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| import subprocess | |
| subprocess.run('sh setup.sh', shell=True) | |
| print("Installed the dependencies!") | |
| from typing import Tuple | |
| import dnnlib | |
| from PIL import Image | |
| import numpy as np | |
| import torch | |
| import legacy | |
| import paddlehub as hub | |
| u2net = hub.Module(name='U2Net') | |
| # gradio app imports | |
| import gradio as gr | |
| from torchvision.transforms import ToTensor, ToPILImage | |
| image_to_tensor = ToTensor() | |
| tensor_to_image = ToPILImage() | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| class_idx = None | |
| truncation_psi = 0.1 | |
| def create_model(network_pkl): | |
| print('Loading networks from "%s"...' % network_pkl) | |
| with dnnlib.util.open_url(network_pkl) as f: | |
| G = legacy.load_network_pkl(f)['G_ema'] # type: ignore | |
| G = G.eval().to(device) | |
| netG_params = sum(p.numel() for p in G.parameters()) | |
| print("Generator Params: {} M".format(netG_params/1e6)) | |
| return G | |
| def fcf_inpaint(G, org_img, erased_img, mask): | |
| label = torch.zeros([1, G.c_dim], device=device) | |
| if G.c_dim != 0: | |
| if class_idx is None: | |
| ValueError("class_idx can't be None.") | |
| label[:, class_idx] = 1 | |
| else: | |
| if class_idx is not None: | |
| print ('warn: --class=lbl ignored when running on an unconditional network') | |
| pred_img = G(img=torch.cat([0.5 - mask, erased_img], dim=1), c=label, truncation_psi=truncation_psi, noise_mode='const') | |
| comp_img = mask.to(device) * pred_img + (1 - mask).to(device) * org_img.to(device) | |
| return comp_img | |
| def show_images(img): | |
| """ Display a batch of images inline. """ | |
| return Image.fromarray(img) | |
| def denorm(img): | |
| img = np.asarray(img[0].cpu(), dtype=np.float32).transpose(1, 2, 0) | |
| img = (img +1) * 127.5 | |
| img = np.rint(img).clip(0, 255).astype(np.uint8) | |
| return img | |
| def pil_to_numpy(pil_img: Image) -> Tuple[torch.Tensor, torch.Tensor]: | |
| img = np.array(pil_img) | |
| return torch.from_numpy(img)[None].permute(0, 3, 1, 2).float() / 127.5 - 1 | |
| def inpaint(input_img, mask, option): | |
| width, height = input_img.size | |
| if option == "Automatic": | |
| result = u2net.Segmentation( | |
| images=[cv2.cvtColor(np.array(input_img), cv2.COLOR_RGB2BGR)], | |
| paths=None, | |
| batch_size=1, | |
| input_size=320, | |
| output_dir='output', | |
| visualization=True) | |
| mask = Image.fromarray(result[0]['mask']) | |
| else: | |
| mask = mask.resize((width,height)) | |
| input_img = input_img.resize(512, 512) | |
| mask = mask.resize(512, 512) | |
| mask = mask.convert('L') | |
| mask = np.array(mask) / 255. | |
| mask_tensor = torch.from_numpy(mask).to(torch.float32) | |
| mask_tensor = mask_tensor.unsqueeze(0) | |
| mask_tensor = mask_tensor.unsqueeze(0).to(device) | |
| rgb = input_img.convert('RGB') | |
| rgb = np.array(rgb) | |
| rgb = rgb.transpose(2,0,1) | |
| rgb = torch.from_numpy(rgb.astype(np.float32)).unsqueeze(0) | |
| rgb = (rgb.to(torch.float32) / 127.5 - 1).to(device) | |
| rgb_erased = rgb.clone() | |
| rgb_erased = rgb_erased * (1 - mask_tensor) # erase rgb | |
| rgb_erased = rgb_erased.to(torch.float32) | |
| # model = create_model("models/places_512.pkl") | |
| # comp_img = fcf_inpaint(G=model, org_img=rgb.to(torch.float32), erased_img=rgb_erased.to(torch.float32), mask=mask_tensor.to(torch.float32)) | |
| rgb_erased = denorm(rgb_erased) | |
| # comp_img = denorm(comp_img) | |
| return show_images(rgb_erased), show_images(rgb_erased) | |
| gradio_inputs = [gr.inputs.Image(type='pil', | |
| tool=None, | |
| label="Input Image"), | |
| gr.inputs.Image(type='pil',source="canvas", label="Mask", invert_colors=True), | |
| gr.inputs.Radio(choices=["Automatic", "Manual"], type="value", default="Manual", label="Masking Choice") | |
| # gr.inputs.Image(type='pil', | |
| # tool=None, | |
| # label="Mask")] | |
| ] | |
| # gradio_outputs = [gr.outputs.Image(label='Auto-Detected Mask (From drawn black pixels)')] | |
| gradio_outputs = [gr.outputs.Image(label='Image with Hole'), | |
| gr.outputs.Image(label='Inpainted Image')] | |
| examples = [['test_512/person512.png', 'test_512/mask_auto.png', 'Automatic'], | |
| ['test_512/a_org.png', 'test_512/a_mask.png', 'Manual'], | |
| ['test_512/c_org.png', 'test_512/b_mask.png', 'Manual'], | |
| ['test_512/b_org.png', 'test_512/c_mask.png', 'Manual'], | |
| ['test_512/d_org.png', 'test_512/d_mask.png', 'Manual'], | |
| ['test_512/e_org.png', 'test_512/e_mask.png', 'Manual'], | |
| ['test_512/f_org.png', 'test_512/f_mask.png', 'Manual'], | |
| ['test_512/g_org.png', 'test_512/g_mask.png', 'Manual'], | |
| ['test_512/h_org.png', 'test_512/h_mask.png', 'Manual'], | |
| ['test_512/i_org.png', 'test_512/i_mask.png', 'Manual']] | |
| title = "FcF-Inpainting" | |
| description = "[Note: Queue time may take upto 20 seconds! The image and mask are resized to 512x512 before inpainting.] To use FcF-Inpainting: \n \ | |
| (1) Upload an Image; \n \ | |
| (2) Draw (Manual) a Mask on the White Canvas or Generate a mask using U2Net by selecting the Automatic option; \n \ | |
| (3) Click on Submit and witness the MAGIC! 🪄 ✨ ✨" | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.10741' target='_blank'> Keys to Better Image Inpainting: Structure and Texture Go Hand in Hand</a> | <a href='https://github.com/SHI-Labs/FcF-Inpainting' target='_blank'>Github Repo</a></p>" | |
| css = ".image-preview {height: 32rem; width: auto;} .output-image {height: 32rem; width: auto;} .panel-buttons { display: flex; flex-direction: row;}" | |
| iface = gr.Interface(fn=inpaint, inputs=gradio_inputs, | |
| outputs=gradio_outputs, | |
| css=css, | |
| layout="vertical", | |
| examples_per_page=5, | |
| thumbnail="fcf_gan.png", | |
| allow_flagging="never", | |
| examples=examples, title=title, | |
| description=description, article=article) | |
| iface.launch(enable_queue=True, | |
| share=True, server_name="0.0.0.0") |