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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 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() | |