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@ -1,3 +1,20 @@ |
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/*
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Text detection model: https://github.com/argman/EAST
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Download link: https://www.dropbox.com/s/r2ingd0l3zt8hxs/frozen_east_text_detection.tar.gz?dl=1
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Text recognition model taken from here: https://github.com/meijieru/crnn.pytorch
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How to convert from pb to onnx: |
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Using classes from here: https://github.com/meijieru/crnn.pytorch/blob/master/models/crnn.py
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import torch |
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import models.crnn as crnn |
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model = CRNN(32, 1, 37, 256) |
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model.load_state_dict(torch.load('crnn.pth')) |
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dummy_input = torch.randn(1, 1, 32, 100) |
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torch.onnx.export(model, dummy_input, "crnn.onnx", verbose=True) |
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*/ |
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#include <opencv2/imgproc.hpp> |
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#include <opencv2/highgui.hpp> |
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#include <opencv2/dnn.hpp> |
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@ -8,15 +25,20 @@ using namespace cv::dnn; |
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const char* keys = |
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"{ help h | | Print help message. }" |
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"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera.}" |
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"{ model m | | Path to a binary .pb file contains trained network.}" |
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"{ model m | | Path to a binary .pb file contains trained detector network.}" |
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"{ ocr | | Path to a binary .pb or .onnx file contains trained recognition network.}" |
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"{ width | 320 | Preprocess input image by resizing to a specific width. It should be multiple by 32. }" |
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"{ height | 320 | Preprocess input image by resizing to a specific height. It should be multiple by 32. }" |
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"{ thr | 0.5 | Confidence threshold. }" |
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"{ nms | 0.4 | Non-maximum suppression threshold. }"; |
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void decode(const Mat& scores, const Mat& geometry, float scoreThresh, |
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void decodeBoundingBoxes(const Mat& scores, const Mat& geometry, float scoreThresh, |
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std::vector<RotatedRect>& detections, std::vector<float>& confidences); |
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void fourPointsTransform(const Mat& frame, Point2f vertices[4], Mat& result); |
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void decodeText(const Mat& scores, std::string& text); |
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int main(int argc, char** argv) |
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{ |
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// Parse command line arguments.
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@ -33,7 +55,8 @@ int main(int argc, char** argv) |
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float nmsThreshold = parser.get<float>("nms"); |
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int inpWidth = parser.get<int>("width"); |
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int inpHeight = parser.get<int>("height"); |
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String model = parser.get<String>("model"); |
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String modelDecoder = parser.get<String>("model"); |
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String modelRecognition = parser.get<String>("ocr"); |
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if (!parser.check()) |
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{ |
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@ -41,17 +64,19 @@ int main(int argc, char** argv) |
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return 1; |
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} |
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CV_Assert(!model.empty()); |
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CV_Assert(!modelDecoder.empty()); |
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// Load network.
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Net net = readNet(model); |
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// Load networks.
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Net detector = readNet(modelDecoder); |
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Net recognizer; |
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if (!modelRecognition.empty()) |
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recognizer = readNet(modelRecognition); |
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// Open a video file or an image file or a camera stream.
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VideoCapture cap; |
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if (parser.has("input")) |
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cap.open(parser.get<String>("input")); |
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else |
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cap.open(0); |
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bool openSuccess = parser.has("input") ? cap.open(parser.get<String>("input")) : cap.open(0); |
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CV_Assert(openSuccess); |
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static const std::string kWinName = "EAST: An Efficient and Accurate Scene Text Detector"; |
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namedWindow(kWinName, WINDOW_NORMAL); |
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@ -62,6 +87,7 @@ int main(int argc, char** argv) |
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outNames[1] = "feature_fusion/concat_3"; |
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Mat frame, blob; |
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TickMeter tickMeter; |
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while (waitKey(1) < 0) |
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{ |
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cap >> frame; |
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@ -72,8 +98,10 @@ int main(int argc, char** argv) |
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} |
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blobFromImage(frame, blob, 1.0, Size(inpWidth, inpHeight), Scalar(123.68, 116.78, 103.94), true, false); |
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net.setInput(blob); |
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net.forward(outs, outNames); |
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detector.setInput(blob); |
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tickMeter.start(); |
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detector.forward(outs, outNames); |
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tickMeter.stop(); |
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Mat scores = outs[0]; |
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Mat geometry = outs[1]; |
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@ -81,42 +109,63 @@ int main(int argc, char** argv) |
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// Decode predicted bounding boxes.
