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 onnxonnx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：
转载：https://blog.csdn.net/m0_37264397/article/details/125791308
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