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GSOC17 - Facemark API (#1257) * Initial commit of facemark API Initial structure of the facemark API and AAM header * make training function as virtual * Add: dataset parser * Bug fix: clear the container before add points * Add: AAM training - procrustes analysis * Add AAM model * Added training function for AAM * Building bot fixes: remove training overload, explicit cast to float for atof * + add dependency: imgcodecs * Build bot fixes: add imgproc.hpp and type casting * Building bot fix: type casting * fixing the AAM training to match with Matlab version fewer model parameters, change the image warp method, change the feature extraction method * add: AAM fitting added several functionalities for fitting * fix warings * Add: transformation for the initial fitting * add sample file for aam implementation * fix warning * Add LFB Header * loadTrainingData: Throw an error message if file not exist * add: LBF prepare training data * add: data augmentation * change to double * add: getMeanShape * shuffling the dataset and parameters initialization * add: initial structure of LBF class * add: getDeltaShapes Difference between the current shape and the desired shape * add: random forest training * generate lbf features * global regression * save training data * fix the parameter initialization * set the default parameters * add: initial version of lbf sample * update the current shape * compute error * add: prediction function * fix some warnings * fitting function the result is mis-aligned, shuould be double checked * add: fitting in the demo * add dependencies * Add: tutorial * add: load model * fixing training * use user defined face detector * Documents, tests, and samples * Allow custom parameters * Cleaning up * Custom parameters for default detector, training, and get custom data * AAM scales * minor fixes , update the opencv_extra files * change path to lbp cascade * face: avoid memory leaks * utilize the filestorage for the model, fixing some minor issues * remove the liblinear dependency * fix the aam test, avoiding to write any files * use RNG and changes the test files