优化代码对齐，Review和调整部分注释
parent
ed6a09bf8b
commit
ce2f195559
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@ -56,7 +56,6 @@ unsigned char * Detection::load_model(const char *filename, int *model_size)
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Detection::Detection()
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{
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/* Create the neural network */
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printf("Loading mode...\n");
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int model_data_size = 0;
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@ -127,7 +126,6 @@ Detection::Detection()
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printf("model input height=%d, width=%d, channel=%d\n", height, width, channel);
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memset(inputs, 0, sizeof(inputs));
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inputs[0].index = 0;
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inputs[0].type = RKNN_TENSOR_UINT8;
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@ -135,8 +133,9 @@ Detection::Detection()
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inputs[0].fmt = RKNN_TENSOR_NHWC;
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inputs[0].pass_through = 0;
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}
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}
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// 这个地方采用直接传值，会影响性能
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detect_result_group_t Detection::outputParse(cv::Mat netInputImg)
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{
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@ -157,7 +156,8 @@ detect_result_group_t Detection::outputParse(cv::Mat netInputImg)
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memset(&pads, 0, sizeof(BOX_RECT));
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cv::Size target_size(width, height);
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cv::Mat resized_img(target_size.height, target_size.width, CV_8UC3);
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// 计算缩放比例
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// 需要将输入图像分辨率转换到检测模型指定的分辨率
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float scale_w = (float)target_size.width / img.cols;
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float scale_h = (float)target_size.height / img.rows;
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@ -39,7 +39,6 @@ void *ranging(void *args) // ranging线程
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int main(int argc, char **argv)
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{
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pthread_t tids[1]; // 执行ranging线程
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int ret = pthread_create(&tids[0], NULL, ranging, NULL);
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@ -1,17 +1,18 @@
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#include <opencv2/opencv.hpp>
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#include <opencv2/highgui.hpp>
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#include <iostream>
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#include <stdio.h>
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#include "ranging.h"
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#include <math.h>
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#include <stdlib.h>
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#include <string.h>
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#include <sys/time.h>
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#include <dlfcn.h>
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// OpenCV相关
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#include "opencv2/core.hpp"
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#include "opencv2/imgcodecs.hpp"
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#include "opencv2/imgproc.hpp"
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#include <opencv2/opencv.hpp>
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#include <opencv2/highgui.hpp>
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#include <sys/stat.h>
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#include <sys/types.h>
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@ -19,19 +20,22 @@
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#include <vector>
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#include <tuple>
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#include <string>
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#include <string.h>
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#include <algorithm>
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// 上面重复无用的头文件应该删除
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// #include <ros/ros.h>
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using namespace cv;
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void Ranging::rectifyImage(Mat &oriImgL, Mat &oriImgR) //重映射函数
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// 重映射函数
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void Ranging::rectifyImage(Mat &oriImgL, Mat &oriImgR)
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{
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remap(oriImgL, oriImgL, mapX1, mapX2, cv::INTER_LINEAR);
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remap(oriImgR, oriImgR, mapY1, mapY2, cv::INTER_LINEAR);
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}
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std::vector<float> Ranging::pic2cam(int u, int v) //像素坐标转相机坐标
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// 像素坐标转相机坐标
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std::vector<float> Ranging::pic2cam(int u, int v)
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{
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//(u,v)->(x,y)"(loc[0],loc[1])"
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std::vector<float> loc;
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@ -40,28 +44,38 @@ std::vector<float> Ranging::pic2cam(int u, int v) //像素坐标转相机坐标
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return loc;
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}
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std::vector<int> Ranging::muban(Mat &left_image, Mat &right_image, const int *coordinates) //模板匹配
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// 模板匹配
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std::vector<int> Ranging::muban(Mat &left_image, Mat &right_image, const int *coordinates)
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{
