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
|
#include <algorithm>
#include <iostream>
#include <cctype>
#include <opencv2/imgproc.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/gapi.hpp>
#include <opencv2/gapi/core.hpp>
#include <opencv2/gapi/imgproc.hpp>
#include <opencv2/gapi/infer.hpp>
#include <opencv2/gapi/render.hpp>
#include <opencv2/gapi/infer/ie.hpp>
#include <opencv2/gapi/cpu/gcpukernel.hpp>
#include <opencv2/gapi/streaming/cap.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/gapi/infer/parsers.hpp>
const std::string about =
"This is an OpenCV-based version of Privacy Masking Camera example";
const std::string keys =
"{ h help | | Print this help message }"
"{ input | | Path to the input video file }"
"{ platm | vehicle-license-plate-detection-barrier-0106.xml | Path to OpenVINO IE vehicle/plate detection model (.xml) }"
"{ platd | CPU | Target device for vehicle/plate detection model (e.g. CPU, GPU, VPU, ...) }"
"{ facem | face-detection-retail-0005.xml | Path to OpenVINO IE face detection model (.xml) }"
"{ faced | CPU | Target device for face detection model (e.g. CPU, GPU, VPU, ...) }"
"{ trad | false | Run processing in a traditional (non-pipelined) way }"
"{ noshow | false | Don't display UI (improves performance) }";
namespace {
std::string weights_path(const std::string &model_path) {
const auto EXT_LEN = 4u;
const auto sz = model_path.size();
CV_Assert(sz > EXT_LEN);
auto ext = model_path.substr(sz - EXT_LEN);
std::transform(ext.begin(), ext.end(), ext.begin(), [](unsigned char c){ return static_cast<unsigned char>(std::tolower(c)); });
CV_Assert(ext == ".xml");
return model_path.substr(0u, sz - EXT_LEN) + ".bin";
}
} // namespace
namespace custom {
G_API_NET(VehLicDetector, <cv::GMat(cv::GMat)>, "vehicle-license-plate-detector");
G_API_NET(FaceDetector, <cv::GMat(cv::GMat)>, "face-detector");
using GDetections = cv::GArray<cv::Rect>;
using GPrims = cv::GArray<cv::gapi::wip::draw::Prim>;
G_API_OP(ToMosaic, <GPrims(GDetections, GDetections)>, "custom.privacy_masking.to_mosaic") {
static cv::GArrayDesc outMeta(const cv::GArrayDesc &, const cv::GArrayDesc &) {
return cv::empty_array_desc();
}
};
GAPI_OCV_KERNEL(OCVToMosaic, ToMosaic) {
static void run(const std::vector<cv::Rect> &in_plate_rcs,
const std::vector<cv::Rect> &in_face_rcs,
std::vector<cv::gapi::wip::draw::Prim> &out_prims) {
out_prims.clear();
const auto cvt = [](cv::Rect rc) {
// Align the mosaic region to mosaic block size
const int BLOCK_SIZE = 24;
const int dw = BLOCK_SIZE - (rc.width % BLOCK_SIZE);
const int dh = BLOCK_SIZE - (rc.height % BLOCK_SIZE);
rc.width += dw;
rc.height += dh;
rc.x -= dw / 2;
rc.y -= dh / 2;
return cv::gapi::wip::draw::Mosaic{rc, BLOCK_SIZE, 0};
};
for (auto &&rc : in_plate_rcs) { out_prims.emplace_back(cvt(rc)); }
for (auto &&rc : in_face_rcs) { out_prims.emplace_back(cvt(rc)); }
}
};
} // namespace custom
int main(int argc, char *argv[])
{
cv::CommandLineParser cmd(argc, argv, keys);
cmd.about(about);
if (cmd.has("help")) {
cmd.printMessage();
return 0;
}
const std::string input = cmd.get<std::string>("input");
const bool no_show = cmd.get<bool>("noshow");
const bool run_trad = cmd.get<bool>("trad");
cv::GMat in;
cv::GMat blob_plates = cv::gapi::infer<custom::VehLicDetector>(in);
cv::GMat blob_faces = cv::gapi::infer<custom::FaceDetector>(in);
// VehLicDetector from Open Model Zoo marks vehicles with label "1" and
// license plates with label "2", filter out license plates only.
cv::GOpaque<cv::Size> sz = cv::gapi::streaming::size(in);
cv::GArray<cv::Rect> rc_plates, rc_faces;
cv::GArray<int> labels;
std::tie(rc_plates, labels) = cv::gapi::parseSSD(blob_plates, sz, 0.5f, 2);
// Face detector produces faces only so there's no need to filter by label,
// pass "-1".
std::tie(rc_faces, labels) = cv::gapi::parseSSD(blob_faces, sz, 0.5f, -1);
cv::GMat out = cv::gapi::wip::draw::render3ch(in, custom::ToMosaic::on(rc_plates, rc_faces));
cv::GComputation graph(in, out);
const auto plate_model_path = cmd.get<std::string>("platm");
auto plate_net = cv::gapi::ie::Params<custom::VehLicDetector> {
plate_model_path, // path to topology IR
weights_path(plate_model_path), // path to weights
cmd.get<std::string>("platd"), // device specifier
};
const auto face_model_path = cmd.get<std::string>("facem");
auto face_net = cv::gapi::ie::Params<custom::FaceDetector> {
face_model_path, // path to topology IR
weights_path(face_model_path), // path to weights
cmd.get<std::string>("faced"), // device specifier
};
auto kernels = cv::gapi::kernels<custom::OCVToMosaic>();
auto networks = cv::gapi::networks(plate_net, face_net);
cv::TickMeter tm;
cv::Mat out_frame;
std::size_t frames = 0u;
std::cout << "Reading " << input << std::endl;
if (run_trad) {
cv::Mat in_frame;
cv::VideoCapture cap(input);
cap >> in_frame;
auto exec = graph.compile(cv::descr_of(in_frame), cv::compile_args(kernels, networks));
tm.start();
do {
exec(in_frame, out_frame);
if (!no_show) {
cv::imshow("Out", out_frame);
cv::waitKey(1);
}
frames++;
} while (cap.read(in_frame));
tm.stop();
} else {
auto pipeline = graph.compileStreaming(cv::compile_args(kernels, networks));
pipeline.setSource(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(input));
pipeline.start();
tm.start();
while (pipeline.pull(cv::gout(out_frame))) {
frames++;
if (!no_show) {
cv::imshow("Out", out_frame);
cv::waitKey(1);
}
}
tm.stop();
}
std::cout << "Processed " << frames << " frames"
<< " (" << frames / tm.getTimeSec() << " FPS)" << std::endl;
return 0;
}
|
