def test_importing_ffmpeg_exact_frame_count_no_ffprobe(is_file,
video_infos_ffprobe):
video_infos_ffprobe.side_effect = ValueError
is_file.return_value = True
mio.import_video('fake_image_being_mocked.avi',
normalize=True,
exact_frame_count=True)
def process_video(file, dest):
if is_video(file):
create_dir(os.path.dirname(dest))
frames = mio.import_video(file, normalise=False)
print('{} contains {} frames'.format(file, len(frames)))
print('writing landmarks to {}...'.format(dest))
frames = frames.map(fit_image)
with open(dest, 'w') as outputfile:
outwriter = csv.writer(outputfile)
try:
for i, frame in enumerate(print_progress(frames)):
if 'final_shape' not in frame.landmarks:
warnings.warn(
'no faces detected in the frame {}, '
'initializing landmarks to -1s...'.format(i))
# dlib does not fitting from previous initial shape so
# leave entire row as -1s
# initial_shape = frames[i - 1].landmarks['final_shape'].lms
# fitting_result = fit_image.fitter.fit_from_shape(frame, initial_shape)
# frame.landmarks['final_shape'] = fitting_result.final_shape
landmarks = [-1] * 136
else:
lmg = frame.landmarks['final_shape']
landmarks = lmg['all'].points.reshape(
(136, )).tolist() # reshape to 136 points
fill_row(outwriter, i, landmarks)
except Exception as e:
warnings.warn('Runtime Error at frame {}'.format(i))
print('initializing landmarks to -1s...')
fill_row(outwriter, i, [-1] * 136)
def process_video(file, dest):
if is_video(file):
create_dir(os.path.dirname(dest))
frames = mio.import_video(file, normalise=False)
print('{} contains {} frames'.format(file, len(frames)))
print('writing landmarks to {}...'.format(dest))
frames = frames.map(fit_image)
with open(dest, 'w') as outputfile:
outwriter = csv.writer(outputfile)
try:
for i, frame in enumerate(print_progress(frames)):
if 'final_shape' not in frame.landmarks:
warnings.warn('no faces detected in the frame {}, '
'initializing landmarks to -1s...'.format(i))
# dlib does not fitting from previous initial shape so
# leave entire row as -1s
# initial_shape = frames[i - 1].landmarks['final_shape'].lms
# fitting_result = fit_image.fitter.fit_from_shape(frame, initial_shape)
# frame.landmarks['final_shape'] = fitting_result.final_shape
landmarks = [-1] * 136
else:
lmg = frame.landmarks['final_shape']
landmarks = lmg['all'].points.reshape((136,)).tolist() # reshape to 136 points
fill_row(outwriter, i, landmarks)
except Exception as e:
warnings.warn('Runtime Error at frame {}'.format(i))
print('initializing landmarks to -1s...')
fill_row(outwriter, i, [-1] * 136)
def test_importing_imageio_ffmpeg_all_bad_frames(is_file, mock_reader):
def fake_get_data(_):
raise ValueError()
mock_reader.return_value.get_length.return_value = 2
mock_reader.return_value.get_data.side_effect = fake_get_data
is_file.return_value = True
ll = mio.import_video('fake_image_being_mocked.avi')
assert len(ll) == 0
def test_importing_imageio_ffmpeg_all_bad_frames(is_file, mock_reader):
def fake_get_data(_):
raise ValueError()
mock_reader.return_value.get_length.return_value = 2
mock_reader.return_value.get_data.side_effect = fake_get_data
is_file.return_value = True
ll = mio.import_video('fake_image_being_mocked.avi')
assert len(ll) == 0
def test_importing_imageio_avi_no_normalise(is_file, mock_image):
mock_image.return_value.get_data.return_value = np.ones((10, 10, 3),
dtype=np.uint8)
mock_image.return_value.get_length.return_value = 1
is_file.return_value = True
ll = mio.import_video('fake_image_being_mocked.avi', normalise=False)
assert len(ll) == 1
im = ll[0]
assert im.shape == (10, 10)
assert im.n_channels == 3
assert im.pixels.dtype == np.uint8
def test_register_video_importer(is_file):
from menpo.image import Image
from menpo.base import LazyList
def foo_importer(filepath, **kwargs):
