Create app.py

app.py

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+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
+
+def process_image(image):
+    """Process uploaded image and extract cell features"""
+    # Convert to BGR if image is RGB
+    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)
+    
+    # Segmentation
+    thresh = cv2.adaptiveThreshold(
+        blurred, 255,
+        cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+        cv2.THRESH_BINARY_INV, 21, 4
+    )
+    
+    # Clean small noise
+    cleaned = morphology.opening(thresh, morphology.disk(2))
+    
+    # Watershed segmentation
+    sure_bg = cv2.dilate(cleaned, morphology.disk(3), iterations=3)
+    dist = cv2.distanceTransform(cleaned, cv2.DIST_L2, 5)
+    ret, sure_fg = cv2.threshold(dist, 0.5*dist.max(), 255, 0)
+    sure_fg = np.uint8(sure_fg)
+    unknown = cv2.subtract(sure_bg, sure_fg)
+    
+    # Marker labelling
+    ret, markers = cv2.connectedComponents(sure_fg)
+    markers += 1
+    markers[unknown == 255] = 0
+    markers = watershed(-dist, markers, mask=cleaned)
+    
+    # Extract features
+    features = []
+    props = measure.regionprops(markers, intensity_image=gray)
+    
+    for i, prop in enumerate(props):
+        if prop.area < 50:  # Filter small regions
+            continue
+        
+        features.append({
+            'cell_id': i+1,
+            'area': prop.area,
+            'perimeter': prop.perimeter,
+            'circularity': (4 * np.pi * prop.area) / (prop.perimeter**2 + 1e-6),
+            'mean_intensity': prop.mean_intensity,
+            'centroid_x': prop.centroid[1],
+            'centroid_y': prop.centroid[0]
+        })
+    
+    # Create visualization
+    vis_img = image.copy()
+    contours = measure.find_contours(markers, 0.5)
+    
+    # Draw contours and cell IDs
+    for contour in contours:
+        coords = contour.astype(int)
+        cv2.drawContours(vis_img, [coords], -1, (0,255,0), 1)
+    
+    for region in measure.regionprops(markers):
+        if region.area >= 50:
+            y, x = region.centroid
+            cv2.putText(vis_img, str(region.label), 
+                       (int(x), int(y)),
+                       cv2.FONT_HERSHEY_SIMPLEX, 
+                       0.4, (255,0,0), 1)
+    
+    # Create summary plots
+    fig, axes = plt.subplots(1, 2, figsize=(12, 6))
+    
+    # Cell size distribution
+    df = pd.DataFrame(features)
+    df['area'].hist(ax=axes[0], bins=20)
+    axes[0].set_title('Cell Size Distribution')
+    axes[0].set_xlabel('Area')
+    axes[0].set_ylabel('Count')
+    
+    # Circularity vs Intensity
+    axes[1].scatter(df['circularity'], df['mean_intensity'])
+    axes[1].set_title('Circularity vs Intensity')
+    axes[1].set_xlabel('Circularity')
+    axes[1].set_ylabel('Mean Intensity')
+    
+    plt.tight_layout()
+    
+    return (
+        cv2.cvtColor(vis_img, cv2.COLOR_BGR2RGB),
+        fig,
+        df
+    )
+
+# Create Gradio interface
+with gr.Blocks(title="Cell Analysis Tool") as demo:
+    gr.Markdown("""
+    # 🔬 Bioengineering Cell Analysis Tool
+    
+    Upload microscopy images to analyze cell properties:
+    - Automated cell detection
+    - Feature extraction (size, shape, intensity)
+    - Statistical analysis
+    
+    **Instructions:**
+    1. Upload an image containing cells
+    2. Wait for analysis to complete
+    3. Review results in three tabs:
+       - Detected cells visualization
+       - Statistical plots
+       - Detailed measurements table
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(
+                label="Upload Image",
+                type="numpy",
+                tool="upload"
+            )
+            analyze_btn = gr.Button(
+                "Analyze Image", 
+                variant="primary"
+            )
+        
+        with gr.Column():
+            with gr.Tabs():
+                with gr.Tab("Detection Results"):
+                    output_image = gr.Image(
+                        label="Detected Cells"
+                    )
+                with gr.Tab("Statistics"):
+                    output_plot = gr.Plot(
+                        label="Statistical Analysis"
+                    )
+                with gr.Tab("Measurements"):
+                    output_table = gr.DataFrame(
+                        label="Cell Features"
+                    )
+    
+    analyze_btn.click(
+        fn=process_image,
+        inputs=input_image,
+        outputs=[output_image, output_plot, output_table]
+    )
+
+# Launch the demo
+if __name__ == "__main__":
+    demo.launch()