-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathPose_single.cpp
262 lines (225 loc) · 9.27 KB
/
Pose_single.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
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
#include <stdio.h>
#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <opencv2/highgui.hpp>
#include <iostream>
#include <opencv2/core/ocl.hpp>
#include "tensorflow/lite/builtin_op_data.h"
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/kernels/register.h"
#include "tensorflow/lite/string_util.h"
#include "tensorflow/lite/model.h"
#include <cmath>
/// Besides your regular TensorFlow Lite and flatbuffers library,
/// you must also compiled TensorFlow Lite from scratch by bazel
/// with the option GPU delegate set, before you can use the GPU delegates
/// see https://qengineering.eu/install-tensorflow-2-lite-on-jetson-nano.html
/// note also, it will not bring any speed improvement.
//#define GPU_DELEGATE //remove comment to deploy GPU delegates
#ifdef GPU_DELEGATE
#include "tensorflow/lite/delegates/gpu/delegate.h"
#endif // GPU_DELEGATE
using namespace cv;
using namespace std;
using namespace tflite;
int model_width;
int model_height;
int model_channels;
std::unique_ptr<Interpreter> interpreter;
/*
2 1 (eyes)
4 0 3 (ears, nose)
6___5
/| |\
8 | | 7
/ 12_11 \
10 / \ 9
14 13
| |
16 15
*/
//-----------------------------------------------------------------------------------------------------------------------
const char* Labels[] {
"NOSE", //0
"LEFT_EYE", //1
"RIGHT_EYE", //2
"LEFT_EAR", //3
"RIGHT_EAR", //4
"LEFT_SHOULDER", //5
"RIGHT_SHOULDER", //6
"LEFT_ELBOW", //7
"RIGHT_ELBOW", //8
"LEFT_WRIST", //9
"RIGHT_WRIST", //10
"LEFT_HIP", //11
"RIGHT_HIP", //12
"LEFT_KNEE", //13
"RIGHT_KNEE", //14
"LEFT_ANKLE", //15
"RIGHT_ANKLE" //16
};
//-----------------------------------------------------------------------------------------------------------------------
void GetImageTFLite(float* out, Mat &src)
{
int i,Len;
float f;
uint8_t *in;
static Mat image;
// copy image to input as input tensor
cv::resize(src, image, Size(model_width,model_height),INTER_NEAREST);
//model posenet_mobilenet_v1_100_257x257_multi_kpt_stripped.tflite runs from -1.0 ... +1.0
//model multi_person_mobilenet_v1_075_float.tflite runs from 0.0 ... +1.0
in=image.data;
Len=image.rows*image.cols*image.channels();
for(i=0;i<Len;i++){
f =in[i];
out[i]=(f - 127.5f) / 127.5f;
}
}
//-----------------------------------------------------------------------------------------------------------------------
void detect_from_video(Mat &src)
{
int i,x,y,j;
static Point Pnt[17]; //heatmap
static float Cnf[17]; //confidence table
static Point Loc[17]; //location in image
const float confidence_threshold = -1.0; //confidence can be negative
GetImageTFLite(interpreter->typed_tensor<float>(interpreter->inputs()[0]), src);
interpreter->Invoke(); // run your model
// 1 * 9 * 9 * 17 contains heatmaps
const float* heatmapShape = interpreter->tensor(interpreter->outputs()[0])->data.f;
// 1 * 9 * 9 * 34 contains offsets
const float* offsetShape = interpreter->tensor(interpreter->outputs()[1])->data.f;
// 1 * 9 * 9 * 32 contains forward displacements
// const float* dispFwdShape = interpreter->tensor(interpreter->outputs()[2])->data.f;
// 1 * 9 * 9 * 32 contains backward displacements
// const float* dispBckShape = interpreter->tensor(interpreter->outputs()[3])->data.f;
// Finds the (row, col) locations of where the keypoints are most likely to be.
for(i=0;i<17;i++){
Cnf[i]=heatmapShape[i]; //x=y=0 -> j=17*(9*0+0)+i; -> j=i
for(y=0;y<9;y++){
for(x=0;x<9;x++){
j=17*(9*y+x)+i;
if(heatmapShape[j]>Cnf[i]){
Cnf[i]=heatmapShape[j]; Pnt[i].x=x; Pnt[i].y=y;
}
}
}
}
// Calculating the x and y coordinates of the keypoints with offset adjustment.