return LazyList([lambda: Image.init_blank((10, 10))])
is_file.return_value = True
with patch.dict(mio.input.extensions.ffmpeg_video_types, {}, clear=True):
mio.register_video_importer('.foo', foo_importer)
new_video = mio.import_video('fake.foo')
assert len(new_video) == 1
def test_importing_imageio_avi_no_normalise(is_file, mock_image):
mock_image.return_value.get_data.return_value = np.ones((10, 10, 3),
dtype=np.uint8)
mock_image.return_value.get_length.return_value = 1
is_file.return_value = True
ll = mio.import_video('fake_image_being_mocked.avi', normalise=False)
assert len(ll) == 1
im = ll[0]
assert im.shape == (10, 10)
assert im.n_channels == 3
assert im.pixels.dtype == np.uint8
def test_register_video_importer(is_file):
from menpo.image import Image
from menpo.base import LazyList
def foo_importer(filepath, **kwargs):
return LazyList([lambda: Image.init_blank((10, 10))])
is_file.return_value = True
with patch.dict(mio.input.extensions.ffmpeg_video_types, {}, clear=True):
mio.register_video_importer('.foo', foo_importer)
new_video = mio.import_video('fake.foo')
assert len(new_video) == 1
def _detect_write_roi_bounds(self, file):
from menpo.io import import_video
frames = import_video(filepath=file, normalize=True, exact_frame_count=True)
bounds = []
for frame_idx, frame in enumerate(frames):
lips_pointcloud, success = _get_roi_pointcloud(frame, self._detect, self._fitter)
if success is True:
roi_bounds = _get_pointcloud_bounds(lips_pointcloud, self._boundary_proportion)
else:
roi_bounds = [-1, -1, -1, -1]
bounds.append(roi_bounds)
self._write_rois_to_file(file, bounds)
def test_importing_ffmpeg_avi_normalize(is_file, video_infos_ffprobe, pipe):
video_infos_ffprobe.return_value = {'duration': 2, 'width': 100,
'height': 150, 'n_frames': 10, 'fps': 5}
is_file.return_value = True
empty_frame = np.zeros(150*100*3, dtype=np.uint8).tostring()
pipe.return_value.stdout.read.return_value = empty_frame
ll = mio.import_video('fake_image_being_mocked.avi', normalize=True)
assert ll.path.name == 'fake_image_being_mocked.avi'
assert ll.fps == 5
assert len(ll) == 5*2
image = ll[0]
assert image.shape == (150, 100)
assert image.n_channels == 3
assert image.pixels.dtype == np.float
def test_importing_imageio_ffmpeg_bad_frames(is_file, mock_reader):
def fake_get_data(index):
if index not in [1, 2]:
raise ValueError()
f = np.ones((10, 10, 3)) * -1
f[0, 0, 0] = index
return f
mock_reader.return_value.get_length.return_value = 4
mock_reader.return_value.get_data.side_effect = fake_get_data
is_file.return_value = True
ll = mio.import_video('fake_image_being_mocked.avi', normalise=False)
assert len(ll) == 2
im = ll[0]
assert im.shape == (10, 10)
assert im.pixels[0, 0, 0] == 1
def test_importing_imageio_ffmpeg_bad_frames(is_file, mock_reader):
def fake_get_data(index):
if index not in [1, 2]:
raise ValueError()
f = np.ones((10, 10, 3)) * -1
f[0, 0, 0] = index
return f
mock_reader.return_value.get_length.return_value = 4
mock_reader.return_value.get_data.side_effect = fake_get_data
is_file.return_value = True
ll = mio.import_video('fake_image_being_mocked.avi', normalise=False)
assert len(ll) == 2
im = ll[0]
assert im.shape == (10, 10)
assert im.pixels[0, 0, 0] == 1
def test_importing_ffmpeg_avi_normalize(is_file, video_infos_ffprobe, pipe):
video_infos_ffprobe.return_value = {
'duration': 2,
'width': 100,
'height': 150,
'n_frames': 10,
'fps': 5
}
is_file.return_value = True
empty_frame = np.zeros(150 * 100 * 3, dtype=np.uint8).tostring()
pipe.return_value.stdout.read.return_value = empty_frame
ll = mio.import_video('fake_image_being_mocked.avi', normalize=True)
assert ll.path.name == 'fake_image_being_mocked.avi'
assert ll.fps == 5
assert len(ll) == 5 * 2
image = ll[0]
assert image.shape == (150, 100)
assert image.n_channels == 3
