(img_tensor, width, height):
|
||
samples = img_tensor.movedim(-1, 1)
|
||
img = common_upscale(samples, width, height, "nearest-exact", "center")
|
||
samples = img.movedim(1, -1)
|
||
return samples
|
||
|
||
def get_local_path(comfy_file_path, model_path):
|
||
path = os.path.join(comfy_file_path, "models", "diffusers", model_path)
|
||
model_path = os.path.normpath(path)
|
||
if sys.platform == 'win32':
|
||
model_path = model_path.replace('\\', "/")
|
||
return model_path
|
||
|
||
def get_instance_path(path):
|
||
instance_path = os.path.normpath(path)
|
||
if sys.platform == 'win32':
|
||
instance_path = instance_path.replace('\\', "/")
|
||
return instance_path
|
||
|
||
|
||
def tensor2cv(tensor_image):
|
||
if len(tensor_image.shape)==4:# b hwc to hwc
|
||
tensor_image=tensor_image.squeeze(0)
|
||
if tensor_image.is_cuda:
|
||
tensor_image = tensor_image.cpu()
|
||
tensor_image=tensor_image.numpy()
|
||
#反归一化
|
||
maxValue=tensor_image.max()
|
||
tensor_image=tensor_image*255/maxValue
|
||
img_cv2=np.uint8(tensor_image)#32 to uint8
|
||
img_cv2=cv2.cvtColor(img_cv2,cv2.COLOR_RGB2BGR)
|
||
return img_cv2
|
||
|
||
def cvargb2tensor(img):
|
||
assert type(img) == np.ndarray, 'the img type is {}, but ndarry expected'.format(type(img))
|
||
img = torch.from_numpy(img.transpose((2, 0, 1)))
|
||
return img.float().div(255).unsqueeze(0) # 255也可以改为256
|
||
|
||
def cv2tensor(img):
|
||
assert type(img) == np.ndarray, 'the img type is {}, but ndarry expected'.format(type(img))
|
||
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
||
img = torch.from_numpy(img.transpose((2, 0, 1)))
|
||
return img.float().div(255).unsqueeze(0) # 255也可以改为256
|
||
|
||
def images_generator(img_list: list,):
|
||
#get img size
|
||
sizes = {}
|
||
for image_ in img_list:
|
||
if isinstance(image_,Image.Image):
|
||
count = sizes.get(image_.size, 0)
|
||
sizes[image_.size] = count + 1
|
||
elif isinstance(image_,np.ndarray):
|
||
count = sizes.get(image_.shape[:2][::-1], 0)
|
||
sizes[image_.shape[:2][::-1]] = count + 1
|
||
else:
|
||
raise "unsupport image list,must be pil or cv2!!!"
|
||
size = max(sizes.items(), key=lambda x: x[1])[0]
|
||
yield size[0], size[1]
|
||
|
||
# any to tensor
|
||
def load_image(img_in):
|
||
if isinstance(img_in, Image.Image):
|
||
img_in=img_in.convert("RGB")
|
||
i = np.array(img_in, dtype=np.float32)
|
||
i = torch.from_numpy(i).div_(255)
|
||
if i.shape[0] != size[1] or i.shape[1] != size[0]:
|
||
i = torch.from_numpy(i).movedim(-1, 0).unsqueeze(0)
|
||
i = common_upscale(i, size[0], size[1], "lanczos", "center")
|
||
i = i.squeeze(0).movedim(0, -1).numpy()
|
||
return i
|
||
elif isinstance(img_in,np.ndarray):
|
||
i=cv2.cvtColor(img_in,cv2.COLOR_BGR2RGB).astype(np.float32)
|
||
i = torch.from_numpy(i).div_(255)
|
||
#print(i.shape)
|
||
return i
|
||
else:
|
||
raise "unsupport image list,must be pil,cv2 or tensor!!!"
|
||
|
||
total_images = len(img_list)
|
||
processed_images = 0
|
||
pbar = ProgressBar(total_images)
|
||
images = map(load_image, img_list)
|
||
try:
|
||
prev_image = next(images)
|
||
while True:
|
||
next_image = next(images)
|
||
yield prev_image
|
||
processed_images += 1
|
||
pbar.update_absolute(processed_images, total_images)
|
||
prev_image = next_image
|
||
except StopIteration:
|
||
pass
|
||
if prev_image is not None:
|
||
yield prev_image
|
||
|
||
def load_images(img_list: list,):
|
||
gen = images_generator(img_list)
|
||
(width, height) = next(gen)
|
||
images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (height, width, 3)))))
|
||
if len(images) == 0:
|
||
raise FileNotFoundError(f"No images could be loaded .")
|
||
return images
|
||
|
||
def tensor2pil(tensor):
|
||
image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
|
||
image = Image.fromarray(image_np, mode='RGB')
|
||
return image
|
||
|
||
|
||
def cf_tensor2cv(tensor,width, height):
|
||
d1, _, _, _ = tensor.size()
|
||
if d1 > 1:
|
||
tensor_list = list(torch.chunk(tensor, chunks=d1))
|
||
tensor = [tensor_list][0]
|
||
cr_tensor=tensor_upscale(tensor,width, height)
|
||
cv_img=tensor2cv(cr_tensor)
|
||
return cv_img
|
||
|
||
def center_resize(img, new_width, new_height):
|
||
|
||
h, w = img.shape[:2]
|
||
if w == h and new_width==new_height:
|
||
if w == new_width:
|
||
return img
|
||
else:
|
||
image = cv2.resize(img, (new_width, new_height))
|
||
return image
|
||
|
||
if w==new_width and h==new_height:
|
||
return img
|
||
|
||
return img
|