app.py

# %%
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

import spaces

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
    ]

@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 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)

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']

with gr.Blocks() as demo:

    with gr.Row():
        with gr.Column(scale=5, min_width=200):
            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("🔴Submit", elem_id="submit_button")
            clear_images_button = gr.Button("🗑️Clear Images")
        
            gr.Markdown('### Load Examples 👇')
            load_images_button = gr.Button("Load", elem_id="load-images-button")
            gr.Gallery(value=downscaled_images[:3] + downscaled_outputs[:3], 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):
            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 [], []
    
    load_images_button.click(load_default_images, outputs=[input_gallery, output_gallery])
    clear_images_button.click(empty_input_and_output, outputs=[input_gallery, output_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
    )


demo.launch(share=True)