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Running
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Zero
| # Author: Huzheng Yang | |
| # %% | |
| USE_SPACES = True | |
| if USE_SPACES: # huggingface ZeroGPU | |
| try: | |
| import spaces | |
| except ImportError: | |
| USE_SPACES = False # run on standard GPU | |
| import os | |
| 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, eigenvector_to_rgb | |
| 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, | |
| sampling_method="fps", | |
| metric="cosine", | |
| ): | |
| logging_str = "" | |
| num_nodes = np.prod(features.shape[:3]) | |
| if num_nodes / 2 < num_eig: | |
| # raise gr.Error("Number of eigenvectors should be less than half the number of nodes.") | |
| gr.Warning("Number of eigenvectors should be less than half the number of nodes.\n" f"Setting num_eig to {num_nodes // 2 - 1}.") | |
| num_eig = num_nodes // 2 - 1 | |
| logging_str += f"Number of eigenvectors should be less than half the number of nodes.\n" f"Setting num_eig to {num_nodes // 2 - 1}.\n" | |
| 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, | |
| sample_method=sampling_method, | |
| distance=metric, | |
| ).fit_transform(features.reshape(-1, features.shape[-1])) | |
| # print(f"NCUT time: {time.time() - start:.2f}s") | |
| logging_str += f"NCUT time: {time.time() - start:.2f}s\n" | |
| start = time.time() | |
| _, rgb = eigenvector_to_rgb( | |
| eigvecs, | |
| method=embedding_method, | |
| num_sample=num_sample_tsne, | |
| perplexity=perplexity, | |
| n_neighbors=n_neighbors, | |
| min_distance=min_dist, | |
| knn=knn_tsne, | |
| device="cuda" if torch.cuda.is_available() else "cpu", | |
| ) | |
| logging_str += f"{embedding_method} time: {time.time() - start:.2f}s\n" | |
| rgb = rgb.reshape(features.shape[:3] + (3,)) | |
| return rgb, logging_str, eigvecs | |
| 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.Resampling.NEAREST) | |
| for image in images | |
| ] | |
| def pil_images_to_video(images, output_path, fps=5): | |
| # from pil images to numpy | |
| images = [np.array(image) for image in images] | |
| print("Saving video to", output_path) | |
| import cv2 | |
| fourcc = cv2.VideoWriter_fourcc(*'mp4v') | |
| height, width, _ = images[0].shape | |
| out = cv2.VideoWriter(output_path, fourcc, fps, (width, height)) | |
| for image in images: | |
| out.write(cv2.cvtColor(image, cv2.COLOR_RGB2BGR)) | |
| out.release() | |
| return output_path | |
| # save up to 100 videos in disk | |
| class VideoCache: | |
| def __init__(self, max_videos=100): | |
| self.max_videos = max_videos | |
| self.videos = {} | |
| def add_video(self, video_path): | |
| if len(self.videos) >= self.max_videos: | |
| pop_path = self.videos.popitem()[0] | |
| try: | |
| os.remove(pop_path) | |
| except: | |
| pass | |
| self.videos[video_path] = video_path | |
| def get_video(self, video_path): | |
| return self.videos.get(video_path, None) | |
| video_cache = VideoCache() | |
| def get_random_path(length=10): | |
| import random | |
| import string | |
| name = ''.join(random.choices(string.ascii_lowercase + string.digits, k=length)) | |
| path = f'/tmp/{name}.mp4' | |
| return path | |
| 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'] | |
| default_outputs_independent = ['./images/ncut_0_independent.jpg', './images/ncut_1_independent.jpg', './images/ncut_2_independent.jpg', './images/ncut_3_independent.jpg', './images/ncut_5_independent.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] | |
| def ncut_run( | |
| 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, | |
| sampling_method="fps", | |
| old_school_ncut=False, | |
| recursion=False, | |
| recursion_l2_n_eigs=50, | |
| recursion_l3_n_eigs=20, | |
| recursion_metric="euclidean", | |
| video_output=False, | |
| ): | |
| logging_str = "" | |
| 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 Perplexity to {num_sample_tsne-1}.") | |
| logging_str += f"Perplexity/n_neighbors must be less than the number of samples.\n" f"Setting Perplexity to {num_sample_tsne-1}.\n" | |
| 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") | |
