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Corrects color channels in face restoration; Fixes #1167
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@ -3,6 +3,7 @@ import torch
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import numpy as np
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import numpy as np
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import warnings
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import warnings
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import sys
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import sys
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import cv2
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pretrained_model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
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pretrained_model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
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@ -40,11 +41,12 @@ class CodeFormerRestoration():
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cf.load_state_dict(checkpoint)
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cf.load_state_dict(checkpoint)
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cf.eval()
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cf.eval()
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image = image.convert('RGB')
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# Codeformer expects BGR image data
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bgrImage = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
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face_helper = FaceRestoreHelper(upscale_factor=1, use_parse=True, device=device)
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face_helper = FaceRestoreHelper(upscale_factor=1, use_parse=True, device=device)
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face_helper.clean_all()
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face_helper.clean_all()
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face_helper.read_image(np.array(image, dtype=np.uint8))
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face_helper.read_image(bgrImage)
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face_helper.get_face_landmarks_5(resize=640, eye_dist_threshold=5)
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face_helper.get_face_landmarks_5(resize=640, eye_dist_threshold=5)
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face_helper.align_warp_face()
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face_helper.align_warp_face()
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@ -71,7 +73,8 @@ class CodeFormerRestoration():
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restored_img = face_helper.paste_faces_to_input_image()
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restored_img = face_helper.paste_faces_to_input_image()
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res = Image.fromarray(restored_img)
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# Convert back to RGB for PIL
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res = Image.fromarray(cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB))
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if strength < 1.0:
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if strength < 1.0:
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# Resize the image to the new image if the sizes have changed
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# Resize the image to the new image if the sizes have changed
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@ -3,6 +3,7 @@ import warnings
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import os
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import os
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import sys
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import sys
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import numpy as np
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import numpy as np
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import cv2
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from PIL import Image
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from PIL import Image
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@ -53,15 +54,18 @@ class GFPGAN():
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f'>> Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth to {self.model_path}, \nor change GFPGAN directory with --gfpgan_dir.'
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f'>> Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth to {self.model_path}, \nor change GFPGAN directory with --gfpgan_dir.'
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)
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)
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image = image.convert('RGB')
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# GFPGAN expects BGR image data
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bgrImage = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
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_, _, restored_img = self.gfpgan.enhance(
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_, _, restored_img = self.gfpgan.enhance(
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np.array(image, dtype=np.uint8),
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bgrImage,
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has_aligned=False,
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has_aligned=False,
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only_center_face=False,
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only_center_face=False,
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paste_back=True,
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paste_back=True,
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)
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)
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res = Image.fromarray(restored_img)
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# Convert back to RGB for PIL
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res = Image.fromarray(cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB))
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if strength < 1.0:
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if strength < 1.0:
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# Resize the image to the new image if the sizes have changed
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# Resize the image to the new image if the sizes have changed
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