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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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ClusterRGB/.gitignore

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+
+.DS_Store
+
+.__pycache__

ClusterRGB/app.py

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+import gradio as gr
+import plotly.graph_objs as go
+from sklearn.cluster import KMeans
+import os
+
+
+def _get_cluster_colors(cluster_centers) -> list[str]:
+    cluster_colors = []
+    for r, g, b in cluster_centers:
+        cluster_colors.append(f"rgb({r},{g},{b})")
+    return cluster_colors
+
+
+def plot_image(Image, N_Clusters: int) -> gr.Plot:
+    img_flat = Image.reshape(-1, 3)
+    kmeans = KMeans(N_Clusters, random_state=1).fit(img_flat)
+    cluster_colors = _get_cluster_colors(kmeans.cluster_centers_)
+
+    fig = go.Figure(data=[go.Scatter3d(
+        x=kmeans.cluster_centers_[:, 0],
+        y=kmeans.cluster_centers_[:, 1],
+        z=kmeans.cluster_centers_[:, 2],
+        mode='markers',
+        marker=dict(
+            color=cluster_colors,  # Set marker color to cluster colors
+            opacity=0.9,
+        )
+    )])
+
+    # Adjust layout, including axis labels
+    fig.update_layout(
+        scene=dict(
+            xaxis_title="Red Channel",
+            yaxis_title="Green Channel",
+            zaxis_title="Blue Channel"
+        ),
+        margin=dict(l=0, r=0, b=0, t=0)
+    )
+    return gr.Plot(fig)
+
+
+interface = gr.Interface(
+    fn=plot_image,
+    title="3D RGB Plot",
+    inputs=[gr.Image(), gr.Slider(minimum=20, maximum=500)],
+    outputs=gr.Plot(),
+    examples=[[os.path.join("examples", image), 100] for image in os.listdir("examples")],
+)
+
+
+if __name__ == "__main__":
+    interface.launch()
+

ClusterRGB/requirements.txt

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+gradio==4.26.0
+plotly==5.20.0
+scikit_learn==1.4.1.post1