def test__given_img__when_read__then_returns_correct_numpy_array(
extension, expected_array):
img_path = IMG_DIR / f'einstein.{extension}'
actual_array = read_img(img_path)
assert actual_array.shape == expected_array.shape
assert numpy.allclose(actual_array, expected_array, atol=20, rtol=20)
def test__given_rgba_img__when_read__then_returns_rgb_img(mocker):
array = np.ones(shape=(10, 10, 4))
imageio = mocker.patch(IMAGEIO)
imageio.imread.return_value = array
actual_img = read_img(MOCK)
expected_img = np.ones(shape=(10, 10, 3))
assert (actual_img == expected_img).all()
def find_faces_post():
detector = managers.plugin_manager.detector
face_plugins = managers.plugin_manager.filter_face_plugins(
_get_face_plugin_names())
faces = detector(img=read_img(request.files['file']),
det_prob_threshold=_get_det_prob_threshold(),
face_plugins=face_plugins)
plugins_versions = {p.slug: str(p) for p in [detector] + face_plugins}
faces = _limit(faces, request.values.get(ARG.LIMIT))
return jsonify(plugins_versions=plugins_versions, result=faces)
def scan_faces_post():
from flask import request
img = read_img(request.files['file'])
limit_faces = _get_limit_faces_fun(request.values.get(ARG.LIMIT))
scanner: FaceScanner = get_scanner()
det_prob_threshold = _get_det_prob_threshold(request)
scanned_faces = scanner.scan(img, det_prob_threshold)
return jsonify(calculator_version=get_scanner().ID,
result=limit_faces(_at_least_one_face(scanned_faces)))
def test__given_truncated_img__when_read__then_does_not_crash():
img_path = IMG_DIR / 'truncated.jpg'
actual_array = read_img(img_path)
assert actual_array.shape == (225, 225, 3)
def act():
read_img(IMG_DIR / file)
def act():
read_img(MOCK)
def _scan_faces_local(img_name):
img = read_img(IMG_DIR / img_name)
return scanner.scan(img)
else:
logger.info(
f"Found all {len(noses)} face{s(len(noses))} in correct places for '{img_name}'"
)
else:
logging.warning(f"Image '{img_name}' is not annotated, skipping")
return error_count
if __name__ == '__main__':
init_runtime(logging_level=LOGGING_LEVEL)
logger.info(ENV.to_json() if ENV_MAIN.IS_DEV_ENV else ENV.to_str())
total_error_count = 0
for img_name in ENV.IMG_NAMES:
boxes = [face.box for face in _scan_faces(img_name)]
noses = name_2_annotation.get(img_name)
error_count = _calculate_errors(boxes, noses, img_name)
total_error_count += error_count
if SAVE_IMG or SAVE_IMG_ON_ERROR and error_count:
img = read_img(IMG_DIR / img_name)
save_img(img, boxes, noses, img_name)
if total_error_count:
logger.error(
f"Found a total of {total_error_count} error{s(total_error_count)}"
)
exit(1)
def _scan_faces_local(scanner_id, img_name):
img = read_img(IMG_DIR / img_name)
scanner: FaceScanner = id_2_face_scanner_cls[scanner_id]()
return scanner.scan(img)
def _get_image(img_name):
image_path = TMP_DIR / 'originalPics' / Path(f'{img_name}.jpg')
return read_img(image_path) if not ENV.DRY_RUN else np.zeros((1, 1, 3))
def scan_faces_post():
faces = scanner.scan(img=read_img(request.files['file']),
det_prob_threshold=_get_det_prob_threshold())
faces = _limit(faces, request.values.get(ARG.LIMIT))
return jsonify(calculator_version=scanner.ID, result=faces)
def scan(img_path, *args, **kwargs):
img = read_img(img_path)
return scanner.scan_(img, *args, **kwargs)