EAST: An Efficient and Accurate Scene Text Detector (https://arxiv.org/abs/1704.03155v2)

doc/tutorials/dnn/dnn_custom_layers/dnn_custom_layers.md

@@ -32,11 +32,11 @@ Unspecified error: Can't create layer "layer_name" of type "MyType" in function
 To import the model correctly you have to derive a class from cv::dnn::Layer with
 the following methods:
 
-@snippet dnn/custom_layers.cpp A custom layer interface
+@snippet dnn/custom_layers.hpp A custom layer interface
 
 And register it before the import:
 
-@snippet dnn/custom_layers.cpp Register a custom layer
+@snippet dnn/custom_layers.hpp Register a custom layer
 
 @note `MyType` is a type of unimplemented layer from the thrown exception.
 
@@ -44,27 +44,27 @@ Let's see what all the methods do:
 
 - Constructor
 
-@snippet dnn/custom_layers.cpp MyLayer::MyLayer
+@snippet dnn/custom_layers.hpp MyLayer::MyLayer
 
 Retrieves hyper-parameters from cv::dnn::LayerParams. If your layer has trainable
 weights they will be already stored in the Layer's member cv::dnn::Layer::blobs.
 
 - A static method `create`
 
-@snippet dnn/custom_layers.cpp MyLayer::create
+@snippet dnn/custom_layers.hpp MyLayer::create
 
 This method should create an instance of you layer and return cv::Ptr with it.
 
 - Output blobs' shape computation
 
-@snippet dnn/custom_layers.cpp MyLayer::getMemoryShapes
+@snippet dnn/custom_layers.hpp MyLayer::getMemoryShapes
 
 Returns layer's output shapes depends on input shapes. You may request an extra
 memory using `internals`.
 
 - Run a layer
 
-@snippet dnn/custom_layers.cpp MyLayer::forward
+@snippet dnn/custom_layers.hpp MyLayer::forward
 
 Implement a layer's logic here. Compute outputs for given inputs.
 
@@ -74,7 +74,7 @@ the second invocation of `forward` will has the same data at `outputs` and `inte
 
 - Optional `finalize` method
 
-@snippet dnn/custom_layers.cpp MyLayer::finalize
+@snippet dnn/custom_layers.hpp MyLayer::finalize
 
 The chain of methods are the following: OpenCV deep learning engine calls `create`
 method once then it calls `getMemoryShapes` for an every created layer then you
@@ -108,11 +108,11 @@ layer {
 
 This way our implementation can look like:
 
-@snippet dnn/custom_layers.cpp InterpLayer
+@snippet dnn/custom_layers.hpp InterpLayer
 
 Next we need to register a new layer type and try to import the model.
 
-@snippet dnn/custom_layers.cpp Register InterpLayer
+@snippet dnn/custom_layers.hpp Register InterpLayer
 
 ## Example: custom layer from TensorFlow
 This is an example of how to import a network with [tf.image.resize_bilinear](https://www.tensorflow.org/versions/master/api_docs/python/tf/image/resize_bilinear)
@@ -185,11 +185,11 @@ Custom layers import from TensorFlow is designed to put all layer's `attr` into
 cv::dnn::LayerParams but input `Const` blobs into cv::dnn::Layer::blobs.
 In our case resize's output shape will be stored in layer's `blobs[0]`.
 
-@snippet dnn/custom_layers.cpp ResizeBilinearLayer
+@snippet dnn/custom_layers.hpp ResizeBilinearLayer
 
 Next we register a layer and try to import the model.
 
-@snippet dnn/custom_layers.cpp Register ResizeBilinearLayer
+@snippet dnn/custom_layers.hpp Register ResizeBilinearLayer
 
 ## Define a custom layer in Python
 The following example shows how to customize OpenCV's layers in Python.

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -826,6 +826,10 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
                                CV_OUT std::vector<int>& indices,
                                const float eta = 1.f, const int top_k = 0);
 
+    CV_EXPORTS void NMSBoxes(const std::vector<RotatedRect>& bboxes, const std::vector<float>& scores,
+                             const float score_threshold, const float nms_threshold,
+                             CV_OUT std::vector<int>& indices,
+                             const float eta = 1.f, const int top_k = 0);
 
