The code in this article is a simple version, if you want to use it, please modify and package it yourself
The openvnio deployment model is more convenient and simple, and it is not easy to make mistakes, but the speed is a bit slower!
The deployment source code is given below
1. Detection model
#include<iostream>#include<string>#include<vector>#include<openvino/openvino.hpp>//openvino header file#include<opencv2/opencv.hpp>//opencv header file
std::vector<cv::Scalar> colors ={
cv::Scalar(0,0,255), cv::Scalar(0,255,0), cv::Scalar(255,0,0),
cv::Scalar(255,100,50), cv::Scalar(50,100,255), cv::Scalar(255,50,100)};const std::vector<std::string> class_names ={
"person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush"};usingnamespace cv;usingnamespace dnn;// Keep the ratio before resize
Mat letterbox(const cv::Mat& source){
int col = source.cols;int row = source.rows;int _max =MAX(col, row);
Mat result =Mat::zeros(_max, _max, CV_8UC3);
source.copyTo(result(Rect(0,0, col, row)));return result;}intmain(int argc,char* argv[]){
// -------- Step 1. Initialize OpenVINO Runtime Core --------
ov::Core core;// -------- Step 2. Compile the Model --------auto compiled_model = core.compile_model("yolov8n.xml","CPU");// -------- Step 3. Create an Inference Request --------
ov::InferRequest infer_request = compiled_model.create_infer_request();// -------- Step 4.Read a picture file and do the preprocess --------
Mat img = cv::imread("bus.jpg");// Preprocess the image
Mat letterbox_img =letterbox(img);float scale = letterbox_img.size[0]/640.0;
Mat blob =blobFromImage(letterbox_img,1.0/255.0,Size(640,640),Scalar(),true);// -------- Step 5. Feed the blob into the input node of the Model -------// Get input port for model with one inputauto input_port = compiled_model.input();// Create tensor from external memory
ov::Tensor input_tensor(input_port.get_element_type(), input_port.get_shape(), blob.ptr(0));// Set input tensor for model with one input
infer_request.set_input_tensor(input_tensor);// -------- Step 6. Start inference --------
infer_request.infer();// -------- Step 7. Get the inference result --------auto output = infer_request.get_output_tensor(0);auto output_shape = output.get_shape();
std::cout <<"The shape of output tensor:"<< output_shape << std::endl;int rows = output_shape[2];//8400int dimensions = output_shape[1];//84: box[cx, cy, w, h]+80 classes scores// -------- Step 8. Postprocess the result --------float* data = output.data<float>();
Mat output_buffer(output_shape[1], output_shape[2], CV_32F, data);transpose(output_buffer, output_buffer);//[8400,84]float score_threshold =0.25;float nms_threshold =0.5;
std::vector<int> class_ids;
std::vector<float> class_scores;
std::vector<Rect> boxes;// Figure out the bbox, class_id and class_scorefor(int i =0; i < output_buffer.rows; i++){
Mat classes_scores = output_buffer.row(i).colRange(4,84);
Point class_id;double maxClassScore;minMaxLoc(classes_scores,0,&maxClassScore,0,&class_id);if(maxClassScore > score_threshold){
class_scores.push_back(maxClassScore);
class_ids.push_back(class_id.x);float cx = output_buffer.at<float>(i,0);float cy = output_buffer.at<float>(i,1);float w = output_buffer.at<float>(i,2);float h = output_buffer.at<float>(i,3);int left =int((cx -0.5* w)* scale);int top =int((cy -0.5* h)* scale);int width =int(w * scale);int height =int(h * scale);
