add part of opencv
This commit is contained in:
Tang1705
161
Lib/opencv/sources/samples/dnn/text_detection.cpp
Normal file
161
Lib/opencv/sources/samples/dnn/text_detection.cpp
Normal file
@@ -0,0 +1,161 @@
|
||||
#include <opencv2/imgproc.hpp>
|
||||
#include <opencv2/highgui.hpp>
|
||||
#include <opencv2/dnn.hpp>
|
||||
|
||||
using namespace cv;
|
||||
using namespace cv::dnn;
|
||||
|
||||
const char* keys =
|
||||
"{ help h | | Print help message. }"
|
||||
"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera.}"
|
||||
"{ model m | | Path to a binary .pb file contains trained network.}"
|
||||
"{ width | 320 | Preprocess input image by resizing to a specific width. It should be multiple by 32. }"
|
||||
"{ height | 320 | Preprocess input image by resizing to a specific height. It should be multiple by 32. }"
|
||||
"{ thr | 0.5 | Confidence threshold. }"
|
||||
"{ nms | 0.4 | Non-maximum suppression threshold. }";
|
||||
|
||||
void decode(const Mat& scores, const Mat& geometry, float scoreThresh,
|
||||
std::vector<RotatedRect>& detections, std::vector<float>& confidences);
|
||||
|
||||
int main(int argc, char** argv)
|
||||
{
|
||||
// Parse command line arguments.
|
||||
CommandLineParser parser(argc, argv, keys);
|
||||
parser.about("Use this script to run TensorFlow implementation (https://github.com/argman/EAST) of "
|
||||
"EAST: An Efficient and Accurate Scene Text Detector (https://arxiv.org/abs/1704.03155v2)");
|
||||
if (argc == 1 || parser.has("help"))
|
||||
{
|
||||
parser.printMessage();
|
||||
return 0;
|
||||
}
|
||||
|
||||
float confThreshold = parser.get<float>("thr");
|
||||
float nmsThreshold = parser.get<float>("nms");
|
||||
int inpWidth = parser.get<int>("width");
|
||||
int inpHeight = parser.get<int>("height");
|
||||
String model = parser.get<String>("model");
|
||||
|
||||
if (!parser.check())
|
||||
{
|
||||
parser.printErrors();
|
||||
return 1;
|
||||
}
|
||||
|
||||
CV_Assert(!model.empty());
|
||||
|
||||
// Load network.
|
||||
Net net = readNet(model);
|
||||
|
||||
// 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);
|
||||
|
||||
static const std::string kWinName = "EAST: An Efficient and Accurate Scene Text Detector";
|
||||
namedWindow(kWinName, WINDOW_NORMAL);
|
||||
|
||||
std::vector<Mat> outs;
|
||||
std::vector<String> outNames(2);
|
||||
outNames[0] = "feature_fusion/Conv_7/Sigmoid";
|
||||
outNames[1] = "feature_fusion/concat_3";
|
||||
|
||||
Mat frame, blob;
|
||||
while (waitKey(1) < 0)
|
||||
{
|
||||
cap >> frame;
|
||||
if (frame.empty())
|
||||
{
|
||||
waitKey();
|
||||
break;
|
||||
}
|
||||
|
||||
blobFromImage(frame, blob, 1.0, Size(inpWidth, inpHeight), Scalar(123.68, 116.78, 103.94), true, false);
|
||||
net.setInput(blob);
|
||||
net.forward(outs, outNames);
|
||||
|
||||
Mat scores = outs[0];
|
||||
Mat geometry = outs[1];
|
||||
|
||||
// Decode predicted bounding boxes.
|
||||
std::vector<RotatedRect> boxes;
|
||||
std::vector<float> confidences;
|
||||
decode(scores, geometry, confThreshold, boxes, confidences);
|
||||
|
||||
// Apply non-maximum suppression procedure.
|
||||
std::vector<int> indices;
|
||||
NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);
|
||||
|
||||
// Render detections.
|
||||
Point2f ratio((float)frame.cols / inpWidth, (float)frame.rows / inpHeight);
|
||||
for (size_t i = 0; i < indices.size(); ++i)
|
||||
{
|
||||
RotatedRect& box = boxes[indices[i]];
|
||||
|
||||
Point2f vertices[4];
|
||||
box.points(vertices);
|
||||
for (int j = 0; j < 4; ++j)
|
||||
{
|
||||
vertices[j].x *= ratio.x;
|
||||
vertices[j].y *= ratio.y;
|
||||
}
|
||||
for (int j = 0; j < 4; ++j)
|
||||
line(frame, vertices[j], vertices[(j + 1) % 4], Scalar(0, 255, 0), 1);
|
||||
}
|
||||
|
||||
// Put efficiency information.
|
||||
std::vector<double> layersTimes;
|
||||
double freq = getTickFrequency() / 1000;
|
||||
double t = net.getPerfProfile(layersTimes) / freq;
|
||||
std::string label = format("Inference time: %.2f ms", t);
|
||||
putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
|
||||
|
||||
imshow(kWinName, frame);
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
void decode(const Mat& scores, const Mat& geometry, float scoreThresh,
|
||||
std::vector<RotatedRect>& detections, std::vector<float>& confidences)
|
||||
{
|
||||
detections.clear();
|
||||
CV_Assert(scores.dims == 4); CV_Assert(geometry.dims == 4); CV_Assert(scores.size[0] == 1);
|
||||
CV_Assert(geometry.size[0] == 1); CV_Assert(scores.size[1] == 1); CV_Assert(geometry.size[1] == 5);
|
||||
CV_Assert(scores.size[2] == geometry.size[2]); CV_Assert(scores.size[3] == geometry.size[3]);
|
||||
|
||||
const int height = scores.size[2];
|
||||
const int width = scores.size[3];
|
||||
for (int y = 0; y < height; ++y)
|
||||
{
|
||||
const float* scoresData = scores.ptr<float>(0, 0, y);
|
||||
const float* x0_data = geometry.ptr<float>(0, 0, y);
|
||||
const float* x1_data = geometry.ptr<float>(0, 1, y);
|
||||
const float* x2_data = geometry.ptr<float>(0, 2, y);
|
||||
const float* x3_data = geometry.ptr<float>(0, 3, y);
|
||||
const float* anglesData = geometry.ptr<float>(0, 4, y);
|
||||
for (int x = 0; x < width; ++x)
|
||||
{
|
||||
float score = scoresData[x];
|
||||
if (score < scoreThresh)
|
||||
continue;
|
||||
|
||||
// Decode a prediction.
|
||||
// Multiple by 4 because feature maps are 4 time less than input image.
|
||||
float offsetX = x * 4.0f, offsetY = y * 4.0f;
|
||||
float angle = anglesData[x];
|
||||
float cosA = std::cos(angle);
|
||||
float sinA = std::sin(angle);
|
||||
float h = x0_data[x] + x2_data[x];
|
||||
float w = x1_data[x] + x3_data[x];
|
||||
|
||||
Point2f offset(offsetX + cosA * x1_data[x] + sinA * x2_data[x],
|
||||
offsetY - sinA * x1_data[x] + cosA * x2_data[x]);
|
||||
Point2f p1 = Point2f(-sinA * h, -cosA * h) + offset;
|
||||
Point2f p3 = Point2f(-cosA * w, sinA * w) + offset;
|
||||
RotatedRect r(0.5f * (p1 + p3), Size2f(w, h), -angle * 180.0f / (float)CV_PI);
|
||||
detections.push_back(r);
|
||||
confidences.push_back(score);
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user