def test_transform_set_select_extract_pad_rejected(self):
with deepstar_path():
video_0001 = os.path.dirname(os.path.realpath(
__file__)) + '/../../support/video_0001.mp4' # noqa
shutil.copyfile(video_0001, VideoFile.path('video_0001.mp4'))
VideoModel().insert('test', 'video_0001.mp4')
DefaultVideoSelectExtractPlugin().video_select_extract(1) # noqa
self.mock_transform_set()
MaxSizeTransformSetSelectExtractPlugin(
).transform_set_select_extract(1, {}) # noqa
transform_model = TransformModel()
transform_model.update(7, rejected=1)
transform_model.update(9, rejected=1)
with mock.patch.dict(os.environ, {'MODEL_LIST_LENGTH': '2'}):
transform_set_id = PadTransformSetSelectExtractPlugin(
).transform_set_select_extract(2, {}) # noqa
self.assertEqual(transform_set_id, 3)
# db
result = TransformSetModel().select(3)
self.assertEqual(result, (3, 'pad', 1, 2))
result = TransformModel().list(3)
self.assertEqual(len(result), 3)
t = list(result[0])
json.loads(t.pop(3))
self.assertEqual(t, [11, 3, 1, 0])
t = list(result[1])
json.loads(t.pop(3))
self.assertEqual(t, [12, 3, 3, 0])
t = list(result[2])
json.loads(t.pop(3))
self.assertEqual(t, [13, 3, 5, 0])
# files
p1 = TransformSetSubDir.path(3)
# transforms
self.assertIsInstance(cv2.imread(TransformFile.path(p1, 11,
'jpg')),
np.ndarray) # noqa
self.assertIsInstance(cv2.imread(TransformFile.path(p1, 12,
'jpg')),
np.ndarray) # noqa
self.assertIsInstance(cv2.imread(TransformFile.path(p1, 13,
'jpg')),
np.ndarray) # noqa
def transform_set_select_curate(self, transform_set_id, opts):
"""
This method automatically curates a transform set and rejects
transforms that are more blurry than the 'max-blur'.
:param int transform_set_id: The transform set ID.
:param dict opts: The dict of options.
:raises: ValueError
:rtype: None
"""
if 'max-blur' not in opts:
raise ValueError(
'The max-blur option is required but was not supplied')
max_blur = float(opts['max-blur'])
transform_model = TransformModel()
length = 100
offset = 0
p1 = TransformSetSubDir.path(transform_set_id)
while True:
transforms = transform_model.list(transform_set_id,
length=length,
offset=offset)
if not transforms:
break
for transform in transforms:
p2 = TransformFile.path(p1, transform[0], 'jpg')
debug(f'Curating transform with ID {transform[0]:08d} at {p2}',
4)
image = cv2.imread(p2)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
h, w = image.shape[:2]
# recommendation to scale down image to ~500
if h > 600 or w > 600:
# imutils.resize preserves aspect ratio.
image = imutils.resize(image, width=500, height=500)
score = cv2.Laplacian(image, cv2.CV_64F).var()
if score < max_blur:
transform_model.update(transform[0], rejected=1)
debug(f'Transform with ID {transform[0]:08d} rejected', 4)
offset += length
def test_transform_set_select_merge_overlay_image_rejected(self):
with deepstar_path():
with mock.patch.dict(os.environ, {'DEBUG_LEVEL': '0'}):
route_handler = VideoCommandLineRouteHandler()
video_0001 = os.path.dirname(os.path.realpath(
__file__)) + '/../../support/video_0001.mp4' # noqa
route_handler.insert_file(video_0001)
route_handler.select_extract([1])
route_handler = FrameSetCommandLineRouteHandler()
route_handler.select_extract([1], 'transform_set', {})
transform_model = TransformModel()
transform_model.update(1, rejected=1)
image_0007 = os.path.dirname(os.path.realpath(
__file__)) + '/../../support/image_0007.png' # noqa
OverlayImageTransformSetSelectMergePlugin(
).transform_set_select_merge([1], {
'image-path': image_0007,
'x1': '0',
'y1': '0'
}) # noqa
# db
result = TransformSetModel().select(2)
self.assertEqual(result, (2, 'overlay_image', None, None))
result = TransformModel().list(2)
self.assertEqual(len(result), 4)
self.assertEqual(result[0], (6, 2, None, None, 0))
self.assertEqual(result[1], (7, 2, None, None, 0))
self.assertEqual(result[2], (8, 2, None, None, 0))
self.assertEqual(result[3], (9, 2, None, None, 0))
# files
p1 = TransformSetSubDir.path(2)
# transforms
self.assertIsInstance(cv2.imread(TransformFile.path(p1, 6, 'jpg')),
np.ndarray) # noqa
self.assertIsInstance(cv2.imread(TransformFile.path(p1, 7, 'jpg')),
np.ndarray) # noqa
self.assertIsInstance(cv2.imread(TransformFile.path(p1, 8, 'jpg')),
np.ndarray) # noqa
self.assertIsInstance(cv2.imread(TransformFile.path(p1, 9, 'jpg')),
np.ndarray) # noqa
def list(self, transform_set_id):
"""
This method lists transforms in the transform collection for a
transform set.
