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Yolo-Detection/yolo+SORT/yoloSORT/yolo.cpp

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first commit
2024-10-11 20:43:44 +08:00
#include"yolo.h"
using namespace std;
using namespace cv;
using namespace cv::dnn;
void Yolov5::LetterBox(const cv::Mat& image, cv::Mat& outImage, cv::Vec4d& params, const cv::Size& newShape,
bool autoShape, bool scaleFill, bool scaleUp, int stride, const cv::Scalar& color)
{
if (false) {
int maxLen = MAX(image.rows, image.cols);
outImage = Mat::zeros(Size(maxLen, maxLen), CV_8UC3);
image.copyTo(outImage(Rect(0, 0, image.cols, image.rows)));
params[0] = 1;
params[1] = 1;
params[3] = 0;
params[2] = 0;
}
cv::Size shape = image.size();
float r = std::min((float)newShape.height / (float)shape.height,
(float)newShape.width / (float)shape.width);
if (!scaleUp)
r = std::min(r, 1.0f);
float ratio[2]{ r, r };
int new_un_pad[2] = { (int)std::round((float)shape.width * r),(int)std::round((float)shape.height * r) };
auto dw = (float)(newShape.width - new_un_pad[0]);
auto dh = (float)(newShape.height - new_un_pad[1]);
if (autoShape)
{
dw = (float)((int)dw % stride);
dh = (float)((int)dh % stride);
}
else if (scaleFill)
{
dw = 0.0f;
dh = 0.0f;
new_un_pad[0] = newShape.width;
new_un_pad[1] = newShape.height;
ratio[0] = (float)newShape.width / (float)shape.width;
ratio[1] = (float)newShape.height / (float)shape.height;
}
dw /= 2.0f;
dh /= 2.0f;
if (shape.width != new_un_pad[0] && shape.height != new_un_pad[1])
{
cv::resize(image, outImage, cv::Size(new_un_pad[0], new_un_pad[1]));
}
else {
outImage = image.clone();
}
int top = int(std::round(dh - 0.1f));
int bottom = int(std::round(dh + 0.1f));
int left = int(std::round(dw - 0.1f));
int right = int(std::round(dw + 0.1f));
params[0] = ratio[0];
params[1] = ratio[1];
params[2] = left;
params[3] = top;
cv::copyMakeBorder(outImage, outImage, top, bottom, left, right, cv::BORDER_CONSTANT, color);
}
bool Yolov5::readModel(Net& net, string& netPath, bool isCuda = false) {
try {
net = readNet(netPath);
#if CV_VERSION_MAJOR==4 &&CV_VERSION_MINOR==7&&CV_VERSION_REVISION==0
net.enableWinograd(false); //bug of opencv4.7.x in AVX only platform ,https://github.com/opencv/opencv/pull/23112 and https://github.com/opencv/opencv/issues/23080
//net.enableWinograd(true); //If your CPU supports AVX2, you can set it true to speed up
#endif
}
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 Yolov5::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();
Vec4d params;
LetterBox(SrcImg, netInputImg, params, cv::Size(_netWidth, _netHeight));
blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(_netWidth, _netHeight), cv::Scalar(0, 0, 0), true, false);
//<2F><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>û<EFBFBD><C3BB><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>µ<EFBFBD><C2B5>ǽ<EFBFBD><C7BD><EFBFBD>ƫ<EFBFBD><C6AB><EFBFBD>ܴ󣬿<DCB4><F3A3ACBF>Գ<EFBFBD><D4B3><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
//blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(_netWidth, _netHeight), cv::Scalar(104, 117, 123), true, false);
//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]); //<2F><>ȡoutput<75><74><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
//net.forward(netOutputImg, net.getUnconnectedOutLayersNames());
std::vector<int> classIds;//<2F><><EFBFBD><EFBFBD>id<69><64><EFBFBD><EFBFBD>
std::vector<float> confidences;//<2F><><EFBFBD><EFBFBD>ÿ<EFBFBD><C3BF>id<69><64>Ӧ<EFBFBD><D3A6><EFBFBD>Ŷ<EFBFBD><C5B6><EFBFBD><EFBFBD><EFBFBD>
std::vector<cv::Rect> boxes;//ÿ<><C3BF>id<69><64><EFBFBD>ο<EFBFBD>
int net_width = _className.size() + 5; //<2F><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>+5
int net_out_width = netOutputImg[0].size[2];
assert(net_out_width == net_width, "Error Wrong number of _className"); //ģ<><C4A3><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ŀ<EFBFBD><C4BF><EFBFBD><EFBFBD>
float* pdata = (float*)netOutputImg[0].data;
int net_height = netOutputImg[0].size[1];
for (int r = 0; r < net_height; ++r) {
float box_score = pdata[4]; ;//<2F><>ȡÿһ<C3BF>е<EFBFBD>box<6F><78><EFBFBD>к<EFBFBD><D0BA><EFBFBD>ij<EFBFBD><C4B3><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ĸ<EFBFBD><C4B8><EFBFBD>
if (box_score >= _classThreshold) {
cv::Mat scores(1, _className.size(), CV_32FC1, pdata + 5);
Point classIdPoint;
double max_class_socre;
minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);
max_class_socre = max_class_socre * box_score;
if (max_class_socre >= _classThreshold) {
//rect [x,y,w,h]
float x = (pdata[0] - params[2]) / params[0];
float y = (pdata[1] - params[3]) / params[1];
float w = pdata[2] / params[0];
float h = pdata[3] / params[1];
int left = MAX(round(x - 0.5 * w + 0.5), 0);
int top = MAX(round(y - 0.5 * h + 0.5), 0);
classIds.push_back(classIdPoint.x);
confidences.push_back(max_class_socre);
boxes.push_back(Rect(left, top, int(w + 0.5), int(h + 0.5)));
}
}
pdata += net_width;//<2F><>һ<EFBFBD><D2BB>
}
//ִ<>зǼ<D0B7><C7BC><EFBFBD>ֵ<EFBFBD><D6B5><EFBFBD>ƣ<EFBFBD>NMS<4D><53><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>нϵ<D0BD><CFB5><EFBFBD><EFBFBD>Ŷȵ<C5B6><C8B5><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ص<EFBFBD><D8B5><EFBFBD>
vector<int> nms_result;
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 Yolov5::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("1", img);
//imwrite("out.bmp", img);
//waitKey();
//destroyAllWindows();
}