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import numpy as np
from PIL import Image
import gradio as gr
import tempfile
import os
def initialize_centroids(X, k):
np.random.seed(42)
indices = np.random.choice(X.shape[0], k, replace=False)
return X[indices]
def compute_centroids(X, labels, k):
centroids = []
for i in range(k):
cluster_points = X[labels == i]
if len(cluster_points) > 0:
centroids.append(cluster_points.mean(axis=0))
else:
centroids.append(X[np.random.choice(len(X))])
return np.array(centroids)
def assign_clusters(X, centroids):
distances = np.linalg.norm(X[:, np.newaxis] - centroids, axis=2)
return np.argmin(distances, axis=1)
def kmeans(X, k, max_iters=100):
centroids = initialize_centroids(X, k)
for _ in range(max_iters):
labels = assign_clusters(X, centroids)
new_centroids = compute_centroids(X, labels, k)
if np.allclose(centroids, new_centroids):
break
centroids = new_centroids
return centroids, labels
def compress_image(img, clusters=16):
if clusters < 2:
return None, "Minimum 3 clusters required."
# Resize for speed & consistency
max_dim = 512
w, h = img.size
scale = max_dim / max(w, h)
img = img.resize((int(w * scale), int(h * scale)))
img_np = np.array(img)
shape = img_np.shape
img_flat = img_np.reshape(-1, 3).astype(float)
centroids, labels = kmeans(img_flat, clusters, max_iters=30)
compressed_flat = centroids[labels].astype(np.uint8)
compressed_img = compressed_flat.reshape(shape)
# Save original and compressed to compare file sizes (use PNG to avoid JPEG inconsistency)
with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as orig:
Image.fromarray(img_np).save(orig.name, format='PNG')
orig_size = os.path.getsize(orig.name)
with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as comp:
Image.fromarray(compressed_img).save(comp.name, format='PNG')
comp_size = os.path.getsize(comp.name)
os.remove(orig.name)
os.remove(comp.name)
percent = 100 * (orig_size - comp_size) / orig_size
if percent < 0:
status = f"⚠️ Larger by {abs(percent):.2f}% (due to more color detail)"
else:
status = f"✅ Compressed by {percent:.2f}%"
return Image.fromarray(compressed_img), status
iface = gr.Interface(
fn=compress_image,
inputs=[
gr.Image(type="pil", label="Upload Image"),
gr.Slider(2, 64, value=16, step=1, label="Number of Clusters")
],
outputs=[
gr.Image(type="pil", label="Compressed Image"),
gr.Label(label="Compression Info")
],
title="Fixed K-Means Image Compressor",
description="Uses KMeans clustering on color pixels. PNG is used to give accurate compression size comparison."
)
if __name__ == "__main__":
iface.launch()
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