 //! @}
 CV__DNN_EXPERIMENTAL_NS_END

modules/dnn/src/nms.cpp

@@ -8,6 +8,8 @@
 #include "precomp.hpp"
 #include "nms.inl.hpp"
 
+#include <opencv2/imgproc.hpp>
+
 namespace cv
 {
 namespace dnn
@@ -28,6 +30,28 @@ void NMSBoxes(const std::vector<Rect>& bboxes, const std::vector<float>& scores,
     NMSFast_(bboxes, scores, score_threshold, nms_threshold, eta, top_k, indices, rectOverlap);
 }
 
+static inline float rotatedRectIOU(const RotatedRect& a, const RotatedRect& b)
+{
+    std::vector<Point2f> inter, hull;
+    int res = rotatedRectangleIntersection(a, b, inter);
+    if (inter.empty() || res == INTERSECT_NONE)
+        return 0.0f;
+    if (res == INTERSECT_FULL)
+        return 1.0f;
+    convexHull(inter, hull);
+    float interArea = contourArea(hull);
+    return interArea / (a.size.area() + b.size.area() - interArea);
+}
+
+void NMSBoxes(const std::vector<RotatedRect>& bboxes, const std::vector<float>& scores,
+              const float score_threshold, const float nms_threshold,
+              std::vector<int>& indices, const float eta, const int top_k)
+{
+    CV_Assert(bboxes.size() == scores.size(), score_threshold >= 0,
+        nms_threshold >= 0, eta > 0);
+    NMSFast_(bboxes, scores, score_threshold, nms_threshold, eta, top_k, indices, rotatedRectIOU);
+}
+
 CV__DNN_EXPERIMENTAL_NS_END
 }// dnn
 }// cv

modules/dnn/src/tensorflow/tf_graph_simplifier.cpp

@@ -538,6 +538,37 @@ public:
     }
 };
 
+// In case of resizing by factor.
+class ResizeBilinearSubgraph : public Subgraph
+{
+public:
+    ResizeBilinearSubgraph()
+    {
+        int input = addNodeToMatch("");
+
+        int shape = addNodeToMatch("Shape", input);
+        int stack = addNodeToMatch("Const");
+        int stack_1 = addNodeToMatch("Const");
+        int stack_2 = addNodeToMatch("Const");
+        int strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
+        int factorY = addNodeToMatch("Const");
+        int mul = addNodeToMatch("Mul", strided_slice, factorY);
+
+        shape = addNodeToMatch("Shape", input);
+        stack = addNodeToMatch("Const");
+        stack_1 = addNodeToMatch("Const");
+        stack_2 = addNodeToMatch("Const");
+        strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
+        int factorX = addNodeToMatch("Const");
+        int mul_1 = addNodeToMatch("Mul", strided_slice, factorX);
+
+        int pack = addNodeToMatch("Pack", mul, mul_1);
+
+        addNodeToMatch("ResizeBilinear", input, pack);
+        setFusedNode("ResizeBilinear", input, factorY, factorX);
+    }
+};
+
 void simplifySubgraphs(tensorflow::GraphDef& net)
 {
     std::vector<Ptr<Subgraph> > subgraphs;
@@ -551,6 +582,7 @@ void simplifySubgraphs(tensorflow::GraphDef& net)
     subgraphs.push_back(Ptr<Subgraph>(new L2NormalizeSubgraph()));
     subgraphs.push_back(Ptr<Subgraph>(new DeconvolutionValidKerasSubgraph()));
     subgraphs.push_back(Ptr<Subgraph>(new DeconvolutionSameKerasSubgraph()));
+    subgraphs.push_back(Ptr<Subgraph>(new ResizeBilinearSubgraph()));
 
     int numNodes = net.node_size();
     std::vector<int> matchedNodesIds;