assert image.pixels.dtype == np.float
def test_importing_imageio_ffmpeg_many_bad_frames_warning_end(is_file, mock_reader):
def fake_get_data(index):
if index > 3:
raise ValueError()
f = np.ones((10, 10, 3)) * -1
f[0, 0, 0] = index
return f
mock_reader.return_value.get_length.return_value = 15
mock_reader.return_value.get_data.side_effect = fake_get_data
is_file.return_value = True
with warnings.catch_warnings(record=True) as w:
ll = mio.import_video('fake_image_being_mocked.avi', normalise=False)
assert len(w) == 1
assert len(ll) == 4
assert ll[0].pixels[0, 0, 0] == 0
assert ll[-1].pixels[0, 0, 0] == 3
def test_importing_ffmpeg_avi_no_normalize(is_file, video_infos_ffprobe, pipe):
video_infos_ffprobe.return_value = {
"duration": 2,
"width": 100,
"height": 150,
"n_frames": 10,
"fps": 5,
}
is_file.return_value = True
empty_frame = np.zeros(150 * 100 * 3, dtype=np.uint8).tostring()
pipe.return_value.stdout.read.return_value = empty_frame
ll = mio.import_video("fake_image_being_mocked.avi", normalize=False)
assert ll.path.name == "fake_image_being_mocked.avi"
assert ll.fps == 5
assert len(ll) == 5 * 2
image = ll[0]
assert image.shape == ((150, 100))
assert image.n_channels == 3
assert image.pixels.dtype == np.uint8
def _compute_3d_dct_menpo(self, file):
r"""
Video import is based on menpo implementation
(LazyList with ffmpeg backend)
:param file:
:return:
"""
from menpo.io import import_video
frames = import_video(filepath=file, normalize=True, exact_frame_count=True)
dct_volume = np.zeros((self._yres, self._xres, len(frames)), dtype=np.float32)
for frame_idx, frame in enumerate(frames):
roi = self._get_roi(frame, frame_idx, file)
dctmat = np.zeros(np.shape(roi))
cv2.dct(roi, dctmat)
dct_volume[:, :, frame_idx] = dctmat
return dct_volume
def test_importing_imageio_ffmpeg_many_bad_frames_warning_end(
is_file, mock_reader):
def fake_get_data(index):
if index > 3:
raise ValueError()
f = np.ones((10, 10, 3)) * -1
f[0, 0, 0] = index
return f
mock_reader.return_value.get_length.return_value = 15
mock_reader.return_value.get_data.side_effect = fake_get_data
is_file.return_value = True
with warnings.catch_warnings(record=True) as w:
ll = mio.import_video('fake_image_being_mocked.avi', normalise=False)
assert len(w) == 1
assert len(ll) == 4
assert ll[0].pixels[0, 0, 0] == 0
assert ll[-1].pixels[0, 0, 0] == 3
def process_video(file, dest):
if is_video(file):
try:
frames = mio.import_video(file, normalise=False)
except IOError:
warnings.warn('IO error reading video file {}, '.format(file) +
'the file may be corrupted or the video format is unsupported, skipping...')
except ValueError as e:
warnings.warn('Value Error reading video file {}, '.format(file) +
e.message)
return
# check if directory is non empty
if os.path.dirname(dest):
create_dir(os.path.dirname(dest))
print('{} contains {} frames'.format(file, len(frames)))
print('writing landmarks to {}...'.format(dest))
frames = frames.map(fit_image)
with open(dest, 'w') as outputfile:
outwriter = csv.writer(outputfile)
try:
for i, frame in enumerate(print_progress(frames)):
if 'final_shape' not in frame.landmarks:
warnings.warn('no faces detected in the frame {}, '
'initializing landmarks to -1s...'.format(i))
# dlib does not fitting from previous initial shape so
# leave entire row as -1s
# initial_shape = frames[i - 1].landmarks['final_shape'].lms
# fitting_result = fit_image.fitter.fit_from_shape(frame, initial_shape)
# frame.landmarks['final_shape'] = fitting_result.final_shape
landmarks = [-1] * NO_LANDMARKS*2
else:
lmg = frame.landmarks['final_shape']
landmarks = lmg['all'].points.reshape((NO_LANDMARKS*2,)).tolist() # reshape to 136 points
fill_row(outwriter, i, landmarks)
except Exception as e:
warnings.warn('Runtime Error at frame {}'.format(i))
print('initializing landmarks to -1s...')
