C++/Yolov8人体特征识别 广场室内 人数统计

C++/Yolov8人体特征识别 广场室内 人数统计

二、使用步骤

1.引入库

#include <iostream>
#include<opencv2/opencv.hpp>
#include<math.h>
#include "yolov8_onnx.h"
#include<time.h>
using namespace std;
using namespace cv;
using namespace dnn;

2.识别图像特征

bool CheckParams(int netHeight, int netWidth, const int* netStride, int strideSize) {
if (netHeight % netStride[strideSize - 1] != 0 || netWidth % netStride[strideSize - 1] != 0)
{
cout << "Error:_netHeight and _netWidth must be multiple of max stride " << netStride[strideSize - 1] << "!" << endl;
return false;
}
return true;
}
void 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;
ratio[0] = (float)newShape.width / (float)shape.width;
ratio[1] = (float)newShape.height / (float)shape.height;
}
dw /= 2.0f;
dh /= 2.0f;
{
}
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);
}
Mat protos = maskProtos.reshape(0, { seg_channels,seg_width * seg_height });
Mat matmul_res = (maskProposals * protos).t();
Mat masks = matmul_res.reshape(output.size(), { seg_width,seg_height });
for (int i = 0; i < output.size(); ++i) {
//sigmoid
dest = 1.0 / (1.0 + dest);
Rect roi(int(params[2] / net_width * seg_width), int(params[3] / net_height * seg_height), int(seg_width - params[2] / 2), int(seg_height - params[3] / 2));
dest = dest(roi);
//crop
Rect temp_rect = output[i].box;
}
}
Rect temp_rect = output.box;
int rang_x = floor((temp_rect.x * params[0] + params[2]) / net_width * seg_width);
int rang_y = floor((temp_rect.y * params[1] + params[3]) / net_height * seg_height);
int rang_w = ceil(((temp_rect.x + temp_rect.width) * params[0] + params[2]) / net_width * seg_width) - rang_x;
int rang_h = ceil(((temp_rect.y + temp_rect.height) * params[1] + params[3]) / net_height * seg_height) - rang_y;
rang_w = MAX(rang_w, 1);
rang_h = MAX(rang_h, 1);
if (rang_x + rang_w > seg_width) {
if (seg_width - rang_x > 0)
rang_w = seg_width - rang_x;
else
rang_x -= 1;
}
if (rang_y + rang_h > seg_height) {
if (seg_height - rang_y > 0)
rang_h = seg_height - rang_y;
else
rang_y -= 1;
}
vector<Range> roi_rangs;
roi_rangs.push_back(Range(0, 1));
roi_rangs.push_back(Range::all());
roi_rangs.push_back(Range(rang_y, rang_h + rang_y));
roi_rangs.push_back(Range(rang_x, rang_w + rang_x));
//crop
Mat protos = temp_mask_protos.reshape(0, { seg_channels,rang_w * rang_h });
Mat matmul_res = (maskProposals * protos).t();
Mat masks_feature = matmul_res.reshape(1, { rang_h,rang_w });
//sigmoid
dest = 1.0 / (1.0 + dest);
int left = floor((net_width / seg_width * rang_x - params[2]) / params[0]);
int top = floor((net_height / seg_height * rang_y - params[3]) / params[1]);
int width = ceil(net_width / seg_width * rang_w / params[0]);
int height = ceil(net_height / seg_height * rang_h / params[1]);
}
void DrawPred(Mat& img, vector<OutputSeg> result, std::vector<std::string> classNames, 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 = classNames[result[i].id] + ":" + to_string(result[i].confidence);
std::ostringstream oss;
oss << round(10 / result[i].confidence * 10) / 10.0;
std::cout << oss.str() << std::endl;
//string label = classNames[result[i].id] + ":" + oss.str();
string label = classNames[result[i].id];
int baseLine;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.8, 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, 0.6, color[result[i].id], 2);
}
imshow("1", img);
//imwrite("out.bmp", img);
waitKey();
//destroyAllWindows();
}

3.参数定义

int main() {
//string img_path = "./data/image/aa.png";
string img_path = "./data/image/11.jpeg";
string detect_model_path = "yolov8n.onnx";
return 0;
}

三、在线协助：

1）远程安装运行环境，代码调试
2）Qt, C++, Python入门指导
3）界面美化
4）软件制作

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