use object detector with camera feed in python

main.py
import cv2

#Load the pre-trained model
model = cv2.dnn.readNetFromTensorflow('frozen_inference_graph.pb', 'ssd_mobilenet_v2_coco_2018_03_29.pbtxt')

#Define the classes for object detection
classNames = {0: 'background', 1: 'person', 2: 'bicycle', 3: 'car', 4: 'motorcycle',
              5: 'airplane', 6: 'bus', 7: 'train', 8: 'truck', 9: 'boat'}

#Create a video capture object
cap = cv2.VideoCapture(0)

while True:
    #Read the frames from the camera feed
    ret, frame = cap.read()

    #Create OpenCV blob from the input frame
    blob = cv2.dnn.blobFromImage(frame, size=(300, 300), swapRB=True)

    #Set the input blob to the model
    model.setInput(blob)

    #Perform object detection on the input frame
    detections = model.forward()

    #Loop through the detections and draw the bounding boxes
    for i in range(0, detections.shape[2]):
        confidence = detections[0, 0, i, 2]

        #Filter out weak detections
        if confidence > 0.5:
            classId = int(detections[0, 0, i, 1])
            className = classNames[classId]

            #Get the bounding box coordinates
            box = detections[0, 0, i, 3:7] * numpy.array([frame.shape[1], frame.shape[0], frame.shape[1], frame.shape[0]])
            (startX, startY, endX, endY) = box.astype("int")

            #Draw the bounding box and label on the frame
            cv2.rectangle(frame, (startX, startY), (endX, endY), (0, 255, 0), 2)
            y = startY - 15 if startY - 15 > 15 else startY + 15
            cv2.putText(frame, className, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    #Show the output
    cv2.imshow('Object Detection using OpenCV', frame)

    #Exit on 'q' press
    if cv2.waitKey(1) == ord('q'):
        break

#Release the video capture object and destroy all windows
cap.release()
cv2.destroyAllWindows()
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