| logging_str += f"Backbone time: {time.time() - start:.2f}s\n" | |
| if recursion: | |
| rgbs = [] | |
| inp = features | |
| for i, n_eigs in enumerate([num_eig, recursion_l2_n_eigs, recursion_l3_n_eigs]): | |
| logging_str += f"Recursion #{i+1}\n" | |
| rgb, _logging_str, eigvecs = compute_ncut( | |
| inp, | |
| num_eig=n_eigs, | |
| 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, | |
| sampling_method=sampling_method, | |
| metric="cosine" if i == 0 else recursion_metric, | |
| ) | |
| logging_str += _logging_str | |
| rgb = dont_use_too_much_green(rgb) | |
| rgbs.append(to_pil_images(rgb)) | |
| inp = eigvecs.reshape(*features.shape[:3], -1) | |
| return rgbs[0], rgbs[1], rgbs[2], logging_str | |
| if old_school_ncut: # individual images | |
| logging_str += "Running NCut for each image independently\n" | |
| rgb = [] | |
| for i_image in range(features.shape[0]): | |
| feature = features[i_image] | |
| _rgb, _logging_str, _ = compute_ncut( | |
| feature[None], | |
| 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, | |
| sampling_method=sampling_method, | |
| ) | |
| logging_str += _logging_str | |
| rgb.append(_rgb[0]) | |
| if not old_school_ncut: # joint across all images | |
| rgb, _logging_str, _ = 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, | |
| sampling_method=sampling_method, | |
| ) | |
| logging_str += _logging_str | |
| rgb = dont_use_too_much_green(rgb) | |
| if video_output: | |
| video_path = get_random_path() | |
| video_cache.add_video(video_path) | |
| pil_images_to_video(to_pil_images(rgb), video_path) | |
| return video_path, logging_str | |
| else: | |
| return to_pil_images(rgb), logging_str | |
| def _ncut_run(*args, **kwargs): | |
| try: | |
| return ncut_run(*args, **kwargs) | |
| except Exception as e: | |
| gr.Error(str(e)) | |
| return [], "Error: " + str(e) | |
| if USE_SPACES: | |
| def quick_run(*args, **kwargs): | |
| return _ncut_run(*args, **kwargs) | |
| def long_run(*args, **kwargs): | |
| return _ncut_run(*args, **kwargs) | |
| def longer_run(*args, **kwargs): | |
| return _ncut_run(*args, **kwargs) | |
| def super_duper_long_run(*args, **kwargs): | |
| return _ncut_run(*args, **kwargs) | |
| if not USE_SPACES: | |
| def quick_run(*args, **kwargs): | |
| return _ncut_run(*args, **kwargs) | |
| def long_run(*args, **kwargs): | |
| return _ncut_run(*args, **kwargs) | |
| def longer_run(*args, **kwargs): | |
| return _ncut_run(*args, **kwargs) | |
| def super_duper_long_run(*args, **kwargs): | |
| return _ncut_run(*args, **kwargs) | |
| def extract_video_frames(video_path, max_frames=100): | |
| from decord import VideoReader | |
| vr = VideoReader(video_path) | |
| num_frames = len(vr) | |
| if num_frames > max_frames: | |
| gr.Warning(f"Video has {num_frames} frames. Only using {max_frames} frames. Evenly spaced.") | |
| frame_idx = np.linspace(0, num_frames - 1, max_frames, dtype=int).tolist() | |
| else: | |
| frame_idx = list(range(num_frames)) | |
| frames = vr.get_batch(frame_idx).asnumpy() | |
| # return as list of PIL images | |
| return [(Image.fromarray(frames[i]), "") for i in range(frames.shape[0])] | |
| def run_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, | |
| sampling_method="fps", | |
| old_school_ncut=False, | |
| max_frames=100, | |
| recursion=False, | |
| recursion_l2_n_eigs=50, | |
| recursion_l3_n_eigs=20, | |
| recursion_metric="euclidean", | |
| ): | |
| print("Running...") | |
| if images is None: | |
| gr.Warning("No images selected.") | |
| return [], "No images selected." | |
| video_output = False | |
| if isinstance(images, str): | |
| images = extract_video_frames(images, max_frames=max_frames) | |
| video_output = True | |
| if sampling_method == "fps": | |
| sampling_method = "farthest" | |
| kwargs = { | |
| "model_name": model_name, | |
| "layer": layer, | |
| "num_eig": num_eig, | |
| "node_type": node_type, | |
| "affinity_focal_gamma": affinity_focal_gamma, | |
| "num_sample_ncut": num_sample_ncut, | |
| "knn_ncut": knn_ncut, | |
| "embedding_method": embedding_method, | |
| "num_sample_tsne": num_sample_tsne, | |
