MVP

Write an MVP that basically integrates with ONE FR core backend and not yet a real db - probably AWS-specific, and implements basic image and person only functionality. A simple Python API will suffice, running locally or in EC2 - future versions could go in Lambda or Beanstalk.

API spec:
```python
class FLDB:
def __init__(self, aws_obj, recognition_coll, bucket_name, create=False, initial_data=None):
# connect up to backed
# optionally create them collection
# optionally load pickled data
def add_person(name):
return person
def get_person(name):
return person
def add_image(url):
image = Image(url)
faces.append = image.get_faces()
return image
def get_image(url):
def delete_all()
def export_db()
def import_db(initial_data)

```
FLDB_data:
- Images
- Faces
- Persons

Image:
- init(url, rekog)
- get_url(s3url)
- get_faces (actually run rekog and cache IndexImage and create new Faces objs)

Person:
- set/get_name/id
- set_faces(confirmed, denied)
- get_faces(threshold, breadth, depth):
- 1 = confirmed
- ~~0 = denied~~
- ~~breadth = check x # of confirmed images to find more unconfirmed~~
- ~~depth = check x # deep for each~~
- basically run get_similar on known good, and then remove any known bad
- add_face(face, deny=False):

Face:
- set_rekog()
- get_similar()
- cache SearchFaces
- set/getb_info(rekogoutput)
- set/get_image:
- set/get_person(:
- get_person easy if set - if not, run get_similar and see what happens

Tests
- add bunch of images
- confirm a few
- workflow where one is added with a name in one shot
- who is this (based on image) Image->get_faces->get_person
- workflow where image is added and outputs who it is in one shot
- more of (based on person) Person->get_faces->get_image
- also more of an unknown person Image->get_faces->get_similar->Image

Backends:
- AWS S3
- ~~optional~~
- AWS Rekognition
- mostly IndexFaces and SearchFaces
- ~~AWS DynamoDB~~
- schema TBD