opencv越來越強大了，可以直接對訓練好的caffe、tensorflow等框架訓練好的模型進行載入，進而完成識別、檢測等任務。

    opencv載入caffe訓練好的模型，採用readNetFromCaffe(arg1,arg2)，第一個引數對應定義模型結構的prototxt檔案，第二個引數對應於訓練好的model，載入完之後，使用blobFromImage函式，將圖片轉換成blob格式，網路接收輸入資料後，通過forward()函式進行前向傳播，即可得到網路輸出的結果，檢測視訊其實也差不多，視訊其實對應於一幀一幀的影象，我們只需要對視訊中每一幀進行檢測，即可得到對視訊的檢測結果。

   程式碼如下：

from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import cv2
import time

ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", required=True, help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True, help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.2, help="min probability. to filter weak detections")
args = vars(ap.parse_args())

# initialize the list of class labels MobileNet SSD was trained to
# detect, then generate a set of bounding box colors for each class
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
	"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
	"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
	"sofa", "train", "tvmonitor"]

COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

# load the serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

# initialize video stream, allow camera sensor to warmup and initialize FPS counter
video_stream = VideoStream(src=0).start()
time.sleep(2.0)
fps = FPS().start()

while True:
    # grab the frame from threaded video stream and resize it to a max width of 400
    frame = video_stream.read()
    frame = imutils.resize(frame, width=400)

    # grab the frame dimensions and convert it to blob
    (h, w) = frame.shape[:2]
    blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 0.007843, (300, 300), 127.5)

    # pass the blob through network
    net.setInput(blob)
    detections = net.forward()

    # loop over the detections
    for i in np.arange(0, detections.shape[2]):
        # extract the confidence
        confidence = detections[0, 0, i, 2]

        # filter weak detections
        if confidence > args["confidence"]:
            # extract index of class label
            idx = int(detections[0, 0, i, 1])
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            # draw predictions in the frame
            label = "{}: {:.2f}%".format(CLASSES[idx], confidence* 100)
            cv2.rectangle(frame, (startX, startY), (endX, endY), COLORS[idx], 2)

            y = startY - 15 if startY - 15 > 15 else startY + 15
            cv2.putText(frame, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)

    # show the output frame
    cv2.imshow("frame", frame)
    key = cv2.waitKey(1) & 0xFF

    # if `q` is pressed, break from loop
    if key == ord('q'):
        break

    # update the fps counter
    fps.update()

# stop the timer and display FPS information
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

# cleanup
cv2.destroyAllWindows()
video_stream.stop()
 

    輸入命令：  

python object_detection.py -p MobileNetSSD_deploy.prototxt.txt -m MobileNetSSD_deploy.caffemodel

   效果如下：