-
Notifications
You must be signed in to change notification settings - Fork 198
/
demo.cpp
186 lines (163 loc) · 7.66 KB
/
demo.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
#include <iostream>
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
using namespace std;
using namespace cv;
using namespace dnn;
vector< pair<cv::dnn::Backend, cv::dnn::Target> > backendTargetPairs = {
std::make_pair<cv::dnn::Backend, cv::dnn::Target>(dnn::DNN_BACKEND_OPENCV, dnn::DNN_TARGET_CPU),
std::make_pair<cv::dnn::Backend, cv::dnn::Target>(dnn::DNN_BACKEND_CUDA, dnn::DNN_TARGET_CUDA),
std::make_pair<cv::dnn::Backend, cv::dnn::Target>(dnn::DNN_BACKEND_CUDA, dnn::DNN_TARGET_CUDA_FP16),
std::make_pair<cv::dnn::Backend, cv::dnn::Target>(dnn::DNN_BACKEND_TIMVX, dnn::DNN_TARGET_NPU),
std::make_pair<cv::dnn::Backend, cv::dnn::Target>(dnn::DNN_BACKEND_CANN, dnn::DNN_TARGET_NPU)};
std::string keys =
"{ help h | | Print help message. }"
"{ model m | text_detection_cn_ppocrv3_2023may.onnx | Usage: Set model type, defaults to text_detection_ch_ppocrv3_2023may.onnx }"
"{ input i | | Usage: Path to input image or video file. Skip this argument to capture frames from a camera.}"
"{ width | 736 | Usage: Resize input image to certain width, default = 736. It should be multiple by 32.}"
"{ height | 736 | Usage: Resize input image to certain height, default = 736. It should be multiple by 32.}"
"{ binary_threshold | 0.3 | Usage: Threshold of the binary map, default = 0.3.}"
"{ polygon_threshold | 0.5 | Usage: Threshold of polygons, default = 0.5.}"
"{ max_candidates | 200 | Usage: Set maximum number of polygon candidates, default = 200.}"
"{ unclip_ratio | 2.0 | Usage: The unclip ratio of the detected text region, which determines the output size, default = 2.0.}"
"{ save s | true | Usage: Specify to save file with results (i.e. bounding box, confidence level). Invalid in case of camera input.}"
"{ viz v | true | Usage: Specify to open a new window to show results. Invalid in case of camera input.}"
"{ backend bt | 0 | Choose one of computation backends: "
"0: (default) OpenCV implementation + CPU, "
"1: CUDA + GPU (CUDA), "
"2: CUDA + GPU (CUDA FP16), "
"3: TIM-VX + NPU, "
"4: CANN + NPU}";
class PPOCRDet {
public:
PPOCRDet(string modPath, Size inSize = Size(736, 736), float binThresh = 0.3,
float polyThresh = 0.5, int maxCand = 200, double unRatio = 2.0,
dnn::Backend bId = DNN_BACKEND_DEFAULT, dnn::Target tId = DNN_TARGET_CPU) : modelPath(modPath), inputSize(inSize), binaryThreshold(binThresh),
polygonThreshold(polyThresh), maxCandidates(maxCand), unclipRatio(unRatio),
backendId(bId), targetId(tId)
{
this->model = TextDetectionModel_DB(readNet(modelPath));
this->model.setPreferableBackend(backendId);
this->model.setPreferableTarget(targetId);
this->model.setBinaryThreshold(binaryThreshold);
this->model.setPolygonThreshold(polygonThreshold);
this->model.setUnclipRatio(unclipRatio);
this->model.setMaxCandidates(maxCandidates);
this->model.setInputParams(1.0 / 255.0, inputSize, Scalar(122.67891434, 116.66876762, 104.00698793));
}
pair< vector<vector<Point>>, vector<float> > infer(Mat image) {
CV_Assert(image.rows == this->inputSize.height && "height of input image != net input size ");
CV_Assert(image.cols == this->inputSize.width && "width of input image != net input size ");
vector<vector<Point>> pt;
vector<float> confidence;
this->model.detect(image, pt, confidence);
return make_pair< vector<vector<Point>> &, vector< float > &>(pt, confidence);
}
private:
string modelPath;
TextDetectionModel_DB model;
Size inputSize;
float binaryThreshold;
float polygonThreshold;
int maxCandidates;
double unclipRatio;
dnn::Backend backendId;
dnn::Target targetId;
};
Mat visualize(Mat image, pair< vector<vector<Point>>, vector<float> >&results, double fps=-1, Scalar boxColor=Scalar(0, 255, 0), Scalar textColor=Scalar(0, 0, 255), bool isClosed=true, int thickness=2)
{
Mat output;
image.copyTo(output);
if (fps > 0)
putText(output, format("FPS: %.2f", fps), Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, textColor);
polylines(output, results.first, isClosed, boxColor, thickness);
return output;
}
int main(int argc, char** argv)
{
CommandLineParser parser(argc, argv, keys);
parser.about("Use this program to run Real-time Scene Text Detection with Differentiable Binarization in opencv Zoo using OpenCV.");
if (parser.has("help"))
{
parser.printMessage();
return 0;
}
int backendTargetid = parser.get<int>("backend");
String modelName = parser.get<String>("model");
if (modelName.empty())
{
CV_Error(Error::StsError, "Model file " + modelName + " not found");
}
Size inpSize(parser.get<int>("width"), parser.get<int>("height"));
float binThresh = parser.get<float>("binary_threshold");
float polyThresh = parser.get<float>("polygon_threshold");
int maxCand = parser.get<int>("max_candidates");
double unRatio = parser.get<float>("unclip_ratio");
bool save = parser.get<bool>("save");
bool viz = parser.get<bool>("viz");
PPOCRDet model(modelName, inpSize, binThresh, polyThresh, maxCand, unRatio, backendTargetPairs[backendTargetid].first, backendTargetPairs[backendTargetid].second);
//! [Open a video file or an image file or a camera stream]
VideoCapture cap;
if (parser.has("input"))
cap.open(parser.get<String>("input"));
else
cap.open(0);
if (!cap.isOpened())
CV_Error(Error::StsError, "Cannot open video or file");
Mat originalImage;
static const std::string kWinName = modelName;
while (waitKey(1) < 0)
{
cap >> originalImage;
if (originalImage.empty())
{
if (parser.has("input"))
{
cout << "Frame is empty" << endl;
break;
}
else
continue;
}
int originalW = originalImage.cols;
int originalH = originalImage.rows;
double scaleHeight = originalH / double(inpSize.height);
double scaleWidth = originalW / double(inpSize.width);
Mat image;
resize(originalImage, image, inpSize);
// inference
TickMeter tm;
tm.start();
pair< vector<vector<Point>>, vector<float> > results = model.infer(image);
tm.stop();
auto x = results.first;
// Scale the results bounding box
for (auto &pts : results.first)
{
for (int i = 0; i < 4; i++)
{
pts[i].x = int(pts[i].x * scaleWidth);
pts[i].y = int(pts[i].y * scaleHeight);
}
}
originalImage = visualize(originalImage, results, tm.getFPS());
tm.reset();
if (parser.has("input"))
{
if (save)
{
cout << "Result image saved to result.jpg\n";
imwrite("result.jpg", originalImage);
}
if (viz)
{
imshow(kWinName, originalImage);
waitKey(0);
}
}
else
imshow(kWinName, originalImage);
}
return 0;
}