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| import os | |
| import cv2 | |
| import torch | |
| import gradio as gr | |
| from torchvision.transforms.functional import normalize | |
| from basicsr.archs.rrdbnet_arch import RRDBNet | |
| from basicsr.utils import imwrite, img2tensor, tensor2img | |
| from basicsr.utils.misc import gpu_is_available, get_device | |
| from basicsr.utils.realesrgan_utils import RealESRGANer | |
| from basicsr.utils.registry import ARCH_REGISTRY | |
| from facelib.utils.face_restoration_helper import FaceRestoreHelper | |
| from facelib.utils.misc import is_gray | |
| def imread(img_path): | |
| img = cv2.imread(img_path) | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
| return img | |
| def set_realesrgan(): | |
| half = True if gpu_is_available() else False | |
| model = RRDBNet( | |
| num_in_ch=3, | |
| num_out_ch=3, | |
| num_feat=64, | |
| num_block=23, | |
| num_grow_ch=32, | |
| scale=2, | |
| ) | |
| upsampler = RealESRGANer( | |
| scale=2, | |
| model_path="CodeFormer/weights/realesrgan/RealESRGAN_x2plus.pth", | |
| model=model, | |
| tile=400, | |
| tile_pad=40, | |
| pre_pad=0, | |
| half=half, | |
| ) | |
| return upsampler | |
| upsampler = set_realesrgan() | |
| device = get_device() | |
| codeformer_net = ARCH_REGISTRY.get("CodeFormer")( | |
| dim_embd=512, | |
| codebook_size=1024, | |
| n_head=8, | |
| n_layers=9, | |
| connect_list=["32", "64", "128", "256"], | |
| ).to(device) | |
| ckpt_path = "CodeFormer/weights/CodeFormer/codeformer.pth" | |
| checkpoint = torch.load(ckpt_path)["params_ema"] | |
| codeformer_net.load_state_dict(checkpoint) | |
| codeformer_net.eval() | |
| os.makedirs('output', exist_ok=True) | |
| def inference(image, background_enhance, face_upsample, upscale, codeformer_fidelity): | |
| """Run a single prediction on the model""" | |
| try: # global try | |
| # take the default setting for the demo | |
| has_aligned = False | |
| only_center_face = False | |
| draw_box = False | |
| detection_model = "retinaface_resnet50" | |
| img = cv2.imread(str(image), cv2.IMREAD_COLOR) | |
| upscale = int(upscale) | |
| if upscale > 4: | |
| upscale = 4 | |
| if upscale > 2 and max(img.shape[:2]) > 1000: | |
| upscale = 2 | |
| if max(img.shape[:2]) > 1500: | |
| upscale = 1 | |
| background_enhance = False | |
| face_upsample = False | |
| face_helper = FaceRestoreHelper( | |
| upscale, | |
| face_size=512, | |
| crop_ratio=(1, 1), | |
| det_model=detection_model, | |
| save_ext="png", | |
| use_parse=True, | |
| device=device, | |
| ) | |
| bg_upsampler = upsampler if background_enhance else None | |
| face_upsampler = upsampler if face_upsample else None | |
| if has_aligned: | |
| # the input faces are already cropped and aligned | |
| img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_LINEAR) | |
| face_helper.is_gray = is_gray(img, threshold=5) | |
| if face_helper.is_gray: | |
| print('\tgrayscale input: True') | |
| face_helper.cropped_faces = [img] | |
| else: | |
| face_helper.read_image(img) | |
| # get face landmarks for each face | |
| num_det_faces = face_helper.get_face_landmarks_5( | |
| only_center_face=only_center_face, resize=640, eye_dist_threshold=5 | |
| ) | |
| print(f'\tdetect {num_det_faces} faces') | |
| # align and warp each face | |
| face_helper.align_warp_face() | |
| # face restoration for each cropped face | |
| for idx, cropped_face in enumerate(face_helper.cropped_faces): | |
| # prepare data | |
| cropped_face_t = img2tensor( | |
| cropped_face / 255.0, bgr2rgb=True, float32=True | |
| ) | |
| normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) | |
| cropped_face_t = cropped_face_t.unsqueeze(0).to(device) | |
| try: | |
| with torch.no_grad(): | |
| output = codeformer_net( | |
| cropped_face_t, w=codeformer_fidelity, adain=True | |
| )[0] | |
| restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1)) | |
| del output | |
| torch.cuda.empty_cache() | |
| except RuntimeError as error: | |
| print(f"Failed inference for CodeFormer: {error}") | |
| restored_face = tensor2img( | |
| cropped_face_t, rgb2bgr=True, min_max=(-1, 1) | |
| ) | |
| restored_face = restored_face.astype("uint8") | |
| face_helper.add_restored_face(restored_face) | |
| if not has_aligned: | |
| # upsample the background | |
| if bg_upsampler is not None: | |
| # Now only support RealESRGAN for upsampling background | |
| bg_img = bg_upsampler.enhance(img, outscale=upscale)[0] | |
| else: | |
| bg_img = None | |
| face_helper.get_inverse_affine(None) | |
| # paste each restored face to the input image | |
| if face_upsample and face_upsampler is not None: | |
| restored_img = face_helper.paste_faces_to_input_image( | |
| upsample_img=bg_img, | |
| draw_box=draw_box, | |
| face_upsampler=face_upsampler, | |
| ) | |
| else: | |
| restored_img = face_helper.paste_faces_to_input_image( | |
| upsample_img=bg_img, draw_box=draw_box | |
| ) | |
| # save restored img | |
| save_path = f'output/out.png' | |
| imwrite(restored_img, str(save_path)) | |
| restored_img = cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB) | |
| return restored_img, save_path | |
| except Exception as error: | |
| print('Global exception', error) | |
| return None, None | |
| title = "CodeFormer: Face Restoration " | |
| demo = gr.Interface( | |
| inference, [ | |
| gr.inputs.Image(type="filepath", label="Input"), | |
| gr.inputs.Checkbox(default=True, label="Background_Enhance"), | |
| gr.inputs.Checkbox(default=True, label="Face_Upsample"), | |
| gr.inputs.Number(default=2, label="Rescaling_Factor (up to 4)"), | |
| gr.Slider(0, 1, value=0.5, step=0.01, label='Codeformer_Fidelity (0 for better quality, 1 for better identity)') | |
| ], [ | |
| gr.outputs.Image(type="numpy", label="Output"), | |
| gr.outputs.File(label="Download the output") | |
| ], | |
| title=title, | |
| ) | |
| demo.queue(concurrency_count=2) | |
| demo.launch() | |