Modified the class heirarchy

modules/text/include/opencv2/text/ocr.hpp

@@ -716,10 +716,6 @@ public:
     /** @brief produces a class confidence row-vector given an image
      */
     CV_WRAP virtual void classify(InputArray image, OutputArray classProbabilities) = 0;
-    /** @brief produces a list of bounding box given an image
-     */
-
-    CV_WRAP virtual void detect(InputArray image, OutputArray classProbabilities) = 0;
 
     /** @brief produces a matrix containing class confidence row-vectors given an collection of images
      */

modules/text/include/opencv2/text/textDetector.hpp

@@ -65,19 +65,131 @@ namespace text
 //detection scenario
 class CV_EXPORTS_W BaseDetector
 {
- public:
+public:
     virtual ~BaseDetector() {};
 
     virtual void run(Mat& image,
-                     std::vector<Rect>* component_rects=NULL,                     
+                     std::vector<Rect>* component_rects=NULL,
                      std::vector<float>* component_confidences=NULL,
                      int component_level=0) = 0;
 
     virtual void run(Mat& image, Mat& mask,
-                     std::vector<Rect>* component_rects=NULL,                     
+                     std::vector<Rect>* component_rects=NULL,
                      std::vector<float>* component_confidences=NULL,
                      int component_level=0) = 0;
 
+};
+/** A virtual class for different models of text detection (including CNN based deep models)
+ */
+
+class CV_EXPORTS_W TextRegionDetector
+{
+protected:
+    /** Stores input and output size
+     */
+    //netGeometry inputGeometry_;
+    //netGeometry outputGeometry_;
+    Size inputGeometry_;
+    Size outputGeometry_;
+    int inputChannelCount_;
+    int outputChannelCount_;
+
+public:
+    virtual ~TextRegionDetector() {}
+
+    /** @brief produces a list of Bounding boxes and an estimate of text-ness confidence of Bounding Boxes
+     */
+    CV_WRAP virtual void detect(InputArray image, OutputArray bboxProb ) = 0;
+
+
+    /** @brief simple getter method returning the size (height, width) of the input sample
+     */
+    CV_WRAP virtual Size  getInputGeometry(){return this->inputGeometry_;}
+
+    /** @brief simple getter method returning the shape of the oputput
+     *   Any text detector should output a number of text regions alongwith a score of text-ness
+     *   From the shape it can be inferred the number of text regions and number of returned value
+     *   for each region
+     */
+    CV_WRAP virtual Size getOutputGeometry(){return this->outputGeometry_;}
+
+
+
+};
+
+/** Generic structure of Deep CNN based Text Detectors
+ * */
+class CV_EXPORTS_W  DeepCNNTextDetector : public TextRegionDetector
+{
+    /** @brief Class that uses a pretrained caffe model for text detection.
+     * Any text detection should
+     * This network is described in detail in:
+     * Minghui Liao et al.: TextBoxes: A Fast Text Detector with a Single Deep Neural Network
+     * https://arxiv.org/abs/1611.06779
+     */
+protected:
+    /** all deep CNN based text detectors have a preprocessor (normally)
+         */
+    Ptr<ImagePreprocessor> preprocessor_;
+    /** @brief all image preprocessing is handled here including whitening etc.
+         *
+         *  @param input the image to be preprocessed for the classifier. If the depth
+         * is CV_U8 values should be in [0,255] otherwise values are assumed to be in [0,1]
+         *
+         * @param output reference to the image to be fed to the classifier, the preprocessor will
+         * resize the image to the apropriate size and convert it to the apropriate depth\
+         *
+         * The method preprocess should never be used externally, it is up to classify and classifyBatch
+         * methods to employ it.
+         */
+    virtual void preprocess(const Mat& input,Mat& output);
+public:
+    virtual ~DeepCNNTextDetector() {};
+
+    /** @brief Constructs a DeepCNNTextDetector object from a caffe pretrained model
+     *
+     * @param archFilename is the path to the prototxt file containing the deployment model architecture description.
+     *
+     * @param weightsFilename is the path to the pretrained weights of the model in binary fdorm.
+     *
+     * @param preprocessor is a pointer to the instance of a ImagePreprocessor implementing the preprocess_ protecteed method;
+     *
+     * @param minibatchSz the maximum number of samples that can processed in parallel. In practice this parameter
+     * has an effect only when computing in the GPU and should be set with respect to the memory available in the GPU.
+     *
+     * @param backEnd integer parameter selecting the coputation framework. For now OCR_HOLISTIC_BACKEND_CAFFE is
+     * the only option
+     */
+    CV_WRAP static Ptr<DeepCNNTextDetector> create(String archFilename,String weightsFilename,Ptr<ImagePreprocessor> preprocessor,int minibatchSz=100,int backEnd=OCR_HOLISTIC_BACKEND_CAFFE);
+
+    /** @brief Constructs a DeepCNNTextDetector intended to be used for text area detection.
+     *
+     * This method loads a pretrained classifier and couples with a preprocessor that preprocess the image with mean subtraction of ()
+     * The architecture and models weights can be downloaded from:
+     * https://github.com/sghoshcvc/TextBox-Models.git (size is around 100 MB)
+
+     * @param archFilename is the path to the prototxt file containing the deployment model architecture description.
+     * When employing OCR_HOLISTIC_BACKEND_CAFFE this is the path to the deploy ".prototxt".
+     *
+     * @param weightsFilename is the path to the pretrained weights of the model. When employing
+     * OCR_HOLISTIC_BACKEND_CAFFE this is the path to the ".caffemodel" file.
+     *
+     * @param backEnd integer parameter selecting the coputation framework. For now OCR_HOLISTIC_BACKEND_CAFFE is
+     * the only option
+     */
+    CV_WRAP static Ptr<DeepCNNTextDetector> createTextBoxNet(String archFilename,String weightsFilename,int backEnd=OCR_HOLISTIC_BACKEND_CAFFE);
+    friend class ImagePreprocessor;
+
+};
+
+/** @brief textDetector class provides the functionallity of text bounding box detection.
+ * A TextRegionDetector is employed to find bounding boxes of text
+ * words given an input image.
+ *
+ * This class implements the logic of providing text bounding boxes in a vector of rects given an TextRegionDetector
+ * The TextRegionDetector can be any text detector
+ *
+ */
 
