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example_usage.py

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+"""
+Example usage of Ultimate V2 Chess Board Segmentation ONNX model
+"""
+
+import onnxruntime as ort
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+
+def load_model(model_path):
+    """Load ONNX model"""
+    session = ort.InferenceSession(model_path)
+    return session
+
+def preprocess_image(image_path):
+    """Preprocess image for model input"""
+    # Load image
+    image = cv2.imread(image_path)
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    
+    # Resize to model input size
+    image_resized = cv2.resize(image_rgb, (256, 256))
+    
+    # Normalize to [0, 1]
+    image_normalized = image_resized.astype(np.float32) / 255.0
+    
+    # Convert to model input format (NCHW)
+    input_tensor = np.transpose(image_normalized, (2, 0, 1))[np.newaxis, ...]
+    
+    return input_tensor, image_rgb
+
+def run_inference(session, input_tensor):
+    """Run inference on the model"""
+    # Get input name
+    input_name = session.get_inputs()[0].name
+    
+    # Run inference
+    outputs = session.run(None, {input_name: input_tensor})
+    
+    # Apply sigmoid to get probabilities
+    mask = 1.0 / (1.0 + np.exp(-outputs[0]))
+    
+    return mask.squeeze()
+
+def visualize_results(original_image, mask, threshold=0.5):
+    """Visualize the segmentation results"""
+    # Create binary mask
+    binary_mask = (mask > threshold).astype(np.uint8) * 255
+    
+    # Create overlay
+    overlay = original_image.copy()
+    overlay[binary_mask > 0] = [255, 0, 0]  # Red overlay
+    
+    # Plot results
+    fig, axes = plt.subplots(1, 4, figsize=(16, 4))
+    
+    axes[0].imshow(original_image)
+    axes[0].set_title('Original Image')
+    axes[0].axis('off')
+    
+    axes[1].imshow(mask, cmap='hot')
+    axes[1].set_title('Segmentation Heatmap')
+    axes[1].axis('off')
+    
+    axes[2].imshow(binary_mask, cmap='gray')
+    axes[2].set_title('Binary Mask')
+    axes[2].axis('off')
+    
+    axes[3].imshow(overlay)
+    axes[3].set_title('Overlay')
+    axes[3].axis('off')
+    
+    plt.tight_layout()
+    plt.show()
+
+def main():
+    """Main example function"""
+    # Load model
+    model_path = "ultimate_v2_breakthrough_accurate.onnx"
+    session = load_model(model_path)
+    
+    # Process image
+    image_path = "chess_board.jpg"  # Replace with your image
+    input_tensor, original_image = preprocess_image(image_path)
+    
+    # Run inference
+    mask = run_inference(session, input_tensor)
+    
+    # Visualize results
+    visualize_results(original_image, mask)
+    
+    print(f"✅ Chess board segmentation completed!")
+    print(f"📊 Mask shape: {mask.shape}")
+    print(f"📈 Mask range: {mask.min():.3f} - {mask.max():.3f}")
+
+if __name__ == "__main__":
+    main()