使用B导的yolov7代码部署，代码地址：https://github.com/bubbliiiing/yolov7-pytorch
模型的的训练看B导即可，up主地址：Bubbliiiing的博客_CSDN博客-神经网络学习小记录,睿智的目标检测,有趣的数据结构算法领域博主
模型训练完成之后，在predict.py中设置mode = "export_onnx"即可生成。
注意，此处有个坑，B导的yolov7代码输出的onnx只有1*class.size*20*20这一层，需要在nets/yolo.py文件中修改一下。
修改之前：（在yolo.py的最下面）
#---------------------------------------------------#
# 第三个特征层
# y3=(batch_size, 36, 80, 80)
#---------------------------------------------------#
out2 = self.yolo_head_P3(P3)
#---------------------------------------------------#
# 第二个特征层
# y2=(batch_size, 36, 40, 40)
#---------------------------------------------------#
out1 = self.yolo_head_P4(P4)
#---------------------------------------------------#
# 第一个特征层
# y1=(batch_size, 36, 20, 20)
#---------------------------------------------------#
out0 = self.yolo_head_P5(P5)
return [out0, out1, out2]
修改之后：
#---------------------------------------------------#
# 第三个特征层
# y3=(batch_size, 36, 80, 80)
#---------------------------------------------------#
out2 = self.yolo_head_P3(P3)
bs, _, ny, nx = out2.shape # x(bs,255,20,20) to x(bs,3,20,20,85)
out2 = out2.view(bs, 3, 12, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
out2 = out2.view(bs * 3 * ny * nx, 12).contiguous()
#---------------------------------------------------#
# 第二个特征层
# y2=(batch_size, 36, 40, 40)
#---------------------------------------------------#
out1 = self.yolo_head_P4(P4)
bs, _, ny, nx = out1.shape
out1 = out1.view(bs, 3, 12, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
out1 = out1.view(bs * 3 * ny * nx, 12).contiguous()
#---------------------------------------------------#
# 第一个特征层
# y1=(batch_size, 36, 20, 20)
#---------------------------------------------------#
out0 = self.yolo_head_P5(P5)
bs, _, ny, nx = out0.shape
out0 = out0.view(bs, 3, 12, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
out0 = out0.view(bs * 3 * ny * nx, 12).contiguous()
#return [out0, out1, out2]
return torch.cat((out2,out1,out0))
这样我们可以看到输出的shape已经变成了25200*12了！
解释下这个数据：网络原本的输出是1*36*80*80，1*36*40*40，1*36*20*20，36是因为我的模型的类别数为7，36=（5+7）*3，5为四个位置信息加置信度，3为anchor数，经过上述代码的操作就把所有的输出拼接起来了，结果为25200*12，一共有25200个预测结果与每个结果对应12个信息。
之后我们就可以利用生成的onnx在vs studio中进行部署啦。
main.cpp：
#include "yolo.h"
#include <iostream>
#include<opencv2//opencv.hpp>
#include<math.h>
using namespace std;
using namespace cv;
using namespace dnn;
int main()
{
string img_path = "3.jpg";
string model_path = "models.onnx";
Yolo test;
Net net;
//加载onnx模型
if (test.readModel(net, model_path, true)) {
cout << "read net ok!" << endl;
}
else {
return -1;
}
//生成随机颜色
vector<Scalar> color;
srand(time(0));
for (int i = 0; i < 80; i++) {
int b = rand() % 256;
int g = rand() % 256;
int r = rand() % 256;
color.push_back(Scalar(b, g, r));
}
vector<Output> result;
Mat img = imread(img_path);
if (test.Detect(img, net, result)) {
test.drawPred(img, result, color);
}
else {
cout << "Detect Failed!" << endl;
}
system("pause");
return 0;
}
yolo.h：
#pragma once
#include<iostream>
#include<math.h>
#include<opencv2/opencv.hpp>
struct Output {
int id;//结果类别id
float confidence;//结果置信度
cv::Rect box;//矩形框
};
class Yolo
{
public:
Yolo() {}
~Yolo(){}
bool readModel(cv::dnn::Net& net, std::string& netPath, bool isCuda);
bool Detect(cv::Mat& SrcImg, cv::dnn::Net& net, std::vector<Output>& output);
void drawPred(cv::Mat& img, std::vector<Output> result, std::vector<cv::Scalar> color);
private:
//计算归一化函数
float Sigmoid(float x) {
return static_cast<float>(1.f / (1.f + exp(-x)));
}
//anchors
const float netAnchors[3][6] = { { 12.0, 16.0, 19.0, 36.0, 40.0, 28.0 },{ 36.0, 75.0, 76.0, 55.0, 72.0, 146.0 },{ 142.0, 110.0, 192.0, 243.0, 459.0, 401.0 } };
//stride
const float netStride[3] = { 8.0, 16.0, 32.0 };
const int netWidth = 640; //网络模型输入大小
const int netHeight = 640;
float nmsThreshold = 0.45;
float boxThreshold = 0.35;
float classThreshold = 0.35;
//我的数据集类名
std::vector<std::string> className = { "scratch","Exposed components","Reverse printing","Missing print","6.8CA","D7","TB20K"};
};
yolo.cpp:
#include "Yolo.h"
using namespace std;
using namespace cv;
using namespace dnn;
bool Yolo::readModel(Net& net, string& netPath, bool isCuda = false) {
