def test_lazylist_repeat():
ll = LazyList.init_from_iterable([0, 1])
ll_repeated = ll.repeat(2)
assert len(ll_repeated) == 4
assert all([a == b for a, b in zip([0, 0, 1, 1], ll_repeated)])
def test_lazylist_init_from_iterable_with_f():
double_func = lambda x: x * 2
ll = LazyList.init_from_iterable([0, 1], f=double_func)
assert ll[0] == 0
assert ll[1] == 2
def test_lazylist_slice_with_ndarray():
index = np.array([1, 0, 3], dtype=int)
l = LazyList.init_from_iterable(["a", "b", "c", "d", "e"])
l_indexed = l[index]
assert list(l_indexed) == ["b", "a", "d"]
def test_lazylist_repeat():
ll = LazyList.init_from_iterable([0, 1])
ll_repeated = ll.repeat(2)
assert len(ll_repeated) == 4
assert all([a == b for a, b in zip([0, 0, 1, 1], ll_repeated)])
def test_lazylist_init_from_iterable_identity():
ll = LazyList.init_from_iterable([0, 1])
assert ll[0] == 0
assert ll[1] == 1
def test_lazylist_init_from_iterable_with_f():
double_func = lambda x: x * 2
ll = LazyList.init_from_iterable([0, 1], f=double_func)
assert ll[0] == 0
assert ll[1] == 2
def test_lazylist_slice_with_ndarray():
index = np.array([1, 0, 3], dtype=np.int)
l = LazyList.init_from_iterable(['a', 'b', 'c', 'd', 'e'])
l_indexed = l[index]
assert list(l_indexed) == ['b', 'a', 'd']
def test_lazylist_init_from_iterable_identity():
ll = LazyList.init_from_iterable([0, 1])
assert ll[0] == 0
assert ll[1] == 1
def kf_sequence_image_paths_with_landmarks(i, expression):
return LazyList.init_from_iterable(
sorted([
p.with_suffix('.png')
for p in kf_sequence_path(i, expression).glob('*.pts')
]))
def kf_sequence_image_paths(i, expression):
return LazyList.init_from_iterable(
mio.image_paths(kf_sequence_path(i, expression)))
def extract_save_features(self, files):
r"""
Uses the input files as train AAMs and store the resulting pickle on the disk
Parameters
----------
files
Returns
-------
"""
# 1. fetch all video frames, attach landmarks
frames = mio.import_video(files[0],
landmark_resolver=self._myresolver,
normalize=True,
exact_frame_count=True)
# frames = frames.map(AAMFeature._preprocess)
idx_above_thresh, idx_lip_opening = landmark_filter(
files[0],
self._landmarkDir,
threshold=self._confidence_thresh,
keep=self._kept_frames)
frames = frames[idx_above_thresh]
frames = frames[idx_lip_opening]
frames = frames.map(attach_semantic_landmarks)
if self._greyscale is True:
frames = frames.map(convert_to_grayscale)
# initial AAM training
if self._warpType == 'holistic':
aam = HolisticAAM(frames,
group=self._landmarkGroup,
holistic_features=self._features,
reference_shape=None,
diagonal=self._diagonal,
scales=self._scales,
max_shape_components=self._max_shape_components,
max_appearance_components=self._max_appearance_components,
verbose=False)
elif self._warpType == 'patch':
aam = PatchAAM(frames,
group=self._landmarkGroup,
holistic_features=self._features,
diagonal=self._diagonal,
scales=self._scales,
max_shape_components=self._max_shape_components,
max_appearance_components=self._max_appearance_components,
patch_shape=self._extractOpts['patch_shape'],
verbose=False)
else:
raise Exception('Unknown warp type. Did you mean holistic/patch ?')
frame_buffer = LazyList.init_from_iterable([])
buffer_len = 256
for idx, file in enumerate(files[1:]):
# useful to check progress
with open('./run/log_' + self._outModelName + '.txt', 'w') as log:
log.write(str(idx) + ' ' + file + '\n')
frames = mio.import_video(file,
landmark_resolver=self._myresolver,
normalize=True,
exact_frame_count=True)
idx_above_thresh, idx_lip_opening = landmark_filter(
file,
landmark_dir=self._landmarkDir,
threshold=self._confidence_thresh,
keep=self._kept_frames)
frames = frames[idx_above_thresh]
frames = frames[idx_lip_opening]
frames = frames.map(attach_semantic_landmarks)
if self._greyscale is True:
frames = frames.map(convert_to_grayscale)
frame_buffer += frames
if len(frame_buffer) > buffer_len:
# 2. retrain AAM
aam.increment(frame_buffer,
group=self._landmarkGroup,
shape_forgetting_factor=1.0,
appearance_forgetting_factor=1.0,
verbose=False,
batch_size=None)
del frame_buffer
frame_buffer = LazyList.init_from_iterable([])
else:
pass
if len(frame_buffer) != 0: #
# deplete remaining frames
aam.increment(frame_buffer,
group=self._landmarkGroup,
shape_forgetting_factor=1.0,
appearance_forgetting_factor=1.0,
verbose=False,
batch_size=None)
del frame_buffer
mio.export_pickle(obj=aam, fp=self._outDir + self._outModelName, overwrite=True, protocol=4)
def shape_nicps(r):
return LazyList.init_from_iterable(paths_shape_nicp(r),
f=_load_shape_nicp_for_path)
