def usage_demo():
print('-'*88)
print("Welcome to the Amazon Rekognition face collection demo!")
print('-'*88)
logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
rekognition_client = boto3.client('rekognition')
images = [
RekognitionImage.from_file(
'.media/pexels-agung-pandit-wiguna-1128316.jpg', rekognition_client,
image_name='sitting'),
RekognitionImage.from_file(
'.media/pexels-agung-pandit-wiguna-1128317.jpg', rekognition_client,
image_name='hopping'),
RekognitionImage.from_file(
'.media/pexels-agung-pandit-wiguna-1128318.jpg', rekognition_client,
image_name='biking')]
collection_mgr = RekognitionCollectionManager(rekognition_client)
collection = collection_mgr.create_collection('doc-example-collection-demo')
print(f"Created collection {collection.collection_id}:")
pprint(collection.describe_collection())
print("Indexing faces from three images:")
for image in images:
collection.index_faces(image, 10)
print("Listing faces in collection:")
faces = collection.list_faces(10)
for face in faces:
pprint(face.to_dict())
input("Press Enter to continue.")
print(f"Searching for faces in the collection that match the first face in the "
f"list (Face ID: {faces[0].face_id}.")
found_faces = collection.search_faces(faces[0].face_id, 80, 10)
print(f"Found {len(found_faces)} matching faces.")
for face in found_faces:
pprint(face.to_dict())
input("Press Enter to continue.")
print(f"Searching for faces in the collection that match the largest face in "
f"{images[0].image_name}.")
image_face, match_faces = collection.search_faces_by_image(images[0], 80, 10)
print(f"The largest face in {images[0].image_name} is:")
pprint(image_face.to_dict())
print(f"Found {len(match_faces)} matching faces.")
for face in match_faces:
pprint(face.to_dict())
input("Press Enter to continue.")
collection.delete_collection()
print('Thanks for watching!')
print('-'*88)
def test_index_faces(make_stubber, make_faces, error_code):
rekognition_client = boto3.client('rekognition')
rekognition_stubber = make_stubber(rekognition_client)
image = RekognitionImage(TEST_IMAGE, 'test-image', rekognition_client)
max_faces = 3
indexed_faces = [
RekognitionFace(face)
for face in make_faces(3, has_details=True, is_index=True)
]
unindexed_faces = [RekognitionFace(face) for face in make_faces(4)]
collection = make_collection(rekognition_client)
rekognition_stubber.stub_index_faces(collection.collection_id,
image,
max_faces,
indexed_faces,
unindexed_faces,
error_code=error_code)
if error_code is None:
got_indexed_faces, got_unindexed_faces = collection.index_faces(
image, max_faces)
assert ([face.to_dict() for face in indexed_faces
] == [face.to_dict() for face in got_indexed_faces])
assert ([face.to_dict() for face in unindexed_faces
] == [face.to_dict() for face in got_unindexed_faces])
else:
with pytest.raises(ClientError) as exc_info:
collection.index_faces(image, max_faces)
assert exc_info.value.response['Error']['Code'] == error_code
def test_search_face_by_image(make_stubber, make_faces, error_code):
rekognition_client = boto3.client('rekognition')
rekognition_stubber = make_stubber(rekognition_client)
collection = make_collection(rekognition_client)
image = RekognitionImage(TEST_IMAGE, 'test-image', rekognition_client)
threshold = 80
max_faces = 3
image_face = RekognitionFace(make_faces(1)[0])
faces = [
RekognitionFace({
'FaceIndex': f'face-{index}',
'ImageIndex': f'image-{index}'
}) for index in range(0, 3)
]
rekognition_stubber.stub_search_faces_by_image(collection.collection_id,
image,
threshold,
max_faces,
image_face,
faces,
error_code=error_code)
if error_code is None:
got_image_face, got_faces = collection.search_faces_by_image(
image, threshold, max_faces)
assert image_face.to_dict() == got_image_face.to_dict()
assert ([face.to_dict()
for face in faces] == [face.to_dict() for face in got_faces])
else:
with pytest.raises(ClientError) as exc_info:
collection.search_faces_by_image(image, threshold, max_faces)
assert exc_info.value.response['Error']['Code'] == error_code
