app.py

import streamlit as st
import cv2
import tempfile
import os

# Load model and labels
config_model = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt.txt'
frozen_model = 'frozen_inference_graph.pb'
model = cv2.dnn_DetectionModel(frozen_model, config_model)

class_labels = []
file_name = 'labels.txt'
with open(file_name, 'rt') as fpt:
    class_labels = fpt.read().rstrip('\n').split('\n')

model.setInputSize(320, 320)
model.setInputScale(1.0 / 127.5)
model.setInputMean((127.5, 127, 5, 127.5))
model.setInputSwapRB(True)

# Streamlit UI
st.title("Object Detection in Videos")

uploaded_file = st.file_uploader("Choose a video...", type=["mp4", "avi", "mov"])
if uploaded_file is not None:
    tfile = tempfile.NamedTemporaryFile(delete=False)
    tfile.write(uploaded_file.read())
    cap = cv2.VideoCapture(tfile.name)

    # Check if video opened successfully
    if not cap.isOpened():
        st.error("Error opening video file")

    # Process video
    font_scale = 1
    font = cv2.FONT_HERSHEY_PLAIN

    # Save processed video
    output_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4')
    frame_width = int(cap.get(3))
    frame_height = int(cap.get(4))
    out = cv2.VideoWriter(output_file.name, cv2.VideoWriter_fourcc(*'mp4v'), 20, (frame_width, frame_height))

    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break

        ClassIndex, confidence, bbox = model.detect(frame, confThreshold=0.55)

        if len(ClassIndex) != 0:
            for ClassInd, conf, boxes in zip(ClassIndex.flatten(), confidence.flatten(), bbox):
                if ClassInd <= 80:
                    cv2.rectangle(frame, boxes, (255, 0, 0), 2)
                    cv2.putText(frame, class_labels[ClassInd - 1], (boxes[0] + 10, boxes[1] + 40), font, fontScale=font_scale, color=(0, 255, 0), thickness=2)

        out.write(frame)

    cap.release()
    out.release()

    # Display processed video
    st.video(output_file.name)

    # Clean up temporary files
    os.unlink(tfile.name)
    os.unlink(output_file.name)