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int x1 = coordinates[0], y1 = coordinates[1], x2 = coordinates[2], y2 = coordinates[3];
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Mat tpl = right_image.rowRange(max(y1 - 2, 0), min(y2 + 2, 479)).colRange(x1, x2); //获取目标框
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Mat target = left_image.rowRange(max(y1 - 20, 0), min(y2 + 20, 479)).colRange(0, 639); //待匹配图像，极线约束，只需要同水平区域
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// 获取目标框
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Mat tpl = right_image.rowRange(max(y1 - 2, 0), min(y2 + 2, 479)).colRange(x1, x2);
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// 待匹配图像，极线约束，只需要同水平区域
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Mat target = left_image.rowRange(max(y1 - 20, 0), min(y2 + 20, 479)).colRange(0, 639);
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int th = tpl.rows, tw = tpl.cols;
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Mat result;
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matchTemplate(target, tpl, result, TM_CCOEFF_NORMED); //匹配方法：归一化相关系数即零均值归一化互相关
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// 匹配方法：归一化相关系数即零均值归一化互相关
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matchTemplate(target, tpl, result, TM_CCOEFF_NORMED);
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double minVal, maxVal;
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Point minLoc, maxLoc;
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minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc); //得到匹配点坐标
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// 得到匹配点坐标
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minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc);
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Point tl = maxLoc, br;
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// 这个地方应该修改为宏
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// 转为像素坐标系 640 - 1
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br.x = min(maxLoc.x + tw, 639);
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// 转为像素坐标系 480 - 1
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br.y = min(maxLoc.y + th, 479);
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br.x = min(maxLoc.x + tw, 639); //转为像素坐标系
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br.y = min(maxLoc.y + th, 479); //转为像素坐标系
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////展示匹配结果
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// br.x = min(maxLoc.x + tw, 319);
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// br.y = min(maxLoc.y + th, 239);
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//rectangle(target, tl, br, (0, 255, 0), 3);
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//imshow("match-", target);
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//waitKey(2);
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/*
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// 展示匹配结果
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br.x = min(maxLoc.x + tw, 319);
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br.y = min(maxLoc.y + th, 239);
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rectangle(target, tl, br, (0, 255, 0), 3);
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imshow("match-", target);
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waitKey(2);
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*/
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std::vector<int> maxloc;
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maxloc.push_back(maxLoc.x);
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@ -69,6 +83,7 @@ std::vector<int> Ranging::muban(Mat &left_image, Mat &right_image, const int *co
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return maxloc;
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}
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// 这个函数暂时不知道作用
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void Ranging::horizon_estimate(Mat& img, Mat& bboxs,int k)
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{
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//保证摄像头与地面平行
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@ -138,49 +153,70 @@ void Ranging::getInfo(Mat &imgL, Mat &imgR, Mat &detBoxes, Mat &info)
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Mat imgGrayL, imgGrayR;
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cvtColor(imgL, imgGrayL, COLOR_BGR2GRAY);
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cvtColor(imgR, imgGrayR, COLOR_BGR2GRAY);
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// 优化：这个地方的imgR_weight没有起到任何作用
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// 纯粹浪费CPU计算了
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Mat imgR_weight = imgR.clone();
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Mat infoRow;
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for (uchar i = 0; i < detBoxes.rows; i++)
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{
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// 获得位置信息
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int x1 = detBoxes.at<float>(i, 0);
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int y1 = detBoxes.at<float>(i, 1);
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int x2 = detBoxes.at<float>(i, 2);
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int y2 = detBoxes.at<float>(i, 3);
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// 获取置信度信息
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float Conf = detBoxes.at<float>(i, 4);
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int cls = detBoxes.at<float>(i, 5);
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if (x1 > 600 || x2 < 50 || y1 < 5 || y2 > 475 || x1 < 2 || x2 > 590 || abs(x2 - x1) > 550) //当目标框偏左、偏右或者过大，略去该物体
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{
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// 当目标框偏左、偏右或者过大，略去该物体
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if (x1 > 600 || x2 < 50 || y1 < 5 || y2 > 475 || x1 < 2 || x2 > 590 || abs(x2 - x1) > 550) {
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continue;
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}
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rectangle(imgR, Point(int(x1), int(y1)),
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Point(int(x2), int(y2)), Scalar(0, 0, 255)); //绘制目标框
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// 绘制目标框
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rectangle(imgR, Point(int(x1), int(y1)),Point(int(x2), int(y2)), Scalar(0, 0, 255));
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// 检测的是右图，利用模板匹配去找左图的位置
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int coordinates[4] = {x1, y1, x2, y2};
