OpenCv 从V3.3版本开始支持调用深度学习模型,例如Caffe, Tensorflow, darknet等.详细见下图,具体的使用方法,可以参考官网:
https://docs.opencv.org/3.4.1/d6/d0f/group__dnn.html

　　目前Opencv可以支持的网络有GoogLeNet, ResNet-50,MobileNet-SSD from Caffe等,具体的可以参考:https://github.com/opencv/opencv/wiki/ChangeLog,里面有对dnn模块的详细介绍.
　　在github上,Opencv也有关于dnn模块的使用例子:https://github.com/opencv/opencv/tree/3.4.1/samples/dnn
　　这里只使用Python接口的Opencv 对Yolo V2(目前Opencv还不支持Yolo V3, 期待下一个版本支持)和Tensorflow训练出来的ssd_inception_v2_coco模型进行说明.

Yolo V2模型:

import cv2
import numpy as np

cap = cv2.VideoCapture('solidYellowLeft.mp4')
def read_cfg_model():
    model_path = '/home/scyang/TiRan/WorkSpace/others/darknet/cfg/yolov2.weights'
    cfg_path = '/home/scyang/TiRan/WorkSpace/others/darknet/cfg/yolov2.cfg'
    yolo_net = cv2.dnn.readNet(model_path, cfg_path, 'darknet')
    while True:
        flag, img = cap.read()
        if flag:
            yolo_net.setInput(cv2.dnn.blobFromImage(img, 1.0/127.5, (416, 416), (127.5, 127.5, 127.5), False, False))
            cvOut = yolo_net.forward()
            for detection in cvOut:
                confidence = np.max(detection[5:])
                if confidence > 0:
                    classIndex = np.argwhere(detection == confidence)[0][0] - 5
                    x_center = detection[0] * cols
                    y_center = detection[1] * rows
                    width = detection[2] * cols
                    height = detection[3] * rows
                    start = (int(x_center - width/2), int(y_center - height/2))
                    end = (int(x_center + width/2), int(y_center + height/2))
                    cv2.rectangle(img,start, end , (23, 230, 210), thickness=2)
        else:
            break
        cv2.imshow('show', img)
        cv2.waitKey(10)

　　这里需要对cvOut的结果说明一下：cvOut的前4个表示检测到的矩形框信息，第5位表示背景，从第6位开始代表检测到的目标置信度及目标属于那个类。
　　因此，下面两处的作用是，从5位开始获取结果中目标的置信度及目标属于那个类。

confidence = np.max(detection[5:])
classIndex = np.argwhere(detection == confidence)[0][0] - 5

结果的截图如下：

Tensorflow模型

cvNet = cv2.dnn.readNetFromTensorflow('model/ssd_inception_v2_coco_2017_11_17.pb','model/ssd_inception_v2_coco_2017_11_17.pbtxt')
while True:
    flag, img = cap.read()
    if flag:
        rows = img.shape[0]
        cols = img.shape[1]
        width = height = 300
        image = cv2.resize(img, ((int(cols * height / rows), width)))
        img = image[0:height, image.shape[1] - width:image.shape[1]]
        cvNet.setInput(cv2.dnn.blobFromImage(img, 1.0/127.5, (300, 300), (127.5, 127.5, 127.5), swapRB=True, crop=False))
        cvOut = cvNet.forward()

         # Network produces output blob with a shape 1x1xNx7 where N is a number of
         # detections and an every detection is a vector of values
         # [batchId, classId, confidence, left, top, right, bottom]

        for detection in cvOut[0,0,:,:]:
            score = float(detection[2])
            if score > 0.3:
                rows = cols = 300
                # print(detection)
                left = detection[3] * cols
                top = detection[4] * rows
                right = detection[5] * cols
                bottom = detection[6] * rows
                cv2.rectangle(img, (int(left), int(top)), (int(right), int(bottom)), (23, 230, 210), thickness=2)

        cv2.imshow('img', img)
        cv2.waitKey(10)
    else:
        break

效果如下：

　　使用方法和Yolo的类似，从最终的效果可以看出，ssd_inception_v2模型要比V2好。
注：blobFromImage的详细介绍及使用方法，可以参考某大神的博客：https://www.pyimagesearch.com/2017/11/06/deep-learning-opencvs-blobfromimage-works/。这里就不在多述了，要学会站在巨人的肩膀上。