add app files

app.py

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+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)

requirements.txt

@@ -0,0 +1,108 @@
+aiofiles==23.2.1
+annotated-types==0.7.0
+anyio==4.8.0
+appnope==0.1.4
+asttokens==3.0.0
+attrs==25.1.0
+backcall==0.2.0
+beautifulsoup4==4.13.3
+bleach==6.2.0
+certifi==2022.9.24
+charset-normalizer==2.1.1
+click==8.1.8
+contourpy==1.0.6
+cycler==0.11.0
+decorator==5.1.1
+defusedxml==0.7.1
+docopt==0.6.2
+exceptiongroup==1.2.2
+executing==2.2.0
+fastapi==0.115.8
+fastjsonschema==2.21.1
+ffmpy==0.5.0
+filelock==3.17.0
+fonttools==4.38.0
+fsspec==2025.2.0
+gradio==5.15.0
+gradio_client==1.7.0
+h11==0.14.0
+httpcore==1.0.7
+httpx==0.28.1
+huggingface-hub==0.28.1
+idna==3.4
+imageio==2.37.0
+ipython==8.12.3
+jedi==0.19.2
+Jinja2==3.1.5
+jsonschema==4.23.0
+jsonschema-specifications==2024.10.1
+jupyter_client==8.6.3
+jupyter_core==5.7.2
+jupyterlab_pygments==0.3.0
+kiwisolver==1.4.4
+lazy_loader==0.4
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.6.2
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+mistune==3.1.1
+nbclient==0.10.2
+nbconvert==7.16.6
+nbformat==5.10.4
+networkx==3.4.2
+numpy==2.2.2
+opencv-python==4.11.0.86
+orjson==3.10.15
+packaging==21.3
+pandas==2.2.3
+pandocfilters==1.5.1
+parso==0.8.4
+pexpect==4.9.0
+pickleshare==0.7.5
+pillow==11.1.0
+pipreqs==0.5.0
+platformdirs==4.3.6
+prompt_toolkit==3.0.50
+ptyprocess==0.7.0
+pure_eval==0.2.3
+pydantic==2.10.6
+pydantic_core==2.27.2
+pydub==0.25.1
+Pygments==2.19.1
+pyparsing==3.0.9
+python-dateutil==2.8.2
+python-multipart==0.0.20
+pytz==2022.6
+PyYAML==6.0.2
+pyzmq==26.2.1
+referencing==0.36.2
+requests==2.28.1
+rich==13.9.4
+rpds-py==0.22.3
+ruff==0.9.4
+safehttpx==0.1.6
+scikit-image==0.25.1
+scipy==1.15.1
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.16.0
+sniffio==1.3.1
+soupsieve==2.6
+stack-data==0.6.3
+starlette==0.45.3
+tifffile==2025.1.10
+tinycss2==1.4.0
+tomlkit==0.13.2
+tornado==6.4.2
+tqdm==4.67.1
+traitlets==5.14.3
+typer==0.15.1
+typing_extensions==4.12.2
+tzdata==2025.1
+urllib3==1.26.12
+uvicorn==0.34.0
+wcwidth==0.2.13
+webencodings==0.5.1
+websockets==14.2
+yarg==0.1.9