boxes.push_back(Rect(left, top, width, height));}}//NMS
std::vector<int> indices;NMSBoxes(boxes, class_scores, score_threshold, nms_threshold, indices);// -------- Visualize the detection results -----------for(size_t i =0; i < indices.size(); i++){
int index = indices[i];int class_id = class_ids[index];rectangle(img, boxes[index], colors[class_id %6],2,8);
std::string label = class_names[class_id]+":"+ std::to_string(class_scores[index]).substr(0,4);
Size textSize = cv::getTextSize(label, FONT_HERSHEY_SIMPLEX,0.5,1,0);
Rect textBox(boxes[index].tl().x, boxes[index].tl().y -15, textSize.width, textSize.height+5);
cv::rectangle(img, textBox, colors[class_id %6], FILLED);putText(img, label,Point(boxes[index].tl().x, boxes[index].tl().y -5), FONT_HERSHEY_SIMPLEX,0.5,Scalar(255,255,255));}namedWindow("YOLOv8 OpenVINO Inference C++ Demo", WINDOW_AUTOSIZE);imshow("YOLOv8 OpenVINO Inference C++ Demo", img);waitKey(0);destroyAllWindows();return0;}
2. Segmentation model
#include<iostream>#include<string>#include<vector>#include<algorithm>#include<openvino/openvino.hpp>//openvino header file#include<opencv2/opencv.hpp>//opencv header fileusingnamespace cv;usingnamespace dnn;
std::vector<Scalar> colors ={
Scalar(255,0,0),Scalar(255,0,255),Scalar(170,0,255),Scalar(255,0,85),Scalar(255,0,170),Scalar(85,255,0),Scalar(255,170,0),Scalar(0,255,0),Scalar(255,255,0),Scalar(0,255,85),Scalar(170,255,0),Scalar(0,85,255),Scalar(0,255,170),Scalar(0,0,255),Scalar(0,255,255),Scalar(85,0,255)};const std::vector<std::string> class_names ={
"person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush"};// Keep the ratio before resize
Mat letterbox(const cv::Mat& source){
int col = source.cols;int row = source.rows;int _max =MAX(col, row);
Mat result =Mat::zeros(_max, _max, CV_8UC3);
source.copyTo(result(Rect(0,0, col, row)));return result;}floatsigmoid_function(float a){
float b =1./(1.+exp(-a));return b;}intmain(int argc,char* argv[]){
// -------- Step 1. Initialize OpenVINO Runtime Core --------
ov::Core core;// -------- Step 2. Compile the Model --------auto compiled_model = core.compile_model("yolov8n-seg.xml","CPU");// -------- Step 3. Create an Inference Request --------
ov::InferRequest infer_request = compiled_model.create_infer_request();// -------- Step 4.Read a picture file and do the preprocess --------
Mat img = cv::imread("bus.jpg");// Preprocess the image
Mat letterbox_img =letterbox(img);float scale = letterbox_img.size[0]/640.0;
Mat blob =blobFromImage(letterbox_img,1.0/255.0,Size(640,640),Scalar(),true);// -------- Step 5. Feed the blob into the input node of the Model -------// Get input port for model with one inputauto input_port = compiled_model.input();// Create tensor from external memory
ov::Tensor input_tensor(input_port.get_element_type(), input_port.get_shape(), blob.ptr(0));// Set input tensor for model with one input
infer_request.set_input_tensor(input_tensor);// -------- Step 6. Start inference --------
infer_request.infer();// -------- Step 7. Get the inference result --------auto output0 = infer_request.get_output_tensor(0);//output0auto output1 = infer_request.get_output_tensor(1);//otuput1auto output0_shape = output0.get_shape();auto output1_shape = output1.get_shape();
std::cout <<"The shape of output0:"<< output0_shape << std::endl;
std::cout <<"The shape of output1:"<< output1_shape << std::endl;// -------- Step 8. Postprocess the result --------
