OpenCV3.3深度神经网络DNN模块 实例4：SSD-MobileNet模型实时对象检测

 1 #include <opencv2/opencv.hpp>
 2 #include <opencv2/dnn.hpp>
 3 #include <iostream>
 4  
 5 using namespace cv;
 6 using namespace cv::dnn;
 7 using namespace std;
 8  
 9 const size_t width = 300;
10 const size_t height = 300;
11 const float meanVal = 127.5;//均值
12 const float scaleFactor = 0.007843f;
13 const char* classNames[] = { "background",
14 "aeroplane", "bicycle", "bird", "boat",
15 "bottle", "bus", "car", "cat", "chair",
16 "cow", "diningtable", "dog", "horse",
17 "motorbike", "person", "pottedplant",
18 "sheep", "sofa", "train", "tvmonitor" };
19 //模型文件
20 String modelFile = "D:/opencv3.3/opencv/sources/samples/data/dnn/MobileNetSSD_deploy.caffemodel";
21 //二进制描述文件
22 String model_text_file = "D:/opencv3.3/opencv/sources/samples/data/dnn/MobileNetSSD_deploy.prototxt";
23  
24 int main(int argc, char** argv) {
25     VideoCapture capture;//读取视频
26     capture.open("01.mp4");
27     namedWindow("input", CV_WINDOW_AUTOSIZE);
28     int w = capture.get(CAP_PROP_FRAME_WIDTH);//获取视频宽度
29     int h = capture.get(CAP_PROP_FRAME_HEIGHT    );//获取视频高度
30     printf("frame width : %d, frame height : %d", w, h);
31  
32     // set up net
33     Net net = readNetFromCaffe(model_text_file, modelFile);
34  
35     Mat frame;
36     while (capture.read(frame)) {
37         imshow("input", frame);
38  
39         // 预测
40         Mat inputblob = blobFromImage(frame, scaleFactor, Size(width, height), meanVal, false);
41         net.setInput(inputblob, "data");
42         Mat detection = net.forward("detection_out");
43  
44         // 绘制
45         Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
46         float confidence_threshold = 0.25;//自信区间，越小检测到的物体越多（>=0.25)
47         for (int i = 0; i < detectionMat.rows; i++) {
48             float confidence = detectionMat.at<float>(i, 2);
49             if (confidence > confidence_threshold) {
50                 size_t objIndex = (size_t)(detectionMat.at<float>(i, 1));
51                 float tl_x = detectionMat.at<float>(i, 3) * frame.cols;
52                 float tl_y = detectionMat.at<float>(i, 4) * frame.rows;
53                 float br_x = detectionMat.at<float>(i, 5) * frame.cols;
54                 float br_y = detectionMat.at<float>(i, 6) * frame.rows;
55  
56                 Rect object_box((int)tl_x, (int)tl_y, (int)(br_x - tl_x), (int)(br_y - tl_y));
57                 rectangle(frame, object_box, Scalar(0, 0, 255), 2, 8, 0);
58                 putText(frame, format("%s", classNames[objIndex]), Point(tl_x, tl_y), FONT_HERSHEY_SIMPLEX, 1.0, Scalar(255, 0, 0), 2);
59             }
60         }
61         imshow("ssd-video-demo", frame);
62         char c = waitKey(5);
63         if (c == 27) { // 如果ESC按下
64             break;
65         }
66     }
67     capture.release();
68     waitKey(0);
69     return 0;
70 }