OpenVINO计算机视觉模型加速-001

#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <iostream>

using namespace cv;
using namespace cv::dnn;
using namespace std;

string model = "D:/tensorflow/ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb";
string config = "D:/tensorflow/ssd_mobilenet_v2_coco_2018_03_29/graph.pbtxt";
void dnn_tf();
void dnn_ir_tf();
int main(int argc, char** argv) {
   dnn_ir_tf();
   waitKey(0);
   destroyAllWindows();
   return 0;
}

void dnn_tf() {
   Mat src = imread("D:/images/test.png");
   if (src.empty()) {
       printf("could not load image...\n");
       return;
   }
   imshow("input", src);

   Net net = readNetFromTensorflow(model, config);
   net.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
   net.setPreferableTarget(DNN_TARGET_CPU);

   printf("ssd network model loaded...\n");
   Mat blob = blobFromImage(src, 1.0, Size(300, 300), Scalar(), true, false, 5);
   net.setInput(blob);

float threshold = 0.5;
Mat detection = net.forward();

   // 获取推断时间
   vector<double> layerTimings;
   double freq = getTickFrequency() / 1000;
   double time = net.getPerfProfile(layerTimings) / freq;
   ostringstream ss;
   ss << "infernece : " << time << " ms";
   putText(src, ss.str(), Point(50, 50), FONT_HERSHEY_SIMPLEX, 1, Scalar(255, 0, 0), 2, 8);

   Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
   for (int i = 0; i < detectionMat.rows; i++) {
       float confidence = detectionMat.at<float>(i, 2);
       if (confidence > threshold) {
           size_t obj_index = (size_t)detectionMat.at<float>(i, 1);
           float tl_x = detectionMat.at<float>(i, 3) * src.cols;
           float tl_y = detectionMat.at<float>(i, 4) * src.rows;
           float br_x = detectionMat.at<float>(i, 5) * src.cols;
           float br_y = detectionMat.at<float>(i, 6) * src.rows;
           Rect object_box((int)tl_x, (int)tl_y, (int)(br_x - tl_x), (int(br_y - tl_y)));
           rectangle(src, object_box, Scalar(0, 0, 255), 2, 8, 0);
       }
   }
   imshow("ssd detection", src);
}

void dnn_ir_tf() {
   string xml = "D:/projects/models/frozen_inference_graph.xml";
   string bin = "D:/projects/models/frozen_inference_graph.bin";
   Mat src = imread("D:/images/test.png");
   if (src.empty()) {
       printf("could not load image...\n");
       return;
   }
   imshow("input", src);

   Net net = readNetFromModelOptimizer(xml, bin);
   net.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
   net.setPreferableTarget(DNN_TARGET_CPU);

   printf("ssd network model loaded...\n");
   Mat blob = blobFromImage(src, 1.0, Size(300, 300), Scalar(), true, false, 5);
   net.setInput(blob);

float threshold = 0.5;
Mat detection = net.forward();

OpenVINO计算机视觉模型加速-001

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