| "knn_tsne": knn_tsne, | |
| "perplexity": perplexity, | |
| "n_neighbors": n_neighbors, | |
| "min_dist": min_dist, | |
| "sampling_method": sampling_method, | |
| "old_school_ncut": old_school_ncut, | |
| "recursion": recursion, | |
| "recursion_l2_n_eigs": recursion_l2_n_eigs, | |
| "recursion_l3_n_eigs": recursion_l3_n_eigs, | |
| "recursion_metric": recursion_metric, | |
| "video_output": video_output, | |
| } | |
| print(kwargs) | |
| num_images = len(images) | |
| if num_images > 100: | |
| return super_duper_long_run(images, **kwargs) | |
| if num_images > 50: | |
| return longer_run(images, **kwargs) | |
| if old_school_ncut: | |
| return longer_run(images, **kwargs) | |
| if num_images > 10: | |
| return long_run(images, **kwargs) | |
| if embedding_method == "UMAP": | |
| if perplexity >= 250 or num_sample_tsne >= 500: | |
| return longer_run(images, **kwargs) | |
| return long_run(images, **kwargs) | |
| if embedding_method == "t-SNE": | |
| if perplexity >= 250 or num_sample_tsne >= 500: | |
| return long_run(images, **kwargs) | |
| return quick_run(images, **kwargs) | |
| return quick_run(images, **kwargs) | |
| def make_input_images_section(): | |
| gr.Markdown('### Input Images') | |
| input_gallery = gr.Gallery(value=None, 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') | |
| return input_gallery, submit_button, clear_images_button | |
| def make_input_video_section(): | |
| gr.Markdown('### Input Video') | |
| input_gallery = gr.Video(value=None, label="Select video", elem_id="video-input", height="auto", show_share_button=False) | |
| max_frames_number = gr.Number(100, label="Max frames", elem_id="max_frames") | |
| submit_button = gr.Button("🔴RUN", elem_id="submit_button") | |
| clear_images_button = gr.Button("🗑️Clear", elem_id='clear_button') | |
| return input_gallery, submit_button, clear_images_button, max_frames_number | |
| def make_example_images_section(): | |
| gr.Markdown('### Load Images 👇') | |
| load_images_button = gr.Button("Load Example", 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, elem_id="example-gallery") | |
| hide_button = gr.Button("Hide Example", elem_id="hide-button") | |
| hide_button.click( | |
| fn=lambda: gr.update(visible=False), | |
| outputs=example_gallery | |
| ) | |
| return load_images_button, example_gallery, hide_button | |
| def make_example_video_section(): | |
| gr.Markdown('### Load Video 👇') | |
| load_video_button = gr.Button("Load Example", elem_id="load-video-button") | |
| return load_video_button | |
| def make_dataset_images_section(): | |
| with gr.Accordion("➡️ Load from dataset", open=True): | |
| dataset_names = [ | |
| 'UCSC-VLAA/Recap-COCO-30K', | |
| 'nateraw/pascal-voc-2012', | |
| 'johnowhitaker/imagenette2-320', | |
| 'jainr3/diffusiondb-pixelart', | |
| 'nielsr/CelebA-faces', | |
| 'JapanDegitalMaterial/Places_in_Japan', | |
| 'Borismile/Anime-dataset', | |
| ] | |
| dataset_dropdown = gr.Dropdown(dataset_names, label="Dataset name", value="UCSC-VLAA/Recap-COCO-30K", elem_id="dataset") | |
| num_images_slider = gr.Slider(1, 200, step=1, label="Number of images", value=9, elem_id="num_images") | |
| random_seed_slider = gr.Number(0, label="Random seed", elem_id="random_seed") | |
| load_dataset_button = gr.Button("Load Dataset", elem_id="load-dataset-button") | |
| def load_dataset_images(dataset_name, num_images=10, random_seed=42): | |
| from datasets import load_dataset | |
| try: | |
| dataset = load_dataset(dataset_name, trust_remote_code=True) | |
| key = list(dataset.keys())[0] | |
| dataset = dataset[key] | |
| except Exception as e: | |
| gr.Error(f"Error loading dataset {dataset_name}: {e}") | |
| return None | |
| if num_images > len(dataset): | |
| num_images = len(dataset) | |
| image_idx = np.random.RandomState(random_seed).choice(len(dataset), num_images, replace=False) | |
| image_idx = image_idx.tolist() | |
| images = [dataset[i]['image'] for i in image_idx] | |
| return images | |
| load_dataset_button.click(load_dataset_images, inputs=[dataset_dropdown, num_images_slider, random_seed_slider], outputs=[input_gallery]) | |
