yolov5 opencv DNN 推理

HySmiley

已于 2022-07-14 20:31:48 修改

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分类专栏： # DNN推理深度学习文章标签： opencv dnn 人工智能

于 2022-07-14 20:28:53 首次发布

本文链接：https://blog.youkuaiyun.com/m0_37264397/article/details/125791308

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DNN推理

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本文档详细介绍了在Ubuntu21.10环境下，使用OpenCV4.5.1进行Yolov5的ONNX模型转换及Python和C++版本的推理过程。通过pythonexport.py将模型转换为onnx和onnx-sim格式，并利用netron验证模型结构。接着，使用cv2.dnn模块进行Python推理，包括输入预处理、非极大值抑制等步骤。最后，展示了C++版本的推理代码，实现了相似的功能。

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一、环境

opencv4.5.1

Ubuntu 21.10

二、onnx转换

切换到yolov5工程中，打开终端运行指令

python export.py --weights yolov5s.pt --include onnx

onnx转onnx-sim

python -m onnxsim yolov5s.onnx yolov5s_sim.onnx

使用netron打开onnx文件

最终的输出1*25200*85，将三个输出整合在一起即：

25200=3*（20*20）+3*（40*40）+3*（80*80）个候选框

三、python版本推理

opecv DNN API:
1.读取模型
net = cv2.dnn.readNetFromONNX(model)
2.输入（类似Tensor）
blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (640, 640), swapRB=True, crop=False)
swapRB：BGR->RGB
net.setInput(blob)
3.推理
preds = net.forward()
4.非极大值抑制
cv2.dnn.NMSBoxes(boxes,confidences,0.25,0.45)

import cv2
import numpy as np


classes=["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"]


def infer(img,pred,shape):
    w_ratio=img.shape[1]/shape[0]
    h_ratio=img.shape[0]/shape[1]
    # print(w_ratio,h_ratio)
    confidences=[]
    boxes=[]
    class_ids=[]
    boxes_num=pred.shape[1]
    data=pred[0]
    # print("data_shape:",data.shape)
    for i in range(boxes_num):
        da=data[i]#[box,conf,cls]
        confidence=da[4]
        if confidence>0.6:
            score=da[5:]*confidence
            _,_,_,max_score_index=cv2.minMaxLoc(score)#
            max_cls_id=max_score_index[1]
            if score[max_cls_id]>0.25:
                confidences.append(confidence)
                class_ids.append(max_cls_id)
                x,y,w,h=da[0].item(),da[1].item(),da[2].item(),da[3].item()
                nx=int((x-w/2.0)*w_ratio)
                ny=int((y-h/2.0)*h_ratio)
                nw=int(w*w_ratio)
                nh=int(h*h_ratio)
                boxes.append(np.array([nx,ny,nw,nh]))

    indexes=cv2.dnn.NMSBoxes(boxes,confidences,0.25,0.45)
    res_ids=[]
    res_confs=[]
    res_boxes=[]
    for i in indexes:
        res_ids.append(class_ids[i])
        res_confs.append(confidences[i])
        res_boxes.append(boxes[i])

    # print(res_ids)
    # print(res_confs)
    # print(res_boxes)
    return res_ids,res_confs,res_boxes

def draw_rect(img,ids,confs,boxes):
    for i in range(len(ids)):
        cv2.rectangle(img, boxes[i], (0,0,255), 2)
        cv2.rectangle(img, (boxes[i][0],boxes[i][1]-20),(boxes[i][0]+boxes[i][2],boxes[i][1]), (200, 200, 200), -1)
        cv2.putText(img, classes[ids[i]], (boxes[i][0], boxes[i][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, .5, (255, 0, 0))
        cv2.putText(img, str(confs[i]), (boxes[i][0]+60, boxes[i][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, .5, (255, 0, 0))

    cv2.imwrite("res.jpg",img)
    cv2.imshow('img',img)
    cv2.waitKey()


if __name__=="__main__":
    import time
    st=time.time()
    shape=(640,640)
    src=cv2.imread("../data/bus.jpg")
    img=src.copy()
    net=cv2.dnn.readNet("../model_m1/yolov5s_sim.onnx")
    blob=cv2.dnn.blobFromImage(img,1/255.,shape,swapRB=True,crop=False)
    net.setInput(blob)
    pred=net.forward()
    print(pred.shape)
    ids,confs,boxes=infer(img,pred,shape)
    et=time.time()
    print("run time:{:.2f}s/{:.2f}FPS".format(et-st,1/(et-st)))
    draw_rect(src,ids,confs,boxes)