:param int transform_set_id: The transform set ID.
:raises: CommandLineRouteHandlerError
:rtype: None
"""
result = TransformSetModel().select(transform_set_id)
if result is None:
raise CommandLineRouteHandlerError(
f'Transform set with ID {transform_set_id:08d} not found')
transform_model = TransformModel()
count = transform_model.count(transform_set_id)
debug(f'{count} results', 3)
debug(
'id | fk_transform_sets | fk_frames | metadata | rejected | '
'(width | height)', 3)
debug(
'-----------------------------------------------------------'
'----------------', 3)
if count == 0:
return
length = int(os.environ.get('MODEL_LIST_LENGTH', '100'))
offset = 0
p1 = TransformSetSubDir.path(transform_set_id)
while True:
transforms = transform_model.list(transform_set_id,
length=length,
offset=offset)
if not transforms:
break
for transform in transforms:
p2 = TransformFile.path(p1, transform[0], 'jpg')
height, width, _ = cv2.imread(p2).shape
debug(
f'{transform[0]} | {transform[1]} | {transform[2]} | '
f'{transform[3]} | {transform[4]} | ({width} | '
f'{height})', 3)
offset += length
def test_select(self):
with deepstar_path():
FrameSetModel().insert(None)
FrameModel().insert(1, 0)
TransformSetModel().insert('test', 1)
transform_model = TransformModel()
transform_model.insert(1, 1, '{}', 0)
result = transform_model.select(1)
self.assertEqual(result, (1, 1, 1, '{}', 0))
def test_transform_set_select_extract_crop_rejected(self):
with deepstar_path():
with mock.patch.dict(os.environ, {'DEBUG_LEVEL': '0'}):
route_handler = VideoCommandLineRouteHandler()
video_0001 = os.path.dirname(os.path.realpath(
__file__)) + '/../../support/video_0001.mp4' # noqa
route_handler.insert_file(video_0001)
route_handler.select_extract([1])
FrameSetCommandLineRouteHandler().select_extract(
[1], 'transform_set', {}) # noqa
TransformModel().update(5, rejected=1)
CropTransformSetSelectExtractPlugin().transform_set_select_extract(
1, {
'x1': '0',
'y1': '0',
'x2': '50',
'y2': '50'
}) # noqa
# db
result = TransformSetModel().select(2)
self.assertEqual(result, (2, 'crop', 1, 1))
result = TransformModel().list(2)
self.assertEqual(len(result), 4)
self.assertEqual(result[0], (6, 2, 1, None, 0))
self.assertEqual(result[1], (7, 2, 2, None, 0))
self.assertEqual(result[2], (8, 2, 3, None, 0))
self.assertEqual(result[3], (9, 2, 4, None, 0))
# files
p1 = TransformSetSubDir.path(2)
# transforms
self.assertEqual(
cv2.imread(TransformFile.path(p1, 6, 'jpg')).shape[:2],
(50, 50)) # noqa
self.assertEqual(
cv2.imread(TransformFile.path(p1, 7, 'jpg')).shape[:2],
(50, 50)) # noqa
self.assertEqual(
cv2.imread(TransformFile.path(p1, 8, 'jpg')).shape[:2],
(50, 50)) # noqa
self.assertEqual(
cv2.imread(TransformFile.path(p1, 9, 'jpg')).shape[:2],
(50, 50)) # noqa
def test_transform_set_select_extract_max_size_rejected(self):
with deepstar_path():
video_0001 = os.path.dirname(os.path.realpath(
__file__)) + '/../../support/video_0001.mp4' # noqa
shutil.copyfile(video_0001, VideoFile.path('video_0001.mp4'))
VideoModel().insert('test', 'video_0001.mp4')
DefaultVideoSelectExtractPlugin().video_select_extract(1) # noqa
self.mock_transform_set()
transform_model = TransformModel()
transform_model.update(2, rejected=1)
transform_model.update(4, rejected=1)
with mock.patch.dict(os.environ, {'MODEL_LIST_LENGTH': '2'}):
transform_set_id = MaxSizeTransformSetSelectExtractPlugin(
).transform_set_select_extract(1, {}) # noqa
self.assertEqual(transform_set_id, 2)
# db
result = TransformSetModel().select(2)
self.assertEqual(result, (2, 'max_size', 1, 1))
result = TransformModel().list(2)
