c++windows+yolov5-6.2+openvino模型部署超详细_yolov5-6.2模型转openvino

自我记录：代码是根据自己的项目需求，进行了修改，主要是需要检测的图片非常大，目标小，所以对图片进行了分割再检测。下载完配置好环境之后可以直接跑。

我的环境是：windows+vs2019+openvino2022.2+opencv4.5.5+cmake3.14.0

步骤：

1、下载openvino，我用的版本是2022.2

官网网址：https://docs.openvino.ai/latest/index.html

 

 就是这个链接：https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/download.html

 

 解压之前记得给电脑设置一下，启动长路径，很简单，教程在这儿：https://blog.csdn.net/weixin_46356818/article/details/121029550

 解压后，配置电脑系统变量，下面是我的：

 下面是代码：

h文件

#pragma once
#include <opencv2/dnn.hpp>
#include <openvino/openvino.hpp>
#include <opencv2/opencv.hpp>
 
using namespace std;
 
class YOLO_OPENVINO
{
public:
	YOLO_OPENVINO();
	~YOLO_OPENVINO();
 
public:
	struct Detection 
	{
		int class_id;
		float confidence;
		cv::Rect box;
	};
 
	struct Resize 
	{
		cv::Mat resized_image;
		int dw;
		int dh;
	};
 
	Resize resize_and_pad(cv::Mat& img, cv::Size new_shape);
	void yolov5_compiled(std::string xml_path, ov::CompiledModel &compiled_model);
	void yolov5_detector(ov::CompiledModel compiled_model, cv::Mat input_detect_img, cv::Mat output_detect_img, vector<cv::Rect>& nms_box);
	
private:
	
	const float SCORE_THRESHOLD = 0.4;
	const float NMS_THRESHOLD = 0.4;
	const float CONFIDENCE_THRESHOLD = 0.4;
 
	vector<cv::Mat>images;//图像容器 
	vector<cv::Rect> boxes;
	vector<int> class_ids;
	vector<float> confidences;
	vector<cv::Rect>output_box;
	Resize resize;
 
};
 
 

cpp文件

#include"yolo_openvino.h"
 
YOLO_OPENVINO::YOLO_OPENVINO()
{
}
 
YOLO_OPENVINO::~YOLO_OPENVINO()
{
}
 
 
YOLO_OPENVINO::Resize YOLO_OPENVINO::resize_and_pad(cv::Mat& img, cv::Size new_shape)
{
    float width = img.cols;
    float height = img.rows;
    float r = float(new_shape.width / max(width, height));
    int new_unpadW = int(round(width * r));
    int new_unpadH = int(round(height * r));
    
    cv::resize(img, resize.resized_image, cv::Size(new_unpadW, new_unpadH), 0, 0, cv::INTER_AREA);
 
    resize.dw = new_shape.width - new_unpadW;//w方向padding值 
    resize.dh = new_shape.height - new_unpadH;//h方向padding值 
    cv::Scalar color = cv::Scalar(100, 100, 100);
    cv::copyMakeBorder(resize.resized_image, resize.resized_image, 0, resize.dh, 0, resize.dw, cv::BORDER_CONSTANT, color);
 
    return resize;
}
 
void YOLO_OPENVINO::yolov5_compiled(std::string xml_path, ov::CompiledModel& compiled_model)
{
    // Step 1. Initialize OpenVINO Runtime core
    ov::Core core;
    // Step 2. Read a model
    //std::shared_ptr<ov::Model> model = core.read_model("best.xml");
    std::shared_ptr<ov::Model> model = core.read_model(xml_path);
    // Step 4. Inizialize Preprocessing for the model 初始化模型的预处理
    ov::preprocess::PrePostProcessor ppp = ov::preprocess::PrePostProcessor(model);
    // Specify input image format 指定输入图像格式
    ppp.input().tensor().set_element_type(ov::element::u8).set_layout("NHWC").set_color_format(ov::preprocess::ColorFormat::BGR);
    // Specify preprocess pipeline to input image without resizing 指定输入图像的预处理管道而不调整大小
    ppp.input().preprocess().convert_element_type(ov::element::f32).convert_color(ov::preprocess::ColorFormat::RGB).scale({ 255., 255., 255. });
    //  Specify model's input layout 指定模型的输入布局
    ppp.input().model().set_layout("NCHW");
    // Specify output results format 指定输出结果格式
    ppp.output().tensor().set_element_type(ov::element::f32);
    // Embed above steps in the graph 在图形中嵌入以上步骤
    model = ppp.build();
    compiled_model = core.compile_model(model, "CPU");
}
 
