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d2aa004 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | import gradio as gr
import numpy as np
import cv2
from skimage.metrics import structural_similarity as ssim
from PIL import Image
def preprocess_image(image, grid_size):
"""Resize image to ensure consistent processing."""
if isinstance(image, str): # If image is a file path
image = cv2.imread(image)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
height, width = image.shape[:2]
max_dim = 512
scale = max_dim / max(height, width)
new_width = int(width * scale)
new_height = int(height * scale)
return cv2.resize(image, (new_width, new_height))
def quantize_colors(image, n_colors=8):
"""Quantize colors using optimized K-means clustering."""
pixels = image.reshape(-1, 3).astype(np.float32) # Flatten image for K-means
# Define K-means criteria and apply clustering
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2)
_, labels, centers = cv2.kmeans(pixels, n_colors, None, criteria, 10, cv2.KMEANS_PP_CENTERS)
# Convert back to uint8 and reshape
centers = np.uint8(centers)
quantized = centers[labels.flatten()]
return quantized.reshape(image.shape)
def create_grid(image, grid_size):
"""Divide image into grid cells."""
height, width = image.shape[:2]
cell_height = height // grid_size
cell_width = width // grid_size
grid = []
for i in range(grid_size):
row = []
for j in range(grid_size):
y_start = i * cell_height
x_start = j * cell_width
y_end = height if i == grid_size - 1 else (i + 1) * cell_height
x_end = width if j == grid_size - 1 else (j + 1) * cell_width
cell = image[y_start:y_end, x_start:x_end]
row.append(cell)
grid.append(row)
return grid
def get_average_color(cell):
"""Calculate average color of a grid cell."""
return np.mean(cell, axis=(0, 1)).astype(np.uint8)
def create_mosaic(image, grid_size, use_color_quantization, n_colors=8):
"""Create mosaic from input image using optional color quantization."""
processed_image = preprocess_image(image, grid_size)
if use_color_quantization:
processed_image = quantize_colors(processed_image, n_colors)
grid = create_grid(processed_image, grid_size)
height, width = processed_image.shape[:2]
cell_height = height // grid_size
cell_width = width // grid_size
mosaic = np.zeros_like(processed_image)
for i in range(grid_size):
for j in range(grid_size):
y_start = i * cell_height
x_start = j * cell_width
y_end = height if i == grid_size - 1 else (i + 1) * cell_height
x_end = width if j == grid_size - 1 else (j + 1) * cell_width
avg_color = get_average_color(grid[i][j])
mosaic[y_start:y_end, x_start:x_end] = avg_color
return processed_image, mosaic
def calculate_similarity(original, mosaic):
"""Calculate similarity between original and mosaic images."""
original_gray = cv2.cvtColor(original, cv2.COLOR_RGB2GRAY)
mosaic_gray = cv2.cvtColor(mosaic, cv2.COLOR_RGB2GRAY)
similarity = ssim(original_gray, mosaic_gray)
mse = np.mean((original - mosaic) ** 2)
return similarity, mse
def process_image(input_image, grid_size, use_quantization, n_colors):
"""Process image with K-means color quantization instead of a fixed palette."""
original, mosaic = create_mosaic(input_image, grid_size, use_quantization, n_colors)
similarity, mse = calculate_similarity(original, mosaic)
return (mosaic,
f"Structural Similarity: {similarity:.4f}",
f"Mean Squared Error: {mse:.4f}")
iface = gr.Interface(
fn=process_image,
inputs=[
gr.Image(type="numpy", label="Upload Image"),
gr.Slider(minimum=8, maximum=64, step=8, value=16, label="Grid Size"),
gr.Checkbox(label="Use Color Quantization"),
gr.Slider(minimum=2, maximum=16, step=1, value=8, label="Number of Colors (K-Means)")
],
outputs=[
gr.Image(type="numpy", label="Mosaic Result"),
gr.Textbox(label="Structural Similarity (SSIM)"),
gr.Textbox(label="Mean Squared Error (MSE)")
],
title="Interactive Image Mosaic Generator (Optimized K-Means)",
description="Upload an image to create a mosaic-style reconstruction. Adjust the grid size and color quantization settings.",
examples=[["https://res.cloudinary.com/daigovpbf/image/upload/c_crop,g_auto,h_800,w_800/samples/cup-on-a-table", 8, False, 0],
["https://res.cloudinary.com/daigovpbf/image/upload/c_crop,g_auto,h_800,w_800/samples/dessert-on-a-plate", 16, True, 8],
["https://res.cloudinary.com/daigovpbf/image/upload/c_crop,g_auto,h_800,w_800/samples/breakfast", 32, True, 4],
["https://res.cloudinary.com/daigovpbf/image/upload/c_crop,g_auto,h_800,w_800/samples/balloons", 64, True, 8]],
theme="default"
)
if __name__ == "__main__":
iface.launch(share=True)
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