app.py

import cv2
import numpy as np
import matplotlib.pyplot as plt
import pywt
from skimage import exposure
import gradio as gr
from io import BytesIO
from PIL import Image

# Optional: Print Gradio version for verification
print("Gradio version:", gr.__version__)

def musica_enhancement(image):
    """
    Enhances a 16-bit TIFF image using wavelet decomposition, CLAHE, gamma correction,
    and edge-preserving sharpening.

    Args:
        image (PIL.Image): Uploaded image.

    Returns:
        enhanced_image (PIL.Image): Enhanced image.
        histogram (PIL.Image): Histogram of the enhanced image.
    """
    # Convert PIL Image to numpy array
    img = np.array(image)

    # Handle different image modes
    if image.mode == 'I;16':
        # 16-bit image
        img = img.astype(np.uint16)
        img_norm = img.astype(np.float32) / 65535.0
    elif image.mode == 'I':
        # 32-bit integer image
        img = img.astype(np.int32)
        img_norm = img.astype(np.float32) / (2**32 - 1)
    elif image.mode in ['RGB', 'RGBA']:
        # Convert to grayscale if it's a color image
        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
        img_norm = img.astype(np.float32) / 255.0
    elif image.mode == 'L':
        # 8-bit grayscale
        img_norm = img.astype(np.float32) / 255.0
    else:
        raise ValueError(f"Unsupported image mode: {image.mode}")

    # 1. Multi-Scale Decomposition (3-level wavelet transform)
    coeffs = pywt.wavedec2(img_norm, 'bior1.3', level=3)
    cA3, (cH3, cV3, cD3), (cH2, cV2, cD2), (cH1, cV1, cD1) = coeffs

    # 2. Adaptive Processing per Sub-band
    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. Reconstruct Enhanced Image 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)  # Critical fix

    # 4. Adaptive CLAHE 
    entropy = -np.sum(img_recon * np.log2(img_recon + 1e-7))  # Now safe
    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-Preserving Sharpening (convert to BGR first)
    img_gamma_8bit = (img_gamma * 255).astype(np.uint8)
    img_bgr = cv2.cvtColor(img_gamma_8bit, cv2.COLOR_GRAY2BGR)  # Convert to 3-channel
    img_sharp = cv2.detailEnhance(img_bgr, sigma_s=12, sigma_r=0.15)
    img_sharp = cv2.cvtColor(img_sharp, cv2.COLOR_BGR2GRAY)  # Convert back to grayscale

    # Convert enhanced image to PIL Image
    enhanced_image = Image.fromarray(img_sharp)

    # Create histogram plot
    plt.figure(figsize=(6, 4))
    plt.hist(img_sharp.ravel(), bins=256, range=(0, 255), color='gray')
    plt.title('Enhanced Histogram')
    plt.xlabel('Pixel Intensity')
    plt.ylabel('Frequency')
    plt.tight_layout()
    buf = BytesIO()
    plt.savefig(buf, format='png')
    plt.close()
    buf.seek(0)
    histogram = Image.open(buf)

    return enhanced_image, histogram

# Define Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Musica Image Enhancement")
    gr.Markdown(
        """
        Upload a 16-bit TIFF image to enhance it using wavelet decomposition, CLAHE,
        gamma correction, and edge-preserving sharpening.
        """
    )
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(
                type="pil",
                label="Upload 16-bit TIFF Image",
                tool="editor",  # Remove 'source' parameter
            )
            run_button = gr.Button("Enhance Image")
        with gr.Column():
            output_image = gr.Image(type="pil", label="Enhanced Image")
            output_hist = gr.Image(type="pil", label="Enhanced Histogram")
    
    run_button.click(
        fn=musica_enhancement,
        inputs=input_image,
        outputs=[output_image, output_hist],
    )

    gr.Examples(
        examples=[
            "sample1.tif",
            "sample2.tif",
            "sample3.tif"
        ],
        inputs=input_image,
        label="Or try one of these examples",
    )

if __name__ == "__main__":
    demo.launch()