modules/dnn/src/tensorflow/tf_importer.cpp

@@ -767,6 +767,26 @@ void TFImporter::populateNet(Net dstNet)
                 }
             }
         }
+        else if (type == "Sub")
+        {
+            bool haveConst = false;
+            for(int ii = 0; !haveConst && ii < layer.input_size(); ++ii)
+            {
+                Pin input = parsePin(layer.input(ii));
+                haveConst = value_id.find(input.name) != value_id.end();
+            }
+            CV_Assert(haveConst);
+
+            layerParams.blobs.resize(1);
+            blobFromTensor(getConstBlob(layer, value_id), layerParams.blobs[0]);
+            layerParams.blobs[0] *= -1;
+
+            int id = dstNet.addLayer(name, "Shift", layerParams);
+            layer_id[name] = id;
+
+            // one input only
+            connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
+        }
         else if (type == "MatMul")
         {
             CV_Assert(layer.input_size() == 2);

modules/dnn/test/test_tf_importer.cpp

@@ -373,9 +373,24 @@ public:
     ResizeBilinearLayer(const LayerParams &params) : Layer(params)
     {
         CV_Assert(!params.get<bool>("align_corners", false));
-        CV_Assert(blobs.size() == 1, blobs[0].type() == CV_32SC1);
-        outHeight = blobs[0].at<int>(0, 0);
-        outWidth = blobs[0].at<int>(0, 1);
+        CV_Assert(!blobs.empty());
+
+        for (size_t i = 0; i < blobs.size(); ++i)
+            CV_Assert(blobs[i].type() == CV_32SC1);
+
+        if (blobs.size() == 1)
+        {
+            CV_Assert(blobs[0].total() == 2);
+            outHeight = blobs[0].at<int>(0, 0);
+            outWidth = blobs[0].at<int>(0, 1);
+        }
+        else
+        {
+            CV_Assert(blobs.size() == 2, blobs[0].total() == 1, blobs[1].total() == 1);
+            factorHeight = blobs[0].at<int>(0, 0);
+            factorWidth = blobs[1].at<int>(0, 0);
+            outHeight = outWidth = 0;
+        }
     }
 
     static Ptr<Layer> create(LayerParams& params)
@@ -391,12 +406,21 @@ public:
         std::vector<int> outShape(4);
         outShape[0] = inputs[0][0];  // batch size
         outShape[1] = inputs[0][1];  // number of channels
-        outShape[2] = outHeight;
-        outShape[3] = outWidth;
+        outShape[2] = outHeight != 0 ? outHeight : (inputs[0][2] * factorHeight);
+        outShape[3] = outWidth != 0 ? outWidth : (inputs[0][3] * factorWidth);
         outputs.assign(1, outShape);
         return false;
     }
 
+    virtual void finalize(const std::vector<Mat*>& inputs, std::vector<Mat> &outputs) CV_OVERRIDE
+    {
+        if (!outWidth && !outHeight)
+        {
+            outHeight = outputs[0].size[2];
+            outWidth = outputs[0].size[3];
+        }
+    }
+
     // This implementation is based on a reference implementation from
     // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
     virtual void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) CV_OVERRIDE
@@ -447,13 +471,51 @@ private:
         return x + size[3] * (y + size[2] * (c + size[1] * b));
     }
 
-    int outWidth, outHeight;
+    int outWidth, outHeight, factorWidth, factorHeight;
 };
 
