C++/Yolov8人体特征识别 广场室内 人数统计

C++/Yolov8人体特征识别人数统计

 

如需安装运行环境或远程调试，见文章底部个人QQ名片，由专业技术人员远程协助！

前言

这篇博客针对<>编写代码，代码整洁，规则，易读。 学习与应用推荐首选。

文章目录

一、所需工具软件

二、使用步骤

        1. 引入库

        2. 识别图像特征

        3. 参数设置

        4. 运行结果

三、在线协助

一、所需工具软件

1. VS2019, C++

2. Yolov8, OpenCV

二、使用步骤

1.引入库

代码如下（示例）：

#include 
#include

#include
#include "yolov8_onnx.h"
#include

using namespace std;
using namespace cv;
using namespace dnn;

2.识别图像特征

代码如下（示例）：

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;
		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);
}

void GetMask(const cv::Mat& maskProposals, const cv::Mat& maskProtos, std::vector& output, const MaskParams& maskParams) {
	//cout << maskProtos.size << endl;

	int seg_channels = maskParams.segChannels;
	int net_width = maskParams.netWidth;
	int seg_width = maskParams.segWidth;
	int net_height = maskParams.netHeight;
	int seg_height = maskParams.segHeight;
	float mask_threshold = maskParams.maskThreshold;
	Vec4f params = maskParams.params;
	Size src_img_shape = maskParams.srcImgShape;

	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 });
	vector maskChannels;
	split(masks, maskChannels);
	for (int i = 0; i < output.size(); ++i) {
		Mat dest, mask;
		//sigmoid
		cv::exp(-maskChannels[i], dest);
		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);
		resize(dest, mask, src_img_shape, INTER_NEAREST);

		//crop
		Rect temp_rect = output[i].box;
		mask = mask(temp_rect) > mask_threshold;
		output[i].boxMask = mask;
	}
}

void GetMask2(const Mat& maskProposals, const Mat& mask_protos, OutputSeg& output, const MaskParams& maskParams) {
	int seg_channels = maskParams.segChannels;
	int net_width = maskParams.netWidth;
	int seg_width = maskParams.segWidth;
	int net_height = maskParams.netHeight;
	int seg_height = maskParams.segHeight;
	float mask_threshold = maskParams.maskThreshold;
	Vec4f params = maskParams.params;
	Size src_img_shape = maskParams.srcImgShape;

	Rect temp_rect = output.box;
	//crop from mask_protos
	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;

	//如果下面的 mask_protos(roi_rangs).clone()位置报错，说明你的output.box数据不对，或者矩形框就1个像素的，开启下面的注释部分防止报错。
	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 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 temp_mask_protos = mask_protos(roi_rangs).clone();
	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 });
	Mat dest, mask;

	//sigmoid
	cv::exp(-masks_feature, dest);
	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]);

	resize(dest, mask, Size(width, height), INTER_NEAREST);
	mask = mask(temp_rect - Point(left, top)) > mask_threshold;
	output.boxMask = mask;

}

void DrawPred(Mat& img, vector result, std::vector classNames, vector color) {
	Mat mask = img.clone();
	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);
		if(result[i].boxMask.rows&& result[i].boxMask.cols>0)
			mask(result[i].box).setTo(color[result[i].id], result[i].boxMask);
		//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);
	}
	addWeighted(img, 0.5, mask, 0.5, 0, img); //add mask to src
	imshow("1", img);
	//imwrite("out.bmp", img);
	waitKey();
	//destroyAllWindows();

}

3.参数定义

代码如下（示例）：

int main() {

	//string img_path = "./data/image/aa.png";
	string img_path = "./data/image/11.jpeg";
	string detect_model_path = "yolov8n.onnx";
	Mat img = imread(img_path);

	Yolov8Onnx task_detect_onnx;
	yolov8_onnx(task_detect_onnx,img,detect_model_path);  //onnxruntime detect


	return 0;
}

4. 运行结果如下

 

三、在线协助：

如需安装运行环境或远程调试，见文章底部个人 QQ 名片，由专业技术人员远程协助！
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

博主推荐文章：python人脸识别统计人数qt窗体-CSDN博客

博主推荐文章：Python Yolov5火焰烟雾识别源码分享-CSDN博客

个人博客主页：alicema1111的博客_CSDN博客-Python,C++,网页领域博主

博主所有文章点这里：alicema1111的博客_CSDN博客-Python,C++,网页领域博主