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