 class CV_EXPORTS_W textDetector : public BaseDetector
 {
@@ -125,9 +237,9 @@ public:
 
 
 
-   /** @brief simple getter for the preprocessing functor
+    /** @brief simple getter for the preprocessing functor
      */
-    CV_WRAP virtual Ptr<TextImageClassifier> getClassifier()=0;
+    CV_WRAP virtual Ptr<TextRegionDetector> getClassifier()=0;
 
     /** @brief Creates an instance of the textDetector class.
 
@@ -135,7 +247,7 @@ public:
 
 
      */
-    CV_WRAP static Ptr<textDetector> create(Ptr<TextImageClassifier> classifierPtr);
+    CV_WRAP static Ptr<textDetector> create(Ptr<TextRegionDetector> classifierPtr);
 
 
     /** @brief Creates an instance of the textDetector class and implicitly also a DeepCNN classifier.

modules/text/src/ocr_holistic.cpp

@@ -459,53 +459,53 @@ protected:
 #endif
     }
 
-    void process_(Mat inputImage, Mat &outputMat)
-    {
-        // do forward pass and stores the output in outputMat
-        //Process one image
-        CV_Assert(this->minibatchSz_==1);
-        //CV_Assert(outputMat.isContinuous());
+//    void process_(Mat inputImage, Mat &outputMat)
+//    {
+//        // do forward pass and stores the output in outputMat
+//        //Process one image
+//        CV_Assert(this->minibatchSz_==1);
+//        //CV_Assert(outputMat.isContinuous());
 
-#ifdef HAVE_CAFFE
-        net_->input_blobs()[0]->Reshape(1, this->channelCount_,this->inputGeometry_.height,this->inputGeometry_.width);
-        net_->Reshape();
-        float* inputBuffer=net_->input_blobs()[0]->mutable_cpu_data();
-        float* inputData=inputBuffer;
+//#ifdef HAVE_CAFFE
+//        net_->input_blobs()[0]->Reshape(1, this->channelCount_,this->inputGeometry_.height,this->inputGeometry_.width);
+//        net_->Reshape();
+//        float* inputBuffer=net_->input_blobs()[0]->mutable_cpu_data();
+//        float* inputData=inputBuffer;
 
-        std::vector<Mat> input_channels;
-        Mat preprocessed;
-        // if the image have multiple color channels the input layer should be populated accordingly
-        for (int channel=0;channel < this->channelCount_;channel++){
+//        std::vector<Mat> input_channels;
+//        Mat preprocessed;
+//        // if the image have multiple color channels the input layer should be populated accordingly
+//        for (int channel=0;channel < this->channelCount_;channel++){
 
