Use CV to grab iPhone mirrored on screen to get screenshots and then send to Rekognition using the boto library to train engine.

Rekognition.py

#----------------------------------------------------------------------------_
# This script can be ran in 2 modes:
#    python play.py train
# or
#    python play.py Run
#
# Use the training mode to train the AI.
# Once the AI has been trained, then run it in the play mode. The matching screenshot
# from the learning mode will be displayed on screen.
#-----------------------------------------------------------------------------

import numpy as np
from PIL import ImageGrab
import cv2
import time
import boto3
import re
import sys

BUCKET_NAME = "somebucket"
mode = 'train'  # modes can be train or run
NUM_IMAGES = 9    # Learning Games has 9 tiles

#-----------------------------------------------------------------------------
# Train: upload image to S3
#-----------------------------------------------------------------------------
def screen_record():
    last_time = time.time()
    fileNameCounter = 11
    counter = 1

    while(counter <= NUM_IMAGES):
        img = ImageGrab.grab(bbox=(0,0,720,1280))
        imageName = str(counter)+'.png'

        printscreen =  np.array(img)

        if (mode == 'train'):

            img.save('./images/' + imageName)

            s3 = boto3.client('s3')
            s3.upload_file('./images/'+imageName, BUCKET_NAME, imageName, ExtraArgs={'ACL':'public-read'})

            #index
            for record in index_faces(BUCKET_NAME, imageName, 'emp__photos',imageName):
            	face = record['Face']
            	# details = record['FaceDetail']
            	#print ("Face " + str(face['Confidence']))
            	#print ("  FaceId: " + face['FaceId'])
            	#print ("  ImageId: " + face['ImageId'])


            #print("Uploaded " + str(counter)+ ".png to S3")



        elif (mode == 'run'):
            # send image to recognition
            img.save('./images/matchThis.png')
            s3 = boto3.client('s3')
            s3.upload_file('./images/matchThis.png', BUCKET_NAME, 'matchThis.png', ExtraArgs={'ACL':'public-read'})

            face_recog(printscreen)

        print(str(counter) + ' - Processing took {} seconds'.format(time.time()-last_time))
        last_time = time.time()
        #cv2.imshow('window',cv2.cvtColor(printscreen, cv2.COLOR_BGR2RGB))
        if cv2.waitKey(25) & 0xFF == ord('q'):
            cv2.destroyAllWindows()
            break

        counter += 1
        fileNameCounter += 1



#-----------------------------------------------------------------------------
# Index faces using Boto
#-----------------------------------------------------------------------------
def index_faces(bucket, key, collection_id, image_id):
	rekognition = boto3.client("rekognition")
	response = rekognition.index_faces(
		Image={
			"S3Object": {
				"Bucket": bucket,
				"Name": key,
			}
		},
		CollectionId=collection_id,
		ExternalImageId=image_id,
	)
	return response['FaceRecords']


#-----------------------------------------------------------------------------
# Run:  Ask rekognition to match the face
#-----------------------------------------------------------------------------
def face_recog(img):
    #cv2.imwrite('./face_recog.jpg', img)

    with open("./images/MatchThis.png", "rb") as imageFile:
        f = imageFile.read()
        buf = bytearray(f)

    client = boto3.client('rekognition')
    response = client.search_faces_by_image(
        CollectionId='emp__photos',
        Image={
            'Bytes': buf
        }
        #MaxFaces=1,
        #FaceMatchThreshold=80
    )
    #print (response)
    if len(response['FaceMatches']) == 0:
        print ("FaceMatches = 0")
        return False
    else:
        res = response['FaceMatches'][0]['Face']['ExternalImageId']
        matchObj = re.match( r'(.*)_([0-9]+).jpg', res, re.M|re.I)
        if matchObj:
            print ("matchObj.group(1) " + matchObj.group(1))
            return matchObj.group(1)
        else:
            print ("Matched this image file that was trained: " + res)
            img = cv2.imread('./images/'+res)
            #cv2.imshow('window',cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
            cv2.imshow('image',img)
            return res

#-------------------------------------------------------
# Main
#-------------------------------------------------------
if __name__ == "__main__":
    # print command line arguments
    for arg in sys.argv[1:]:
        if arg == 'train':
            mode = 'train'
        elif (arg == 'run'):
            mode = 'run'

    print ("Mode: "+ mode)
    screen_record()