Added comments

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

@@ -633,6 +633,16 @@ public:
      */
     CV_WRAP void preprocess(InputArray input,OutputArray output,Size sz,int outputChannels);
 
+    /** @brief this method in provides public acces to set the mean of the input images
+     * mean can be a mat either of same size of the image or one value per color channel
+     * A preprocessor can be created without the mean( the pre processor will calculate mean for every image
+     * in that case
+     *
+
+     * @param mean which will be subtracted from the images
+     *
+     */
+
     CV_WRAP void set_mean(Mat mean);
 
     /** @brief Creates a functor that only resizes and changes the channels of the input
@@ -655,6 +665,10 @@ public:
      * @return shared pointer to generated preprocessor
      */
     CV_WRAP static Ptr<ImagePreprocessor> createImageMeanSubtractor(InputArray meanImg);
+    /** @brief
+     * create a functor with the parameters, parameters can be changes by corresponding set functions
+     * @return shared pointer to generated preprocessor
+     */
 
     CV_WRAP static Ptr<ImagePreprocessor>createImageCustomPreprocessor(double rawval=1.0,String channel_order="BGR");
 

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

@@ -62,7 +62,7 @@ namespace text
 
 
 //base class BaseDetector declares a common API that would be used in a typical text
-//recognition scenario
+//detection scenario
 class CV_EXPORTS_W BaseDetector
 {
  public:
@@ -78,46 +78,7 @@ class CV_EXPORTS_W BaseDetector
                      std::vector<float>* component_confidences=NULL,
                      int component_level=0) = 0;
 
-    /** @brief Main functionality of the OCR Hierarchy. Subclasses provide
-     * default parameters for all parameters other than the input image.
-     */
-//    virtual std::vector<Rect>* run(InputArray image){
-//        //std::string res;
-//        std::vector<Rect> component_rects;
-//        std::vector<float> component_confidences;
-//        //std::vector<std::string> component_texts;
-//        Mat inputImage=image.getMat();
-//        this->run(inputImage,&component_rects,
-//                  &component_confidences,OCR_LEVEL_WORD);
-//        return *component_rects;
-//    }
-
-};
-
-
-//Classifiers should provide diferent backends
-//For the moment only caffe is implemeted
-//enum{
-//    OCR_HOLISTIC_BACKEND_NONE,
-//    OCR_HOLISTIC_BACKEND_CAFFE
-//};
-
 
-
-
-
-/** @brief OCRHolisticWordRecognizer class provides the functionallity of segmented wordspotting.
- * Given a predefined vocabulary , a TextImageClassifier is employed to select the most probable
- * word given an input image.
- *
- * This class implements the logic of providing transcriptions given a vocabulary and and an image
- * classifer. The classifier has to be any TextImageClassifier but the classifier for which this
- * class was built is the DictNet. In order to load it the following files should be downloaded:
-
- * <http://nicolaou.homouniversalis.org/assets/vgg_text/dictnet_vgg_deploy.prototxt>
- * <http://nicolaou.homouniversalis.org/assets/vgg_text/dictnet_vgg.caffemodel>
- * <http://nicolaou.homouniversalis.org/assets/vgg_text/dictnet_vgg_labels.txt>
- */
 class CV_EXPORTS_W textDetector : public BaseDetector
 {
 public:
@@ -125,7 +86,7 @@ public:
                      std::vector<float>* component_confidences=NULL,
                      int component_level=OCR_LEVEL_WORD)=0;
 
-    /** @brief Recognize text using a segmentation based word-spotting/classifier cnn.
+    /** @brief detect text with a cnn, input is one image with (multiple) ocuurance of text.
 
     Takes image on input and returns recognized text in the output_text parameter. Optionally
     provides also the Rects for individual text elements found (e.g. words), and the list of those
@@ -135,16 +96,12 @@ public:
 
     @param mask is totally ignored and is only available for compatibillity reasons
 
-    @param output_text Output text of the the word spoting, always one that exists in the dictionary.
 
-    @param component_rects Not applicable for word spotting can be be NULL if not, a single elemnt will
-        be put in the vector.
+    @param component_rects a vector of Rects, each rect is one text bounding box.
 
