07639e75
 Hu Chunming
实现狗狗姿态检测
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
|
#include "AlgorithmResult.h"
#include "opencv2/opencv.hpp"
void ResultYolov5::read_class_names(std::string path_name)
{
std::ifstream infile;
infile.open(path_name.data()); //将文件流对象与文件连接起来
assert(infile.is_open()); //若失败,则输出错误消息,并终止程序运行
std::string str;
while (getline(infile, str)) {
class_names.push_back(str);
str.clear();
}
infile.close(); //关闭文件输入流
}
std::vector<DogPoseResult> ResultYolov5::yolov5_result(float* result, float threshold) {
cv::Mat det_output = cv::Mat(25200, 13, CV_32F, result);
//// post-process
std::vector<cv::Rect> position_boxes;
std::vector<int> classIds;
std::vector<float> confidences;
//std::cout << det_output.rows << std::endl;
for (int i = 0; i < det_output.rows; i++) {
float confidence = det_output.at<float>(i, 4);
if (confidence < 0.2) {
continue;
}
//std::cout << "confidence " << confidence << std::endl;
cv::Mat classes_scores = det_output.row(i).colRange(5, 13);
cv::Point classIdPoint;
double score;
// 获取一组数据中最大值及其位置
minMaxLoc(classes_scores, 0, &score, 0, &classIdPoint);
// 置信度 0～1之间
if (score > 0.25)
{
float cx = det_output.at<float>(i, 0);
float cy = det_output.at<float>(i, 1);
float ow = det_output.at<float>(i, 2);
float oh = det_output.at<float>(i, 3);
int x = static_cast<int>((cx - 0.5 * ow) * factor);
int y = static_cast<int>((cy - 0.5 * oh) * factor);
int width = static_cast<int>(ow * factor);
int height = static_cast<int>(oh * factor);
cv::Rect box;
box.x = x;
box.y = y;
box.width = width;
box.height = height;
position_boxes.push_back(box);
classIds.push_back(classIdPoint.x);
confidences.push_back(score);
}
}
// NMS
std::vector<int> indexes;
cv::dnn::NMSBoxes(position_boxes, confidences, 0.25, 0.45, indexes);
//for (size_t i = 0; i < indexes.size(); i++) {
// int index = indexes[i];
// int idx = classIds[index];
// cv::rectangle(image, position_boxes[index], cv::Scalar(0, 0, 255), 2, 8);
// cv::rectangle(image, cv::Point(position_boxes[index].tl().x, position_boxes[index].tl().y - 20),
// cv::Point(position_boxes[index].br().x, position_boxes[index].tl().y), cv::Scalar(0, 255, 255), -1);
// cv::putText(image, class_names[idx], cv::Point(position_boxes[index].tl().x, position_boxes[index].tl().y - 10), cv::FONT_HERSHEY_SIMPLEX, .5, cv::Scalar(0, 0, 0));
//}
std::vector<DogPoseResult> vecPoseResult;
for (size_t i = 0; i < indexes.size(); i++) {
int index = indexes[i];
int idx = classIds[index];
DogPoseResult poseResult;
poseResult.x = position_boxes[index].x;
poseResult.y = position_boxes[index].y;
poseResult.width = position_boxes[index].width;
poseResult.height = position_boxes[index].height;
poseResult.confidence = confidences[index];
poseResult.classId = classIds[index];
poseResult.className = class_names[idx];
vecPoseResult.push_back(poseResult);
}
return vecPoseResult;
}
|