 TEST(Test_TensorFlow, resize_bilinear)
 {
     CV_DNN_REGISTER_LAYER_CLASS(ResizeBilinear, ResizeBilinearLayer);
     runTensorFlowNet("resize_bilinear");
+    runTensorFlowNet("resize_bilinear_factor");
+    LayerFactory::unregisterLayer("ResizeBilinear");
+}
+
+// inp = cv.imread('opencv_extra/testdata/cv/ximgproc/sources/08.png')
+// inp = inp[:,:,[2, 1, 0]].astype(np.float32).reshape(1, 512, 512, 3)
+// outs = sess.run([sess.graph.get_tensor_by_name('feature_fusion/Conv_7/Sigmoid:0'),
+//                  sess.graph.get_tensor_by_name('feature_fusion/concat_3:0')],
+//                 feed_dict={'input_images:0': inp})
+// scores = np.ascontiguousarray(outs[0].transpose(0, 3, 1, 2))
+// geometry = np.ascontiguousarray(outs[1].transpose(0, 3, 1, 2))
+// np.save('east_text_detection.scores.npy', scores)
+// np.save('east_text_detection.geometry.npy', geometry)
+TEST(Test_TensorFlow, EAST_text_detection)
+{
+    CV_DNN_REGISTER_LAYER_CLASS(ResizeBilinear, ResizeBilinearLayer);
+    std::string netPath = findDataFile("dnn/frozen_east_text_detection.pb", false);
+    std::string imgPath = findDataFile("cv/ximgproc/sources/08.png", false);
+    std::string refScoresPath = findDataFile("dnn/east_text_detection.scores.npy", false);
+    std::string refGeometryPath = findDataFile("dnn/east_text_detection.geometry.npy", false);
+
+    Net net = readNet(findDataFile("dnn/frozen_east_text_detection.pb", false));
+
+    Mat img = imread(imgPath);
+    Mat inp = blobFromImage(img, 1.0, Size(), Scalar(123.68, 116.78, 103.94), true, false);
+    net.setInput(inp);
+
+    std::vector<Mat> outs;
+    std::vector<String> outNames(2);
+    outNames[0] = "feature_fusion/Conv_7/Sigmoid";
+    outNames[1] = "feature_fusion/concat_3";
+    net.forward(outs, outNames);
+
+    Mat scores = outs[0];
+    Mat geometry = outs[1];
+
+    normAssert(scores, blobFromNPY(refScoresPath), "scores");
+    normAssert(geometry, blobFromNPY(refGeometryPath), "geometry", 5e-5, 1e-3);
     LayerFactory::unregisterLayer("ResizeBilinear");
 }
 

samples/cpp/tutorial_code/dnn/custom_layers.cpp → samples/dnn/custom_layers.hpp

-#include <opencv2/dnn.hpp>
-
-//! [A custom layer interface]
-class MyLayer : public cv::dnn::Layer
-{
-public:
-    //! [MyLayer::MyLayer]
-    MyLayer(const cv::dnn::LayerParams &params);
-    //! [MyLayer::MyLayer]
-
-    //! [MyLayer::create]
-    static cv::Ptr<cv::dnn::Layer> create(cv::dnn::LayerParams& params);
-    //! [MyLayer::create]
-
-    //! [MyLayer::getMemoryShapes]
-    virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
-                                 const int requiredOutputs,
-                                 std::vector<std::vector<int> > &outputs,
-                                 std::vector<std::vector<int> > &internals) const CV_OVERRIDE;
-    //! [MyLayer::getMemoryShapes]
-
-    //! [MyLayer::forward]
-    virtual void forward(std::vector<cv::Mat*> &inputs, std::vector<cv::Mat> &outputs, std::vector<cv::Mat> &internals) CV_OVERRIDE;
-    //! [MyLayer::forward]
-
-    //! [MyLayer::finalize]
-    virtual void finalize(const std::vector<cv::Mat*> &inputs, std::vector<cv::Mat> &outputs) CV_OVERRIDE;
-    //! [MyLayer::finalize]
+#ifndef __OPENCV_SAMPLES_DNN_CUSTOM_LAYERS__
+#define __OPENCV_SAMPLES_DNN_CUSTOM_LAYERS__
 
-    virtual void forward(cv::InputArrayOfArrays inputs, cv::OutputArrayOfArrays outputs, cv::OutputArrayOfArrays internals) CV_OVERRIDE;
-};
-//! [A custom layer interface]
+#include <opencv2/dnn.hpp>
+#include <opencv2/dnn/shape_utils.hpp>    // getPlane
 
 //! [InterpLayer]
 class InterpLayer : public cv::dnn::Layer
@@ -113,15 +86,33 @@ private:
 //! [InterpLayer]
 