self.assertEqual(len(result), 3)
t = list(result[0])
json.loads(t.pop(3))
self.assertEqual(t, [6, 2, 1, 0])
t = list(result[1])
json.loads(t.pop(3))
self.assertEqual(t, [7, 2, 3, 0])
t = list(result[2])
json.loads(t.pop(3))
self.assertEqual(t, [8, 2, 5, 0])
# files
p1 = TransformSetSubDir.path(2)
# transforms
self.assertEqual(
cv2.imread(TransformFile.path(p1, 6, 'jpg')).shape[0],
299) # noqa
self.assertEqual(
cv2.imread(TransformFile.path(p1, 7, 'jpg')).shape[0],
299) # noqa
self.assertEqual(
cv2.imread(TransformFile.path(p1, 8, 'jpg')).shape[0],
299) # noqa
def put(self, transform_set_id, transform_id):
transform_model = TransformModel()
result = transform_model.update(
transform_id, rejected=request.get_json()['rejected'])
if result is False:
abort(404)
result = transform_model.select(transform_id)
if result is None:
abort(404)
return jsonify(result)
def _pad(self, transform_set_path, transform_id, frame_id, metadata,
target_set_id, size):
"""
This method pads a transform.
:param str transform_set_path: The transform set path.
:param int transform_id: The transform ID.
:param int frame_id: The frame ID.
:param str metadata: Metadata for the transform.
:param int target_set_id: The new transform set ID.
:param int size: The size to which to pad.
:rtype: None
"""
transform_path = TransformFile().path(transform_set_path, transform_id,
'jpg')
img = cv2.imread(transform_path)
img_height, img_width = img.shape[:2]
img_padded = np.zeros((size, size, 3), dtype=np.uint8)
img_padded[:img_height, :img_width, :] = img.copy()
target_id = TransformModel().insert(target_set_id, frame_id,
json.dumps(metadata), 0)
output_path = TransformFile.path(
TransformSetSubDir.path(target_set_id), target_id, 'jpg')
cv2.imwrite(output_path, img_padded, [cv2.IMWRITE_JPEG_QUALITY, 100])
debug(
f'Transform with ID {target_id:08d} at {output_path} extracted '
f'from transform with ID {transform_id:08d} at {transform_path}',
4)
def transform_set_select_extract(self, transform_set_id, opts):
"""
This method extracts just the mouth from an image of a face.
:param int transform_set_id: The transform set ID.
:rtype: int
"""
self._offset_percent = int(opts.get('offset-percent', 20)) / 100
transform_set_path = TransformSetSubDir.path(transform_set_id)
_, _, frame_set_id, _ = TransformSetModel().select(transform_set_id)
target_set_id = TransformSetModel().insert(f'{self.name}',
frame_set_id,
transform_set_id)
target_path = TransformSetSubDir.path(target_set_id)
os.makedirs(target_path)
result = TransformModel().list(transform_set_id)
for transform_id, _, frame_id, metadata, rejected in result:
if rejected == 1:
continue
metadata = json.loads(metadata)
self._get_mouth(transform_set_path, transform_id, frame_id,
metadata, target_set_id)
return target_set_id
def mock_transform_set(self):
image_0001 = os.path.dirname(os.path.realpath(
__file__)) + '/../../support/image_0001.jpg' # noqa
TransformSetModel().insert('face', 1, None)
p1 = TransformSetSubDir.path(1)
os.mkdir(p1)
transform_model = TransformModel()
for i in range(0, 5):
transform_model.insert(1, i + 1, '{}', 0)
shutil.copy(image_0001, TransformFile.path(p1, i + 1, 'jpg'))
def _resize(self, transform_set_path, transform_id, frame_id, metadata,
target_set_id, max_size):
"""
This method resizes a transform.
:param str transform_set_path: The transform set path.
:param int transform_id: The transform ID.
:param int frame_id: The frame ID.
:param str metadata: Metadata for the transform.
:param int target_set_id: The new transform set ID.
:param int max_size: The max size.
