First things first, the documentation [here](https://learn.microsoft.com/en-us/python/api/azure-cognitiveservices-vision-face/azure.cognitiveservices.vision.face.operations.persongrouppersonoperations?view=azure-python) says "JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size is from 1KB to 6MB."

I am sending a .jpg that is ~1.4 MB
In my search, others who had this issue were custom forming packets and ran into issues chunk transfering images.
however unlike the [others](https://stackoverflow.com/questions/51433688/invalidimagesize-message-image-size-is-too-small) I am not forming my own API call, just passing a jpg to the python sdk.
What is going wrong/what am I missing?

The error is:

```
getting image, start time
opening image:  2019_11_30_18_40_21.jpg
time elapsed for capturing image: 8.007975816726685
time elapsed for detecting image: 0.0017137527465820312
appending face found in image
identifying face
time elapsed for identifying image: 0.8008027076721191
Person for face ID e7b2c3fe-6a62-471f-8371-8c1e96608362 is identified in 2019_11_30_18_40_21.jpg with a confidence of 0.68515.
Traceback (most recent call last):
File "./GreeterCam_V0.1 - testing.py", line 116, in <module>
face_client.person_group_person.add_face_from_stream(PERSON_GROUP_ID, face.candidates[0].person_id, image)
File "/home/pi/.local/lib/python3.7/site-packages/azure/cognitiveservices/vision/face/operations/_person_group_person_operations.py", line 785, in add_face_from_stream
raise models.APIErrorException(self._deserialize, response)
azure.cognitiveservices.vision.face.models._models_py3.APIErrorException: (InvalidImageSize) Image size is too small.  

```

my source code is:

```
if __name__ == '__main__':
    FRAMES_PER_SECOND = 0.13
    ENDPOINT = os.environ['COGNITIVE_SERVICE_ENDPOINT']
    KEY = os.environ['COGNITIVE_SERVICE_KEY']
    face_client = FaceClient(ENDPOINT, CognitiveServicesCredentials(KEY))
    PERSON_GROUP_ID = 'my-unique-person-group'
    #IMAGES_FOLDER = os.path.join(os.path.dirname(os.path.realpath(__file__)))
    #camera = PiCamera()
    #camera.start_preview()
    test_images = [file for file in glob.glob('*.jpg')]
    #webcam = cv2.VideoCapture(0)
    while(True):
        start_time = time.time()
        print('getting image, start time')
        for image_name in test_images:
            image = open(image_name, 'r+b')
            print("opening image: ", image_name)
            time.sleep(5)
            faces = face_client.face.detect_with_stream(image)     
            #image = open(os.path.join(IMAGES_FOLDER, imageName), 'r+b')
            face_ids = []
            time1 = time.time()
            print('time elapsed for capturing image: ' + str(time1-start_time))
            # detect faces in image

            time2 = time.time()
            print('time elapsed for detecting image: ' + str(time2-time1))
            for face in faces:
                print('appending face found in image')
                face_ids.append(face.face_id)
            if face_ids:
                print('identifying face')
                # if there are faces, identify person matching face
                results = face_client.face.identify(face_ids, PERSON_GROUP_ID)
                time3 = time.time()
                print('time elapsed for identifying image: ' + str(time3-time2))
                name = 'person-created-' + str(time.strftime("%Y_%m_%d_%H_%M_%S"))
                if not results:
                    #if there are no matching persons, make a new person and add face
                    print('No person in the person group for faces from {}.'.format(imageName))
                    new_person = face_client.person_group_person.create(PERSON_GROUP_ID, name)
                    face_client.person_group_person.add_face_from_stream(PERSON_GROUP_ID, new_person.person_id, image)
                    time4 = time.time()
                    print('time elapsed for creating new person: ' + str(time4-time3))
                    print('New Person Created: {}'.format(new_person.person_id))
                for face in results:
                    if not face.candidates:
                        new_person = face_client.person_group_person.create(PERSON_GROUP_ID, name)
                        face_client.person_group_person.add_face_from_stream(PERSON_GROUP_ID, new_person.person_id, image)
                    else:
                        #add face to person if match was found
                        print('Person for face ID {} is identified in {} with a confidence of {}.'.format(face.face_id, os.path.basename(image.name), face.candidates[0].confidence)) # Get topmost confidence score
                        face_client.person_group_person.add_face_from_stream(PERSON_GROUP_ID, face.candidates[0].person_id, image)
                        time4 = time.time()
                        print('time elapsed for creating new person: ' + str(time4-time3))   

```

Also this is on Raspbian on a pi 3B(+?)