def test_detect_faces(make_stubber, make_faces, error_code):
rekognition_client = boto3.client('rekognition')
rekognition_stubber = make_stubber(rekognition_client)
image = RekognitionImage(TEST_IMAGE, 'test-image', rekognition_client)
faces = [RekognitionFace(face) for face in make_faces(3, True)]
rekognition_stubber.stub_detect_faces(image.image,
faces,
error_code=error_code)
if error_code is None:
got_faces = image.detect_faces()
assert ([face.to_dict()
for face in faces] == [face.to_dict() for face in got_faces])
else:
with pytest.raises(ClientError) as exc_info:
image.detect_faces()
assert exc_info.value.response['Error']['Code'] == error_code
def test_detect_text(make_stubber, make_texts, error_code):
rekognition_client = boto3.client('rekognition')
rekognition_stubber = make_stubber(rekognition_client)
image = RekognitionImage(TEST_IMAGE, 'test-image', rekognition_client)
texts = [RekognitionText(text) for text in make_texts(3)]
rekognition_stubber.stub_detect_text(image.image,
texts,
error_code=error_code)
if error_code is None:
got_texts = image.detect_text()
assert ([text.to_dict()
for text in texts] == [text.to_dict() for text in got_texts])
else:
with pytest.raises(ClientError) as exc_info:
image.detect_text()
assert exc_info.value.response['Error']['Code'] == error_code
def test_detect_labels(make_stubber, make_labels, error_code):
rekognition_client = boto3.client('rekognition')
rekognition_stubber = make_stubber(rekognition_client)
image = RekognitionImage(TEST_IMAGE, 'test-image', rekognition_client)
labels = [RekognitionLabel(label) for label in make_labels(3)]
max_labels = 3
rekognition_stubber.stub_detect_labels(image.image,
max_labels,
labels,
error_code=error_code)
if error_code is None:
got_labels = image.detect_labels(max_labels)
assert ([label.to_dict() for label in labels
] == [label.to_dict() for label in got_labels])
else:
with pytest.raises(ClientError) as exc_info:
image.detect_labels(max_labels)
assert exc_info.value.response['Error']['Code'] == error_code
def test_compare_faces(make_stubber, make_faces, error_code):
rekognition_client = boto3.client('rekognition')
rekognition_stubber = make_stubber(rekognition_client)
source_image = RekognitionImage(TEST_IMAGE, 'source-image',
rekognition_client)
target_image = RekognitionImage(TEST_IMAGE, 'target-image',
rekognition_client)
matches = [RekognitionFace(face) for face in make_faces(1)]
unmatches = [RekognitionFace(face) for face in make_faces(2)]
similarity = 80
rekognition_stubber.stub_compare_faces(source_image.image,
target_image.image,
similarity,
matches,
unmatches,
error_code=error_code)
if error_code is None:
got_matches, got_unmatches = source_image.compare_faces(
target_image, similarity)
assert ([face.to_dict() for face in matches
] == [face.to_dict() for face in got_matches])
assert ([face.to_dict() for face in unmatches
] == [face.to_dict() for face in got_unmatches])
else:
with pytest.raises(ClientError) as exc_info:
source_image.compare_faces(target_image, similarity)
assert exc_info.value.response['Error']['Code'] == error_code
def test_recognize_celebrities(make_stubber, make_faces, error_code):
rekognition_client = boto3.client('rekognition')
rekognition_stubber = make_stubber(rekognition_client)
image = RekognitionImage(TEST_IMAGE, 'test-image', rekognition_client)
celebrities = [
RekognitionCelebrity(face) for face in make_faces(3, is_celebrity=True)
]
normals = [RekognitionFace(face) for face in make_faces(2)]
rekognition_stubber.stub_recognize_celebrities(image.image,
celebrities,
normals,
error_code=error_code)
if error_code is None:
got_celebrities, got_normals = image.recognize_celebrities()
assert ([celeb.to_dict() for celeb in celebrities
] == [celeb.to_dict() for celeb in got_celebrities])
assert ([normal.to_dict() for normal in normals
] == [normal.to_dict() for normal in got_normals])
else:
with pytest.raises(ClientError) as exc_info:
image.recognize_celebrities()
assert exc_info.value.response['Error']['Code'] == error_code