import streamlit as st import cv2 import numpy as np import matplotlib.pyplot as plt import pandas as pd import plotly.express as px from PIL import Image def analyze_crack(image): # Convert image to grayscale gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Edge detection edges = cv2.Canny(gray, 50, 150) # Finding contours contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Calculate crack metrics crack_lengths = [cv2.arcLength(cnt, True) for cnt in contours] crack_widths = [cv2.boundingRect(cnt)[2] for cnt in contours] return edges, crack_lengths, crack_widths def main(): st.set_page_config(page_title='Structural Integrity Analyst', layout='wide') st.title('🏗️ Structural Integrity Analyst') st.sidebar.header("Upload Crack Image") uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) if uploaded_file is not None: image = Image.open(uploaded_file) image = np.array(image) edges, crack_lengths, crack_widths = analyze_crack(image) st.subheader("Uploaded Image") st.image(uploaded_file, caption="Uploaded Image", use_column_width=True) # Display processed image st.subheader("Processed Crack Detection") fig, ax = plt.subplots() ax.imshow(edges, cmap='gray') ax.axis("off") st.pyplot(fig) # Data Analysis data = pd.DataFrame({ "Crack Length (pixels)": crack_lengths, "Crack Width (pixels)": crack_widths }) st.subheader("Crack Metrics") st.dataframe(data) # Visualization fig1 = px.histogram(data, x="Crack Length (pixels)", title="Crack Length Distribution", nbins=10) fig2 = px.histogram(data, x="Crack Width (pixels)", title="Crack Width Distribution", nbins=10) st.plotly_chart(fig1, use_container_width=True) st.plotly_chart(fig2, use_container_width=True) if __name__ == "__main__": main()