app.py

import streamlit as st
from ultralytics import YOLO
from utils import detect_objects, load_image, draw_bounding_boxes

models = {
    "YOLOv8l": YOLO('yolov8l.pt'),
    "YOLOv8l-OIV7": YOLO('yolov8l-oiv7.pt'),
}

st.title("Object Detection with YOLOv8 Model")
st.write("Upload an image and click 'Analyse Image' to detect objects with different models.")


# Image uploader widget
uploaded_file = st.file_uploader("Choose an image to analyze...", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Display the uploaded image
    image = load_image(uploaded_file)
    st.image(image, caption='Uploaded Image', use_column_width=True)

    # Button to trigger image analysis
    if st.button("Analyse Image"):
        st.write("Detecting objects...")

        all_results = {}
        for model_name, model in models.items():
            detected_labels_confidences, detection_time, bounding_boxes = detect_objects(model, image)
            all_results[model_name] = {"detections": detected_labels_confidences,
                                       "time": detection_time,
                                       "Boxes": bounding_boxes}

        # Display the results for each model
        for model_name, results in all_results.items():
            st.write(f"Results for {model_name}:")
            st.write(f"Time taken for detection: {results['time']:.2f} seconds")
            st.write(f"Number of detected objects: {len(results['detections'])}")

            labels = []
            for label, confidence in results["detections"]:
                labels.append(label)
                st.write(f'Label: {label}, Confidence: {confidence * 100:.2f}%')

            st.write(f"Names of the components detected in the uploaded image: {labels}")

            image_with_bb = image.copy()

            # Draw bounding boxes on the image for specific model
            image_with_bounding_boxes = draw_bounding_boxes(image_with_bb, all_results[model_name]["detections"],
                                                            all_results[model_name]["Boxes"])

            # Display the image with bounding boxes
            st.image(image_with_bounding_boxes, caption=f"Bounding Boxes for {model_name}", use_column_width=True)

            # clear the image after each plot
            image = image.copy()