-            cv::Mat netInputWraped(this->inputGeometry_.height, this->inputGeometry_.width, CV_32FC1, inputData);
-            input_channels.push_back(netInputWraped);
-            //input_data += width * height;
-            inputData+=(this->inputGeometry_.height*this->inputGeometry_.width);
-        }
-        this->preprocess(inputImage,preprocessed);
-        split(preprocessed, input_channels);
+//            cv::Mat netInputWraped(this->inputGeometry_.height, this->inputGeometry_.width, CV_32FC1, inputData);
+//            input_channels.push_back(netInputWraped);
+//            //input_data += width * height;
+//            inputData+=(this->inputGeometry_.height*this->inputGeometry_.width);
+//        }
+//        this->preprocess(inputImage,preprocessed);
+//        split(preprocessed, input_channels);
 
-        //preprocessed.copyTo(netInputWraped);
+//        //preprocessed.copyTo(netInputWraped);
 
 
-        this->net_->Forward();
-        const float* outputNetData=net_->output_blobs()[0]->cpu_data();
-        // const float* outputNetData1=net_->output_blobs()[1]->cpu_data();
+//        this->net_->Forward();
+//        const float* outputNetData=net_->output_blobs()[0]->cpu_data();
+//        // const float* outputNetData1=net_->output_blobs()[1]->cpu_data();
 
 
 
 
-        this->outputGeometry_ = Size(net_->output_blobs()[0]->width(),net_->output_blobs()[0]->height());
-        int outputSz = this->outputSize_ * this->outputGeometry_.height * this->outputGeometry_.width;
-        outputMat.create(this->outputGeometry_.height , this->outputGeometry_.width,CV_32FC1);
-        float*outputMatData=(float*)(outputMat.data);
+//        this->outputGeometry_ = Size(net_->output_blobs()[0]->width(),net_->output_blobs()[0]->height());
+//        int outputSz = this->outputSize_ * this->outputGeometry_.height * this->outputGeometry_.width;
+//        outputMat.create(this->outputGeometry_.height , this->outputGeometry_.width,CV_32FC1);
+//        float*outputMatData=(float*)(outputMat.data);
 
-        memcpy(outputMatData,outputNetData,sizeof(float)*outputSz);
+//        memcpy(outputMatData,outputNetData,sizeof(float)*outputSz);
 
 
 
-#endif
-    }
+//#endif
+//    }
 
 
 
@@ -587,15 +587,15 @@ public:
         inputImageList.push_back(image.getMat());
         classifyBatch(inputImageList,classProbabilities);
     }
-    void detect(InputArray image, OutputArray Bbox_prob)
-    {
+//    void detect(InputArray image, OutputArray Bbox_prob)
+//    {
 
-        Bbox_prob.create(this->outputGeometry_,CV_32F); // dummy initialization is it needed
-        Mat outputMat = Bbox_prob.getMat();
-        process_(image.getMat(),outputMat);
-        //copy back to outputArray
-        outputMat.copyTo(Bbox_prob);
-    }
+//        Bbox_prob.create(this->outputGeometry_,CV_32F); // dummy initialization is it needed
+//        Mat outputMat = Bbox_prob.getMat();
+//        process_(image.getMat(),outputMat);
+//        //copy back to outputArray
+//        outputMat.copyTo(Bbox_prob);
+//    }
 
     void classifyBatch(InputArrayOfArrays inputImageList, OutputArray classProbabilities)
     {

modules/text/src/text_detector.cpp

@@ -23,6 +23,8 @@
 namespace cv { namespace text {
 
 
+
+
 class textDetectImpl: public textDetector{
 private:
     struct NetOutput{
@@ -60,9 +62,9 @@ private:
     };
 protected:
 
-    Ptr<TextImageClassifier> classifier_;
+    Ptr<TextRegionDetector> classifier_;
 public:
-    textDetectImpl(Ptr<TextImageClassifier> classifierPtr):classifier_(classifierPtr)
+    textDetectImpl(Ptr<TextRegionDetector> classifierPtr):classifier_(classifierPtr)
     {
 
     }
@@ -131,13 +133,13 @@ public:
 
 
 
-    Ptr<TextImageClassifier> getClassifier()
+    Ptr<TextRegionDetector> getClassifier()
     {
         return this->classifier_;
     }
 };
 
