from flask import Flask, render_template, Response import cv2 import os app = Flask(__name__) thres = 0.45 # Threshold to detect objects classNames = [] # Load class names script_dir = os.path.dirname(os.path.realpath(__file__)) classFile = os.path.join(script_dir, 'coco.names') with open(classFile, 'rt') as f: classNames = f.read().rstrip('\n').split('\n') # Load the detection model ssd_path = os.path.join(script_dir, 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt') frozen_path = os.path.join(script_dir, 'frozen_inference_graph.pb') net = cv2.dnn_DetectionModel(frozen_path, ssd_path) net.setInputSize(320, 320) net.setInputScale(1.0 / 127.5) net.setInputMean((127.5, 127.5, 127.5)) net.setInputSwapRB(True) # Function to process frames def process_frame(frame): classIds, confs, bbox = net.detect(frame, confThreshold=thres) if len(classIds) != 0: for classId, confidence, box in zip(classIds.flatten(), confs.flatten(), bbox): if 0 <= classId - 1 < len(classNames): class_name = classNames[classId - 1].upper() cv2.rectangle(frame, box, color=(0, 255, 0), thickness=2) cv2.putText(frame, class_name, (box[0] + 10, box[1] + 30), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2) cv2.putText(frame, str(round(confidence * 100, 2)), (box[0] + 200, box[1] + 30), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2) return frame # Camera capture function def generate_frames(): cap = cv2.VideoCapture(0) while True: success, frame = cap.read() if not success: break else: frame = process_frame(frame) ret, buffer = cv2.imencode('.jpg', frame) frame = buffer.tobytes() yield (b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n') @app.route('/') def index(): return render_template('index.html') @app.route('/video_feed') def video_feed(): return Response(generate_frames(), mimetype='multipart/x-mixed-replace; boundary=frame') if __name__ == '__main__': app.run(debug=True)