f4334277
 Hu Chunming
提交3rdparty
|
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
|
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/objdetect.hpp>
#include <iostream>
using namespace cv;
using namespace std;
static Mat visualize(Mat input, Mat faces, int thickness=2)
{
Mat output = input.clone();
for (int i = 0; i < faces.rows; i++)
{
// Print results
cout << "Face " << i
<< ", top-left coordinates: (" << faces.at<float>(i, 0) << ", " << faces.at<float>(i, 1) << "), "
<< "box width: " << faces.at<float>(i, 2) << ", box height: " << faces.at<float>(i, 3) << ", "
<< "score: " << faces.at<float>(i, 14) << "\n";
// Draw bounding box
rectangle(output, Rect2i(int(faces.at<float>(i, 0)), int(faces.at<float>(i, 1)), int(faces.at<float>(i, 2)), int(faces.at<float>(i, 3))), Scalar(0, 255, 0), thickness);
// Draw landmarks
circle(output, Point2i(int(faces.at<float>(i, 4)), int(faces.at<float>(i, 5))), 2, Scalar(255, 0, 0), thickness);
circle(output, Point2i(int(faces.at<float>(i, 6)), int(faces.at<float>(i, 7))), 2, Scalar( 0, 0, 255), thickness);
circle(output, Point2i(int(faces.at<float>(i, 8)), int(faces.at<float>(i, 9))), 2, Scalar( 0, 255, 0), thickness);
circle(output, Point2i(int(faces.at<float>(i, 10)), int(faces.at<float>(i, 11))), 2, Scalar(255, 0, 255), thickness);
circle(output, Point2i(int(faces.at<float>(i, 12)), int(faces.at<float>(i, 13))), 2, Scalar( 0, 255, 255), thickness);
}
return output;
}
int main(int argc, char ** argv)
{
CommandLineParser parser(argc, argv,
"{help h | | Print this message.}"
"{input i | | Path to the input image. Omit for detecting on default camera.}"
"{model m | yunet.onnx | Path to the model. Download yunet.onnx in https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx.}"
"{score_threshold | 0.9 | Filter out faces of score < score_threshold.}"
"{nms_threshold | 0.3 | Suppress bounding boxes of iou >= nms_threshold.}"
"{top_k | 5000 | Keep top_k bounding boxes before NMS.}"
"{save s | false | Set true to save results. This flag is invalid when using camera.}"
"{vis v | true | Set true to open a window for result visualization. This flag is invalid when using camera.}"
);
if (argc == 1 || parser.has("help"))
{
parser.printMessage();
return -1;
}
String modelPath = parser.get<String>("model");
float scoreThreshold = parser.get<float>("score_threshold");
float nmsThreshold = parser.get<float>("nms_threshold");
int topK = parser.get<int>("top_k");
bool save = parser.get<bool>("save");
bool vis = parser.get<bool>("vis");
// Initialize FaceDetectorYN
Ptr<FaceDetectorYN> detector = FaceDetectorYN::create(modelPath, "", Size(320, 320), scoreThreshold, nmsThreshold, topK);
// If input is an image
if (parser.has("input"))
{
String input = parser.get<String>("input");
Mat image = imread(input);
// Set input size before inference
detector->setInputSize(image.size());
// Inference
Mat faces;
detector->detect(image, faces);
// Draw results on the input image
Mat result = visualize(image, faces);
// Save results if save is true
if(save)
{
cout << "Results saved to result.jpg\n";
imwrite("result.jpg", result);
}
// Visualize results
if (vis)
{
namedWindow(input, WINDOW_AUTOSIZE);
imshow(input, result);
waitKey(0);
}
}
else
{
int deviceId = 0;
VideoCapture cap;
cap.open(deviceId, CAP_ANY);
int frameWidth = int(cap.get(CAP_PROP_FRAME_WIDTH));
int frameHeight = int(cap.get(CAP_PROP_FRAME_HEIGHT));
detector->setInputSize(Size(frameWidth, frameHeight));
Mat frame;
TickMeter tm;
String msg = "FPS: ";
while(waitKey(1) < 0) // Press any key to exit
{
// Get frame
if (!cap.read(frame))
{
cerr << "No frames grabbed!\n";
break;
}
// Inference
Mat faces;
tm.start();
detector->detect(frame, faces);
tm.stop();
// Draw results on the input image
Mat result = visualize(frame, faces);
putText(result, msg + to_string(tm.getFPS()), Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
// Visualize results
imshow("Live", result);
tm.reset();
}
}
}
|