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std::vector<RotatedRect> boxes; |
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std::vector<float> confidences; |
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decode(scores, geometry, confThreshold, boxes, confidences); |
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decodeBoundingBoxes(scores, geometry, confThreshold, boxes, confidences); |
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// Apply non-maximum suppression procedure.
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std::vector<int> indices; |
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NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices); |
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// Render detections.
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Point2f ratio((float)frame.cols / inpWidth, (float)frame.rows / inpHeight); |
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// Render text.
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for (size_t i = 0; i < indices.size(); ++i) |
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{ |
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RotatedRect& box = boxes[indices[i]]; |
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Point2f vertices[4]; |
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box.points(vertices); |
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for (int j = 0; j < 4; ++j) |
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{ |
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vertices[j].x *= ratio.x; |
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vertices[j].y *= ratio.y; |
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} |
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if (!modelRecognition.empty()) |
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{ |
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Mat cropped; |
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fourPointsTransform(frame, vertices, cropped); |
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cvtColor(cropped, cropped, cv::COLOR_BGR2GRAY); |
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Mat blobCrop = blobFromImage(cropped, 1.0/127.5, Size(), Scalar::all(127.5)); |
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recognizer.setInput(blobCrop); |
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tickMeter.start(); |
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Mat result = recognizer.forward(); |
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tickMeter.stop(); |
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std::string wordRecognized = ""; |
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decodeText(result, wordRecognized); |
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putText(frame, wordRecognized, vertices[1], FONT_HERSHEY_SIMPLEX, 1.5, Scalar(0, 0, 255)); |
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} |
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for (int j = 0; j < 4; ++j) |
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line(frame, vertices[j], vertices[(j + 1) % 4], Scalar(0, 255, 0), 1); |
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} |
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// Put efficiency information.
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std::vector<double> layersTimes; |
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double freq = getTickFrequency() / 1000; |
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double t = net.getPerfProfile(layersTimes) / freq; |
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std::string label = format("Inference time: %.2f ms", t); |
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std::string label = format("Inference time: %.2f ms", tickMeter.getTimeMilli()); |
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putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0)); |
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imshow(kWinName, frame); |
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tickMeter.reset(); |
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} |
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return 0; |
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} |
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void decode(const Mat& scores, const Mat& geometry, float scoreThresh, |
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void decodeBoundingBoxes(const Mat& scores, const Mat& geometry, float scoreThresh, |
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std::vector<RotatedRect>& detections, std::vector<float>& confidences) |
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{ |
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detections.clear(); |
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@ -159,3 +208,51 @@ void decode(const Mat& scores, const Mat& geometry, float scoreThresh, |
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} |
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} |
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} |
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void fourPointsTransform(const Mat& frame, Point2f vertices[4], Mat& result) |
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{ |
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const Size outputSize = Size(100, 32); |
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Point2f targetVertices[4] = {Point(0, outputSize.height - 1), |
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Point(0, 0), Point(outputSize.width - 1, 0), |
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Point(outputSize.width - 1, outputSize.height - 1), |
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}; |
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Mat rotationMatrix = getPerspectiveTransform(vertices, targetVertices); |
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warpPerspective(frame, result, rotationMatrix, outputSize); |
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} |
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void decodeText(const Mat& scores, std::string& text) |
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{ |
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static const std::string alphabet = "0123456789abcdefghijklmnopqrstuvwxyz"; |
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Mat scoresMat = scores.reshape(1, scores.size[0]); |
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std::vector<char> elements; |
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elements.reserve(scores.size[0]); |
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for (int rowIndex = 0; rowIndex < scoresMat.rows; ++rowIndex) |
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{ |
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Point p; |
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minMaxLoc(scoresMat.row(rowIndex), 0, 0, 0, &p); |
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if (p.x > 0 && static_cast<size_t>(p.x) <= alphabet.size()) |
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{ |
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elements.push_back(alphabet[p.x - 1]); |
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} |
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else |
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{ |
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elements.push_back('-'); |
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} |
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} |
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if (elements.size() > 0 && elements[0] != '-') |
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text += elements[0]; |
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for (size_t elementIndex = 1; elementIndex < elements.size(); ++elementIndex) |
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{ |
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if (elementIndex > 0 && elements[elementIndex] != '-' && |
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elements[elementIndex - 1] != elements[elementIndex]) |
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{ |
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text += elements[elementIndex]; |
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} |
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} |
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} |
Alexander Alekhin
5 years ago