Mat output_buffer(output0_shape[1], output0_shape[2], CV_32F, output0.data<float>());
Mat proto(32,25600, CV_32F, output1.data<float>());//[32,25600]transpose(output_buffer, output_buffer);//[8400,116]float score_threshold =0.25;float nms_threshold =0.5;
std::vector<int> class_ids;
std::vector<float> class_scores;
std::vector<Rect> boxes;
std::vector<Mat> mask_confs;// Figure out the bbox, class_id and class_scorefor(int i =0; i < output_buffer.rows; i++){
Mat classes_scores = output_buffer.row(i).colRange(4,84);
Point class_id;double maxClassScore;minMaxLoc(classes_scores,0,&maxClassScore,0,&class_id);if(maxClassScore > score_threshold){
class_scores.push_back(maxClassScore);
class_ids.push_back(class_id.x);float cx = output_buffer.at<float>(i,0);float cy = output_buffer.at<float>(i,1);float w = output_buffer.at<float>(i,2);float h = output_buffer.at<float>(i,3);int left =int((cx -0.5* w)* scale);int top =int((cy -0.5* h)* scale);int width =int(w * scale);int height =int(h * scale);
cv::Mat mask_conf = output_buffer.row(i).colRange(84,116);
mask_confs.push_back(mask_conf);
boxes.push_back(Rect(left, top, width, height));}}//NMS
std::vector<int> indices;NMSBoxes(boxes, class_scores, score_threshold, nms_threshold, indices);// -------- Visualize the detection results -----------
cv::Mat rgb_mask = cv::Mat::zeros(img.size(), img.type());
cv::Mat masked_img;
cv::RNG rng;for(size_t i =0; i < indices.size(); i++){
// Visualize the objectsint index = indices[i];int class_id = class_ids[index];rectangle(img, boxes[index], colors[class_id %16],2,8);
std::string label = class_names[class_id]+":"+ std::to_string(class_scores[index]).substr(0,4);
Size textSize = cv::getTextSize(label, FONT_HERSHEY_SIMPLEX,0.5,1,0);
Rect textBox(boxes[index].tl().x, boxes[index].tl().y -15, textSize.width, textSize.height+5);
cv::rectangle(img, textBox, colors[class_id %16], FILLED);putText(img, label,Point(boxes[index].tl().x, boxes[index].tl().y -5), FONT_HERSHEY_SIMPLEX,0.5,Scalar(255,255,255));// Visualize the Masks
Mat m = mask_confs[i]* proto;for(int col =0; col < m.cols; col++){
m.at<float>(0, col)=sigmoid_function(m.at<float>(0, col));}
cv::Mat m1 = m.reshape(1,160);// 1x25600 -> 160x160int x1 = std::max(0, boxes[index].x);int y1 = std::max(0, boxes[index].y);int x2 = std::max(0, boxes[index].br().x);int y2 = std::max(0, boxes[index].br().y);int mx1 =int(x1 / scale *0.25);int my1 =int(y1 / scale *0.25);int mx2 =int(x2 / scale *0.25);int my2 =int(y2 / scale *0.25);
cv::Mat mask_roi =m1(cv::Range(my1, my2), cv::Range(mx1, mx2));
cv::Mat rm, det_mask;
cv::resize(mask_roi, rm, cv::Size(x2 - x1, y2 - y1));for(int r =0; r < rm.rows; r++){
for(int c =0; c < rm.cols; c++){
float pv = rm.at<float>(r, c);if(pv >0.5){
rm.at<float>(r, c)=1.0;}else{
rm.at<float>(r, c)=0.0;}}}
rm = rm * rng.uniform(0,255);
rm.convertTo(det_mask, CV_8UC1);if((y1 + det_mask.rows)>= img.rows){
y2 = img.rows -1;}if((x1 + det_mask.cols)>= img.cols){
x2 = img.cols -1;}
cv::Mat mask = cv::Mat::zeros(cv::Size(img.cols, img.rows), CV_8UC1);det_mask(cv::Range(0, y2 - y1), cv::Range(0, x2 - x1)).copyTo(mask(cv::Range(y1, y2), cv::Range(x1, x2)));add(rgb_mask, cv::Scalar(rng.uniform(0,255), rng.uniform(0,255), rng.uniform(0,255)), rgb_mask, mask);addWeighted(img,0.5, rgb_mask,0.5,0, masked_img);}namedWindow("YOLOv8-Seg OpenVINO Inference C++ Demo", WINDOW_AUTOSIZE);imshow("YOLOv8-Seg OpenVINO Inference C++ Demo", masked_img);waitKey(0);destroyAllWindows();return0;}
