202 lines
6.5 KiB
Python
202 lines
6.5 KiB
Python
# !/usr/bin/env python
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# -*- coding: UTF-8 -*-
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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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from huggingface_hub import hf_hub_download
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from comfy.utils import common_upscale,ProgressBar
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from .utils.extractor import extract_faces
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from .utils.merger import paste_foreground_onto_background
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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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def anonymize_faces_in_image(
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image,
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face_alignment,
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pipe,
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generator: torch.Generator = None,
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face_image_size: int = 512,
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num_inference_steps: int = 50,
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guidance_scale: float = 4,
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anonymization_degree: float = 1.25,
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image_emb=None,
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cond_imag_emb=None,
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) -> Image:
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face_images, image_to_face_matrices = extract_faces(
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face_alignment, image, face_image_size
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)
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anon_image = image
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for face_image, image_to_face_mat in zip(face_images, image_to_face_matrices):
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# generate an image that anonymizes faces
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anon_face_image = pipe(
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source_image=face_image,
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conditioning_image=face_image,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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generator=generator,
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anonymization_degree=anonymization_degree,
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image_emb=image_emb,
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cond_imag_emb=cond_imag_emb
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).images[0]
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anon_image = paste_foreground_onto_background(
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anon_face_image, anon_image, image_to_face_mat
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)
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return anon_image
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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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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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return file_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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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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return file_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 tensor_to_pil(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 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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img_pil = tensor_to_pil(samples)
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return img_pil
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def cf_tensor2cv(tensor,width, height):
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d1, _, _, _ = tensor.size()
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if d1 > 1:
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tensor_list = list(torch.chunk(tensor, chunks=d1))
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tensor = [tensor_list][0]
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cr_tensor=tensor_upscale(tensor,width, height)
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cv_img=tensor2cv(cr_tensor)
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return cv_img
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def tensor_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 tensor2cv(tensor_image):
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if len(tensor_image.shape)==4:# b hwc to hwc
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tensor_image=tensor_image.squeeze(0)
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if tensor_image.is_cuda:
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tensor_image = tensor_image.cpu()
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tensor_image=tensor_image.numpy()
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#反归一化
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maxValue=tensor_image.max()
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tensor_image=tensor_image*255/maxValue
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img_cv2=np.uint8(tensor_image)#32 to uint8
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img_cv2=cv2.cvtColor(img_cv2,cv2.COLOR_RGB2BGR)
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return img_cv2
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def cvargb2tensor(img):
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assert type(img) == np.ndarray, 'the img type is {}, but ndarry expected'.format(type(img))
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img = torch.from_numpy(img.transpose((2, 0, 1)))
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return img.float().div(255).unsqueeze(0) # 255也可以改为256
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def cv2tensor(img):
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assert type(img) == np.ndarray, 'the img type is {}, but ndarry expected'.format(type(img))
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img = torch.from_numpy(img.transpose((2, 0, 1)))
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return img.float().div(255).unsqueeze(0) # 255也可以改为256
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def images_generator(img_list: list,):
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#get img size
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sizes = {}
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for image_ in img_list:
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if isinstance(image_,Image.Image):
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count = sizes.get(image_.size, 0)
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sizes[image_.size] = count + 1
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elif isinstance(image_,np.ndarray):
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count = sizes.get(image_.shape[:2][::-1], 0)
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sizes[image_.shape[:2][::-1]] = count + 1
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else:
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raise "unsupport image list,must be pil or cv2!!!"
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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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# any to tensor
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def load_image(img_in):
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if isinstance(img_in, Image.Image):
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img_in=img_in.convert("RGB")
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i = np.array(img_in, dtype=np.float32)
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i = torch.from_numpy(i).div_(255)
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if i.shape[0] != size[1] or i.shape[1] != size[0]:
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i = torch.from_numpy(i).movedim(-1, 0).unsqueeze(0)
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i = common_upscale(i, size[0], size[1], "lanczos", "center")
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i = i.squeeze(0).movedim(0, -1).numpy()
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return i
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elif isinstance(img_in,np.ndarray):
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i=cv2.cvtColor(img_in,cv2.COLOR_BGR2RGB).astype(np.float32)
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i = torch.from_numpy(i).div_(255)
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#print(i.shape)
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return i
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else:
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raise "unsupport image list,must be pil,cv2 or tensor!!!"
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total_images = len(img_list)
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processed_images = 0
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pbar = ProgressBar(total_images)
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images = map(load_image, img_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_list(img_list: list,):
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gen = images_generator(img_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 .")
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return images
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