import spaces import gradio as gr import torch from PIL import Image import numpy as np import time import gradio as gr from backbone import extract_features from ncut_pytorch import NCUT, rgb_from_tsne_3d, rgb_from_umap_3d def compute_ncut( features, num_eig=100, num_sample_ncut=10000, affinity_focal_gamma=0.3, knn_ncut=10, knn_tsne=10, embedding_method="UMAP", num_sample_tsne=300, perplexity=150, n_neighbors=150, min_dist=0.1, ): start = time.time() eigvecs, eigvals = NCUT( num_eig=num_eig, num_sample=num_sample_ncut, device="cuda" if torch.cuda.is_available() else "cpu", affinity_focal_gamma=affinity_focal_gamma, knn=knn_ncut, ).fit_transform(features.reshape(-1, features.shape[-1])) print(f"NCUT time: {time.time() - start:.2f}s") start = time.time() if embedding_method == "UMAP": X_3d, rgb = rgb_from_umap_3d( eigvecs, n_neighbors=n_neighbors, min_dist=min_dist, device="cuda" if torch.cuda.is_available() else "cpu", ) print(f"UMAP time: {time.time() - start:.2f}s") elif embedding_method == "t-SNE": X_3d, rgb = rgb_from_tsne_3d( eigvecs, num_sample=num_sample_tsne, perplexity=perplexity, knn=knn_tsne, device="cuda" if torch.cuda.is_available() else "cpu", ) print(f"t-SNE time: {time.time() - start:.2f}s") else: raise ValueError(f"Embedding method {embedding_method} not supported.") rgb = rgb.reshape(features.shape[:3] + (3,)) return rgb def dont_use_too_much_green(image_rgb): # make sure the foval 40% of the image is red leading x1, x2 = int(image_rgb.shape[1] * 0.3), int(image_rgb.shape[1] * 0.7) y1, y2 = int(image_rgb.shape[2] * 0.3), int(image_rgb.shape[2] * 0.7) sum_values = image_rgb[:, x1:x2, y1:y2].mean((0, 1, 2)) sorted_indices = sum_values.argsort(descending=True) image_rgb = image_rgb[:, :, :, sorted_indices] return image_rgb def to_pil_images(images): return [ Image.fromarray((image * 255).cpu().numpy().astype(np.uint8)).resize((256, 256), Image.NEAREST) for image in images ] default_images = ['./images/image_0.jpg', './images/image_1.jpg', './images/image_2.jpg', './images/image_3.jpg', './images/image_5.jpg'] default_outputs = ['./images/ncut_0.jpg', './images/ncut_1.jpg', './images/ncut_2.jpg', './images/ncut_3.jpg', './images/ncut_5.jpg'] downscaled_images = ['./images/image_0_small.jpg', './images/image_1_small.jpg', './images/image_2_small.jpg', './images/image_3_small.jpg', './images/image_5_small.jpg'] downscaled_outputs = ['./images/ncut_0_small.jpg', './images/ncut_1_small.jpg', './images/ncut_2_small.jpg', './images/ncut_3_small.jpg', './images/ncut_5_small.jpg'] example_items = downscaled_images[:3] + downscaled_outputs[:3] @spaces.GPU(duration=30) def main_fn( images, model_name="SAM(sam_vit_b)", layer=-1, num_eig=100, node_type="block", affinity_focal_gamma=0.3, num_sample_ncut=10000, knn_ncut=10, embedding_method="UMAP", num_sample_tsne=1000, knn_tsne=10, perplexity=500, n_neighbors=500, min_dist=0.1, ): if len(images) is None: return [], example_items if perplexity >= num_sample_tsne or n_neighbors >= num_sample_tsne: # raise gr.Error("Perplexity must be less than the number of samples for t-SNE.") gr.Warning("Perplexity/n_neighbors must be less than the number of samples.