app.py

import streamlit as st
import cv2
import tempfile
from ultralytics import YOLO
import numpy as np
import time

alerting_classes = {
    0: 'People',
    2: 'Car',
    7: 'Truck',
    24: 'Backpack',
    65: 'Suspicious handheld device',
    26: 'Handbag',
    28: 'Suitcase',
}

red_tint = np.array([[[0, 0, 255]]], dtype=np.uint8)

model1 = YOLO('yolov8n.pt')

st.title("Object Detection and Recognition")

video_file = st.file_uploader("Choose a video file", type=["mp4"])

if video_file is not None:
    # Create temporary file for uploaded video
    tfile = tempfile.NamedTemporaryFile(delete=False)
    tfile.write(video_file.read())

    # Open video capture using temporary file path
    cap = cv2.VideoCapture(tfile.name)
    alert_set = set(alerting_classes.keys())
    alert_set.remove(0)

    # Create red-tinted overlay
    red_tinted_overlay = np.tile(red_tint, (1, 1, 1))

    stop_button = st.button("Stop Inference")
    processing_interrupted = False

    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    progress_bar_processing_slot = st.empty()

    # Collect frames in a list
    frames = []

    while cap.isOpened() and not processing_interrupted:
        alert_flag = False
        alert_reason = []

        success, frame = cap.read()

        # if the frame is read correctly ret is True
        if not success:
            # st.warning("Can't receive frame (stream end?). Exiting ...")
            break

        if success:
            # Check if the stop button is clicked
            if stop_button:
                processing_interrupted = True
                break

            # Perform YOLO object detection
            results = model1(frame, conf=0.35, verbose=False, classes=list(alerting_classes.keys()))

            class_ids = results[0].boxes.cls.tolist()
            class_counts = {cls: class_ids.count(cls) for cls in set(class_ids)}

            for cls in alert_set:
                if cls in class_counts and class_counts[cls] > 0:
                    alert_flag = True
                    alert_reason.append((cls, class_counts[cls]))

            if class_counts.get(0, 0) > 5:
                alert_flag = True
                alert_reason.append((0, class_counts[0]))

            text = 'ALERT!'
            font = cv2.FONT_HERSHEY_DUPLEX
            font_scale = 0.75
            thickness = 2

            size = cv2.getTextSize(text, font, font_scale, thickness)
            x = 0
            y = int((2 + size[0][1]))

            img = results[0].plot()
            if alert_flag:
                # Resize the red-tinted overlay to match the image size
                red_tinted_overlay = cv2.resize(red_tinted_overlay, (img.shape[1], img.shape[0]))
                img = cv2.addWeighted(img, 0.7, red_tinted_overlay, 0.3, 0)
                cv2.putText(img, text, (x, y), font, font_scale, (0, 0, 0), thickness)

                y += int(size[0][1]) + 10  # Move to the next line

                for cls, count in alert_reason:
                    alert_text = f'{count} {alerting_classes[cls]}'
                    cv2.putText(img, alert_text, (x, y), font, font_scale, (0, 0, 0), thickness)
                    y += int(size[0][1]) + 10  # Move to the next line

            # Append the frame to the list
            frames.append(img)

            del results

            # Update processing progress bar
            current_frame_processing = int(cap.get(cv2.CAP_PROP_POS_FRAMES))
            progress = current_frame_processing / total_frames
            progress_bar_processing_slot.progress(progress)
            progress_bar_processing_slot.text(f"Processing... {int(progress * 100)}%")

    # Release resources
    cap.release()
    tfile.close()

    # Display frames one by one as a video with 24 FPS
    if processing_interrupted:
        st.text("User interrupted processing.")
    else:
        progress_bar_processing_slot.text("Done!")
        video_placeholder = st.image([], channels="BGR", caption="YOLOv8 Inference")
        progress_bar_display = st.progress(0)
        fps_delay = 1 / 24  # Delay to achieve 24 FPS