-Ptr<textDetector> textDetector::create(Ptr<TextImageClassifier> classifierPtr)
+Ptr<textDetector> textDetector::create(Ptr<TextRegionDetector> classifierPtr)
 {
     return Ptr<textDetector>(new textDetectImpl(classifierPtr));
 }
@@ -155,7 +157,7 @@ Ptr<textDetector> textDetector::create(String modelArchFilename, String modelWei
     textbox_mean.at<uchar>(0,2)=123;
     preprocessor->set_mean(textbox_mean);
 // create a pointer to text box detector(textDetector)
-    Ptr<TextImageClassifier> classifierPtr(DeepCNN::create(modelArchFilename,modelWeightsFilename,preprocessor,1));
+    Ptr<TextRegionDetector> classifierPtr(DeepCNNTextDetector::create(modelArchFilename,modelWeightsFilename,preprocessor,1));
     return Ptr<textDetector>(new textDetectImpl(classifierPtr));
 }
 

modules/text/src/text_detectorCNN.cpp

@@ -0,0 +1,343 @@
+#include "precomp.hpp"
+#include "opencv2/imgproc.hpp"
+#include "opencv2/core.hpp"
+
+
+
+#include <iostream>
+#include <fstream>
+#include <sstream>
+#include <queue>
+#include <algorithm>
+#include <iosfwd>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+
+#ifdef HAVE_CAFFE
+#include "caffe/caffe.hpp"
+#endif
+namespace cv { namespace text {
+
+inline bool fileExists (String filename) {
+    std::ifstream f(filename.c_str());
+    return f.good();
+}
+
+//************************************************************************************
+//******************   TextImageClassifier   *****************************************
+//************************************************************************************
+
+//void TextImageClassifier::preprocess(const Mat& input,Mat& output)
+//{
+//    this->preprocessor_->preprocess_(input,output,this->inputGeometry_,this->channelCount_);
+//}
+
+//void TextImageClassifier::setPreprocessor(Ptr<ImagePreprocessor> ptr)
+//{
+//    CV_Assert(!ptr.empty());
+//    preprocessor_=ptr;
+//}
+
+//Ptr<ImagePreprocessor> TextImageClassifier::getPreprocessor()
+//{
+//    return preprocessor_;
+//}
+
+
+class DeepCNNTextDetectorCaffeImpl: public DeepCNNTextDetector{
+protected:
+
+
+    void process_(Mat inputImage, Mat &outputMat)
+    {
+        // do forward pass and stores the output in outputMat
+        //Process one image
+        // CV_Assert(this->outputGeometry_.batchSize==1);
+        //CV_Assert(outputMat.isContinuous());
+
+#ifdef HAVE_CAFFE
+        net_->input_blobs()[0]->Reshape(1, this->inputChannelCount_,this->inputGeometry_.height,this->inputGeometry_.width);
+        net_->Reshape();
+        float* inputBuffer=net_->input_blobs()[0]->mutable_cpu_data();
+        float* inputData=inputBuffer;
+
+        std::vector<Mat> input_channels;
+        Mat preprocessed;
+        // if the image have multiple color channels the input layer should be populated accordingly
+        for (int channel=0;channel < this->inputChannelCount_;channel++){
+
+            cv::Mat netInputWraped(this->inputGeometry_.height, this->inputGeometry_.width, CV_32FC1, inputData);
+            input_channels.push_back(netInputWraped);
+            //input_data += width * height;
+            inputData+=(this->inputGeometry_.height*this->inputGeometry_.width);
+        }
+        this->preprocess(inputImage,preprocessed);
+        split(preprocessed, input_channels);
+
+        //preprocessed.copyTo(netInputWraped);
+
+
+        this->net_->Forward();
+        const float* outputNetData=net_->output_blobs()[0]->cpu_data();
+        // const float* outputNetData1=net_->output_blobs()[1]->cpu_data();
+
+
+
+
+        this->outputGeometry_.height = net_->output_blobs()[0]->height();
+        this->outputGeometry_.width = net_->output_blobs()[0]->width();
+        this->outputChannelCount_ = net_->output_blobs()[0]->channels();