try {
net = readNetFromONNX(netPath);
}
catch (const std::exception&) {
return false;
}
//cuda
if (isCuda) {
net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);
}
//cpu
else {
net.setPreferableBackend(cv::dnn::DNN_BACKEND_DEFAULT);
net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
}
return true;
}
bool Yolo::Detect(Mat& SrcImg, Net& net, vector<Output>& output) {
Mat blob;
int col = SrcImg.cols;
int row = SrcImg.rows;
int maxLen = MAX(col, row);
Mat netInputImg = SrcImg.clone();
if (maxLen > 1.2 * col || maxLen > 1.2 * row) {
Mat resizeImg = Mat::zeros(maxLen, maxLen, CV_8UC3);
SrcImg.copyTo(resizeImg(Rect(0, 0, col, row)));
netInputImg = resizeImg;
}
blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(netWidth, netHeight), cv::Scalar(104, 117, 123), true, false);
//blob = blobFromImage(netInputImg, 1 / 255.0, cv::Size(netWidth, netHeight), cv::Scalar(0, 0,0), true, false);//如果训练集未对图片进行减去均值操作，则需要设置为这句
//blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(netWidth, netHeight), cv::Scalar(114, 114,114), true, false);
net.setInput(blob);
std::vector<cv::Mat> netOutputImg;
//vector<string> outputLayerName{"345","403", "461","output" };
//net.forward(netOutputImg, outputLayerName[3]); //获取output的输出
net.forward(netOutputImg, net.getUnconnectedOutLayersNames());
std::vector<int> classIds;//结果id数组
std::vector<float> confidences;//结果每个id对应置信度数组
std::vector<cv::Rect> boxes;//每个id矩形框
float ratio_h = (float)netInputImg.rows / netHeight;
float ratio_w = (float)netInputImg.cols / netWidth;
int net_width = className.size() + 5; //输出的网络宽度是类别数+5
float* pdata = (float*)netOutputImg[0].data;
for (int stride = 0; stride < 3; stride++) { //stride
int grid_x = (int)(netWidth / netStride[stride]);
int grid_y = (int)(netHeight / netStride[stride]);
for (int anchor = 0; anchor < 3; anchor++) { //anchors
const float anchor_w = netAnchors[stride][anchor * 2];
const float anchor_h = netAnchors[stride][anchor * 2 + 1];
for (int i = 0; i < grid_x; i++) {
for (int j = 0; j < grid_y; j++) {
float box_score = Sigmoid(pdata[4]);//获取每一行的box框中含有某个物体的概率
if (box_score > boxThreshold) {
//为了使用minMaxLoc(),将85长度数组变成Mat对象
cv::Mat scores(1, className.size(), CV_32FC1, pdata + 5);
Point classIdPoint;
double max_class_socre;
//cout << scores << endl;
minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);
max_class_socre = Sigmoid((float)max_class_socre);
if (max_class_socre > classThreshold) {
//rect [x,y,w,h]
float x = (Sigmoid(pdata[0]) * 2.f - 0.5f + j) * netStride[stride]; //x
float y = (Sigmoid(pdata[1]) * 2.f - 0.5f + i) * netStride[stride]; //y
float w = powf(Sigmoid(pdata[2]) * 2.f, 2.f) * anchor_w; //w
float h = powf(Sigmoid(pdata[3]) * 2.f, 2.f) * anchor_h; //h
int left = (x - 0.5 * w) * ratio_w;
int top = (y - 0.5 * h) * ratio_h;
classIds.push_back(classIdPoint.x);
confidences.push_back(max_class_socre * box_score);
boxes.push_back(Rect(left, top, int(w * ratio_w), int(h * ratio_h)));
}
}
pdata += net_width;//指针移到下一行
}
}
}
}
vector<int> nms_result;
dnn::NMSBoxes(boxes, confidences, classThreshold, nmsThreshold, nms_result);
for (int i = 0; i < nms_result.size(); i++) {
int idx = nms_result[i];
Output result;
result.id = classIds[idx];
result.confidence = confidences[idx];
result.box = boxes[idx];
output.push_back(result);
}
if (output.size())
return true;
else
return false;
}
void Yolo::drawPred(Mat& img, vector<Output> result, vector<Scalar> color) {
for (int i = 0; i < result.size(); i++) {
int left, top;
left = result[i].box.x;
top = result[i].box.y;
int color_num = i;
rectangle(img, result[i].box, color[result[i].id], 2, 8);
string label = className[result[i].id] + ":" + to_string(result[i].confidence);
int baseLine;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
top = max(top, labelSize.height);
//rectangle(frame, Point(left, top - int(1.5 * labelSize.height)), Point(left + int(1.5 * labelSize.width), top + baseLine), Scalar(0, 255, 0), FILLED);
putText(img, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 1, color[result[i].id], 2);
}
imshow("res", img);
waitKey();
}
预测结果：
大功告成啦，不得不说yolov7的效果相当的好，也感谢B导大大啦