 //! [ResizeBilinearLayer]
-class ResizeBilinearLayer : public cv::dnn::Layer
+class ResizeBilinearLayer CV_FINAL : public cv::dnn::Layer
 {
 public:
     ResizeBilinearLayer(const cv::dnn::LayerParams &params) : Layer(params)
     {
         CV_Assert(!params.get<bool>("align_corners", false));
-        CV_Assert(blobs.size() == 1, blobs[0].type() == CV_32SC1);
-        outHeight = blobs[0].at<int>(0, 0);
-        outWidth = blobs[0].at<int>(0, 1);
+        CV_Assert(!blobs.empty());
+
+        for (size_t i = 0; i < blobs.size(); ++i)
+            CV_Assert(blobs[i].type() == CV_32SC1);
+
+        // There are two cases of input blob: a single blob which contains output
+        // shape and two blobs with scaling factors.
+        if (blobs.size() == 1)
+        {
+            CV_Assert(blobs[0].total() == 2);
+            outHeight = blobs[0].at<int>(0, 0);
+            outWidth = blobs[0].at<int>(0, 1);
+            factorHeight = factorWidth = 0;
+        }
+        else
+        {
+            CV_Assert(blobs.size() == 2, blobs[0].total() == 1, blobs[1].total() == 1);
+            factorHeight = blobs[0].at<int>(0, 0);
+            factorWidth = blobs[1].at<int>(0, 0);
+            outHeight = outWidth = 0;
+        }
     }
 
     static cv::Ptr<cv::dnn::Layer> create(cv::dnn::LayerParams& params)
@@ -130,25 +121,32 @@ public:
     }
 
     virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
-                                 const int requiredOutputs,
+                                 const int,
                                  std::vector<std::vector<int> > &outputs,
-                                 std::vector<std::vector<int> > &internals) const CV_OVERRIDE
+                                 std::vector<std::vector<int> > &) const CV_OVERRIDE
     {
-        CV_UNUSED(requiredOutputs); CV_UNUSED(internals);
         std::vector<int> outShape(4);
         outShape[0] = inputs[0][0];  // batch size
         outShape[1] = inputs[0][1];  // number of channels
-        outShape[2] = outHeight;
-        outShape[3] = outWidth;
+        outShape[2] = outHeight != 0 ? outHeight : (inputs[0][2] * factorHeight);
+        outShape[3] = outWidth != 0 ? outWidth : (inputs[0][3] * factorWidth);
         outputs.assign(1, outShape);
         return false;
     }
 
+    virtual void finalize(const std::vector<cv::Mat*>&, std::vector<cv::Mat> &outputs) CV_OVERRIDE
+    {
+        if (!outWidth && !outHeight)
+        {
+            outHeight = outputs[0].size[2];
+            outWidth = outputs[0].size[3];
+        }
+    }
+
     // This implementation is based on a reference implementation from
     // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
-    virtual void forward(std::vector<cv::Mat*> &inputs, std::vector<cv::Mat> &outputs, std::vector<cv::Mat> &internals) CV_OVERRIDE
+    virtual void forward(std::vector<cv::Mat*> &inputs, std::vector<cv::Mat> &outputs, std::vector<cv::Mat> &) CV_OVERRIDE
     {
-        CV_UNUSED(internals);
         cv::Mat& inp = *inputs[0];
         cv::Mat& out = outputs[0];
         const float* inpData = (float*)inp.data;
@@ -195,19 +193,54 @@ private:
         return x + size[3] * (y + size[2] * (c + size[1] * b));
     }
 
-    int outWidth, outHeight;
+    int outWidth, outHeight, factorWidth, factorHeight;
 };
 //! [ResizeBilinearLayer]
 
+//
+// The folowing code is used only to generate tutorials documentation.
+//
+
+//! [A custom layer interface]
+class MyLayer : public cv::dnn::Layer
+{
+public:
+    //! [MyLayer::MyLayer]
+    MyLayer(const cv::dnn::LayerParams &params);
+    //! [MyLayer::MyLayer]
+
+    //! [MyLayer::create]
+    static cv::Ptr<cv::dnn::Layer> create(cv::dnn::LayerParams& params);
+    //! [MyLayer::create]
+
+    //! [MyLayer::getMemoryShapes]
+    virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
+                                 const int requiredOutputs,
+                                 std::vector<std::vector<int> > &outputs,
+                                 std::vector<std::vector<int> > &internals) const CV_OVERRIDE;
+    //! [MyLayer::getMemoryShapes]
+
+    //! [MyLayer::forward]
+    virtual void forward(std::vector<cv::Mat*> &inputs, std::vector<cv::Mat> &outputs, std::vector<cv::Mat> &internals) CV_OVERRIDE;
+    //! [MyLayer::forward]
+
+    //! [MyLayer::finalize]
+    virtual void finalize(const std::vector<cv::Mat*> &inputs, std::vector<cv::Mat> &outputs) CV_OVERRIDE;
+    //! [MyLayer::finalize]
+
+    virtual void forward(cv::InputArrayOfArrays inputs, cv::OutputArrayOfArrays outputs, cv::OutputArrayOfArrays internals) CV_OVERRIDE;
+};
+//! [A custom layer interface]
+
 //! [Register a custom layer]
-#include <opencv2/dnn/layer.details.hpp>  // CV_DNN_REGISTER_LAYER_CLASS macro
+#include <opencv2/dnn/layer.details.hpp>  // CV_DNN_REGISTER_LAYER_CLASS
 