+        int outputSz = this->outputChannelCount_ * this->outputGeometry_.height * this->outputGeometry_.width;
+        outputMat.create(this->outputGeometry_.height , this->outputGeometry_.width,CV_32FC1);
+        float*outputMatData=(float*)(outputMat.data);
+
+        memcpy(outputMatData,outputNetData,sizeof(float)*outputSz);
+
+
+
+#endif
+    }
+
+
+
+#ifdef HAVE_CAFFE
+    Ptr<caffe::Net<float> > net_;
+#endif
+    //Size inputGeometry_;
+    int minibatchSz_;//The existence of the assignment operator mandates this to be nonconst
+    //int outputSize_;
+public:
+    DeepCNNTextDetectorCaffeImpl(const DeepCNNTextDetectorCaffeImpl& dn):
+        minibatchSz_(dn.minibatchSz_){
+        outputGeometry_=dn.outputGeometry_;
+        inputGeometry_=dn.inputGeometry_;
+        //Implemented to supress Visual Studio warning "assignment operator could not be generated"
+#ifdef HAVE_CAFFE
+        this->net_=dn.net_;
+#endif
+    }
+    DeepCNNTextDetectorCaffeImpl& operator=(const DeepCNNTextDetectorCaffeImpl &dn)
+    {
+#ifdef HAVE_CAFFE
+        this->net_=dn.net_;
+#endif
+        this->setPreprocessor(dn.preprocessor_);
+        this->inputGeometry_=dn.inputGeometry_;
+        this->inputChannelCount_=dn.inputChannelCount_;
+        this->outputChannelCount_ = dn.outputChannelCount_;
+        // this->minibatchSz_=dn.minibatchSz_;
+        //this->outputGeometry_=dn.outputSize_;
+        this->preprocessor_=dn.preprocessor_;
+        this->outputGeometry_=dn.outputGeometry_;
+        return *this;
+        //Implemented to supress Visual Studio warning "assignment operator could not be generated"
+    }
+
+    DeepCNNTextDetectorCaffeImpl(String modelArchFilename, String modelWeightsFilename,Ptr<ImagePreprocessor> preprocessor, int maxMinibatchSz)
+        :minibatchSz_(maxMinibatchSz)
+    {
+
+        CV_Assert(this->minibatchSz_>0);
+        CV_Assert(fileExists(modelArchFilename));
+        CV_Assert(fileExists(modelWeightsFilename));
+        CV_Assert(!preprocessor.empty());
+        this->setPreprocessor(preprocessor);
+#ifdef HAVE_CAFFE
+        this->net_.reset(new caffe::Net<float>(modelArchFilename, caffe::TEST));
+        CV_Assert(net_->num_inputs()==1);
+        CV_Assert(net_->num_outputs()==1);
+        CV_Assert(this->net_->input_blobs()[0]->channels()==1
+                ||this->net_->input_blobs()[0]->channels()==3);
+        //        this->channelCount_=this->net_->input_blobs()[0]->channels();
+
+
+
+        this->net_->CopyTrainedLayersFrom(modelWeightsFilename);
+
+        caffe::Blob<float>* inputLayer = this->net_->input_blobs()[0];
+
+        this->inputGeometry_.height = inputLayer->height();
+        this->inputGeometry_.width = inputLayer->width();
+        this->inputChannelCount_ = inputLayer->channels();
+        //this->inputGeometry_.batchSize =1;
+
+        inputLayer->Reshape(this->minibatchSz_,this->inputChannelCount_,this->inputGeometry_.height, this->inputGeometry_.width);
+        net_->Reshape();
+        this->outputChannelCount_ = net_->output_blobs()[0]->channels();
+        //this->outputGeometry_.batchSize =1;
+        this->outputGeometry_.height =net_->output_blobs()[0]->height();
+        this->outputGeometry_.width = net_->output_blobs()[0]->width();
+
+
+
+
+
+#else
+        CV_Error(Error::StsError,"Caffe not available during compilation!");
+#endif
+    }
+
+
+    void detect(InputArray image, OutputArray Bbox_prob)
+    {
+        Size outSize = Size(this->outputGeometry_.height,outputGeometry_.width);
+        Bbox_prob.create(outSize,CV_32F); // dummy initialization is it needed
+        Mat outputMat = Bbox_prob.getMat();
+        process_(image.getMat(),outputMat);
+        //copy back to outputArray
+        outputMat.copyTo(Bbox_prob);
+    }
+
+
+
+    //int getOutputSize()
+    //{