-    @param component_texts Not applicable for word spotting can be be NULL if not, a single elemnt will
-        be put in the vector.
 
-    @param component_confidences Not applicable for word spotting can be be NULL if not, a single elemnt will
-        be put in the vector.
+
+    @param component_confidences A vector of float returns confidence of text bounding boxes
 
     @param component_level must be OCR_LEVEL_WORD.
      */
@@ -155,76 +112,43 @@ public:
 
 
     /**
-    @brief Method that provides a quick and simple interface to a single word image classifcation
+    @brief Method that provides a quick and simple interface to detect text inside an image
 
-    @param inputImage an image expected to be a CV_U8C1 or CV_U8C3 of any size
+    @param inputImage an image expected to be a CV_U8C3 of any size
 
-    @param transcription an opencv string that will store the detected word transcription
+    @param Bbox a vector of Rect that will store the detected word bounding box
 
-    @param confidence a double that will be updated with the confidence the classifier has for the selected word
+    @param confidence a vector of float that will be updated with the confidence the classifier has for the selected bounding box
     */
     CV_WRAP virtual void textDetectInImage(InputArray inputImage,CV_OUT std::vector<Rect>& Bbox,CV_OUT std::vector<float>& confidence)=0;
 
-    /**
-    @brief Method that provides a quick and simple interface to a multiple word image classifcation taking advantage
-    the classifiers parallel capabilities.
-
-    @param inputImageList an list of images expected to be a CV_U8C1 or CV_U8C3 each image can be of any size and is assumed
-    to contain a single word.
 
-    @param transcriptions a vector of opencv strings that will store the detected word transcriptions, one for each
-    input image
-
-    @param confidences a vector of double that will be updated with the confidence the classifier has for each of the
-    selected words.
-    */
-    //CV_WRAP virtual void recogniseImageBatch(InputArrayOfArrays inputImageList,CV_OUT std::vector<String>& transcriptions,CV_OUT std::vector<double>& confidences)=0;
 
 
    /** @brief simple getter for the preprocessing functor
      */
     CV_WRAP virtual Ptr<TextImageClassifier> getClassifier()=0;
 
-    /** @brief Creates an instance of the OCRHolisticWordRecognizer class.
+    /** @brief Creates an instance of the textDetector class.
 
     @param classifierPtr an instance of TextImageClassifier, normaly a DeepCNN instance
 
-    @param vocabularyFilename the relative or absolute path to the file containing all words in the vocabulary. Each text line
-    in the file is assumed to be a single word. The number of words in the vocabulary must be exactly the same as the outputSize
-    of the classifier.
+
      */
     CV_WRAP static Ptr<textDetector> create(Ptr<TextImageClassifier> classifierPtr);
 
 
-    /** @brief Creates an instance of the OCRHolisticWordRecognizer class and implicitly also a DeepCNN classifier.
+    /** @brief Creates an instance of the textDetector class and implicitly also a DeepCNN classifier.
 
     @param modelArchFilename the relative or absolute path to the prototxt file describing the classifiers architecture.
 
     @param modelWeightsFilename the relative or absolute path to the file containing the pretrained weights of the model in caffe-binary form.
 
-    @param vocabularyFilename the relative or absolute path to the file containing all words in the vocabulary. Each text line
-    in the file is assumed to be a single word. The number of words in the vocabulary must be exactly the same as the outputSize
-    of the classifier.
+
     */
     CV_WRAP static Ptr<textDetector> create(String modelArchFilename, String modelWeightsFilename);
 
-    /** @brief
-     *
-     * @param classifierPtr
-     *
-     * @param vocabulary
-     */
- //   CV_WRAP static Ptr<textDetectImage> create(Ptr<TextImageClassifier> classifierPtr,const std::vector<String>& vocabulary);
-
-    /** @brief
-     *
-     * @param modelArchFilename
-     *
-     * @param modelWeightsFilename
-     *
-     * @param vocabulary
-     */
- //   CV_WRAP static Ptr<textDetectImage> create (String modelArchFilename, String modelWeightsFilename, const std::vector<String>& vocabulary);
+
 };