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| import cv2 | |
| import numpy as np | |
| import gradio as gr | |
| import pywt | |
| from skimage import exposure | |
| from PIL import Image | |
| def musica_enhancement(img): | |
| # Convert PIL Image to NumPy array | |
| img = np.array(img) | |
| # Convert from RGB to grayscale if needed | |
| if len(img.shape) == 3: | |
| img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) | |
| # Normalize to [0, 1] | |
| img_norm = img.astype(np.float32) / 255.0 | |
| # 1. Wavelet Decomposition | |
| coeffs = pywt.wavedec2(img_norm, 'bior1.3', level=3) | |
| cA3, (cH3, cV3, cD3), (cH2, cV2, cD2), (cH1, cV1, cD1) = coeffs | |
| # 2. Frequency Band Processing | |
| cD1 = pywt.threshold(cD1, 0.05*np.max(cD1), mode='soft') | |
| cD2 = pywt.threshold(cD2, 0.07*np.max(cD2), mode='soft') | |
| cH1 = cH1 * 1.2 | |
| cV1 = cV1 * 1.2 | |
| # 3. Reconstruction with Clipping | |
| coeffs_enhanced = [cA3, (cH3, cV3, cD3), (cH2, cV2, cD2), (cH1, cV1, cD1)] | |
| img_recon = pywt.waverec2(coeffs_enhanced, 'bior1.3') | |
| img_recon = np.clip(img_recon, 0, 1) | |
| # 4. Adaptive CLAHE | |
| entropy = -np.sum(img_recon * np.log2(img_recon + 1e-7)) | |
| clip_limit = 0.02 if entropy > 7 else 0.05 | |
| img_clahe = exposure.equalize_adapthist(img_recon, clip_limit=clip_limit, kernel_size=64) | |
| # 5. Gamma Correction | |
| p5, p95 = np.percentile(img_clahe, (5, 95)) | |
| gamma = 0.7 if (p95 - p5) < 0.3 else 0.9 | |
| img_gamma = exposure.adjust_gamma(img_clahe, gamma=gamma) | |
| # 6. Edge Enhancement | |
| img_gamma_8bit = (img_gamma * 255).astype(np.uint8) | |
| img_bgr = cv2.cvtColor(img_gamma_8bit, cv2.COLOR_GRAY2BGR) | |
| img_sharp = cv2.detailEnhance(img_bgr, sigma_s=12, sigma_r=0.15) | |
| img_sharp = cv2.cvtColor(img_sharp, cv2.COLOR_BGR2RGB) | |
| return img_sharp | |
| def process_image(input_img): | |
| # Convert Gradio input (PIL Image) to NumPy array | |
| input_img = np.array(input_img) | |
| # Enhance the image | |
| enhanced_img = musica_enhancement(input_img) | |
| # Convert enhanced image back to PIL for display | |
| enhanced_img = Image.fromarray(enhanced_img) | |
| return input_img, enhanced_img | |
| # Gradio Interface | |
| demo = gr.Interface( | |
| fn=process_image, | |
| inputs=gr.Image(label="Upload X-ray (TIFF or JPEG/PNG)"), | |
| outputs=[ | |
| gr.Image(label="Original Image"), | |
| gr.Image(label="Enhanced Image") | |
| ], | |
| title="X-ray Enhancement (MUSICA®-Style)", | |
| description="Upload a TIFF, JPEG, or PNG X-ray image to enhance it using multi-scale wavelet processing and adaptive contrast adjustment." | |
| ) | |
| demo.launch() |