-
Notifications
You must be signed in to change notification settings - Fork 31
/
yolov5_seg.cpp
141 lines (127 loc) · 5.9 KB
/
yolov5_seg.cpp
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
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
#include"yolov5_seg.h"
using namespace std;
using namespace cv;
using namespace cv::dnn;
bool Yolov5Seg::ReadModel(Net& net, string& netPath, bool isCuda = false) {
try {
if (!CheckModelPath(netPath))
return false;
net = readNetFromONNX(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;
}
if (isCuda) {
//cuda
net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA); //or DNN_TARGET_CUDA_FP16
}
else {
//cpu
cout << "Inference device: CPU" << endl;
net.setPreferableBackend(cv::dnn::DNN_BACKEND_DEFAULT);
net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
}
return true;
}
bool Yolov5Seg::Detect(Mat& srcImg, Net& net, vector<OutputSeg>& output) {
Mat blob;
output.clear();
int col = srcImg.cols;
int row = srcImg.rows;
Mat netInputImg;
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);
//**************************************************************************************************************************************************/
//如果在其他设置没有问题的情况下但是结果偏差很大，可以尝试下用下面两句语句
// If there is no problem with other settings, but results are a lot different from Python-onnx , you can try to use the following two sentences
//
//$ 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> net_output_img;
//*********************************************************************************************************************************
//net.forward(net_output_img, net.getUnconnectedOutLayersNames());
//opencv4.5.x和4.6.x这里输出不一致，推荐使用下面的固定名称输出
// 如果使用net.forward(net_output_img, net.getUnconnectedOutLayersNames())，需要确认下net.getUnconnectedOutLayersNames()返回值中output0在前，output1在后，否者出错
//
// The outputs of opencv4.5.x and 4.6.x are inconsistent.Please make sure "output0" is in front of "output1" if you use net.forward(net_output_img, net.getUnconnectedOutLayersNames())
//*********************************************************************************************************************************
vector<string> output_layer_names{ "output0","output1" };
net.forward(net_output_img, output_layer_names); //获取output的输出
std::vector<int> class_ids;//结果id数组
std::vector<float> confidences;//结果每个id对应置信度数组
std::vector<cv::Rect> boxes;//每个id矩形框
std::vector<vector<float>> picked_proposals; //output0[:,:, 5 + _className.size():net_width]===> for mask
int net_width = net_output_img[0].size[2];
int net_height = net_output_img[0].size[1];
int score_length = net_width - 37;
float* pdata = (float*)net_output_img[0].data;
for (int r = 0; r < net_height; r++) { //lines
float box_score = pdata[4];
if (box_score >= _classThreshold) {
cv::Mat scores(1, score_length, CV_32FC1, pdata + 5);
Point classIdPoint;
double max_class_socre;
minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);
max_class_socre = (float)max_class_socre;
if (max_class_socre >= _classThreshold) {
vector<float> temp_proto(pdata + 5 + score_length, pdata + net_width);
picked_proposals.push_back(temp_proto);
//rect [x,y,w,h]
float x = (pdata[0] - params[2]) / params[0]; //x
float y = (pdata[1] - params[3]) / params[1]; //y
float w = pdata[2] / params[0]; //w
float h = pdata[3] / params[1]; //h
int left = MAX(int(x - 0.5 * w + 0.5), 0);
int top = MAX(int(y - 0.5 * h + 0.5), 0);
class_ids.push_back(classIdPoint.x);
confidences.push_back(max_class_socre * box_score);
boxes.push_back(Rect(left, top, int(w + 0.5), int(h + 0.5)));
}
}
pdata += net_width;//下一行
}
//NMS
vector<int> nms_result;
cv::dnn::NMSBoxes(boxes, confidences, _classThreshold, _nmsThreshold, nms_result);
std::vector<vector<float>> temp_mask_proposals;
Rect holeImgRect(0, 0, srcImg.cols, srcImg.rows);
for (int i = 0; i < nms_result.size(); ++i) {
int idx = nms_result[i];
OutputSeg result;
result.id = class_ids[idx];
result.confidence = confidences[idx];
result.box = boxes[idx] & holeImgRect;
temp_mask_proposals.push_back(picked_proposals[idx]);
output.push_back(result);
}
MaskParams mask_params;
mask_params.params = params;
mask_params.srcImgShape = srcImg.size();
mask_params.maskThreshold = _maskThreshold;
mask_params.netHeight = _netWidth;
mask_params.netWidth = _netWidth;
for (int i = 0; i < temp_mask_proposals.size(); ++i) {
GetMask2(Mat(temp_mask_proposals[i]).t(), net_output_img[1], output[i], mask_params);
}
//******************** ****************
// 老版本的方案，如果上面GetMask2出错，建议使用这个。
// If the GetMask2() still reports errors , it is recommended to use GetMask().
//Mat mask_proposals;
//for (int i = 0; i < temp_mask_proposals.size(); ++i) {
// mask_proposals.push_back(Mat(temp_mask_proposals[i]).t());
//}
//GetMask(mask_proposals, net_output_img[1], output, mask_params);
//*****************************************************/
if (output.size())
return true;
else
return false;
}