3. Classification model
#include<iostream>#include<string>#include<vector>#include<algorithm>#include<openvino/openvino.hpp>//openvino header file#include<opencv2/opencv.hpp>//opencv header fileusingnamespace cv;usingnamespace dnn;// Keep the ratio before resize
Mat letterbox(const cv::Mat& source){
int col = source.cols;int row = source.rows;int _max =MAX(col, row);
Mat result =Mat::zeros(_max, _max, CV_8UC3);
source.copyTo(result(Rect(0,0, col, row)));return result;}intmain(int argc,char* argv[]){
// -------- Step 1. Initialize OpenVINO Runtime Core --------
ov::Core core;// -------- Step 2. Compile the Model --------auto compiled_model = core.compile_model("yolov8n-cls.xml","CPU");// -------- Step 3. Create an Inference Request --------
ov::InferRequest infer_request = compiled_model.create_infer_request();// -------- Step 4.Read a picture file and do the preprocess --------
Mat img = cv::imread("bus.jpg");// Preprocess the image
Mat letterbox_img =letterbox(img);
Mat blob =blobFromImage(letterbox_img,1.0/255.0,Size(224,224),Scalar(),true);// -------- Step 5. Feed the blob into the input node of the Model -------// Get input port for model with one inputauto input_port = compiled_model.input();// Create tensor from external memory
ov::Tensor input_tensor(input_port.get_element_type(), input_port.get_shape(), blob.ptr(0));// Set input tensor for model with one input
infer_request.set_input_tensor(input_tensor);// -------- Step 6. Start inference --------
infer_request.infer();// -------- Step 7. Get the inference result --------auto output = infer_request.get_output_tensor(0);auto output_shape = output.get_shape();
std::cout <<"The shape of output tensor:"<< output_shape << std::endl;// -------- Step 8. Postprocess the result --------float* output_buffer = output.data<float>();
std::vector<float>result(output_buffer, output_buffer + output_shape[1]);auto max_idx = std::max_element(result.begin(), result.end());int class_id = max_idx - result.begin();float score =*max_idx;
std::cout <<"Class ID:"<< class_id <<" Score:"<<score<< std::endl;return0;}
4. Pose Model
#include<iostream>#include<string>#include<vector>#include<algorithm>#include<openvino/openvino.hpp>//openvino header file#include<opencv2/opencv.hpp>//opencv header fileusingnamespace cv;usingnamespace dnn;//Colors for 17 keypoints
std::vector<cv::Scalar> colors ={
Scalar(255,0,0),Scalar(255,0,255),Scalar(170,0,255),Scalar(255,0,85),Scalar(255,0,170),Scalar(85,255,0),Scalar(255,170,0),Scalar(0,255,0),Scalar(255,255,0),Scalar(0,255,85),Scalar(170,255,0),Scalar(0,85,255),Scalar(0,255,170),Scalar(0,0,255),Scalar(0,255,255),Scalar(85,0,255),Scalar(0,170,255)};// Keep the ratio before resize
Mat letterbox(const cv::Mat& source){
int col = source.cols;int row = source.rows;int _max =MAX(col, row);
Mat result =Mat::zeros(_max, _max, CV_8UC3);
source.copyTo(result(Rect(0,0, col, row)));return result;}intmain(int argc,char* argv[]){
// -------- Step 1. Initialize OpenVINO Runtime Core --------
ov::Core core;// -------- Step 2. Compile the Model --------auto compiled_model = core.compile_model("yolov8n-pose.xml","CPU");// -------- Step 3. Create an Inference Request --------
ov::InferRequest infer_request = compiled_model.create_infer_request();// -------- Step 4.Read a picture file and do the preprocess --------
