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app.py

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+import numpy as np
+import gradio as gr
+from PIL import Image
+import cv2
+from skimage import color
+from sklearn.cluster import KMeans
+from typing import Tuple
+
+f = "view.png"
+img = Image.open(f)
+img = np.array(img)[..., :3]
+
+
+def proc(img: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    assert img.shape[-1] == 3
+    k_size = 11
+    sigma = 11
+    blurred = cv2.GaussianBlur(img, (k_size, k_size), sigma)
+    blurred_small = cv2.resize(blurred, (80, 80))
+    labs = color.rgb2lab(blurred_small)
+    lab_vectors = labs.reshape(-1, 3)
+
+    num_colors = 5
+    num_bins = 10
+    km = KMeans(n_clusters=num_colors)
+    km.fit(lab_vectors)
+    centroid_labs = km.cluster_centers_  # N x (L, a, b)
+    centroid_labs = np.array(
+        sorted(centroid_labs, key=lambda x: x[1] ** 2 + x[2] ** 2)
+    )  # sort by L
+    centroid_ls = (
+        np.arange(0, 100, num_bins).reshape(1, num_bins, 1).repeat(num_colors, axis=0)
+    )
+    centroid_abs = centroid_labs[:, np.newaxis, 1:].repeat(num_bins, axis=1)
+    centroid_labs = np.concatenate([centroid_ls, centroid_abs], axis=-1).reshape(
+        num_colors, num_bins, 3
+    )
+
+    unique_indices = [0] + [
+        i
+        for i in range(1, num_colors)
+        if np.linalg.norm(centroid_labs[i] - centroid_labs[i - 1]) > 10
+    ]
+    centroid_labs = centroid_labs[unique_indices, :, :]
+
+    centroid_rgbs = (color.lab2rgb(centroid_labs) * 255).astype(np.uint8)
+    centroid_rgb_vis = cv2.resize(
+        centroid_rgbs,
+        (int(img.shape[0] / num_colors * num_bins), img.shape[0]),
+        interpolation=cv2.INTER_NEAREST,
+    )
+    return centroid_rgb_vis
+
+
+demo = gr.Interface(fn=proc, inputs="image", outputs="image")
+
+if __name__ == "__main__":
+    demo.launch()