:rtype: None
"""
transform_path = TransformFile().path(transform_set_path, transform_id,
'jpg')
img = cv2.imread(transform_path)
img_height, img_width = img.shape[:2]
if img_height > max_size or img_width > max_size:
if img_height > img_width:
img = imutils.resize(img, height=max_size)
else:
img = imutils.resize(img, width=max_size)
target_id = TransformModel().insert(target_set_id, frame_id,
json.dumps(metadata), 0)
output_path = TransformFile.path(
TransformSetSubDir.path(target_set_id), target_id, 'jpg')
cv2.imwrite(output_path, img, [cv2.IMWRITE_JPEG_QUALITY, 100])
debug(f'Transform with ID {target_id:08d} at {output_path} extracted '
f'from transform with ID {transform_id:08d} at {transform_path}',
4)
def test_fk_transform_sets_fk_frames_constraint(self):
with deepstar_path():
FrameSetModel().insert(None)
TransformSetModel().insert('test', 1)
with self.assertRaises(sqlite3.IntegrityError):
TransformModel().insert(1, 1, '{}', 0)
def frame_set_select_extract(self, frame_set_id, opts):
"""
This method extracts a frame set to a transform set.
:param int frame_set_id: The frame set ID.
:param dict opts: The dict of options.
:rtype: int
"""
transform_set_id = TransformSetModel().insert('transform_set',
frame_set_id)
p1 = TransformSetSubDir.path(transform_set_id)
os.mkdir(p1)
frame_model = FrameModel()
transform_model = TransformModel()
length = int(os.environ.get('MODEL_LIST_LENGTH', '100'))
offset = 0
p2 = FrameSetSubDir.path(frame_set_id)
while True:
frames = frame_model.list(frame_set_id, length=length,
offset=offset, rejected=False)
if not frames:
break
for frame in frames:
transform_id = transform_model.insert(transform_set_id,
frame[0], None, 0)
p3 = FrameFile.path(p2, frame[0], 'jpg')
p4 = TransformFile.path(p1, transform_id, 'jpg')
shutil.copy(p3, p4)
debug(f'Transform with ID {transform_id:08d} at {p4} '
f'extracted from frame with ID {frame[0]:08d} at '
f'{p3}', 4)
offset += length
return transform_set_id
def test_select_extract_face(self):
with deepstar_path():
with mock.patch.dict(os.environ, {'DEBUG_LEVEL': '0'}):
route_handler = VideoCommandLineRouteHandler()
video_0001 = os.path.dirname(os.path.realpath(__file__)) + '/../../support/video_0001.mp4' # noqa
route_handler.insert_file(video_0001)
route_handler.select_extract([1])
args = ['main.py', 'select', 'frame_sets', '1', 'extract', 'face'] # noqa
opts = {}
route_handler = FrameSetCommandLineRouteHandler()
try:
sys.stdout = StringIO()
route_handler.handle(args, opts)
actual = sys.stdout.getvalue().strip()
finally:
sys.stdout = sys.__stdout__
# stdout
self.assertEqual(actual, 'transform_set_id=1, name=face, fk_frame_sets=1, fk_prev_transform_sets=None') # noqa
# db
result = TransformSetModel().select(1)
self.assertEqual(result, (1, 'face', 1, None))
result = TransformModel().list(1)
self.assertEqual(len(result), 5)
t = list(result[0])
json.loads(t.pop(3))
self.assertEqual(t, [1, 1, 1, 0])
t = list(result[1])
json.loads(t.pop(3))
self.assertEqual(t, [2, 1, 2, 0])
t = list(result[2])
json.loads(t.pop(3))
self.assertEqual(t, [3, 1, 3, 0])
t = list(result[3])
json.loads(t.pop(3))
self.assertEqual(t, [4, 1, 4, 0])
t = list(result[4])
json.loads(t.pop(3))
self.assertEqual(t, [5, 1, 5, 0])
# files
p1 = TransformSetSubDir.path(1)
# transforms
self.assertTrue(os.path.isfile(TransformFile.path(p1, 1, 'jpg')))
self.assertTrue(os.path.isfile(TransformFile.path(p1, 2, 'jpg')))
self.assertTrue(os.path.isfile(TransformFile.path(p1, 3, 'jpg')))
self.assertTrue(os.path.isfile(TransformFile.path(p1, 4, 'jpg')))
self.assertTrue(os.path.isfile(TransformFile.path(p1, 5, 'jpg')))
def transform_set_select_curate(self, transform_set_id, opts):
"""
This method automatically curates a transform set and rejects
transforms with width or height less than 'min-size'.
:param int transform_set_id: The transform set ID.
:param dict opts: The dict of options.
