openCV调用YOLOv5 ONNX模型
导出U版YOLOv5的ONNX模型，openCV直接读取会失败，原因在于Pytorch2ONNX不支持对slice对象赋值，需要更改下列两个地方代码：
yolov5/models/common.py
class Focus(nn.Module):
# Focus wh information into c-space
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
super(Focus, self).__init__()
self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
# self.contract = Contract(gain=2)
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
# return self.conv(self.contract(x))
更改为：
class Focus(nn.Module):
# Focus wh information into c-space
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
super(Focus, self).__init__()
self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
# self.contract = Contract(gain=2)
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
# return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
N, C, H, W = x.size() # assert (H / s == 0) and (W / s == 0), 'Indivisible gain'
s = 2
x = x.view(N, C, H // s, s, W // s, s) # x(1,64,40,2,40,2)
x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40)
y = x.view(N, C * s * s, H // s, W // s) # x(1,256,40,40)
return self.conv(y)具体原因可看博客2021.03.11更新 c++下使用opencv部署yolov5模型（一）_爱晚乏客游的博客-CSDN博客和Pytorch转ONNX-实战篇2（实战踩坑总结） - 知乎，这里不做过多解释
yolov5/models/yolo.py
class Detect(nn.Module):
stride = None # strides computed during build
export = False # onnx export
def __init__(self, nc=80, anchors=(), ch=()): # detection layer
super(Detect, self).__init__()
self.nc = nc # number of classes
self.no = nc + 5 # number of outputs per anchor
self.nl = len(anchors) # number of detection layers
self.na = len(anchors[0]) // 2 # number of anchors
self.grid = [torch.zeros(1)] * self.nl # init grid
a = torch.tensor(anchors).float().view(self.nl, -1, 2)
self.register_buffer('anchors', a) # shape(nl,na,2)
self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2)
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
def forward(self, x):
# x = x.copy() # for profiling
z = [] # inference output
self.training |= self.export
for i in range(self.nl):
x[i] = self.m[i](x[i]) # conv
bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
x[i] = x[i].view(bs * self.na * ny * nx, self.no).contiguous()
return torch.cat(x)
# if not self.training: # inference
# if self.grid[i].shape[2:4] != x[i].shape[2:4]:
# self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
#
# y = x[i].sigmoid()
# y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy
# y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
# z.append(y.view(bs, -1, self.no))
#
# return x if self.training else (torch.cat(z, 1), x)更改完成之后，使用netron查看导出的onnx模型，可发现上述操作把YOLO层的三个输出合并为一个输出，格式类似yolov4的格式。
python
import netron
netron.start("best.onnx")
接下来就是使用openCV调用导出的onnx，本文使用的openCV版本为4.5.1，使用4.4版本的可能会出现错误。代码参考了博客2021.09.02更新说明 c++下使用opencv部署yolov5模型 （三）_爱晚乏客游的博客-CSDN博客，我也是依葫芦画瓢
yolo5.h
#pragma once
#include
#include
#include
#include
#include
#include
#include
using namespace cv;
using namespace dnn;
using namespace std;
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() {};
bool readModel(Net &net, string &netPath, bool isCuda = false);
bool Detect(cv::Mat &SrcImg, cv::dnn::Net &net, std::vector yolo5.cpp
#include "yolo5.h"
#include
#include
using namespace cv;
using namespace std;
bool YOLO::readModel(Net & net, string & netPath, bool isCuda)
{
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(cv::Mat & SrcImg, cv::dnn::Net & net, std::vector main.cpp
#include
#include
#include
#include
#include "yolo5.h"
using namespace cv;
using namespace cv::dnn;
using namespace std;
int main()
{
string img_path = "./test.jpg";
string model_path = "./best.onnx";
YOLO test;
Net net;
if (test.readModel(net, model_path, false)) {
cout << "read net ok!" << endl;
}
else {
return -1;
}
//生成随机颜色
vector 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 我做了一个夜间车辆检测，下面贴出运行结果。感觉检测结果边框有点变形，回头再看看哪出错了。模型文件可在我的博客资源里下载yolov5夜间车辆检测模型-深度学习文档类资源-CSDN文库
