196 lines
6.7 KiB
Python
196 lines
6.7 KiB
Python
# !/usr/bin/env python
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# -*- coding: UTF-8 -*-
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import os
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import cv2
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import numpy as np
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import torch
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import torch.nn as nn
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from torchvision import transforms
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from PIL import Image
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from .network import MFF_MoE
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import folder_paths
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from comfy.utils import ProgressBar,common_upscale
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os.environ['CUDA_VISIBLE_DEVICES'] = '0'
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device = (
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"cuda"
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if torch.cuda.is_available()
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else "mps" if torch.backends.mps.is_available() else "cpu"
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)
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current_path = os.path.dirname(os.path.abspath(__file__))
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def tensor2pil(tensor):
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image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
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image = Image.fromarray(image_np, mode='RGB')
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return image
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def phi2narry(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 nomarl_upscale(img_tensor, width, height):
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samples = img_tensor.movedim(-1, 1)
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img = common_upscale(samples, width, height, "nearest-exact", "center")
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samples = img.movedim(1, -1)
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return samples
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def nomarl_upscale_topil(img_tensor, width, height):
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samples = img_tensor.movedim(-1, 1)
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img = common_upscale(samples, width, height, "nearest-exact", "center")
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samples = img.movedim(1, -1)
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img_pil = tensor2pil(samples)
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return img_pil
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def images_generator(file_list):# form VH
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sizes = {}
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for i in file_list:
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count = sizes.get(i.size, 0)
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sizes[i.size] = count +1
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size = max(sizes.items(), key=lambda x: x[1])[0]
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yield size[0], size[1]
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total_images = len(file_list)
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processed_images = 0
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pbar = ProgressBar(total_images)
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images = map(phi2narry, file_list)
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try:
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prev_image = next(images)
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while True:
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next_image = next(images)
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yield prev_image
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processed_images += 1
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pbar.update_absolute(processed_images, total_images)
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prev_image = next_image
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except StopIteration:
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pass
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if prev_image is not None:
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yield prev_image
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def load_images(file_list):
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gen = images_generator(file_list)
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(width, height) = next(gen)
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images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (height, width, 3)))))
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if len(images) == 0:
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raise FileNotFoundError(f"No images could be loaded from directory '{file_list}'.")
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return images
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class DeepFakeDefender_Loader:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"ckpt_path": ("STRING", {"default": "DeepFakeDefender"}),
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}
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}
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RETURN_TYPES = ("MODEL","MODEL",)
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RETURN_NAMES = ("net","transform_val",)
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FUNCTION = "test"
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CATEGORY = "DeepFakeDefender_Gold"
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def test(self,ckpt_path):
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weigths_current_path = os.path.join(folder_paths.models_dir, ckpt_path)
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if not os.path.exists(weigths_current_path):
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os.makedirs(weigths_current_path)
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net = MFF_MoE(pretrained=False)
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net.load(path=weigths_current_path)
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net = nn.DataParallel(net).cuda()
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net.eval()
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transform_val = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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transforms.Resize((512, 512), antialias=True),
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])
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return (net,transform_val,)
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class DeepFakeDefender_Sampler:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"net": ("MODEL",),
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"transform_val": ("MODEL",),
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"threshold": ("FLOAT", {
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"default": 0.500000000,
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"min": 0.000000001,
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"max": 0.999999999,
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"step": 0.001,
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"round": 0.0000000001,
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"display": "number",
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}),
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"crop_width": ("INT", {"default": 512, "min": 256, "max": 4096, "step": 32, "display": "number"}),
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"crop_height": ("INT", {"default": 512, "min": 256, "max": 4096, "step": 32, "display": "number"}),
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},
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}
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RETURN_TYPES = ("STRING","IMAGE","IMAGE",)
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RETURN_NAMES = ("string","above","below",)
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FUNCTION = "test"
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CATEGORY = "DeepFakeDefender_Gold"
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def test(self,image,net,transform_val,threshold,crop_width,crop_height):
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empty_img = Image.new('RGB', (512, 512), (255, 255, 255))
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B, _, _, _ = image.shape
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if B==1:
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origin_img_list =[tensor2pil(image.copy())]
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img_list=[nomarl_upscale_topil(image, crop_width, crop_height)]
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else:
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img_list = list(torch.chunk(image, chunks=B))
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origin_img_list = [tensor2pil(img) for img in img_list.copy()]
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img_list = [nomarl_upscale_topil(img, crop_width, crop_height) for img in img_list]
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out_str=''
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below_img=[]
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above_img=[]
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pred_check=np.array([threshold])
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for i,img in enumerate(img_list):
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#x = img_list[i]
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# x = cv2.imread(input_path)[..., ::-1]
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# x=np.array(x)
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#x=np.asarray(x)
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# x = cv2.cvtColor(x, cv2.COLOR_RGB2BGR)[..., ::-1]
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#x = Image.fromarray(np.uint8(x))
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y = transform_val(img).unsqueeze(0).cuda()
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pred = net(y)
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pred = pred.detach().cpu().numpy()
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print('Prediction of this image [%s] being Deepfake(这张照片是深度伪造的预测值为): %10.9f' % (i+1,pred))
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pred = np.around(pred, decimals=9)
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string = f"\nPrediction of this image ({i+1}) being Deepfake: {pred}. \n 这张照片({i+1})是深度伪造的预测值为：{pred}.\n"
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out_str += string
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if np.less_equal(pred,pred_check)==[True] :
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below_img.append(origin_img_list[i])
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if np.greater(pred,pred_check)==[True] :
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above_img.append(origin_img_list[i])
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if below_img:
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below_image=load_images(below_img)
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else:
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below_image = load_images([empty_img])
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if above_img:
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above_image = load_images(above_img)
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else:
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above_image = load_images([empty_img])
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return (out_str,above_image,below_image)
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# A dictionary that contains all nodes you want to export with their names
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# NOTE: names should be globally unique
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NODE_CLASS_MAPPINGS = {
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"DeepFakeDefender_Loader": DeepFakeDefender_Loader,
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"DeepFakeDefender_Sampler":DeepFakeDefender_Sampler
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}
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# A dictionary that contains the friendly/humanly readable titles for the nodes
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NODE_DISPLAY_NAME_MAPPINGS = {
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"DeepFakeDefender_Loader": "DeepFakeDefender_Loader",
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"DeepFakeDefender_Sampler":"DeepFakeDefender_Sampler"
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}
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