| return dataset_dropdown, num_images_slider, random_seed_slider, load_dataset_button | |
| def make_output_images_section(): | |
| 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") | |
| return output_gallery | |
| def make_parameters_section(): | |
| gr.Markdown('### Parameters') | |
| model_dropdown = gr.Dropdown(["SAM(sam_vit_b)", "MobileSAM", "DiNO(dinov2_vitb14_reg)", "CLIP(openai/clip-vit-base-patch16)", "MAE(vit_base)"], label="Backbone", value="SAM(sam_vit_b)", elem_id="model_name") | |
| layer_slider = gr.Slider(0, 11, step=1, label="Backbone: Layer index", value=11, elem_id="layer") | |
| node_type_dropdown = gr.Dropdown(["attn: attention output", "mlp: mlp output", "block: sum of residual"], label="Backbone: Layer type", value="block: sum of residual", elem_id="node_type", info="which feature to take from each layer?") | |
| num_eig_slider = gr.Slider(1, 1000, step=1, label="NCUT: 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="NCUT: Affinity focal gamma", value=0.5, elem_id="affinity_focal_gamma", info="decrease for shaper segmentation") | |
| with gr.Accordion("➡️ Click to expand: more parameters", open=False): | |
| num_sample_ncut_slider = gr.Slider(100, 50000, step=100, label="NCUT: num_sample", value=10000, elem_id="num_sample_ncut", info="Nyström approximation") | |
| sampling_method_dropdown = gr.Dropdown(["fps", "random"], label="NCUT: Sampling method", value="fps", elem_id="sampling_method", info="Nyström approximation") | |
| knn_ncut_slider = gr.Slider(1, 100, step=1, label="NCUT: KNN", value=10, elem_id="knn_ncut", info="Nyström approximation") | |
| embedding_method_dropdown = gr.Dropdown(["tsne_3d", "umap_3d", "umap_shpere", "tsne_2d", "umap_2d"], label="Coloring method", value="tsne_3d", elem_id="embedding_method") | |
| num_sample_tsne_slider = gr.Slider(100, 1000, step=100, label="t-SNE/UMAP: num_sample", value=300, elem_id="num_sample_tsne", info="Nyström approximation") | |
| knn_tsne_slider = gr.Slider(1, 100, step=1, label="t-SNE/UMAP: KNN", value=10, elem_id="knn_tsne", info="Nyström approximation") | |
| perplexity_slider = gr.Slider(10, 500, step=10, label="t-SNE: Perplexity", value=150, elem_id="perplexity") | |
| n_neighbors_slider = gr.Slider(10, 500, step=10, label="UMAP: n_neighbors", value=150, elem_id="n_neighbors") | |
| min_dist_slider = gr.Slider(0.1, 1, step=0.1, label="UMAP: min_dist", value=0.1, elem_id="min_dist") | |
| return [model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, | |
| 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, | |
| sampling_method_dropdown] | |
| with gr.Blocks() as demo: | |
| with gr.Tab('AlignedCut'): | |
| with gr.Row(): | |
| with gr.Column(scale=5, min_width=200): | |
| input_gallery, submit_button, clear_images_button = make_input_images_section() | |
| load_images_button, example_gallery, hide_button = make_example_images_section() | |
| dataset_dropdown, num_images_slider, random_seed_slider, load_dataset_button = make_dataset_images_section() | |
| with gr.Column(scale=5, min_width=200): | |
| output_gallery = make_output_images_section() | |
| [ | |
| model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, | |
| 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, | |
| sampling_method_dropdown | |
| ] = make_parameters_section() | |
| # logging text box | |
| logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information") | |
| load_images_button.click(lambda x: (default_images, default_outputs), outputs=[input_gallery, output_gallery]) | |
| clear_images_button.click(lambda x: ([], []), outputs=[input_gallery, output_gallery]) | |
| submit_button.click( | |
| run_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, sampling_method_dropdown | |
| ], | |
| outputs=[output_gallery, logging_text] | |
| ) | |
| with gr.Tab('NCut (Legacy)'): | |
| gr.Markdown('#### Ncut, not aligned, no Nyström approximation') | |
| gr.Markdown('Each image is solved independently, <em>color is <b>not</b> aligned across images</em>') | |
| gr.Markdown('---') | |
