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Bioengineering_Query_Tool_image_based / 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
import base64
from datetime import datetime

def apply_color_transformation(image, transform_type):
    """Apply different color transformations to the image"""
    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

def process_image(image, transform_type):
    """Process uploaded image and extract cell features"""
    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
    )

# 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__":
    demo.launch()

	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"""
	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

	def process_image(image, transform_type):
	"""Process uploaded image and extract cell features"""
	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
	)

	# 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__":
	demo.launch()