\n" f"Setting to {num_sample_tsne-1}.") perplexity = num_sample_tsne - 1 n_neighbors = num_sample_tsne - 1 node_type = node_type.split(":")[0].strip() images = [image[0] for image in images] # remove the label start = time.time() features = extract_features( images, model_name=model_name, node_type=node_type, layer=layer ) print(f"Feature extraction time (gpu): {time.time() - start:.2f}s") rgb = compute_ncut( features, num_eig=num_eig, num_sample_ncut=num_sample_ncut, affinity_focal_gamma=affinity_focal_gamma, knn_ncut=knn_ncut, knn_tsne=knn_tsne, num_sample_tsne=num_sample_tsne, embedding_method=embedding_method, perplexity=perplexity, n_neighbors=n_neighbors, min_dist=min_dist, ) rgb = dont_use_too_much_green(rgb) return to_pil_images(rgb), [] with gr.Blocks() as demo: with gr.Row(): with gr.Column(scale=5, min_width=200): gr.Markdown('### Input Images') input_gallery = gr.Gallery(value=[], label="Select images", show_label=False, elem_id="images", columns=[3], rows=[1], object_fit="contain", height="auto", type="pil", show_share_button=False) submit_button = gr.Button("🔴RUN", elem_id="submit_button") clear_images_button = gr.Button("🗑️Clear", elem_id='clear_button') gr.Markdown('### Load Examples 👇') load_images_button = gr.Button("Load", elem_id="load-images-button") example_gallery = gr.Gallery(value=example_items, label="Example Set A", show_label=False, columns=[3], rows=[2], object_fit="scale-down", height="200px", show_share_button=False) with gr.Column(scale=5, min_width=200): gr.Markdown('### Output Images') output_gallery = gr.Gallery(value=[], label="NCUT Embedding", show_label=False, elem_id="ncut", columns=[3], rows=[1], object_fit="contain", height="auto") model_dropdown = gr.Dropdown(["SAM(sam_vit_b)", "MobileSAM", "DiNO(dinov2_vitb14_reg)", "CLIP(openai/clip-vit-base-patch16)"], label="Model", value="SAM(sam_vit_b)", elem_id="model_name") layer_slider = gr.Slider(0, 11, step=1, label="Layer", value=11, elem_id="layer") num_eig_slider = gr.Slider(1, 1000, step=1, label="Number of eigenvectors", value=100, elem_id="num_eig", info='increase for more clusters') affinity_focal_gamma_slider = gr.Slider(0.01, 1, step=0.01, label="Affinity focal gamma", value=0.3, elem_id="affinity_focal_gamma", info="decrease for shaper NCUT") with gr.Accordion("Additional Parameters", open=False): node_type_dropdown = gr.Dropdown(["attn: attention output", "mlp: mlp output", "block: sum of residual"], label="Node type", value="block: sum of residual", elem_id="node_type", info="which feature to take from each layer?") num_sample_ncut_slider = gr.Slider(100, 50000, step=100, label="num_sample (NCUT)", value=10000, elem_id="num_sample_ncut", info="Nyström approximation") knn_ncut_slider = gr.Slider(1, 100, step=1, label="KNN (NCUT)", value=10, elem_id="knn_ncut", info="Nyström approximation") embedding_method_dropdown = gr.Dropdown(["t-SNE", "UMAP"], label="Embedding method", value="t-SNE", elem_id="embedding_method") num_sample_tsne_slider = gr.Slider(100, 1000, step=100, label="num_sample (t-SNE/UMAP)", value=300, elem_id="num_sample_tsne", info="Nyström approximation") knn_tsne_slider = gr.Slider(1, 100, step=1, label="KNN (t-SNE/UMAP)", value=10, elem_id="knn_tsne", info="Nyström approximation") perplexity_slider = gr.Slider(10, 500, step=10, label="Perplexity (t-SNE)", value=150, elem_id="perplexity") n_neighbors_slider = gr.Slider(10, 500, step=10, label="n_neighbors (UMAP)", value=150, elem_id="n_neighbors") min_dist_slider = gr.Slider(0.1, 1, step=0.1, label="min_dist (UMAP)", value=0.1, elem_id="min_dist") def load_default_images(): return default_images, default_outputs, [] def empty_input_and_output(): return [], [], example_items load_images_button.click(load_default_images, outputs=[input_gallery, output_gallery, example_gallery]) clear_images_button.click(empty_input_and_output, outputs=[input_gallery, output_gallery, example_gallery]) submit_button.click( main_fn, inputs=[ input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, embedding_method_dropdown, num_sample_tsne_slider, knn_tsne_slider, perplexity_slider, n_neighbors_slider, min_dist_slider ], outputs=[output_gallery, example_gallery] ) demo.launch()