real_time_detection.py

import numpy as np
import cv2

model_bin = "MobileNetSSD_deploy.caffemodel"
config_text = "MobileNetSSD_deploy.prototxt"

objName = ["aeroplane", "background", "bicycle", "bird", "boat",
           "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
           "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
           "sofa", "train", "tvmonitor"]

net = cv2.dnn.readNetFromCaffe(config_text, model_bin)

cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    if ret is False:
        break
    h, w = frame.shape[:2]
    
    blobImage = cv2.dnn.blobFromImage(frame, 0.007843, (300, 300), (127.5, 127.5, 127.5), True, False)
    net.setInput(blobImage)
    cvOut = net.forward()
    
    for detection in cvOut[0, 0, :, :]:
        score = float(detection[2])
        objIndex = int(detection[1])
        if score > 0.5:
            left = detection[3]*w
            top = detection[4]*h
            right = detection[5]*w
            bottom = detection[6]*h
            
            cv2.rectangle(frame, (int(left), int(top)), (int(right), int(bottom)), (255, 0, 0), thickness = 2)
            cv2.putText(frame, "Score:%.2f, %s" %(score, objName[objIndex]), 
                        (int(left) -10, int(top) -5), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2, 8)
    cv2.imshow("Video-ssd-demo", frame)
    c = cv2.waitKey(10)
    if c == 27:
        break

cv2.waitKey(0)