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Update inference.py
Browse files- inference.py +9 -10
inference.py
CHANGED
@@ -33,7 +33,7 @@ def main():
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print('[Info] Using {} for inference.'.format(device))
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os.makedirs(os.path.join('temp', args.tmp_dir), exist_ok=True)
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enhancer = FaceEnhancement(base_dir='checkpoints', size=
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sr_model='rrdb_realesrnet_psnr', channel_multiplier=2, narrow=1, device=device)
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restorer = GFPGANer(model_path='checkpoints/GFPGANv1.3.pth', upscale=1, arch='clean', \
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channel_multiplier=2, bg_upsampler=None)
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@@ -249,24 +249,23 @@ def main():
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ff = xf.copy()
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ff[y1:y2, x1:x2] = p
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pp = np.uint8(cv2.resize(np.clip(ff, 0 ,512), (width, height)))
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pp, orig_faces, enhanced_faces = enhancer.process(pp, xf, bbox=c, face_enhance=True, possion_blending=False)
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# month region enhancement by GFPGAN
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cropped_faces, restored_faces, restored_img = restorer.enhance(
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# 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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mm = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 255, 255, 255, 0, 0, 0, 0, 0, 0]
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mouse_mask = np.zeros_like(restored_img)
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tmp_mask = enhancer.faceparser.process(restored_img[y1:y2, x1:x2], mm)[0]
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mouse_mask[y1:y2, x1:x2]= cv2.resize(tmp_mask, (x2 - x1, y2 - y1))[:, :, np.newaxis] / 255.
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restored_img, ff, full_mask = [cv2.resize(x, (
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img = Laplacian_Pyramid_Blending_with_mask(restored_img, ff, full_mask[:, :, 0], 10)
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pp = np.uint8(cv2.resize(np.clip(img, 0 ,
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out.write(pp)
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out.release()
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print('[Info] Using {} for inference.'.format(device))
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os.makedirs(os.path.join('temp', args.tmp_dir), exist_ok=True)
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enhancer = FaceEnhancement(base_dir='checkpoints', size=512, model='GPEN-BFR-512', use_sr=False, \
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sr_model='rrdb_realesrnet_psnr', channel_multiplier=2, narrow=1, device=device)
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restorer = GFPGANer(model_path='checkpoints/GFPGANv1.3.pth', upscale=1, arch='clean', \
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channel_multiplier=2, bg_upsampler=None)
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ff = xf.copy()
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ff[y1:y2, x1:x2] = p
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# month region enhancement by GFPGAN
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cropped_faces, restored_faces, restored_img = restorer.enhance(
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ff, has_aligned=False, only_center_face=True, paste_back=True)
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# 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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mm = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 255, 255, 255, 0, 0, 0, 0, 0, 0]
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mouse_mask = np.zeros_like(restored_img)
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tmp_mask = enhancer.faceparser.process(restored_img[y1:y2, x1:x2], mm)[0]
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mouse_mask[y1:y2, x1:x2]= cv2.resize(tmp_mask, (x2 - x1, y2 - y1))[:, :, np.newaxis] / 255.
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height, width = ff.shape[:2]
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restored_img, ff, full_mask = [cv2.resize(x, (512, 512)) for x in (restored_img, ff, np.float32(mouse_mask))]
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img = Laplacian_Pyramid_Blending_with_mask(restored_img, ff, full_mask[:, :, 0], 10)
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pp = np.uint8(cv2.resize(np.clip(img, 0 ,255), (width, height)))
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pp, orig_faces, enhanced_faces = enhancer.process(pp, xf, bbox=c, face_enhance=False, possion_blending=True)
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out.write(pp)
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out.release()
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