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| import matplotlib.pyplot as plt | |
| import torch | |
| import torchvision.transforms as T | |
| from PIL import Image | |
| import gradio as gr | |
| from featup.util import norm, unnorm, pca, remove_axes | |
| from pytorch_lightning import seed_everything | |
| import os | |
| def plot_feats(image, lr, hr): | |
| assert len(image.shape) == len(lr.shape) == len(hr.shape) == 3 | |
| seed_everything(0) | |
| [lr_feats_pca, hr_feats_pca], _ = pca([lr.unsqueeze(0), hr.unsqueeze(0)], dim=9) | |
| fig, ax = plt.subplots(3, 3, figsize=(15, 15)) | |
| ax[0, 0].imshow(image.permute(1, 2, 0).detach().cpu()) | |
| ax[1, 0].imshow(image.permute(1, 2, 0).detach().cpu()) | |
| ax[2, 0].imshow(image.permute(1, 2, 0).detach().cpu()) | |
| ax[0, 0].set_title("Image", fontsize=22) | |
| ax[0, 1].set_title("Original", fontsize=22) | |
| ax[0, 2].set_title("Upsampled Features", fontsize=22) | |
| ax[0, 1].imshow(lr_feats_pca[0, :3].permute(1, 2, 0).detach().cpu()) | |
| ax[0, 0].set_ylabel("PCA Components 1-3", fontsize=22) | |
| ax[0, 2].imshow(hr_feats_pca[0, :3].permute(1, 2, 0).detach().cpu()) | |
| ax[1, 1].imshow(lr_feats_pca[0, 3:6].permute(1, 2, 0).detach().cpu()) | |
| ax[1, 0].set_ylabel("PCA Components 4-6", fontsize=22) | |
| ax[1, 2].imshow(hr_feats_pca[0, 3:6].permute(1, 2, 0).detach().cpu()) | |
| ax[2, 1].imshow(lr_feats_pca[0, 6:9].permute(1, 2, 0).detach().cpu()) | |
| ax[2, 0].set_ylabel("PCA Components 7-9", fontsize=22) | |
| ax[2, 2].imshow(hr_feats_pca[0, 6:9].permute(1, 2, 0).detach().cpu()) | |
| remove_axes(ax) | |
| plt.tight_layout() | |
| plt.close(fig) # Close plt to avoid additional empty plots | |
| return fig | |
| if __name__ == "__main__": | |
| import requests | |
| import os | |
| def download_image(url, save_path): | |
| response = requests.get(url) | |
| with open(save_path, 'wb') as file: | |
| file.write(response.content) | |
| base_url = "https://marhamilresearch4.blob.core.windows.net/feature-upsampling-public/sample_images/" | |
| sample_images_urls = { | |
| "skate.jpg": base_url + "skate.jpg", | |
| "car.jpg": base_url + "car.jpg", | |
| "plant.png": base_url + "plant.png", | |
| } | |
| sample_images_dir = "sample_images" | |
| # Ensure the directory for sample images exists | |
| os.makedirs(sample_images_dir, exist_ok=True) | |
| # Download each sample image | |
| for filename, url in sample_images_urls.items(): | |
| save_path = os.path.join(sample_images_dir, filename) | |
| # Download the image if it doesn't already exist | |
| if not os.path.exists(save_path): | |
| print(f"Downloading {filename}...") | |
| download_image(url, save_path) | |
| else: | |
| print(f"{filename} already exists. Skipping download.") | |
| os.environ['TORCH_HOME'] = '/tmp/.cache' | |
| options = ['dino16', 'vit', 'dinov2', 'clip', 'resnet50'] | |
| image_input = gr.Image(label="Choose an image to featurize", | |
| height=480, | |
| type="pil", | |
| image_mode='RGB', | |
| sources=['upload', 'webcam', 'clipboard'] | |
| ) | |
| model_option = gr.Radio(options, value="dino16", label='Choose a backbone to upsample') | |
| models = {o: torch.hub.load("mhamilton723/FeatUp", o) for o in options} | |
| def upsample_features(image, model_option): | |
| # Image preprocessing | |
| input_size = 224 | |
| transform = T.Compose([ | |
| T.Resize(input_size), | |
| T.CenterCrop((input_size, input_size)), | |
| T.ToTensor(), | |
| norm | |
| ]) | |
| image_tensor = transform(image).unsqueeze(0).cuda() | |
| # Load the selected model | |
| upsampler = models[model_option].cuda() | |
| hr_feats = upsampler(image_tensor) | |
| lr_feats = upsampler.model(image_tensor) | |
| upsampler.cpu() | |
| return plot_feats(unnorm(image_tensor)[0], lr_feats[0], hr_feats[0]) | |
| demo = gr.Interface(fn=upsample_features, | |
| inputs=[image_input, model_option], | |
| outputs="plot", | |
| title="Feature Upsampling Demo", | |
| description="This demo allows you to upsample features of an image using selected models.", | |
| examples=[ | |
| ["sample_images/skate.jpg", "dino16"], | |
| ["sample_images/car.jpg", "dinov2"], | |
| ["sample_images/plant.png", "dino16"], | |
| ] | |
| ) | |
| demo.launch(server_name="0.0.0.0", server_port=7860, debug=True) | |