Face-recognition

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* You can now pass model="small" to face_landmarks() to use the 5-point face model instead of the 68-point model.
* Now officially supporting Python 3.7
* New example of using this library in a Jupyter Notebook

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* Added the face_detection CLI command
* Removed dependencies on scipy to make installation easier
* Cleaned up KNN example and fixed a bug with drawing fonts to label detected faces in the demo

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* Fixed version numbering inside of module code.

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* Fixed a bug where batch size parameter didn't work correctly when doing batch face detections on GPU.
* Updated OpenCV examples to do proper BGR -> RGB conversion
* Updated webcam examples to avoid common mistakes and reduce support questions
* Added a KNN classification example
* Added an example of automatically blurring faces in images or videos
* Updated Dockerfile example to use dlib v19.9 which removes the boost dependency.

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* Will use dlib's 5-point face pose estimator when possible for speed (instead of 68-point face pose esimator)
* dlib v19.7 is now the minimum required version
* face_recognition_models v0.3.0 is now the minimum required version

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* Added support for dlib's CNN face detection model via model="cnn" parameter on face detecion call
* Added support for GPU batched face detections using dlib's CNN face detector model
* Added find_faces_in_picture_cnn.py to examples
* Added find_faces_in_batches.py to examples
* Added face_rec_from_video_file.py to examples
* dlib v19.5 is now the minimum required version
* face_recognition_models v0.2.0 is now the minimum required version