bacancydataprophets/Metal_Defect_Detection

1import streamlit as st2from PIL import Image3import matplotlib.pyplot as plt4import matplotlib.patches as patches5from detect import Detection6from classify import Classification7from compare import Compare8 9# Streamlit app10def main():11    st.title("Metal Defect Detection and Classification App")12    det_res = Detection()13    cls_res = Classification()14    comp = Compare()15    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])16 17    if uploaded_file is not None:18        # Display uploaded image19        image = Image.open(uploaded_file)20        st.image(image, caption='Uploaded Image', use_column_width=True)21 22        df = det_res.detect_defect(image)23        df1 = cls_res.classify_defect(image)24 25        # Perform comparison between scores of detection and classification26        detection_results = comp.comparison(df, df1)27 28        # Display results29        fig, ax = plt.subplots(1)30        ax.imshow(image)31 32        for index, row in detection_results.iterrows():33            x1, y1, x2, y2 = row['x1'], row['y1'], row['x2'], row['y2']34            width, height = x2 - x1, y2 - y135            rect = patches.Rectangle((x1, y1), width, height, linewidth=1, edgecolor='r', facecolor='none')36            ax.add_patch(rect)37            ax.text(x1, y1 - 5, f"{row['fnl_cls']}: {row['fnl_pred']:.2f}", color='r')38 39        ax.axis('off')40        st.pyplot(fig)41 42# Run the app43if __name__ == "__main__":44    main()