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text_detection.cpp
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text_detection.cpp
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/*
Text detection model: https://github.com/argman/EAST
Download link: https://www.dropbox.com/s/r2ingd0l3zt8hxs/frozen_east_text_detection.tar.gz?dl=1
Text recognition models can be downloaded directly here:
Download link: https://drive.google.com/drive/folders/1cTbQ3nuZG-EKWak6emD_s8_hHXWz7lAr?usp=sharing
and doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown
How to convert from pb to onnx:
Using classes from here: https://github.com/meijieru/crnn.pytorch/blob/master/models/crnn.py
import torch
from models.crnn import CRNN
model = CRNN(32, 1, 37, 256)
model.load_state_dict(torch.load('crnn.pth'))
dummy_input = torch.randn(1, 1, 32, 100)
torch.onnx.export(model, dummy_input, "crnn.onnx", verbose=True)
For more information, please refer to doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown and doc/tutorials/dnn/dnn_OCR/dnn_OCR.markdown
*/
#include <iostream>
#include <fstream>
#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.}"
"{ detModel dmp | | Path to a binary .pb file contains trained detector network.}"
"{ width | 320 | Preprocess input image by resizing to a specific width. It should be a multiple of 32. }"
"{ height | 320 | Preprocess input image by resizing to a specific height. It should be a multiple of 32. }"
"{ thr | 0.5 | Confidence threshold. }"
"{ nms | 0.4 | Non-maximum suppression threshold. }"
"{ recModel rmp | | Path to a binary .onnx file contains trained CRNN text recognition model. "
"Download links are provided in doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown}"
"{ RGBInput rgb |0| 0: imread with flags=IMREAD_GRAYSCALE; 1: imread with flags=IMREAD_COLOR. }"
"{ vocabularyPath vp | alphabet_36.txt | Path to benchmarks for evaluation. "
"Download links are provided in doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown}";
void fourPointsTransform(const Mat& frame, const Point2f vertices[], Mat& result);
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 width = parser.get<int>("width");
int height = parser.get<int>("height");
int imreadRGB = parser.get<int>("RGBInput");
String detModelPath = parser.get<String>("detModel");
String recModelPath = parser.get<String>("recModel");
String vocPath = parser.get<String>("vocabularyPath");
if (!parser.check())
{
parser.printErrors();
return 1;
}
// Load networks.
CV_Assert(!detModelPath.empty() && !recModelPath.empty());
TextDetectionModel_EAST detector(detModelPath);
detector.setConfidenceThreshold(confThreshold)
.setNMSThreshold(nmsThreshold);
TextRecognitionModel recognizer(recModelPath);
// Load vocabulary
CV_Assert(!vocPath.empty());
std::ifstream vocFile;
vocFile.open(samples::findFile(vocPath));
CV_Assert(vocFile.is_open());
String vocLine;
std::vector<String> vocabulary;
while (std::getline(vocFile, vocLine)) {
vocabulary.push_back(vocLine);
}
recognizer.setVocabulary(vocabulary);
recognizer.setDecodeType("CTC-greedy");
// Parameters for Recognition
double recScale = 1.0 / 127.5;
Scalar recMean = Scalar(127.5, 127.5, 127.5);
Size recInputSize = Size(100, 32);
recognizer.setInputParams(recScale, recInputSize, recMean);
// Parameters for Detection
double detScale = 1.0;
Size detInputSize = Size(width, height);
Scalar detMean = Scalar(123.68, 116.78, 103.94);
bool swapRB = true;
detector.setInputParams(detScale, detInputSize, detMean, swapRB);
// Open a video file or an image file or a camera stream.
VideoCapture cap;
bool openSuccess = parser.has("input") ? cap.open(parser.get<String>("input")) : cap.open(0);
CV_Assert(openSuccess);
static const std::string kWinName = "EAST: An Efficient and Accurate Scene Text Detector";
Mat frame;
while (waitKey(1) < 0)
{
cap >> frame;
if (frame.empty())
{
waitKey();
break;
}
std::cout << frame.size << std::endl;
// Detection
std::vector< std::vector<Point> > detResults;
detector.detect(frame, detResults);
Mat frame2 = frame.clone();
if (detResults.size() > 0) {
// Text Recognition
Mat recInput;
if (!imreadRGB) {
cvtColor(frame, recInput, cv::COLOR_BGR2GRAY);
} else {
recInput = frame;
}
std::vector< std::vector<Point> > contours;
for (uint i = 0; i < detResults.size(); i++)
{
const auto& quadrangle = detResults[i];
CV_CheckEQ(quadrangle.size(), (size_t)4, "");
contours.emplace_back(quadrangle);
std::vector<Point2f> quadrangle_2f;
for (int j = 0; j < 4; j++)
quadrangle_2f.emplace_back(quadrangle[j]);
Mat cropped;
fourPointsTransform(recInput, &quadrangle_2f[0], cropped);
std::string recognitionResult = recognizer.recognize(cropped);
std::cout << i << ": '" << recognitionResult << "'" << std::endl;
putText(frame2, recognitionResult, quadrangle[3], FONT_HERSHEY_SIMPLEX, 1.5, Scalar(0, 0, 255), 2);
}
polylines(frame2, contours, true, Scalar(0, 255, 0), 2);
}
imshow(kWinName, frame2);
}
return 0;
}
void fourPointsTransform(const Mat& frame, const Point2f vertices[], Mat& result)
{
const Size outputSize = Size(100, 32);
Point2f targetVertices[4] = {
Point(0, outputSize.height - 1),
Point(0, 0), Point(outputSize.width - 1, 0),
Point(outputSize.width - 1, outputSize.height - 1)
};
Mat rotationMatrix = getPerspectiveTransform(vertices, targetVertices);
warpPerspective(frame, result, rotationMatrix, outputSize);
}