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                Horngurke, Ananas und Sternfrucht
                 Kiwano, Pineapple and Carambola

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The project was created in XCode, uses both opencv for iOS and for Linux/Mac.

In order to reach better performance, project was divided into two subprojects. The main idea behind this choice was to save time while training data, which shouldn't be done on the fly, in a mobile device with lower performance. So that it was divided to two steps, in first step, training data is trained and information is saved to a file. In second step, while running applicaiton on an iPhone, device only need to load this file in order to be trained.

1) Trainer application for Mac

	* trainer/main.cpp file contains the whole code.

	Reads given train data and trains it with BOWKMeansTrainer and 
	extracts  descriptors  with BOWImgDescriptorExtractor. As soon 
	as training is done, application will save the state to a file.

2) Classifier application for iOS

	* Classifier_ios/OpencvClassifier.cpp contains the main opencv part
	
	While  classifying first  image,  the application will load the 
	saved BOWKMeansTrainer and BOWImgDescriptorExtractor. This step
	needs to be executed only once.  Application  immediately tries
	to classify given image and write the prediction. 

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OpenCV's features extractor, descriptors and classifiers used:

FlannBased DescriptorMatcher and SURF DescriptorExtractor were used in BOWImgDescriptorExtractor, in order to perform "BagOfWords" based image classification.

SURF FeatureDetector was also used to detect features.

Train images were described with BOWImgDescriptorExtractor and trained with BOWKMeansTrainer.

At the end NormalBayesClassifier was trained, in order to be able to predict classes of given images.

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