:raises: ValueError
:rtype: None
"""
if 'min-size' not in opts:
raise ValueError(
'The min-size option is required but was not supplied')
min_length = int(opts['min-size'])
transform_model = TransformModel()
length = 100
offset = 0
p1 = TransformSetSubDir.path(transform_set_id)
while True:
transforms = transform_model.list(transform_set_id,
length=length,
offset=offset)
if not transforms:
break
for transform in transforms:
p2 = TransformFile.path(p1, transform[0], 'jpg')
debug(f'Curating transform with ID {transform[0]:08d} at {p2}',
4)
h, w = cv2.imread(p2).shape[:2]
if h < min_length or w < min_length:
transform_model.update(transform[0], rejected=1)
debug(f'Transform with ID {transform[0]:08d} rejected', 4)
offset += length
def test_transform_set_select_merge_overlay(self):
with deepstar_path():
with mock.patch.dict(os.environ, {'DEBUG_LEVEL': '0'}):
route_handler = VideoCommandLineRouteHandler()
video_0001 = os.path.dirname(os.path.realpath(
__file__)) + '/../../support/video_0001.mp4' # noqa
route_handler.insert_file(video_0001)
route_handler.select_extract([1])
route_handler = FrameSetCommandLineRouteHandler()
route_handler.select_extract([1], 'transform_set', {})
route_handler.select_extract([1], 'transform_set', {})
OverlayTransformSetSelectMergePlugin().transform_set_select_merge(
[1, 2], {
'x1': '0',
'y1': '0'
}) # noqa
# db
result = TransformSetModel().select(3)
self.assertEqual(result, (3, 'overlay', None, None))
result = TransformModel().list(3)
self.assertEqual(len(result), 5)
self.assertEqual(result[0], (11, 3, None, None, 0))
self.assertEqual(result[1], (12, 3, None, None, 0))
self.assertEqual(result[2], (13, 3, None, None, 0))
self.assertEqual(result[3], (14, 3, None, None, 0))
self.assertEqual(result[4], (15, 3, None, None, 0))
# files
p1 = TransformSetSubDir.path(3)
# transforms
self.assertIsInstance(cv2.imread(TransformFile.path(p1, 11,
'jpg')),
np.ndarray) # noqa
self.assertIsInstance(cv2.imread(TransformFile.path(p1, 12,
'jpg')),
np.ndarray) # noqa
self.assertIsInstance(cv2.imread(TransformFile.path(p1, 13,
'jpg')),
np.ndarray) # noqa
self.assertIsInstance(cv2.imread(TransformFile.path(p1, 14,
'jpg')),
np.ndarray) # noqa
self.assertIsInstance(cv2.imread(TransformFile.path(p1, 15,
'jpg')),
np.ndarray) # noqa
def test_transform_set_select_extract_slice_rejected(self):
with deepstar_path():
with mock.patch.dict(os.environ, {'DEBUG_LEVEL': '0'}):
route_handler = VideoCommandLineRouteHandler()
video_0001 = os.path.dirname(os.path.realpath(
__file__)) + '/../../support/video_0001.mp4' # noqa
route_handler.insert_file(video_0001)
route_handler.select_extract([1])
FrameSetCommandLineRouteHandler().select_extract(
[1], 'transform_set', {}) # noqa
TransformModel().update(4, rejected=1)
TransformModel().update(5, rejected=1)
SliceTransformSetSelectExtractPlugin(
).transform_set_select_extract(1, {
'start': '2',
'end': '4'
}) # noqa
# db
result = TransformSetModel().select(2)
self.assertEqual(result, (2, 'slice', 1, 1))
result = TransformModel().list(2)
self.assertEqual(len(result), 2)
self.assertEqual(result[0], (6, 2, 2, None, 0))
self.assertEqual(result[1], (7, 2, 3, None, 0))
# files
p1 = TransformSetSubDir.path(2)
# transforms
self.assertTrue(os.path.isfile(TransformFile.path(p1, 6, 'jpg')))
self.assertTrue(os.path.isfile(TransformFile.path(p1, 7, 'jpg')))
def test_transform_set_select_extract_resize_height(self):
with deepstar_path():
with mock.patch.dict(os.environ, {'DEBUG_LEVEL': '0'}):
route_handler = VideoCommandLineRouteHandler()
video_0001 = os.path.dirname(os.path.realpath(
__file__)) + '/../../support/video_0001.mp4' # noqa
route_handler.insert_file(video_0001)
