app.py

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:
        if len(image.shape) == 3:
            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        
        if transform_type == "Original":
            return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        elif transform_type == "Grayscale":
            return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        elif transform_type == "Binary":
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
            _, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
            return binary
        elif transform_type == "CLAHE":
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
            clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
            return clahe.apply(gray)
        return image
    except Exception as e:
        print(f"Error in apply_color_transformation: {e}")
        return None

def process_image(image, transform_type):
    """Process uploaded image and extract cell features"""
    try:
        if image is None:
            return None, None, None, None
        
        # Store original image for color transformations
        original_image = image.copy()
        
        # Process image as before
        if len(image.shape) == 3:
            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        
        # Basic preprocessing
        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)
        
        # [Rest of the processing code remains the same until visualization]
        
        # Create enhanced visualization with timestamp
        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        vis_img = image.copy()
        contours = measure.find_contours(markers, 0.5)
        
        # Draw contours and cell IDs with improved visibility
        for contour in contours:
            coords = contour.astype(int)
            cv2.drawContours(vis_img, [coords], -1, (0,255,0), 2)  # Thicker lines
        
        for region in measure.regionprops(markers):
            if region.area >= 50:
                y, x = region.centroid
                # Add white background for better text visibility
                cv2.putText(vis_img, str(region.label), 
                           (int(x), int(y)),
                           cv2.FONT_HERSHEY_SIMPLEX, 
                           0.5, (255,255,255), 2)  # White outline
                cv2.putText(vis_img, str(region.label), 
                           (int(x), int(y)),
                           cv2.FONT_HERSHEY_SIMPLEX, 
                           0.5, (0,0,255), 1)  # Red text
        
        # Add timestamp and cell count
        cv2.putText(vis_img, f"Analyzed: {timestamp}", 
                    (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 
                    0.7, (255,255,255), 2)
        
        # Create summary plots with improved styling
        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')
            axes[0,0].set_xlabel('Area')
            axes[0,0].set_ylabel('Count')
            
            df['circularity'].hist(ax=axes[0,1], bins=20, color='lightgreen', edgecolor='black')
            axes[0,1].set_title('Circularity Distribution')
            axes[0,1].set_xlabel('Circularity')
            axes[0,1].set_ylabel('Count')
            
            # Scatter plots
            axes[1,0].scatter(df['circularity'], df['mean_intensity'], 
                             alpha=0.6, c='purple')
            axes[1,0].set_title('Circularity vs Intensity')
            axes[1,0].set_xlabel('Circularity')
            axes[1,0].set_ylabel('Mean Intensity')
            
            # Add box plot
            df.boxplot(column=['area', 'circularity'], ax=axes[1,1])
            axes[1,1].set_title('Feature Distributions')
        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 to original image
        transformed_image = apply_color_transformation(original_image, transform_type)
        
        return (
            cv2.cvtColor(vis_img, cv2.COLOR_BGR2RGB),
            transformed_image,
            fig,
            df
        )
    except Exception as e:
        print(f"Error in process_image: {e}")
        return None, None, None, None

# 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)  <!-- Add your GitHub URL -->
    """)
    
    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}")