Update app.py

app.py

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+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import gradio as gr
+
+def retinex(image, sigma_list):
+    """
+    Apply Retinex algorithm to enhance image.
+    
+    :param image: Input image (BGR format)
+    :param sigma_list: List of sigma values for Gaussian blur
+    :return: Retinex enhanced image
+    """
+    # Convert image to float32
+    image = np.float32(image) + 1.0
+
+    # Initialize the Retinex result
+    retinex_result = np.zeros_like(image)
+
+    for sigma in sigma_list:
+        # Apply Gaussian blur
+        blurred = cv2.GaussianBlur(image, (0, 0), sigma)
+        # Compute the Retinex result
+        retinex_result += np.log(image) - np.log(blurred)
+
+    # Normalize and convert back to uint8
+    retinex_result = retinex_result / len(sigma_list)
+    retinex_result = np.exp(retinex_result)
+    retinex_result = cv2.normalize(retinex_result, None, 0, 255, cv2.NORM_MINMAX)
+    retinex_result = np.uint8(retinex_result)
+
+    return retinex_result
+
+def enhance_feeble_light_signals(image, alpha, beta, clip_limit, gamma, sigma_list):
+    # Apply Retinex enhancement
+    retinex_image = retinex(image, sigma_list)
+
+    # Convert to LAB color space
+    lab_image = cv2.cvtColor(retinex_image, cv2.COLOR_BGR2LAB)
+    
+    # Split the LAB image into channels
+    l, a, b = cv2.split(lab_image)
+    
+    # Apply CLAHE (Contrast Limited Adaptive Histogram Equalization) to the L channel
+    clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=(8,8))
+    cl = clahe.apply(l)
+    
+    # Merge the CLAHE enhanced L channel back with a and b channels
+    lab_image_clahe = cv2.merge((cl, a, b))
+    
+    # Convert back to BGR color space
+    enhanced_image = cv2.cvtColor(lab_image_clahe, cv2.COLOR_LAB2BGR)
+    
+    # Brighten the image by adjusting contrast (alpha) and brightness (beta)
+    brightened_image = cv2.convertScaleAbs(enhanced_image, alpha=alpha, beta=beta)
+    
+    # Apply Gamma Correction
+    gamma_corrected = np.power(brightened_image / 255.0, gamma)
+    gamma_corrected = np.uint8(gamma_corrected * 255)
+    
+    return gamma_corrected
+
+def process_image(input_image, alpha, beta, clip_limit, gamma):
+    # Convert image to the format compatible with OpenCV
+    input_image = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR)
+    
+    # Define sigma values for Retinex algorithm
+    sigma_list = [15, 80, 250]  # You can adjust this as needed
+    
+    # Enhance the image using Retinex and other adjustments
+    output_image = enhance_feeble_light_signals(input_image, alpha, beta, clip_limit, gamma, sigma_list)
+    
+    # Convert output image back to RGB for displaying
+    output_image = cv2.cvtColor(output_image, cv2.COLOR_BGR2RGB)
+    
+    return input_image, output_image
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=process_image,
+    inputs=[
+        gr.Image(type="numpy", label="Input Image"),
+        gr.Slider(minimum=1.0, maximum=10.0, value=3.0, label="Alpha (Contrast)"),
+        gr.Slider(minimum=0, maximum=100, value=20, label="Beta (Brightness)"),
+        gr.Slider(minimum=1.0, maximum=15.0, value=10.0, label="CLAHE Clip Limit"),
+        gr.Slider(minimum=0.1, maximum=10.0, value=1.5, label="Gamma Correction"),
+    ],
+    outputs=gr.Image(type="numpy", label="Enhanced Image"),  # Only the enhanced image is shown
+    title="Feeble Light Signal Image Enhancer",
+    description="Upload a dark image, and enhance it using Retinex, CLAHE, contrast, brightness, and gamma correction."
+)
+
+# Launch the Gradio app
+interface.launch()