opencv越来越强大了，可以直接对训练好的caffe、tensorflow等框架训练好的模型进行加载，进而完成识别、检测等任务。
opencv加载caffe训练好的模型，采用readNetFromCaffe(arg1,arg2)，第一个参数对应定义模型结构的prototxt文件，第二个参数对应于训练好的model，加载完之后，使用blobFromImage函数，将图片转换成blob格式，网络接收输入数据后，通过forward()函数进行前向传播，即可得到网络输出的结果，检测视频其实也差不多，视频其实对应于一帧一帧的图像，我们只需要对视频中每一帧进行检测，即可得到对视频的检测结果。
blobFromImage()
Mat cv::dnn::blobFromImage (InputArray image,
double scalefactor = 1.0,
const Size & size = Size(),
const Scalar & mean = Scalar(),
bool swapRB = true,
bool crop = true
)
Python:
retval = cv.dnn.blobFromImage(image[, scalefactor[, size[, mean[, swapRB[, crop]]]]] )
对应第一个参数为图像数据，第二个为缩放因子，第三个为缩放后图像大小，第四个为均值，第五个为交换颜色通道标志，第六个为是否进行裁剪操作。
代码如下：
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()输入命令：
python object_detection.py -p MobileNetSSD_deploy.prototxt.txt -m MobileNetSSD_deploy.caffemodel效果如下：