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std::vector<int> disp_pixel = muban(imgGrayL, imgGrayR, coordinates); //模板匹配
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float disp_pixel_x = disp_pixel[0] - x1; //ˮ计算水平视差
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float disp_pixel_y = disp_pixel[1] - y1; //
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disp_pixel_x = (int)(disp_pixel_x + disp_pixel_x * 0.12); //0.12为模板匹配产生的误差，为经验值，通过拟合得到
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// 模板匹配
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std::vector<int> disp_pixel = muban(imgGrayL, imgGrayR, coordinates);
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// 这里用disp_x 有点奇怪？用dispDiffX
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// 计算水平视差 左图匹配的x坐标和右图的x坐标之差
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float disp_pixel_x = disp_pixel[0] - x1;
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// 垂直的误差没有起到作用
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float disp_pixel_y = disp_pixel[1] - y1;
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// 0.12为模板匹配产生的误差，为经验值，通过拟合得到
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disp_pixel_x = (int)(disp_pixel_x + disp_pixel_x * 0.12);
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//Mat disp_matrix = Mat(1, 1, CV_32F, Scalar(disp_pixel_x)), disp_pixel_xyz;
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Mat disp_matrix = Mat(imgGrayL.rows, imgGrayL.cols, CV_32F, Scalar(disp_pixel_x)); //定义视差矩阵，所有值均为水平视差，方便转换为三维坐标，并具有水平距离信息
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// 定义视差矩阵，所有值均为水平视差，方便转换为三维坐标，并具有水平距离信息
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Mat disp_matrix = Mat(imgGrayL.rows, imgGrayL.cols, CV_32F, Scalar(disp_pixel_x));
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Mat threed_pixel_xyz, threedImage;
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reprojectImageTo3D(disp_matrix, threedImage, q, false); //2d->3d
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threed_pixel_xyz = threedImage.mul(threedImage); //每一像素点求平方，
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// 通过quat将显示图像从2d映射到3d
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reprojectImageTo3D(disp_matrix, threedImage, q, false);
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// 图像的3个通道，每一像素点求平方
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threed_pixel_xyz = threedImage.mul(threedImage);
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std::vector<Mat> channels;
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// 求平方后的结果，分离为3个通道，然后求和即是欧式距离
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split(threed_pixel_xyz.clone(), channels);
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threed_pixel_xyz = channels[0] + channels[1] + channels[2]; //计算欧式距离
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// 计算欧式距离 = r^2 + g^2 + b^2
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threed_pixel_xyz = channels[0] + channels[1] + channels[2];
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threed_pixel_xyz.forEach<float>([](float &value, const int *position) { value = sqrt(value); }); // 获得距离d
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int mid_pixel = int((x1 + x2) / 2);
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std::vector<float> mid = pic2cam(imgGrayR.cols / 2, imgGrayR.rows); //计算角度，从像素坐标转为相机坐标
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// 计算角度，从像素坐标转为相机坐标
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// 这里用结构体比用vector更加节省CPU运算
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std::vector<float> mid = pic2cam(imgGrayR.cols / 2, imgGrayR.rows);
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std::vector<float> loc_tar = pic2cam(mid_pixel, imgGrayR.rows);
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float alfa = atan((loc_tar[0] - mid[0]) / q.at<double>(2, 3));
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if (disp_pixel_x > 240) // 距离太近，视差过大
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// 距离太近，视差过大
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if (disp_pixel_x > 240)
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{
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char cm[15];
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//sprintf(cm, "cannot match !");
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@ -217,13 +253,13 @@ void Ranging::getInfo(Mat &imgL, Mat &imgR, Mat &detBoxes, Mat &info)
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//sprintf(tf, "Too far!");
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char cm[15];
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//sprintf(cm, "cannot match !");
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sprintf(cm, "%d , %.2f", cls,Conf);
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sprintf(cm, "%d , %.2f", cls, Conf);
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putText(imgR, cm, Point((x1), (y1)), FONT_HERSHEY_PLAIN, 2.2, Scalar(0, 0, 255), 2);
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infoRow = (Mat_<float>(1, 4) << -1, -1, -1, -1);
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infoRow.copyTo(info.row(i));
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continue;
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}
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// <20><>ͼ<EFBFBD><CDBC><EFBFBD>ϻ<EFBFBD><CFBB><EFBFBD><EFBFBD><EFBFBD>Ϣ
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// char dc[50], wh[50];
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// std::string cname = className[cls + 1];
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// sprintf(dc, "dis:%.2fcm %d", median, cls);
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@ -231,15 +267,13 @@ void Ranging::getInfo(Mat &imgL, Mat &imgR, Mat &detBoxes, Mat &info)
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// putText(imgR, dc, Point(x1, y2), FONT_HERSHEY_PLAIN, 1.5, Scalar(0, 0, 255), 2);
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// putText(imgR, wh, Point(x1, y1), FONT_HERSHEY_PLAIN, 1.5, Scalar(0, 0, 255), 1.5);
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//返回数据
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infoRow = (Mat_<float>(1, 4) << median, w1, h1, alfa);
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infoRow.copyTo(info.row(i));
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};
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}
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// cv::imshow("kk",imgR);
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// cv::waitKey(1);
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//cv::imshow("kk",imgR);