-int main(int argc, char** argv)
+static inline void loadNet()
 {
-    CV_DNN_REGISTER_LAYER_CLASS(MyType, MyLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Interp, InterpLayer);
     // ...
     //! [Register a custom layer]
-    CV_UNUSED(argc); CV_UNUSED(argv);
+
     //! [Register InterpLayer]
     CV_DNN_REGISTER_LAYER_CLASS(Interp, InterpLayer);
     cv::dnn::Net caffeNet = cv::dnn::readNet("/path/to/config.prototxt", "/path/to/weights.caffemodel");
@@ -217,16 +250,8 @@ int main(int argc, char** argv)
     CV_DNN_REGISTER_LAYER_CLASS(ResizeBilinear, ResizeBilinearLayer);
     cv::dnn::Net tfNet = cv::dnn::readNet("/path/to/graph.pb");
     //! [Register ResizeBilinearLayer]
-}
 
-cv::Ptr<cv::dnn::Layer> MyLayer::create(cv::dnn::LayerParams& params)
-{
-    return cv::Ptr<cv::dnn::Layer>(new MyLayer(params));
+    if (false) loadNet();  // To prevent unused function warning.
 }
-MyLayer::MyLayer(const cv::dnn::LayerParams&) {}
-bool MyLayer::getMemoryShapes(const std::vector<std::vector<int> >&, const int,
-                              std::vector<std::vector<int> >&,
-                              std::vector<std::vector<int> >&) const { return false; }
-void MyLayer::forward(std::vector<cv::Mat*>&, std::vector<cv::Mat>&, std::vector<cv::Mat>&) {}
-void MyLayer::finalize(const std::vector<cv::Mat*>&, std::vector<cv::Mat>&) {}
-void MyLayer::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays) {}
+
+#endif  // __OPENCV_SAMPLES_DNN_CUSTOM_LAYERS__

samples/dnn/text_detection.cpp

+#include <opencv2/imgproc.hpp>
+#include <opencv2/highgui.hpp>
+#include <opencv2/dnn.hpp>
+
+#include "custom_layers.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");
+    CV_Assert(parser.has("model"));
+    String model = parser.get<String>("model");
+
+    // Register a custom layer.
+    CV_DNN_REGISTER_LAYER_CLASS(ResizeBilinear, ResizeBilinearLayer);
+
+    // 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, geometry.dims == 4, scores.size[0] == 1,
+              geometry.size[0] == 1, scores.size[1] == 1, geometry.size[1] == 5,
+              scores.size[2] == geometry.size[2], scores.size[3] == geometry.size[3]);
+
+    const int height = scores.size[2];
+    const int width = scores.size[3];
+    const int planeSize = height * width;
+
+    float* scoresData = (float*)scores.data;
+    float* geometryData = (float*)geometry.data;
+    float* x0_data = geometryData;
+    float* x1_data = geometryData + planeSize;
+    float* x2_data = geometryData + planeSize * 2;
+    float* x3_data = geometryData + planeSize * 3;
+    float* anglesData = geometryData + planeSize * 4;
+    for (int y = 0; y < height; ++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);
+        }
+        scoresData += width;
+        x0_data += width;
+        x1_data += width;
+        x2_data += width;
+        x3_data += width;
+        anglesData += width;
+    }
+}