route_handler.select_extract([1])
FrameSetCommandLineRouteHandler().select_extract(
[1], 'transform_set', {}) # noqa
ResizeTransformSetSelectExtractPlugin(
).transform_set_select_extract(1, {'height': '100'}) # noqa
# db
result = TransformSetModel().select(2)
self.assertEqual(result, (2, 'resize', 1, 1))
result = TransformModel().list(2)
self.assertEqual(len(result), 5)
self.assertEqual(result[0], (6, 2, 1, None, 0))
self.assertEqual(result[1], (7, 2, 2, None, 0))
self.assertEqual(result[2], (8, 2, 3, None, 0))
self.assertEqual(result[3], (9, 2, 4, None, 0))
self.assertEqual(result[4], (10, 2, 5, None, 0))
# files
p1 = TransformSetSubDir.path(2)
# transforms
self.assertEqual(
cv2.imread(TransformFile.path(p1, 6, 'jpg')).shape[:2],
(100, 177)) # noqa
self.assertEqual(
cv2.imread(TransformFile.path(p1, 7, 'jpg')).shape[:2],
(100, 177)) # noqa
self.assertEqual(
cv2.imread(TransformFile.path(p1, 8, 'jpg')).shape[:2],
(100, 177)) # noqa
self.assertEqual(
cv2.imread(TransformFile.path(p1, 9, 'jpg')).shape[:2],
(100, 177)) # noqa
self.assertEqual(
cv2.imread(TransformFile.path(p1, 10, 'jpg')).shape[:2],
(100, 177)) # noqa
def test_frame_set_select_extract_face(self):
with deepstar_path():
video_0001 = os.path.dirname(os.path.realpath(__file__)) + '/../../support/video_0001.mp4' # noqa
shutil.copyfile(video_0001, VideoFile.path('video_0001.mp4'))
VideoModel().insert('test', 'video_0001.mp4')
DefaultVideoSelectExtractPlugin().video_select_extract(1) # noqa
with mock.patch.dict(os.environ, {'MODEL_LIST_LENGTH': '2'}):
transform_set_id = MTCNNFrameSetSelectExtractPlugin().frame_set_select_extract(1, {}) # noqa
self.assertEqual(transform_set_id, 1)
# db
result = TransformSetModel().select(1)
self.assertEqual(result, (1, 'face', 1, None))
result = TransformModel().list(1)
self.assertEqual(len(result), 5)
t = list(result[0])
json.loads(t.pop(3))
self.assertEqual(t, [1, 1, 1, 0])
t = list(result[1])
json.loads(t.pop(3))
self.assertEqual(t, [2, 1, 2, 0])
t = list(result[2])
json.loads(t.pop(3))
self.assertEqual(t, [3, 1, 3, 0])
t = list(result[3])
json.loads(t.pop(3))
self.assertEqual(t, [4, 1, 4, 0])
t = list(result[4])
json.loads(t.pop(3))
self.assertEqual(t, [5, 1, 5, 0])
# files
p1 = TransformSetSubDir.path(1)
# transforms
self.assertIsInstance(cv2.imread(TransformFile.path(p1, 1, 'jpg')), np.ndarray) # noqa
self.assertIsInstance(cv2.imread(TransformFile.path(p1, 2, 'jpg')), np.ndarray) # noqa
self.assertIsInstance(cv2.imread(TransformFile.path(p1, 3, 'jpg')), np.ndarray) # noqa
self.assertIsInstance(cv2.imread(TransformFile.path(p1, 4, 'jpg')), np.ndarray) # noqa
self.assertIsInstance(cv2.imread(TransformFile.path(p1, 5, 'jpg')), np.ndarray) # noqa
def test_fk_transform_sets_on_delete_cascade(self):
with deepstar_path():
FrameSetModel().insert(None)
FrameModel().insert(1, 0)
transform_set_model = TransformSetModel()
transform_set_model.insert('test', 1)
transform_model = TransformModel()
transform_model.insert(1, 1, '{}', 0)
self.assertEqual(transform_model.list(1), [(1, 1, 1, '{}', 0)])
transform_set_model.delete(1)
self.assertIsNone(transform_model.list(1))
def get(self, transform_set_id):
if 'offset' in request.args:
offset = int(request.args['offset'])
else:
offset = 0
if 'length' in request.args:
length = int(request.args['length'])
else:
length = 100
result = TransformModel().list(transform_set_id,
length=length,
offset=offset)
if result is None:
abort(404)
return jsonify(result)
def test_count_rejected_false(self):
with deepstar_path():
FrameSetModel().insert(None)
frame_model = FrameModel()
frame_model.insert(1, 0)
frame_model.insert(1, 0)
