Face alignment (#1199)
* This commit will add a new functionality of one millisecond face_alignment to OpenCV.
Face alignment is a computer vision technology for identifying the geometric structure of human faces in digital images.
Given the location and size of a face, it automatically determines the shape
of the face components such as eyes and nose.
Added following functions :
1) Application to train a face landmark detector.
2) Application to detect face landmarks using a trained model.
3) Application to swap faces using face landmark detection
4) Application to detect landmarks in a video.
Merged the code with a global facemark API.
Added Doxygen Documentation for the Class created.
Added tutorials for the samples added.
Added visualisations depicting error rate and training time.
Made desired changes
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* face: drop duplicated file
-face_alignmentImpl.hpp
+face_alignmentimpl.hpp
* face: minor refactoring
- replace license headers
- fix usage of "precomp.hpp"
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
predict method rewritten to use with PredictCollector interface (abstract class)
now face recognizer allow to perform different scenarios of result handling
Brian Wignall
Krutika Bapat
berak
Alexander Alekhin
sukhad-app
kurnianggoro
Vladislav Sovrasov
Yuki Furuta
Maksim Shabunin
comdiv
Vadim Pisarevsky
Vlad Shakhuro