+    //  return this->outputSize_;
+    //}
+    Size getOutputGeometry()
+    {
+        return this->outputGeometry_;
+    }
+    Size getinputGeometry()
+    {
+        return this->inputGeometry_;
+    }
+
+    int getMinibatchSize()
+    {
+        return this->minibatchSz_;
+    }
+
+    int getBackend()
+    {
+        return OCR_HOLISTIC_BACKEND_CAFFE;
+    }
+    void setPreprocessor(Ptr<ImagePreprocessor> ptr)
+    {
+        CV_Assert(!ptr.empty());
+        preprocessor_=ptr;
+    }
+
+    Ptr<ImagePreprocessor> getPreprocessor()
+    {
+        return preprocessor_;
+    }
+};
+
+
+Ptr<DeepCNNTextDetector> DeepCNNTextDetector::create(String archFilename,String weightsFilename,Ptr<ImagePreprocessor> preprocessor,int minibatchSz,int backEnd)
+{
+    if(preprocessor.empty())
+    {
+        // create a custom preprocessor with rawval
+        Ptr<ImagePreprocessor> preprocessor=ImagePreprocessor::createImageCustomPreprocessor(255);
+        // set the mean for the preprocessor
+
+        Mat textbox_mean(1,3,CV_8U);
+        textbox_mean.at<uchar>(0,0)=104;
+        textbox_mean.at<uchar>(0,1)=117;
+        textbox_mean.at<uchar>(0,2)=123;
+        preprocessor->set_mean(textbox_mean);
+    }
+    switch(backEnd){
+    case OCR_HOLISTIC_BACKEND_CAFFE:
+
+        return Ptr<DeepCNNTextDetector>(new DeepCNNTextDetectorCaffeImpl(archFilename, weightsFilename,preprocessor, minibatchSz));
+        break;
+    case OCR_HOLISTIC_BACKEND_NONE:
+    default:
+        CV_Error(Error::StsError,"DeepCNN::create backend not implemented");
+        return Ptr<DeepCNNTextDetector>();
+        break;
+    }
+    return Ptr<DeepCNNTextDetector>();
+
+}
+
+
+Ptr<DeepCNNTextDetector> DeepCNNTextDetector::createTextBoxNet(String archFilename,String weightsFilename,int backEnd)
+{
+
+    // create a custom preprocessor with rawval
+    Ptr<ImagePreprocessor> preprocessor=ImagePreprocessor::createImageCustomPreprocessor(255);
+    // set the mean for the preprocessor
+
+    Mat textbox_mean(1,3,CV_8U);
+    textbox_mean.at<uchar>(0,0)=104;
+    textbox_mean.at<uchar>(0,1)=117;
+    textbox_mean.at<uchar>(0,2)=123;
+    preprocessor->set_mean(textbox_mean);
+    switch(backEnd){
+    case OCR_HOLISTIC_BACKEND_CAFFE:
+        return Ptr<DeepCNNTextDetector>(new DeepCNNTextDetectorCaffeImpl(archFilename, weightsFilename,preprocessor, 100));
+        break;
+    case OCR_HOLISTIC_BACKEND_NONE:
+    default:
+        CV_Error(Error::StsError,"DeepCNN::create backend not implemented");
+        return Ptr<DeepCNNTextDetector>();
+        break;
+    }
+    return Ptr<DeepCNNTextDetector>();
+
+}
+
+void DeepCNNTextDetector::preprocess(const Mat& input,Mat& output)
+{
+    Size inputHtWd = Size(this->inputGeometry_.height,this->inputGeometry_.width);
+    this->preprocessor_->preprocess(input,output,inputHtWd,this->inputChannelCount_);
+}
+
+//namespace cnn_config{
+//namespace caffe_backend{
+
+//#ifdef HAVE_CAFFE
+
+//bool getCaffeGpuMode()
+//{
+//    return caffe::Caffe::mode()==caffe::Caffe::GPU;
+//}
+
+//void setCaffeGpuMode(bool useGpu)
+//{
+//    if(useGpu)
+//    {
+//        caffe::Caffe::set_mode(caffe::Caffe::GPU);
+//    }else
+//    {
+//        caffe::Caffe::set_mode(caffe::Caffe::CPU);
+//    }
+//}
+
+//bool getCaffeAvailable()
+//{
+//    return true;
+//}
+
+//#else
+
+//bool getCaffeGpuMode()
+//{
+//    CV_Error(Error::StsError,"Caffe not available during compilation!");
+//    return 0;
+//}
+
+//void setCaffeGpuMode(bool useGpu)
+//{
+//    CV_Error(Error::StsError,"Caffe not available during compilation!");
+//    CV_Assert(useGpu==1);//Compilation directives force
+//}
+
+//bool getCaffeAvailable(){
+//    return 0;
+//}
+
+//#endif
+
+//}//namespace caffe
+//}//namespace cnn_config
+
+}  } //namespace text namespace cv
+