| gr.Markdown('<p style="text-align: center;"><b>NCut vs. AlignedCut</b></p>') | |
| with gr.Row(): | |
| with gr.Column(scale=5, min_width=200): | |
| gr.Markdown('#### Pros') | |
| gr.Markdown('- Easy Solution. Use less eigenvectors.') | |
| gr.Markdown('- Exact solution. No Nyström approximation.') | |
| with gr.Column(scale=5, min_width=200): | |
| gr.Markdown('#### Cons') | |
| gr.Markdown('- Not aligned. Distance is not preserved across images. No pseudo-labeling or correspondence.') | |
| gr.Markdown('- Poor complexity scaling. Unable to handle large number of pixels.') | |
| gr.Markdown('---') | |
| with gr.Row(): | |
| with gr.Column(scale=5, min_width=200): | |
| gr.Markdown(' ') | |
| with gr.Column(scale=5, min_width=200): | |
| gr.Markdown('<em>color is <b>not</b> aligned across images</em> 👇') | |
| with gr.Row(): | |
| with gr.Column(scale=5, min_width=200): | |
| input_gallery, submit_button, clear_images_button = make_input_images_section() | |
| load_images_button, example_gallery, hide_button = make_example_images_section() | |
| dataset_dropdown, num_images_slider, random_seed_slider, load_dataset_button = make_dataset_images_section() | |
| example_gallery.visible = False | |
| hide_button.visible = False | |
| with gr.Column(scale=5, min_width=200): | |
| output_gallery = make_output_images_section() | |
| [ | |
| model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, | |
| 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, | |
| sampling_method_dropdown | |
| ] = make_parameters_section() | |
| old_school_ncut_checkbox = gr.Checkbox(label="Old school NCut", value=True, elem_id="old_school_ncut") | |
| invisible_list = [old_school_ncut_checkbox, num_sample_ncut_slider, knn_ncut_slider, | |
| num_sample_tsne_slider, knn_tsne_slider, sampling_method_dropdown] | |
| for item in invisible_list: | |
| item.visible = False | |
| # logging text box | |
| logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information") | |
| load_images_button.click(lambda x: (default_images, default_outputs_independent), outputs=[input_gallery, output_gallery]) | |
| clear_images_button.click(lambda x: ([], []), outputs=[input_gallery, output_gallery]) | |
| submit_button.click( | |
| run_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, sampling_method_dropdown, | |
| old_school_ncut_checkbox | |
| ], | |
| outputs=[output_gallery, logging_text] | |
| ) | |
| with gr.Tab('Recursive Cut'): | |
| gr.Markdown('NCUT can be applied recursively, the eigenvectors from previous iteration is the input for the next iteration NCUT. ') | |
| gr.Markdown('__Recursive NCUT__ amplifies small object parts, please see [Documentation](https://ncut-pytorch.readthedocs.io/en/latest/how_to_get_better_segmentation/#recursive-ncut)') | |
| gr.Markdown('---') | |
| with gr.Row(): | |
| with gr.Column(scale=5, min_width=200): | |
| input_gallery, submit_button, clear_images_button = make_input_images_section() | |
| dataset_dropdown, num_images_slider, random_seed_slider, load_dataset_button = make_dataset_images_section() | |
| num_images_slider.value = 100 | |
| dataset_dropdown.value = 'nielsr/CelebA-faces' | |
| with gr.Column(scale=5, min_width=200): | |
| with gr.Accordion("➡️ Recursion config", open=True): | |
| l1_num_eig_slider = gr.Slider(1, 1000, step=1, label="Recursion #1: N eigenvectors", value=100, elem_id="l1_num_eig") | |
| l2_num_eig_slider = gr.Slider(1, 1000, step=1, label="Recursion #2: N eigenvectors", value=50, elem_id="l2_num_eig") | |
| l3_num_eig_slider = gr.Slider(1, 1000, step=1, label="Recursion #3: N eigenvectors", value=25, elem_id="l3_num_eig") | |
| metric_dropdown = gr.Dropdown(["euclidean", "cosine"], label="Recursion distance metric", value="cosine", elem_id="recursion_metric") | |
| [ | |
| model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, | |
| 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, | |
| sampling_method_dropdown | |
| ] = make_parameters_section() | |
| num_eig_slider.visible = False | |
| model_dropdown.value = 'DiNO(dinov2_vitb14_reg)' | |
| layer_slider.value = 6 | |
| node_type_dropdown.value = 'attn: attention output' | |
