/
mask_ultra.cpp
166 lines (134 loc) · 6.27 KB
/
mask_ultra.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
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <iostream>
#include <string>
#include <vector>
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include <opencv2/highgui.hpp>
#include "paddle_api.h" // NOLINT
#include "paddle_use_kernels.h" // NOLINT
#include "paddle_use_ops.h" // NOLINT
#include "UltraFace.hpp"
using namespace std;
using namespace paddle::lite_api; // NOLINT
int main(int argc,char ** argv)
{
float f;
float FPS[16];
int i, Fcnt=0;
cv::Mat frame;
int classify_w = 128;
int classify_h = 128;
float scale_factor = 1.f / 256;
int FaceImgSz = classify_w * classify_h;
// Mask detection (second phase, when the faces are located)
MobileConfig Mconfig;
std::shared_ptr<PaddlePredictor> Mpredictor;
//some timing
chrono::steady_clock::time_point Tbegin, Tend;
for(i=0;i<16;i++) FPS[i]=0.0;
//load SSD face detection model and get predictor
// UltraFace ultraface("slim_320.bin","slim_320.param", 320, 240, 2, 0.7); // config model input
UltraFace ultraface("RFB-320.bin","RFB-320.param", 320, 240, 2, 0.7); // config model input
//load mask detection model
Mconfig.set_model_from_file("mask_detector_opt2.nb");
Mpredictor = CreatePaddlePredictor<MobileConfig>(Mconfig);
std::cout << "Load classification model succeed." << std::endl;
// Get Input Tensor
std::unique_ptr<Tensor> input_tensor1(std::move(Mpredictor->GetInput(0)));
input_tensor1->Resize({1, 3, classify_h, classify_w});
// Get Output Tensor
std::unique_ptr<const Tensor> output_tensor1(std::move(Mpredictor->GetOutput(0)));
cv::VideoCapture cap("Face_Mask_Video.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){
// frame=cv::imread("Face_2.jpg"); //if you want to run just one picture need to refresh frame before class detection
cap >> frame;
if (frame.empty()) {
cerr << "ERROR: Unable to grab from the camera" << endl;
break;
}
Tbegin = chrono::steady_clock::now();
ncnn::Mat inmat = ncnn::Mat::from_pixels(frame.data, ncnn::Mat::PIXEL_BGR2RGB, frame.cols, frame.rows);
//get the faces
std::vector<FaceInfo> face_info;
ultraface.detect(inmat, face_info);
auto* input_data = input_tensor1->mutable_data<float>();
for(long unsigned int i = 0; i < face_info.size(); i++) {
auto face = face_info[i];
//enlarge 10%
float w = (face.x2 - face.x1)/20.0;
float h = (face.y2 - face.y1)/20.0;
cv::Point pt1(std::max(face.x1-w,float(0.0)),std::max(face.y1-h,float(0.0)));
cv::Point pt2(std::min(face.x2+w,float(frame.cols)),std::min(face.y2+h,float(frame.rows)));
//RecClip is completly inside the frame
cv::Rect RecClip(pt1, pt2);
cv::Mat resized_img;
cv::Mat imgf;
if(RecClip.width>0 && RecClip.height>0){
//roi has size RecClip
cv::Mat roi = frame(RecClip);
//resized_img has size 128x128 (uchar)
cv::resize(roi, resized_img, cv::Size(classify_w, classify_h), 0.f, 0.f, cv::INTER_CUBIC);
//imgf has size 128x128 (float in range 0.0 - +1.0)
resized_img.convertTo(imgf, CV_32FC3, scale_factor);
//input tensor has size 128x128 (float in range -0.5 - +0.5)
// fill tensor with mean and scale and trans layout: nhwc -> nchw, neon speed up
//offset_nchw(n, c, h, w) = n * CHW + c * HW + h * W + w
//offset_nhwc(n, c, h, w) = n * HWC + h * WC + w * C + c
const float* dimg = reinterpret_cast<const float*>(imgf.data);
float* dout_c0 = input_data;
float* dout_c1 = input_data + FaceImgSz;
float* dout_c2 = input_data + FaceImgSz * 2;
for(int i=0;i<FaceImgSz;i++){
*(dout_c0++) = (*(dimg++) - 0.5);
*(dout_c1++) = (*(dimg++) - 0.5);
*(dout_c2++) = (*(dimg++) - 0.5);
}
// Classification Model Run
Mpredictor->Run();
auto* outptr = output_tensor1->data<float>();
float prob = outptr[1];
// Draw Detection and Classification Results
bool flag_mask = prob > 0.5f;
cv::Scalar roi_color;
if(flag_mask) roi_color = cv::Scalar(0, 255, 0);
else roi_color = cv::Scalar(0, 0, 255);
// Draw roi object
cv::rectangle(frame, RecClip, roi_color, 2);
}
}
Tend = chrono::steady_clock::now();
//calculate frame rate
f = chrono::duration_cast <chrono::milliseconds> (Tend - Tbegin).count();
if(f>0.0) FPS[((Fcnt++)&0x0F)]=1000.0/f;
for(f=0.0, i=0;i<16;i++){ f+=FPS[i]; }
cv::putText(frame, cv::format("FPS %0.2f", f/16),cv::Point(10,20),cv::FONT_HERSHEY_SIMPLEX,0.6, cv::Scalar(0, 0, 255));
//cv::imwrite("FaceResult.jpg",frame); //in case you run only a jpg picture
//show output
cv::imshow("RPi 64 OS - 1,95 GHz - 2 Mb RAM", frame);
char esc = cv::waitKey(5);
if(esc == 27) break;
}
cout << "Closing the camera" << endl;
cv::destroyAllWindows();
cout << "Bye!" << endl;
return 0;
}