import cv2 import numpy as np import pandas as pd import gradio as gr from skimage import measure, morphology from skimage.segmentation import watershed import matplotlib.pyplot as plt import base64 from datetime import datetime def apply_color_transformation(image, transform_type): """Apply different color transformations to the image""" try: # Convert to BGR if needed if len(image.shape) == 3 and image.shape[2] == 3: image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) if transform_type == "Original": return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) if len(image.shape) == 3 else image elif transform_type == "Grayscale": return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if len(image.shape) == 3 else image elif transform_type == "Binary": gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if len(image.shape) == 3 else image _, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY) return binary elif transform_type == "CLAHE": gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if len(image.shape) == 3 else image clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8)) return clahe.apply(gray) return image except Exception as e: print(f"Transformation error: {str(e)}") return None def process_image(image, transform_type): """Process uploaded image and extract cell features""" try: if image is None: return [None]*4 # Store original image for color transformations original_image = image.copy() # Convert to BGR for OpenCV processing if len(image.shape) == 3: image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # Preprocessing pipeline gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8)) enhanced = clahe.apply(gray) blurred = cv2.medianBlur(enhanced, 5) # Thresholding _, thresh = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) # Noise removal kernel = np.ones((3,3), np.uint8) opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2) # Sure background area sure_bg = cv2.dilate(opening, kernel, iterations=3) # Finding sure foreground area dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5) _, sure_fg = cv2.threshold(dist_transform, 0.7*dist_transform.max(), 255, 0) sure_fg = np.uint8(sure_fg) # Unknown region unknown = cv2.subtract(sure_bg, sure_fg) # Marker labelling _, markers = cv2.connectedComponents(sure_fg) markers += 1 markers[unknown == 255] = 0 # Watershed algorithm markers = cv2.watershed(image, markers) # Feature extraction features = [] vis_img = image.copy() for region in measure.regionprops(markers): if region.area >= 50: y, x = region.centroid # Store features features.append({ 'label': region.label, 'area': region.area, 'circularity': (4 * np.pi * region.area) / (region.perimeter ** 2) if region.perimeter > 0 else 0, 'mean_intensity': region.mean_intensity }) # Draw text with contrast cv2.putText(vis_img, str(region.label), (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 2) cv2.putText(vis_img, str(region.label), (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255), 1) # Convert visualization image back to RGB vis_img = cv2.cvtColor(vis_img, cv2.COLOR_BGR2RGB) # Create analysis plots plt.style.use('seaborn') fig, axes = plt.subplots(2, 2, figsize=(15, 12)) fig.suptitle('Cell Analysis Results', fontsize=16, y=0.95) df = pd.DataFrame(features) if not df.empty: # Distribution plots df['area'].hist(ax=axes[0,0], bins=20, color='skyblue', edgecolor='black') axes[0,0].set_title('Cell Size Distribution') df['circularity'].hist(ax=axes[0,1], bins=20, color='lightgreen', edgecolor='black') axes[0,1].set_title('Circularity Distribution') # Scatter plot axes[1,0].scatter(df['circularity'], df['mean_intensity'], alpha=0.6, c='purple') axes[1,0].set_title('Circularity vs Intensity') # Box plot df.boxplot(column=['area', 'circularity'], ax=axes[1,1]) else: for ax in axes.flat: ax.text(0.5, 0.5, 'No cells detected', ha='center', va='center') plt.tight_layout() # Apply color transformation transformed_img = apply_color_transformation(original_image, transform_type) if transformed_img is not None and len(transformed_img.shape) == 2: transformed_img = cv2.cvtColor(transformed_img, cv2.COLOR_GRAY2RGB) return ( vis_img, transformed_img if transformed_img is not None else original_image, fig, df ) except Exception as e: print(f"Processing error: {str(e)}") return [None]*4 # Create enhanced Gradio interface with gr.Blocks(title="Advanced Cell Analysis Tool", theme=gr.themes.Soft()) as demo: gr.Markdown(""" # 🔬 Advanced Bioengineering Cell Analysis Tool ## Features - 🔍 Automated cell detection and measurement - 📊 Comprehensive statistical analysis - 🎨 Multiple visualization options - 📥 Downloadable results ## Author - **Muhammad Ibrahim Qasmi** - [LinkedIn](https://www.linkedin.com/in/muhammad-ibrahim-qasmi-9876a1297/) - [GitHub](https://github.com/yourusername) """) with gr.Row(): with gr.Column(scale=1): input_image = gr.Image( label="Upload Image", type="numpy" ) transform_type = gr.Dropdown( choices=["Original", "Grayscale", "Binary", "CLAHE"], value="Original", label="Image Transform" ) analyze_btn = gr.Button( "Analyze Image", variant="primary", size="lg" ) with gr.Column(scale=2): with gr.Tabs(): with gr.Tab("Analysis Results"): output_image = gr.Image( label="Detected Cells" ) gr.Markdown("*Green contours show detected cells, red numbers are cell IDs*") with gr.Tab("Image Transformations"): transformed_image = gr.Image( label="Transformed Image" ) gr.Markdown("*Select different transformations from the dropdown menu*") with gr.Tab("Statistics"): output_plot = gr.Plot( label="Statistical Analysis" ) gr.Markdown("*Hover over plots for detailed values*") with gr.Tab("Data"): output_table = gr.DataFrame( label="Cell Features" ) download_btn = gr.Button( "Download Results", variant="secondary" ) # Add footer gr.Markdown(""" --- ### 📝 Notes - Supported image formats: PNG, JPG, JPEG - Minimum recommended resolution: 512x512 pixels - Processing time varies with image size and cell count *Last updated: January 2025* """) analyze_btn.click( fn=process_image, inputs=[input_image, transform_type], outputs=[output_image, transformed_image, output_plot, output_table] ) # Launch the demo if __name__ == "__main__": try: demo.launch() except Exception as e: print(f"Error launching Gradio interface: {e}")