frame_model.insert(1, 0)
TransformSetModel().insert('test', 1)
transform_model = TransformModel()
transform_model.insert(1, 1, '{}', 0)
transform_model.insert(1, 2, '{}', 1)
transform_model.insert(1, 3, '{}', 0)
self.assertEqual(transform_model.count(1, rejected=False), 2)
def test_transform_set_select_extract_adjust_color(self):
with deepstar_path():
with mock.patch.dict(os.environ, {'DEBUG_LEVEL': '0'}):
route_handler = VideoCommandLineRouteHandler()
video_0001 = os.path.dirname(os.path.realpath(
__file__)) + '/../../support/video_0001.mp4' # noqa
route_handler.insert_file(video_0001)
route_handler.select_extract([1])
FrameSetCommandLineRouteHandler().select_extract(
[1], 'transform_set', {}) # noqa
AdjustColorTransformSetSelectExtractPlugin(
).transform_set_select_extract(1, {
'r': '+10',
'g': '-10',
'b': '+10'
}) # noqa
# db
result = TransformSetModel().select(2)
self.assertEqual(result, (2, 'adjust_color', 1, 1))
result = TransformModel().list(2)
self.assertEqual(len(result), 5)
self.assertEqual(result[0], (6, 2, 1, None, 0))
self.assertEqual(result[1], (7, 2, 2, None, 0))
self.assertEqual(result[2], (8, 2, 3, None, 0))
self.assertEqual(result[3], (9, 2, 4, None, 0))
self.assertEqual(result[4], (10, 2, 5, None, 0))
# files
p1 = TransformSetSubDir.path(2)
# transforms
self.assertTrue(os.path.isfile(TransformFile.path(p1, 6, 'jpg')))
self.assertTrue(os.path.isfile(TransformFile.path(p1, 7, 'jpg')))
self.assertTrue(os.path.isfile(TransformFile.path(p1, 8, 'jpg')))
self.assertTrue(os.path.isfile(TransformFile.path(p1, 9, 'jpg')))
self.assertTrue(os.path.isfile(TransformFile.path(p1, 10, 'jpg')))
def test_update(self):
with deepstar_path():
FrameSetModel().insert(None)
FrameModel().insert(1, 0)
TransformSetModel().insert('test', 1)
transform_model = TransformModel()
transform_model.insert(1, 1, 'test1', 0)
result = transform_model.select(1)
self.assertEqual(result, (1, 1, 1, 'test1', 0))
result = transform_model.update(1, 'test2', 1)
self.assertTrue(result)
result = transform_model.select(1)
self.assertEqual(result, (1, 1, 1, 'test2', 1))
def transform_set_select_extract(self, transform_set_id, opts):
"""
This method pads each transform in a transform set.
:param int transform_set_id: The transform set ID.
:param dict opts: The dict of options.
:rtype: int
"""
size = int(opts.get('size', 299))
transform_set_path = TransformSetSubDir.path(transform_set_id)
_, _, frame_set_id, _ = TransformSetModel().select(transform_set_id)
target_set_id = TransformSetModel().insert(f'{self.name}',
frame_set_id,
transform_set_id)
target_path = TransformSetSubDir.path(target_set_id)
os.makedirs(target_path)
length = int(os.environ.get('MODEL_LIST_LENGTH', '100'))
offset = 0
while True:
result = TransformModel().list(transform_set_id,
length=length,
offset=offset,
rejected=False)
if not result:
break
for transform_id, _, frame_id, metadata, rejected in result:
self._pad(transform_set_path, transform_id, frame_id, metadata,
target_set_id, size)
offset += length
return target_set_id
def test_count(self):
with deepstar_path():
FrameSetModel().insert(None)
frame_model = FrameModel()
frame_model.insert(1, 0)
frame_model.insert(1, 0)
frame_model.insert(1, 0)
TransformSetModel().insert('test', 1)
transform_model = TransformModel()
transform_model.insert(1, 1, '{}', 0)
transform_model.insert(1, 2, '{}', 0)
transform_model.insert(1, 3, '{}', 0)
self.assertEqual(frame_model.count(1), 3)
def test_transform_set_select_curate_min_size(self):
with deepstar_path():
video_0001 = os.path.dirname(os.path.realpath(
__file__)) + '/../../support/video_0001.mp4' # noqa
shutil.copyfile(video_0001, VideoFile.path('video_0001.mp4'))
