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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):
# Convert PIL Image to numpy array
img = np.array(image)
# Debugging: Print image properties
print(f"Uploaded image shape: {img.shape}, dtype: {img.dtype}")
print(f"Image min: {img.min()}, max: {img.max()}")
# Convert RGB to grayscale if necessary
if len(img.shape) == 3 and img.shape[2] == 3: # Check for RGB
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
print("Converted RGB image to grayscale.")
# Ensure the image has 16-bit depth
if img.dtype != np.uint16:
img = img.astype(np.uint16) # Scale if necessary
print("Converted image to 16-bit.")
# Normalize to [0, 1]
img_norm = img.astype(np.float32) / 65535.0
print(f"Normalized image min: {img_norm.min()}, max: {img_norm.max()}")
# Wavelet decomposition and reconstruction
coeffs = pywt.wavedec2(img_norm, 'bior1.3', level=3)
cA3, (cH3, cV3, cD3), (cH2, cV2, cD2), (cH1, cV1, cD1) = coeffs
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
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)
print(f"Reconstructed image min: {img_recon.min()}, max: {img_recon.max()}")
# CLAHE
img_clahe = exposure.equalize_adapthist(img_recon, clip_limit=0.02, kernel_size=64)
# Gamma correction
img_gamma = exposure.adjust_gamma(img_clahe, gamma=0.9)
# Convert to 8-bit
img_gamma_8bit = (img_gamma * 255).astype(np.uint8)
# Convert to RGB for better viewing compatibility
img_rgb = cv2.cvtColor(img_gamma_8bit, cv2.COLOR_GRAY2RGB)
enhanced_image = Image.fromarray(img_rgb)
# Create histogram plot
plt.figure(figsize=(6, 4))
plt.hist(img_gamma_8bit.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",
)
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=[
"examples/sample1.tif",
"examples/sample2.tif",
"examples/sample3.tif"
],
inputs=input_image,
label="Or try one of these examples",
)
if __name__ == "__main__":
demo.launch()
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