real_time_detection.py

import cv2 as cv

path = "text\\"

model_bin = (path + "MobileNetSSD_deploy.caffemodel")
config_text = (path + "MobileNetSSD_deploy.prototxt")

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

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

cap = cv.VideoCapture(1)
while True:
    ret, frame = cap.read()
    if ret is False:
        break
    h, w = frame.shape[:2]
    blobImage = cv.dnn.blobFromImage(frame, 0.008444, (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

            cv.rectangle(frame, (int(left), int(top)), (int(right), int(bottom)), (255, 0, 0), thickness=2)
            cv.putText(frame, "score: %2.f, %s" % (score, objName[objIndex]),
                       (int(left) - 10, int(top) - 5), cv.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2, 8)

    cv.imshow("video-ssd-demo", frame)
    c = cv.waitKey(10)
    if c == 27:
        break

cv.waitKey(0)