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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 cv2 | |
| from streamlit_drawable_canvas import st_canvas | |
| import streamlit as st | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| class_idx = None | |
| truncation_psi = 0.1 | |
| title = "FcF-Inpainting" | |
| description = "<p style='color:royalblue; font-size: 14px; font-weight: w300;'> \ | |
| [Note: The image and mask are resized to 512x512 before inpainting. The <span style='color:#E0B941;'>Run FcF-Inpainting</span> button will automatically appear after you draw a mask.] To use FcF-Inpainting: <br> \ | |
| (1) <span style='color:#E0B941;'>Upload an Image</span> or <span style='color:#E0B941;'> select a sample image on the left</span>. <br> \ | |
| (2) Adjust the brush stroke width and <span style='color:#E0B941;'>draw the mask on the image</span>. You may also change the drawing tool on the sidebar. <br>\ | |
| (3) After drawing a mask, click the <span style='color:#E0B941;'>Run FcF-Inpainting</span> and witness the MAGIC! 🪄 ✨ ✨<br> \ | |
| (4) You may <span style='color:#E0B941;'>download/undo/redo/delete</span> the changes on the image using the options below the image box.</p>" | |
| article = "<p style='color: #E0B941; font-size: 16px; font-weight: w500; text-align: center'> <a style='color: #E0B941;' href='https://praeclarumjj3.github.io/fcf-inpainting/' target='_blank'>Project Page</a> | <a style='color: #E0B941;' href='https://github.com/SHI-Labs/FcF-Inpainting' target='_blank'> Keys to Better Image Inpainting: Structure and Texture Go Hand in Hand</a> | <a style='color: #E0B941;' href='https://github.com/SHI-Labs/FcF-Inpainting' target='_blank'>Github</a></p>" | |
| 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 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 process_mask(input_img, mask): | |
| rgb = cv2.cvtColor(input_img, cv2.COLOR_RGBA2RGB) | |
| mask = 255 - mask[:,:,3] | |
| mask = (mask > 0) * 1 | |
| rgb = np.array(rgb) | |
| mask_tensor = torch.from_numpy(mask).to(torch.float32) | |
| mask_tensor = mask_tensor.unsqueeze(0) | |
| mask_tensor = mask_tensor.unsqueeze(0).to(device) | |
| 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) | |
| rgb_erased = denorm(rgb_erased) | |
| return rgb_erased | |
| def inpaint(input_img, mask, model): | |
| rgb = cv2.cvtColor(input_img, cv2.COLOR_RGBA2RGB) | |
| mask = 255 - mask[:,:,3] | |
| mask = (mask > 0) * 1 | |
| rgb = np.array(rgb) | |
| mask_tensor = torch.from_numpy(mask).to(torch.float32) | |
| mask_tensor = mask_tensor.unsqueeze(0) | |
| mask_tensor = mask_tensor.unsqueeze(0).to(device) | |
| 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) | |
| 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 comp_img | |
| def run_app(model): | |
| if "button_id" not in st.session_state: | |
| st.session_state["button_id"] = "" | |
| if "color_to_label" not in st.session_state: | |
| st.session_state["color_to_label"] = {} | |
| image_inpainting(model) | |
| with st.sidebar: | |
| st.markdown("---") | |
| def image_inpainting(model): | |
| if 'reuse_image' not in st.session_state: | |
| st.session_state.reuse_image = None | |
| st.title(title) | |
| st.markdown(article, unsafe_allow_html=True) | |
| st.markdown(description, unsafe_allow_html=True) | |
| image = st.sidebar.file_uploader("Upload an Image", type=["png", "jpg", "jpeg"]) | |
| sample_image = st.sidebar.radio('Choose a Sample Image', [ | |
| 'scene-background.png', | |
| 'fence-background.png', | |
| 'bench.png', | |
| 'house.png', | |
| 'landscape.png', | |
| 'truck.png', | |
| 'scenery.png', | |
| 'grass-texture.png', | |
| 'mapview-texture.png', | |
| ]) | |
| drawing_mode = st.sidebar.selectbox( | |
| "Drawing tool:", ("freedraw", "line") | |
| ) | |
| image = Image.open(image).convert("RGBA") if image else Image.open(f"./test_512/{sample_image}").convert("RGBA") | |
| image = image.resize((512, 512)) | |
| width, height = image.size | |
| stroke_width = st.sidebar.slider("Stroke width: ", 1, 100, 20) | |
| canvas_result = st_canvas( | |
| stroke_color="rgba(255, 0, 255, 0.8)", | |
| stroke_width=stroke_width, | |
| background_image=image, | |
| height=height, | |
| width=width, | |
| drawing_mode=drawing_mode, | |
| key="canvas", | |
| ) | |
| if canvas_result.image_data is not None and image and len(canvas_result.json_data["objects"]) > 0: | |
| im = canvas_result.image_data.copy() | |
| background = np.where( | |
| (im[:, :, 0] == 0) & | |
| (im[:, :, 1] == 0) & | |
| (im[:, :, 2] == 0) | |
| ) | |
| drawing = np.where( | |
| (im[:, :, 0] == 255) & | |
| (im[:, :, 1] == 0) & | |
| (im[:, :, 2] == 255) | |
| ) | |
| im[background]=[0,0,0,255] | |
| im[drawing]=[0,0,0,0] #RGBA | |
| if st.button('Run FcF-Inpainting'): | |
| col1, col2 = st.columns([1,1]) | |
| with col1: | |
| # if st.button('Show Image with Holes'): | |
| st.write("Masked Image") | |
| mask_show = process_mask(np.array(image), np.array(im)) | |
| st.image(mask_show) | |
| with col2: | |
| st.write("Inpainted Image") | |
| inpainted_img = inpaint(np.array(image), np.array(im), model) | |
| st.image(inpainted_img) | |
| if __name__ == "__main__": | |
| st.set_page_config( | |
| page_title="FcF-Inpainting", page_icon=":sparkles:" | |
| ) | |
| st.sidebar.subheader("Configuration") | |
| model = create_model("models/places_512.pkl") | |
| run_app(model) |