VideoModel().insert('test', 'video_0001.mp4')
DefaultVideoSelectExtractPlugin().video_select_extract(1) # noqa
self.mock_transform_set()
MinSizeTransformSetSelectCuratePlugin(
).transform_set_select_curate(1, {'min-size': '300'}) # noqa
# db
result = TransformSetModel().select(1)
self.assertEqual(result, (1, 'face', 1, None))
result = TransformModel().list(1)
self.assertEqual(len(result), 5)
t = list(result[0])
json.loads(t.pop(3))
self.assertEqual(t, [1, 1, 1, 0])
t = list(result[1])
json.loads(t.pop(3))
self.assertEqual(t, [2, 1, 2, 1])
t = list(result[2])
json.loads(t.pop(3))
self.assertEqual(t, [3, 1, 3, 0])
t = list(result[3])
json.loads(t.pop(3))
self.assertEqual(t, [4, 1, 4, 1])
t = list(result[4])
json.loads(t.pop(3))
self.assertEqual(t, [5, 1, 5, 0])
def test_transform_set_select_extract_pad_size(self):
with deepstar_path():
video_0001 = os.path.dirname(os.path.realpath(
__file__)) + '/../../support/video_0001.mp4' # noqa
shutil.copyfile(video_0001, VideoFile.path('video_0001.mp4'))
VideoModel().insert('test', 'video_0001.mp4')
DefaultVideoSelectExtractPlugin().video_select_extract(1) # noqa
self.mock_transform_set()
MaxSizeTransformSetSelectExtractPlugin(
).transform_set_select_extract(1, {'max-size': '200'}) # noqa
with mock.patch.dict(os.environ, {'MODEL_LIST_LENGTH': '2'}):
PadTransformSetSelectExtractPlugin(
).transform_set_select_extract(2, {'size': '200'}) # noqa
# db
result = TransformSetModel().select(3)
self.assertEqual(result, (3, 'pad', 1, 2))
result = TransformModel().list(3)
self.assertEqual(len(result), 5)
t = list(result[0])
json.loads(t.pop(3))
self.assertEqual(t, [11, 3, 1, 0])
t = list(result[1])
json.loads(t.pop(3))
self.assertEqual(t, [12, 3, 2, 0])
t = list(result[2])
json.loads(t.pop(3))
self.assertEqual(t, [13, 3, 3, 0])
t = list(result[3])
json.loads(t.pop(3))
self.assertEqual(t, [14, 3, 4, 0])
t = list(result[4])
json.loads(t.pop(3))
self.assertEqual(t, [15, 3, 5, 0])
# files
p1 = TransformSetSubDir.path(3)
# transforms
self.assertEqual(
cv2.imread(TransformFile.path(p1, 11, 'jpg')).shape[:2],
(200, 200)) # noqa
self.assertEqual(
cv2.imread(TransformFile.path(p1, 12, 'jpg')).shape[:2],
(200, 200)) # noqa
self.assertEqual(
cv2.imread(TransformFile.path(p1, 13, 'jpg')).shape[:2],
(200, 200)) # noqa
self.assertEqual(
cv2.imread(TransformFile.path(p1, 14, 'jpg')).shape[:2],
(200, 200)) # noqa
self.assertEqual(
cv2.imread(TransformFile.path(p1, 15, 'jpg')).shape[:2],
(200, 200)) # noqa
def test_list(self):
with deepstar_path():
FrameSetModel().insert(None)
FrameModel().insert(1, 0)
TransformSetModel().insert('test', 1)
transform_model = TransformModel()
transform_model.insert(1, 1, '{}', 0)
transform_model.insert(1, 1, '{}', 1)
transform_model.insert(1, 1, '{}', 0)
result = transform_model.list(1)
self.assertEqual(len(result), 3)
self.assertEqual(result[0], (1, 1, 1, '{}', 0))
self.assertEqual(result[1], (2, 1, 1, '{}', 1))
self.assertEqual(result[2], (3, 1, 1, '{}', 0))
result = transform_model.list(1, length=2)
self.assertEqual(len(result), 2)
self.assertEqual(result[0], (1, 1, 1, '{}', 0))
self.assertEqual(result[1], (2, 1, 1, '{}', 1))
result = transform_model.list(1, offset=1)
self.assertEqual(len(result), 2)
self.assertEqual(result[0], (2, 1, 1, '{}', 1))
self.assertEqual(result[1], (3, 1, 1, '{}', 0))
result = transform_model.list(1, length=1, offset=1)
self.assertEqual(len(result), 1)
self.assertEqual(result[0], (2, 1, 1, '{}', 1))
result = transform_model.list(1, rejected=False)
self.assertEqual(len(result), 2)
self.assertEqual(result[0], (1, 1, 1, '{}', 0))
self.assertEqual(result[1], (3, 1, 1, '{}', 0))
