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sghoshcvc 8 years ago
parent fc9c41b8d4
commit e494efb4b0
2 changed files with 28 additions and 90 deletions
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  1. 14
      modules/text/include/opencv2/text/ocr.hpp
  2. 104
      modules/text/include/opencv2/text/textDetector.hpp

14
modules/text/include/opencv2/text/ocr.hpp
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@ -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");

104
modules/text/include/opencv2/text/textDetector.hpp
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@ -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);
};

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