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import streamlit as st
import cv2
import numpy as np
from PIL import Image
# Helper function to preprocess each image
def preprocess_image(image):
# Convert to NumPy array and apply preprocessing
image_np = np.array(image)
# Convert to grayscale
image_gray = cv2.cvtColor(image_np, cv2.COLOR_BGR2GRAY)
# CLAHE equalization
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
image_clahe = clahe.apply(image_gray)
# Sobel X and Y gradients
sobel_x = cv2.Sobel(image_clahe, cv2.CV_64F, 1, 0)
sobel_y = cv2.Sobel(image_clahe, cv2.CV_64F, 0, 1)
# Magnitude of gradients
sobel_magnitude = cv2.magnitude(sobel_x, sobel_y)
# Binarization
_, binary_image = cv2.threshold(sobel_magnitude, 70, 100, cv2.THRESH_BINARY)
# Erosion and dilation
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
dilated_image = cv2.dilate(binary_image, kernel, iterations=1)
final_image = cv2.erode(dilated_image, kernel, iterations=1)
# Normalize the final image to [0.0, 1.0] if float
final_image_normalized = cv2.normalize(final_image, None, 0, 1, cv2.NORM_MINMAX)
return final_image_normalized
# Streamlit App
st.title('Image Processing and Display')
# Upload image or images
uploaded_files = st.file_uploader("Choose images", type=['png', 'jpg', 'jpeg'], accept_multiple_files=True)
# Button to trigger preprocessing
if st.button('Process Images'):
if uploaded_files:
st.write(f"Total files uploaded: {len(uploaded_files)}")
for uploaded_file in uploaded_files:
# Load and display original image
image = Image.open(uploaded_file)
# Preprocess the image
preprocessed_image = preprocess_image(image)
# Display original and preprocessed images side by side
col1, col2 = st.columns(2)
with col1:
st.subheader(f"Original Image: {uploaded_file.name}")
st.image(image, caption='Original Image', use_column_width=True)
with col2:
st.subheader("Processed image")
st.image(preprocessed_image, caption='Image with Blood vessels extracted', use_column_width=True, clamp=True)
else:
st.warning("Please upload images before processing.") |