运行结果：

四、C++ 版本推理

cdnn.cpp

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


void infer_res(cv::Mat& img,cv::Mat& preds,std::vector<cv::Rect> &boxes,std::vector<float>&confidences,std::vector<int> &classIds,std::vector<int>&indexs)
{
    float w_ratio=img.cols/640.0;
    float h_ratio=img.rows/640.0;

    cv::Mat data(preds.size[1],preds.size[2],CV_32F,preds.ptr<float>());
    for(int i=0;i<data.rows;i++)
    {
        float conf=data.at<float>(i,4);
        if(conf<0.45)
        {
            continue;
        }

        cv::Mat clsP=data.row(i).colRange(5,85)*conf;
        cv::Point IndexId;
        double score;
        minMaxLoc(clsP,0,&score,0,&IndexId);
        if(score>0.25)
        {
            float x=data.at<float>(i,0);
            float y=data.at<float>(i,1);
            float w=data.at<float>(i,2);
            float h=data.at<float>(i,3);

            int nx=int((x-w/2.0)*w_ratio);
            int ny=int((y-h/2.0)*h_ratio);
            int nw=int(w*w_ratio);
            int nh=int(h*h_ratio);

            cv::Rect box;
            box.x=nx;
            box.y=ny;
            box.width=nw;
            box.height=nh;
            boxes.push_back(box);
            classIds.push_back(IndexId.x);
            confidences.push_back(score);

        }

    }

//    std::vector<int>indexs;
    cv::dnn::NMSBoxes(boxes,confidences,0.25,0.45,indexs);

}
//void draw_label(cv::Mat& img,std::vector<cv::Rect> &boxes,std::vector<float>&confidences,std::vector<int> &classIds,std::vector<int>&indexs,std::string& classes[])
//{
//    for(int i=0;i<boxes.size();i++)
//    {
//        cv::rectangle(img,boxes[i],(0,0,0),1);
//
//    }
//    cv::imshow("img",img);
//    cv::waitKey();
//
//}


int main()
{
    clock_t st=clock();
    cv::Mat src=cv::imread("../../data/bus.jpg");

    std::string classNames[]={ "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"};

    cv::Mat img=src.clone();
    cv::dnn::Net net=cv::dnn::readNet("../../model_m1/yolov5s_sim.onnx");
    cv::Mat blob=cv::dnn::blobFromImage(img,1/255.0,cv::Size(640,640), cv::Scalar(0, 0, 0), true, false);
    net.setInput(blob);
    cv::Mat preds=net.forward();
    std::vector<cv::Rect>boxes;
    std::vector<float>confidences;
    std::vector<int> classIds;
    std::vector<int>indexs;
    infer_res(src,preds,boxes,confidences,classIds,indexs);
    clock_t et=clock();
    std::cout<<"run time:"<<(double)(et-st)/CLOCKS_PER_SEC<<std::endl;
//    draw_label(img,boxes,confidences,classIds,indexs,&classNames);
    for(int i=0;i<indexs.size();i++)
    {
        cv::rectangle(src,boxes[indexs[i]],(0,0,255),2);
        cv::rectangle(src,cv::Point(boxes[indexs[i]].tl().x,boxes[indexs[i]].tl().y-20),
        cv::Point(boxes[indexs[i]].tl().x+boxes[indexs[i]].br().x,boxes[indexs[i]].tl().y),cv::Scalar(200,200,200),-1);
        cv::putText(src,classNames[classIds[indexs[i]]], cv::Point(boxes[indexs[i]].tl().x+5, boxes[indexs[i]].tl().y - 10), cv::FONT_HERSHEY_SIMPLEX, .5, cv::Scalar(0, 0, 0));
        std::ostringstream conf;
        conf<<confidences[indexs[i]];
        cv::putText(src,conf.str(), cv::Point(boxes[indexs[i]].tl().x+60, boxes[indexs[i]].tl().y - 10), cv::FONT_HERSHEY_SIMPLEX, .5, cv::Scalar(0, 0, 0));

    }
    cv::imwrite("res_.jpg",src);
    cv::imshow("img",src);
    cv::waitKey();


    return 0;
}

CMakeLists.txt

cmake_minimum_required(VERSION 2.8)
project(cdnn)
find_package(OpenCV 4.5.1 REQUIRED)
include_directories(${OpenCV_INCLUDES_DIRS})
add_executable(cdnn cdnn.cpp)
target_link_libraries(cdnn ${OpenCV_LIBS})

执行：

mkdir build
cd build
cmake ..
make
./cdnn

运行结果：