Create app.py

app.py

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+
+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("[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()   
+