Mat img = cv::imread("bus.jpg");// Preprocess the image
Mat letterbox_img =letterbox(img);float scale = letterbox_img.size[0]/640.0;
Mat blob =blobFromImage(letterbox_img,1.0/255.0,Size(640,640),Scalar(),true);// -------- Step 5. Feed the blob into the input node of the Model -------// Get input port for model with one inputauto input_port = compiled_model.input();// Create tensor from external memory
ov::Tensor input_tensor(input_port.get_element_type(), input_port.get_shape(), blob.ptr(0));// Set input tensor for model with one input
infer_request.set_input_tensor(input_tensor);// -------- Step 6. Start inference --------
infer_request.infer();// -------- Step 7. Get the inference result --------auto output = infer_request.get_output_tensor(0);auto output_shape = output.get_shape();
std::cout <<"The shape of output tensor:"<< output_shape << std::endl;// -------- Step 8. Postprocess the result --------float* data = output.data<float>();
Mat output_buffer(output_shape[1], output_shape[2], CV_32F, data);transpose(output_buffer, output_buffer);//[8400,56]float score_threshold =0.25;float nms_threshold =0.5;
std::vector<int> class_ids;
std::vector<float> class_scores;
std::vector<Rect> boxes;
std::vector<std::vector<float>> objects_keypoints;// //56: box[cx, cy, w, h] + Score + [17,3] keypointsfor(int i =0; i < output_buffer.rows; i++){
float class_score = output_buffer.at<float>(i,4);if(class_score > score_threshold){
class_scores.push_back(class_score);
class_ids.push_back(0);//{0:"person"}float cx = output_buffer.at<float>(i,0);float cy = output_buffer.at<float>(i,1);float w = output_buffer.at<float>(i,2);float h = output_buffer.at<float>(i,3);// Get the boxint left =int((cx -0.5* w)* scale);int top =int((cy -0.5* h)* scale);int width =int(w * scale);int height =int(h * scale);// Get the keypoints
std::vector<float> keypoints;
Mat kpts = output_buffer.row(i).colRange(5,56);for(int i =0; i <17; i++){
float x = kpts.at<float>(0, i *3+0)* scale;float y = kpts.at<float>(0, i *3+1)* scale;float s = kpts.at<float>(0, i *3+2);
keypoints.push_back(x);
keypoints.push_back(y);
keypoints.push_back(s);}
boxes.push_back(Rect(left, top, width, height));
objects_keypoints.push_back(keypoints);}}//NMS
std::vector<int> indices;NMSBoxes(boxes, class_scores, score_threshold, nms_threshold, indices);// -------- Visualize the detection results -----------for(size_t i =0; i < indices.size(); i++){
int index = indices[i];// Draw bounding boxrectangle(img, boxes[index],Scalar(0,0,255),2,8);
std::string label ="Person:"+ std::to_string(class_scores[index]).substr(0,4);
Size textSize = cv::getTextSize(label, FONT_HERSHEY_SIMPLEX,0.5,1,0);
Rect textBox(boxes[index].tl().x, boxes[index].tl().y -15, textSize.width, textSize.height+5);
cv::rectangle(img, textBox,Scalar(0,0,255), FILLED);putText(img, label,Point(boxes[index].tl().x, boxes[index].tl().y -5), FONT_HERSHEY_SIMPLEX,0.5,Scalar(255,255,255));// Draw keypoints
std::vector<float> object_keypoints = objects_keypoints[index];for(int i =0; i <17; i++){
int x = std::clamp(int(object_keypoints[i*3+0]),0, img.cols);int y = std::clamp(int(object_keypoints[i*3+1]),0, img.rows);//Draw pointcircle(img,Point(x, y),5, colors[i],-1);}}namedWindow("YOLOv8-Pose OpenVINO Inference C++ Demo", WINDOW_AUTOSIZE);imshow("YOLOv8-Pose OpenVINO Inference C++ Demo", img);waitKey(0);destroyAllWindows();return0;}