Nomeroff-net

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**training**
* Re-train mask-rcnn model.

**bugfix**
* Fix rounding bug in RectDetect

**tools**
* Add Mask RCCN dataset tools to auto-nomer-tool

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**features**
* Added experimental support for recognition of Kazakhstan (kz) 2 line box numbers. Recognition Accuracy 95%.

**training**
* Re-train Kazakhstan (kz) numbers recognition model. Get Recognition Accuracy 94%.
* Re-train options numbers classification model with ["xx_unknown", "eu_ua_2015", "eu_ua_2004", "eu_ua_1995", "eu", "xx_transit", "ru", "kz", "kz_box"] classes output. Get Classification Accuracy 99,9%.
* Set simplified convolutional network architecture for numberplate classification by default.

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**features**
* RectDetector: A new perspective distortion correction mechanism has been added, which more accurately positions the number frame. It is activated using the "fixGeometry" parameter, fixGeometry = true
* Added experimental support for recognition of Kazakhstan (kz) numbers. Recognition Accuracy 91%

**training**
* Added a simplified convolutional network architecture for numberplate classification. To train a simplified model, pass the cnn == "simple" to the train method.

**bugfix**
* Fixed a critical bug in a RectDetector that could lead to python sticking

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**features**
* Added CPU and GPU docker files.
* Added ru region detection in license plate classification.
* Added ocr russian number plate detector.

**training**
* Update augmentation(use module imgaug).
* Added freeze model graph and use .pb models in prediction.

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**features**
* OCR: [GRU-network](https://github.com/ria-com/nomeroff-net/blob/master/docs/OCR.md)
trained on Ukrainian and European license plates are used instead of tesseract).
* Implemented batch processing of multiple images.
* The license plate classification model has been improved.
Now, a single pass classification has become possible according to different criteria:
by type of the license plate and by characteristic are painted / not painted.

**optimizations**
* Implemented asynchronous versions of the set of methods, which gives a performance increase of up to 10%.
* Optimized code for use on Nvidia GPUs.

**training**
* A small [nodejs admin panel](https://github.com/ria-com/nomeroff-net/blob/master/moderation/README.md) was created, with which you can prepare your dataset
for license plate classification or OCR text detection tasks.
* Prepare example script for [OCR train](https://github.com/ria-com/nomeroff-net/blob/master/train/trainOcrTextDetectorExample.ipynb).
* Prepare example script for [Options Classification](https://github.com/ria-com/nomeroff-net/blob/master/train/trainOptionDetectorExample.ipynb).
* Added numberplate [MaskRCNN](https://github.com/ria-com/nomeroff-net/blob/master/train/mrcnnTrainExample.ipynb) example script.

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**features**
* Add online demo numberplate recognition https://nomeroff.net.ua/onlinedemo.html