void YOLO_OPENVINO::yolov5_detector(ov::CompiledModel compiled_model, cv::Mat input_detect_img, cv::Mat output_detect_img, vector<cv::Rect>& nms_box)
{
    // Step 3. Read input image
    cv::Mat img = input_detect_img.clone();
    int img_height = img.rows;
    int img_width = img.cols;
    if (img_height < 5000 && img_width < 5000)
    {
        images.push_back(img);
    }
    else
    {
        images.push_back(img(cv::Range(0, 0.6 * img_height), cv::Range(0, 0.6 * img_width)));
        images.push_back(img(cv::Range(0, 0.6 * img_height), cv::Range(0.4 * img_width, img_width)));
        images.push_back(img(cv::Range(0.4 * img_height, img_height), cv::Range(0, 0.6 * img_width)));
        images.push_back(img(cv::Range(0.4 * img_height, img_height), cv::Range(0.4 * img_width, img_width)));
    }
 
    for (int m = 0; m < images.size(); m++)
    {
        // resize image
        Resize res = resize_and_pad(images[m], cv::Size(1280, 1280));
        // Step 5. Create tensor from image
        float* input_data = (float*)res.resized_image.data;//缩放后图像数据
        ov::Tensor input_tensor = ov::Tensor(compiled_model.input().get_element_type(), compiled_model.input().get_shape(), input_data);
 
 
        // Step 6. Create an infer request for model inference 
        ov::InferRequest infer_request = compiled_model.create_infer_request();
        infer_request.set_input_tensor(input_tensor);
        infer_request.infer();
 
 
        //Step 7. Retrieve inference results 
        const ov::Tensor& output_tensor = infer_request.get_output_tensor();
        ov::Shape output_shape = output_tensor.get_shape();
        float* detections = output_tensor.data<float>();
 
        for (int i = 0; i < output_shape[1]; i++)//遍历所有框
        {
            float* detection = &detections[i * output_shape[2]];//bbox（x y w h obj cls)
 
            float confidence = detection[4];//当前bbox的obj
            if (confidence >= CONFIDENCE_THRESHOLD) //判断是否为前景
            {
                float* classes_scores = &detection[5];
                cv::Mat scores(1, output_shape[2] - 5, CV_32FC1, classes_scores);
                cv::Point class_id;
                double max_class_score;
                cv::minMaxLoc(scores, 0, &max_class_score, 0, &class_id);//返回最大得分和最大类别
 
                if (max_class_score > SCORE_THRESHOLD)//满足得分
                {
                    confidences.push_back(confidence);
 
                    class_ids.push_back(class_id.x);
 
                    float x = detection[0];//框中心x 
                    float y = detection[1];//框中心y 
                    float w = detection[2];//49
                    float h = detection[3];//50
 
                    float rx = (float)images[m].cols / (float)(res.resized_image.cols - res.dw);//x方向映射比例
                    float ry = (float)images[m].rows / (float)(res.resized_image.rows - res.dh);//y方向映射比例
 
                    x = rx * x;
                    y = ry * y;
                    w = rx * w;
                    h = ry * h;
 
                    if (m == 0)
                    {
                        x = x;
                        y = y;
                    }
                    else if (m == 1)
                    {
                        x = x + 0.4 * img_width;
                        y = y;
 