for(i=0;i<17;i++){
x=Pnt[i].x; y=Pnt[i].y; j=34*(9*y+x)+i;
Loc[i].y=(y*src.rows)/8 + offsetShape[j ];
Loc[i].x=(x*src.cols)/8 + offsetShape[j+17];
}
for(i=5;i<17;i++){
if(Cnf[i]>confidence_threshold){
circle(src,Loc[i],4,Scalar( 255, 255, 0 ),FILLED);
}
}
if(Cnf[ 5]>confidence_threshold){
if(Cnf[ 6]>confidence_threshold) line(src,Loc[ 5],Loc[ 6],Scalar( 255, 255, 0 ),2);
if(Cnf[ 7]>confidence_threshold) line(src,Loc[ 5],Loc[ 7],Scalar( 255, 255, 0 ),2);
if(Cnf[11]>confidence_threshold) line(src,Loc[ 5],Loc[11],Scalar( 255, 255, 0 ),2);
}
if(Cnf[ 6]>confidence_threshold){
if(Cnf[ 8]>confidence_threshold) line(src,Loc[ 6],Loc[ 8],Scalar( 255, 255, 0 ),2);
if(Cnf[12]>confidence_threshold) line(src,Loc[ 6],Loc[12],Scalar( 255, 255, 0 ),2);
}
if(Cnf[ 7]>confidence_threshold){
if(Cnf[ 9]>confidence_threshold) line(src,Loc[ 7],Loc[ 9],Scalar( 255, 255, 0 ),2);
}
if(Cnf[ 8]>confidence_threshold){
if(Cnf[10]>confidence_threshold) line(src,Loc[ 8],Loc[10],Scalar( 255, 255, 0 ),2);
}
if(Cnf[11]>confidence_threshold){
if(Cnf[12]>confidence_threshold) line(src,Loc[11],Loc[12],Scalar( 255, 255, 0 ),2);
if(Cnf[13]>confidence_threshold) line(src,Loc[11],Loc[13],Scalar( 255, 255, 0 ),2);
}
if(Cnf[13]>confidence_threshold){
if(Cnf[15]>confidence_threshold) line(src,Loc[13],Loc[15],Scalar( 255, 255, 0 ),2);
}
if(Cnf[14]>confidence_threshold){
if(Cnf[12]>confidence_threshold) line(src,Loc[14],Loc[12],Scalar( 255, 255, 0 ),2);
if(Cnf[16]>confidence_threshold) line(src,Loc[14],Loc[16],Scalar( 255, 255, 0 ),2);
}
}
//-----------------------------------------------------------------------------------------------------------------------
int main(int argc,char ** argv)
{
float f;
float FPS[16];
int i;
int In;
int Fcnt=0;
Mat frame;
chrono::steady_clock::time_point Tbegin, Tend;
for(i=0;i<16;i++) FPS[i]=0.0;
// Load model
std::unique_ptr<FlatBufferModel> model = FlatBufferModel::BuildFromFile("posenet_mobilenet_v1_100_257x257_multi_kpt_stripped.tflite");
// Build the interpreter
ops::builtin::BuiltinOpResolver resolver;
InterpreterBuilder(*model.get(), resolver)(&interpreter);
#ifdef GPU_DELEGATE
TfLiteDelegate *MyDelegate = NULL;
const TfLiteGpuDelegateOptionsV2 options = {
.is_precision_loss_allowed = 1, //FP16,
.inference_preference = TFLITE_GPU_INFERENCE_PREFERENCE_FAST_SINGLE_ANSWER,
.inference_priority1 = TFLITE_GPU_INFERENCE_PRIORITY_MIN_LATENCY,
.inference_priority2 = TFLITE_GPU_INFERENCE_PRIORITY_AUTO,
.inference_priority3 = TFLITE_GPU_INFERENCE_PRIORITY_AUTO,
};
MyDelegate = TfLiteGpuDelegateV2Create(&options);
if(interpreter->ModifyGraphWithDelegate(MyDelegate) != kTfLiteOk) {
cerr << "ERROR: Unable to use delegate" << endl;
return 0;
}
#endif // GPU_DELEGATE
interpreter->AllocateTensors();
interpreter->SetAllowFp16PrecisionForFp32(true);
interpreter->SetNumThreads(4); //quad core
// Get input dimension from the input tensor metadata
// Assuming one input only
In = interpreter->inputs()[0];
model_height = interpreter->tensor(In)->dims->data[1];
model_width = interpreter->tensor(In)->dims->data[2];
model_channels = interpreter->tensor(In)->dims->data[3];
cout << "height : "<< model_height << endl;
cout << "width : "<< model_width << endl;
cout << "channels : "<< model_channels << endl;
VideoCapture cap("Dance.mp4");
if (!cap.isOpened()) {
cerr << "ERROR: Unable to open the camera" << endl;
return 0;
}
cout << "Start grabbing, press ESC on Live window to terminate" << endl;
while(1){
cap >> frame;
if (frame.empty()) {
cerr << "End of movie" << endl;
break;
}
detect_from_video(frame);
Tend = chrono::steady_clock::now();
//calculate frame rate
f = chrono::duration_cast <chrono::milliseconds> (Tend - Tbegin).count();
Tbegin = chrono::steady_clock::now();
FPS[((Fcnt++)&0x0F)]=1000.0/f;
for(f=0.0, i=0;i<16;i++){ f+=FPS[i]; }
putText(frame, format("FPS %0.2f",f/16),Point(10,20),FONT_HERSHEY_SIMPLEX,0.6, Scalar(0, 0, 255));
//show output
imshow("Jetson Nano", frame);
char esc = waitKey(5);
if(esc == 27) break;
}
cout << "Closing the camera" << endl;
// When everything done, release the video capture and write object
cap.release();
destroyAllWindows();
#ifdef GPU_DELEGATE
interpreter.reset();
TfLiteGpuDelegateV2Delete(MyDelegate);
#endif // GPU_DELEGATE
cout << "Bye!" << endl;
return 0;
}