| affinity_focal_gamma_slider.value = 0.25 | |
| # logging text box | |
| with gr.Row(): | |
| with gr.Column(scale=5, min_width=200): | |
| gr.Markdown('### Output (Recursion #1)') | |
| l1_gallery = gr.Gallery(value=[], label="Recursion #1", show_label=False, elem_id="ncut_l1", columns=[3], rows=[5], object_fit="contain", height="auto") | |
| with gr.Column(scale=5, min_width=200): | |
| gr.Markdown('### Output (Recursion #2)') | |
| l2_gallery = gr.Gallery(value=[], label="Recursion #2", show_label=False, elem_id="ncut_l2", columns=[3], rows=[5], object_fit="contain", height="auto") | |
| with gr.Column(scale=5, min_width=200): | |
| gr.Markdown('### Output (Recursion #3)') | |
| l3_gallery = gr.Gallery(value=[], label="Recursion #3", show_label=False, elem_id="ncut_l3", columns=[3], rows=[5], object_fit="contain", height="auto") | |
| with gr.Row(): | |
| with gr.Column(scale=5, min_width=200): | |
| gr.Markdown(' ') | |
| with gr.Column(scale=5, min_width=200): | |
| gr.Markdown(' ') | |
| with gr.Column(scale=5, min_width=200): | |
| logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information") | |
| true_placeholder = gr.Checkbox(label="True placeholder", value=True, elem_id="true_placeholder") | |
| true_placeholder.visible = False | |
| false_placeholder = gr.Checkbox(label="False placeholder", value=False, elem_id="false_placeholder") | |
| false_placeholder.visible = False | |
| number_placeholder = gr.Number(0, label="Number placeholder", elem_id="number_placeholder") | |
| number_placeholder.visible = False | |
| clear_images_button.click(lambda x: ([], [], [], []), outputs=[input_gallery, l1_gallery, l2_gallery, l3_gallery]) | |
| submit_button.click( | |
| run_fn, | |
| inputs=[ | |
| input_gallery, model_dropdown, layer_slider, l1_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, sampling_method_dropdown, | |
| false_placeholder, number_placeholder, true_placeholder, | |
| l2_num_eig_slider, l3_num_eig_slider, metric_dropdown, | |
| ], | |
| outputs=[l1_gallery, l2_gallery, l3_gallery, logging_text] | |
| ) | |
| with gr.Tab('AlignedCut (Video)'): | |
| with gr.Row(): | |
| with gr.Column(scale=5, min_width=200): | |
| input_gallery, submit_button, clear_images_button, max_frame_number = make_input_video_section() | |
| # load_video_button = make_example_video_section() | |
| with gr.Column(scale=5, min_width=200): | |
| output_gallery = gr.Video(value=None, label="NCUT Embedding", elem_id="ncut", height="auto", show_share_button=False) | |
| gr.Markdown('_image backbone model is used to extract features from each frame, NCUT is computed on all frames_') | |
| [ | |
| model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, | |
| 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, | |
| sampling_method_dropdown | |
| ] = make_parameters_section() | |
| num_sample_tsne_slider.value = 1000 | |
| perplexity_slider.value = 500 | |
| n_neighbors_slider.value = 500 | |
| knn_tsne_slider.value = 20 | |
| # logging text box | |
| logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information") | |
| load_images_button.click(lambda x: (default_images, default_outputs), outputs=[input_gallery, output_gallery]) | |
| # load_video_button.click(lambda x: './images/ego4d_dog.mp4', outputs=[input_gallery]) | |
| clear_images_button.click(lambda x: (None, []), outputs=[input_gallery, output_gallery]) | |
| place_holder_false = gr.Checkbox(label="Place holder", value=False, elem_id="place_holder_false") | |
| place_holder_false.visible = False | |
| submit_button.click( | |
| run_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, sampling_method_dropdown, | |
| place_holder_false, max_frame_number | |
| ], | |
| outputs=[output_gallery, logging_text] | |
| ) | |
| with gr.Tab('AlignedCut (Text)'): | |
| gr.Markdown('=== under construction ===') | |
| gr.Markdown('Please see the [Documentation](https://ncut-pytorch.readthedocs.io/en/latest/gallery_llama3/) for example of NCUT on text input.') | |
| gr.Markdown('---') | |
| gr.Markdown('') | |
| demo.launch(share=True) | |
| # %% | |