                    }
                    else if (m == 2)
                    {
                        x = x;
                        y = y + 0.4 * img_height;
                    }
                    else if (m == 3)
                    {
                        x = x + 0.4 * img_width;
                        y = y + 0.4 * img_height;
                    }
 
                    float xmin = x - (w / 2);//bbox左上角x
                    float ymin = y - (h / 2);//bbox左上角y
                    boxes.push_back(cv::Rect(xmin, ymin, w, h));
                }
            }
        }
    }
 
    std::vector<int> nms_result;
    cv::dnn::NMSBoxes(boxes, confidences, SCORE_THRESHOLD, NMS_THRESHOLD, nms_result);
    std::vector<Detection> output;
    
    for (int i = 0; i < nms_result.size(); i++)
    {
        Detection result;
        int idx = nms_result[i];
        result.class_id = class_ids[idx];
        result.confidence = confidences[idx];
        result.box = boxes[idx];
        nms_box.push_back(result.box);//传给Qt NMS后的box
        output.push_back(result);
    }
 
    // Step 9. Print results and save Figure with detections
    for (int i = 0; i < output.size(); i++)
    {
        auto detection = output[i];
        auto box = detection.box;
        auto classId = detection.class_id;
        auto confidence = detection.confidence;
 
        /*cout << "Bbox" << i + 1 << ": Class: " << classId << " "
            << "Confidence: " << confidence << " Scaled coords: [ "
            << "cx: " << (float)(box.x + (box.width / 2)) / img.cols << ", "
            << "cy: " << (float)(box.y + (box.height / 2)) / img.rows << ", "
            << "w: " << (float)box.width / img.cols << ", "
            << "h: " << (float)box.height / img.rows << " ]" << endl;*/
        float xmax = box.x + box.width;
        float ymax = box.y + box.height;
        
        cv::rectangle(img, cv::Point(box.x, box.y), cv::Point(xmax, ymax), cv::Scalar(0, 255, 0), 3);
        cv::rectangle(img, cv::Point(box.x, box.y - 20), cv::Point(xmax, box.y), cv::Scalar(0, 255, 0), cv::FILLED);
        cv::putText(img, std::to_string(classId), cv::Point(box.x, box.y - 5), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
        img.copyTo(output_detect_img);
    }
 
    cv::imwrite("./fz.jpg", output_detect_img);
 
}
 
 

main.cpp

#include"yolo_openvino.h"
 
using namespace std;
 
YOLO_OPENVINO yolo_openvino;
std::string path = "./best.xml";
ov::CompiledModel model;
cv::Mat input_img, output_img;
vector<cv::Rect>output_box;
int main()
{
    input_img = cv::imread("140_0_0.jpg");
    yolo_openvino.yolov5_compiled(path, model);
    yolo_openvino.yolov5_detector(model, input_img, output_img, output_box);
/*    for (int i = 0; i < output_box.size(); i++)
    {
        cv::rectangle(input_img, cv::Point(output_box[i].x, output_box[i].y), cv::Point(output_box[i].x + output_box[i].width, output_box[i].y + output_box[i].height), cv::Scalar(0, 255, 0), 3);
    }
    cv::imshow("a", input_img);
    cv::waitKey(0)*/;
	return 0;
}

接下来配置项目的包含目录、库目录、附加依赖项

 2、下载cmake3.14.0

这个下完之后解压，然后配置个环境变量就行，不下cmake应该也是可以的。

 

 3、跑代码：

放一个onnx转xml、bin文件的方法，现在可以直接从Yolov5中用export_openvino直接导出，其导出函数定义为：

def export_openvino(model, im, file, prefix=colorstr('OpenVINO:')):
    # YOLOv5 OpenVINO export
    try:
        check_requirements(('openvino-dev',))  # requires openvino-dev: https://pypi.org/project/openvino-dev/
        import openvino.inference_engine as ie
 
        LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
        f = str(file).replace('.pt', '_openvino_model' + os.sep)
 
        cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f}"
        subprocess.check_output(cmd, shell=True)
 
        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')

所以只需要在yolov5里面运行下面命令：

“mo --input_model {file.with_suffix('.onnx')} --output_dir {f}”

///然后代码里的模型路径改成你自己的，就可以跑了。