app.py

import time
import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.ndimage import gaussian_filter
from skimage.data import coins
from skimage.transform import rescale
from sklearn.cluster import AgglomerativeClustering
from sklearn.feature_extraction.image import grid_to_graph


plt.switch_backend('agg')

def cluster(gf_sigma, scale, anti_alias, mode, n_clusters,linkage):
    orig_coins = coins()
    smoothened_coins = gaussian_filter(orig_coins, sigma=gf_sigma)

    # Resize it to 20% of the original size to speed up the processing Applying a Gaussian filter for smoothing 
    # prior to down-scaling reduces aliasing artifacts.

    rescaled_coins = rescale(
        smoothened_coins,
        scale = scale,
        mode="reflect",
        anti_aliasing=False,
    )

    X = np.reshape(rescaled_coins, (-1, 1))


    connectivity = grid_to_graph(*rescaled_coins.shape)

    
    result = ""
    result += "Compute structured hierarchical clustering...\n"
    st = time.time()
    ward = AgglomerativeClustering(
        n_clusters=n_clusters, linkage="ward", connectivity=connectivity
    )
    ward.fit(X)
    label = np.reshape(ward.labels_, rescaled_coins.shape)
    
    result += f"Elapsed time: {time.time() - st:.3f}s \n"
    result += f"Number of pixels: {label.size} \n"
    result += f"Number of clusters: {np.unique(label).size} \n"


    fig = plt.figure(figsize=(7, 7))
    plt.imshow(rescaled_coins, cmap=plt.cm.gray)
    for l in range(n_clusters):
        plt.contour(
            label == l,
            colors=[
                plt.cm.nipy_spectral(l / float(n_clusters)),
            ],
        )
    plt.axis("off")

    return result, fig

## https://scikit-learn.org/stable/auto_examples/cluster/plot_coin_ward_segmentation.html

title = "A demo of structured Ward hierarchical clustering on an image of coins"


def do_submit(gf_sigma, scale, anti_alias, mode, n_clusters,linkage):
    gf_sigma = float(gf_sigma)
    scale = float(scale)
    anti_alias = True if anti_alias == "True" else False
    n_clusters = int(n_clusters)

    result, fig = cluster(gf_sigma, scale, anti_alias, mode, n_clusters,linkage)
    return result, fig



with gr.Blocks(title=title) as demo:
    gr.Markdown(f"## {title}")
    gr.Markdown("This is an interactive demo for this [scikit-learn example](https://scikit-learn.org/stable/auto_examples/cluster/plot_coin_ward_segmentation.html).")
    gr.Markdown("Compute the segmentation of a 2D image with Ward hierarchical clustering. \
        The clustering is spatially constrained in order for each segmented region to be in one piece.")

    with gr.Row(variant="evenly-spaced"):
        gf_sigma = gr.Slider(minimum=1, maximum=10, label="Gaussian Filter Sigma",  value=2, \
            info="Standard deviation for Gaussian filtering before down-scaling.", step=0.1)
        
        scale = gr.Slider(minimum=0.1, maximum=0.7, label="Scale",  value=0.2, \
            info="Scale factor for the image.", step=0.1)

        anti_alias = gr.Radio(["True","False"], label="Anti Aliasing", value="False", \
            info="Whether to apply a Gaussian filter to smooth the image prior to down-scaling. \
                It is crucial to filter when down-sampling the image to avoid aliasing artifacts.\
                If input image data type is bool, no anti-aliasing is applied.")

        mode = gr.Dropdown(
                ["constant", "edge", "symmetric", "reflect", "wrap"], value=["reflect"], multiselect=False, label="mode",\
            info="Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of numpy.pad."
            )

    with gr.Row():
        ## Agglomerative Clustering parameters
        n_clusters = gr.Slider(minimum=2, maximum=70,label="Number of Clusters", value=27, \
            info="The number of clusters to find.", step=1)
        linkage = gr.Dropdown(["ward", "complete", "average", "single"], value=["ward"], multiselect=False, label="linkage",\
            info="Which linkage criterion to use. The linkage criterion determines which distance to use between sets of observation. ")
        
    output = gr.Textbox(label="Output Box")
    plt_out = gr.Plot()

    submit_btn = gr.Button("Submit")
    submit_btn.click(fn=do_submit, inputs=[gf_sigma, scale, anti_alias, mode, n_clusters,linkage], outputs=[output, plt_out])

if __name__ == "__main__":
    demo.launch()