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//cv::waitKey(1);
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}
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Ranging::Ranging(int index, int imgw, int imgh) : //初始化
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@ -266,38 +300,40 @@ Ranging::Ranging(int index, int imgw, int imgh) : //初始化
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stereoRectify(cam_matrix_left.t(), distortion_l, cam_matrix_right.t(), distortion_r,
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imgSize, rotate.t(), trans, r1, r2, p1, p2, q);//立体校正
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initUndistortRectifyMap(cam_matrix_left.t(), distortion_l, r1, p1, imgSize, CV_32F, mapX1, mapX2);//计算无畸变和修正转换映射
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initUndistortRectifyMap(cam_matrix_right.t(), distortion_r, r2, p2, imgSize, CV_32F, mapY1, mapY2);//计算无畸变和修正转换映射
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// 计算无畸变和修正转换映射
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initUndistortRectifyMap(cam_matrix_left.t(), distortion_l, r1, p1, imgSize, CV_32F, mapX1, mapX2);
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// 计算无畸变和修正转换映射
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initUndistortRectifyMap(cam_matrix_right.t(), distortion_r, r2, p2, imgSize, CV_32F, mapY1, mapY2);
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// RKNN_Create(&hdx, modelPath); // 初始化检测模型
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std::cout<< " ******************* CAMERA initialization ********************" << std::endl;
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}
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std::vector<Mat> Ranging::get_range()
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{
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double rang_old, rang_now;
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// rang_old = ros::Time::now().toSec(); //测试运行时间
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Mat frame, lframe, rframe;
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// vcapture.read(frame); //获取视频帧
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// 获取视频帧
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vcapture >> frame;
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// cv::imshow("frame",frame);
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// 显示原始图像
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//cv::imshow("frame",frame);
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if (!frame.empty())
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{
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int64 t = getTickCount();
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Mat lframe(frame.colRange(0, imgw).clone()); //拷贝左图
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Mat rframe(frame.colRange(imgw, imgw * 2).clone()); //拷贝右图
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// 拷贝左图
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Mat lframe(frame.colRange(0, imgw).clone());
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// 拷贝右图
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Mat rframe(frame.colRange(imgw, imgw * 2).clone());
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rectifyImage(lframe, rframe); //
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rectifyImage(lframe, rframe);
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// 这里拷贝一次，然后再yolo里面再拷贝一次，以及函数传参也拷贝了一次?
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cv::Mat Rframe = rframe.clone();
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// yolov5s.outputPars修改为detectImage()
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detect_result_group = yolov5s.outputParse(Rframe);
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if (detect_result_group.count<=0)
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@ -305,13 +341,17 @@ std::vector<Mat> Ranging::get_range()
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std::cout<<"detect nothing"<<std::endl;
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}
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// detction box transfor to our format
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Mat detBoxes(detect_result_group.count, 5, CV_32F); //定义矩阵，存储目标检测内容，存储格式(x,y,x,y,conf,cls)
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// 定义矩阵，存储目标检测内容，存储格式(x,y,x,y,conf,cls)
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Mat detBoxes(detect_result_group.count, 5, CV_32F);
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char text[256];
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for (int i = 0; i < detect_result_group.count; i++) //存储目标检测内容 (x,y,x,y,conf,cls)
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// 存储目标检测内容 (x,y,x,y,conf,cls)
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// 这里应该写一个函数drawDetectBobox()
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for (int i = 0; i < detect_result_group.count; i++)
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||||
{
|
||||
detect_result_t *det_result = &(detect_result_group.results[i]);
|
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sprintf(text, "%s %.1f%%", det_result->name, det_result->prop * 100);
|
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|
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// printf("%s @ (%d %d %d %d) %f\n", det_result->name, det_result->box.left, det_result->box.top,
|
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// det_result->box.right, det_result->box.bottom, det_result->prop);
|
||||
|
||||
|
|
@ -338,9 +378,15 @@ std::vector<Mat> Ranging::get_range()
|
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// }
|
||||
}
|
||||
|
||||
Mat info(detect_result_group.count, 4, CV_32F); // 存储测距信息，存储格式：距离d，宽w，高h，角度α
|
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// 存储测距信息，存储格式：距离d，宽w，高h，角度α
|
||||
Mat info(detect_result_group.count, 4, CV_32F);
|
||||
|
||||
// 如果有检测目标
|
||||
if (detect_result_group.count)
|
||||
{
|
||||
// getInfo()修改为calObjectRanging()
|
||||
// 在这里测量目标的距离并存储到info中
|
||||
// 优化: 这个地方重复的将左右视图转换为灰度图，浪费CPU的算力
|
||||
getInfo(lframe, rframe, detBoxes, info);
|
||||
|
||||
//show stereo distance
|
||||
|
|
@ -367,6 +413,7 @@ std::vector<Mat> Ranging::get_range()
|
|||
// }
|
||||
}
|
||||
|
||||
// 这个地方应该整为一个函数，负责显示帧率图像
|
||||
t = getTickCount() - t;
|
||||
char fps[50];
|
||||
sprintf(fps, "fps: %d", int(1 / (t / getTickFrequency())));
|
||||
|
|
@ -374,6 +421,7 @@ std::vector<Mat> Ranging::get_range()
|
|||
|
||||
cv::imshow("k",Rframe);
|
||||
cv::waitKey(1);
|
||||
|
||||
return std::vector<Mat>{rframe, detBoxes, info};
|
||||
}
|
||||
return std::vector<Mat>{rframe};
|
||||
|
|
|
|||
Loading…
Reference in New Issue