fill_row(outwriter, i, [-1] * NO_LANDMARKS*2)
def test_importing_ffmpeg_exact_frame_count_no_ffprobe(
is_file, video_infos_ffprobe):
video_infos_ffprobe.side_effect = ValueError
is_file.return_value = True
mio.import_video('fake_image_being_mocked.avi', normalize=True,
exact_frame_count=True)
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 get_feature(self, file, process_opts=None):
r"""
Computes the AAM features, according to the `process_opts`
Parameters
----------
file
process_opts
Returns
-------
A dictionary of five elements, each representing a variation of the computed features
(shape and appearance alone or concatenated, with or without derivatives)
"""
self._maybe_start_logging(file)
self._load_landmark_fitter()
frames = mio.import_video(file,
landmark_resolver=self._myresolver,
normalize=True,
exact_frame_count=True)
feat_shape = []
feat_app = []
feat_shape_app = []
for frameIdx, frame in enumerate(frames):
bounding_boxes = self._face_detect(frame)
if len(bounding_boxes) > 0:
initial_bbox = bounding_boxes[0]
if self._log_errors is True:
gt_shape = frame.landmarks['pts_face']
else:
gt_shape = None
if isinstance(self._landmark_fitter, LucasKanadeAAMFitter):
result = self._landmark_fitter.fit_from_bb(
frame, initial_bbox,
max_iters=self._max_iters,
gt_shape=gt_shape)
elif isinstance(self._landmark_fitter, DlibWrapper): # DLIB fitter, doesn't have max_iters
result = self._landmark_fitter.fit_from_bb(
frame,
initial_bbox,
gt_shape=gt_shape)
else:
raise Exception('incompatible landmark fitter')
self._maybe_append_to_log(file, frameIdx, result)
if self._shape == 'face':
if self._parameters == 'lk_fitting':
# skip the first 4 similarity params, probably not useful for classification
shape_param_frame = result.shape_parameters[-1][4:]
app_param_frame = result.appearance_parameters[-1]
elif self._parameters == 'aam_projection':
result_aam = self._projection_fitter.fit_from_shape(
frame,
result.final_shape,
max_iters=[0, 0, 0])
# TODO: analyse the case when aam true components are less than max components
shape_param_frame = result_aam.shape_parameters[-1][4:]
app_param_frame = result_aam.appearance_parameters[-1]
feat_shape.append(shape_param_frame)
feat_app.append(app_param_frame)
feat_shape_app.append(np.concatenate((shape_param_frame, app_param_frame)))
elif self._shape == 'lips':
# extract lips landmarks from the final face fitting to initialize the part model fitting
aam_lips = mio.import_pickle(self._part_aam)
fitter_lips = LucasKanadeAAMFitter(aam_lips, lk_algorithm_cls=WibergInverseCompositional,
n_shape=[10, 20], n_appearance=[20, 150])
result_lips = fitter_lips.fit_from_shape(
image=frame,
initial_shape=_pointcloud_subset(result.final_shape, 'lips'),
max_iters=[5, 5])
shape_param_frame_lips = result_lips.shape_parameters[-1][4:]
app_param_frame_lips = result_lips.appearance_parameters[-1]
feat_shape.append(shape_param_frame_lips)
feat_app.append(app_param_frame_lips)
feat_shape_app.append(np.concatenate((shape_param_frame_lips, app_param_frame_lips)))
elif self._shape == 'chin':
# extract chin and lips landmarks from the final face fitting to initialize the part model fitting
aam_chin = mio.import_pickle(self._part_aam)
fitter_chin = LucasKanadeAAMFitter(aam_chin, lk_algorithm_cls=WibergInverseCompositional,
n_shape=[10, 20, 25], n_appearance=[20, 50, 150])
result_chin = fitter_chin.fit_from_shape(
image=frame,
initial_shape=_pointcloud_subset(result.final_shape, 'chin'),
max_iters=[10, 10, 5])
shape_param_frame_mchin = result_chin.shape_parameters[-1][4:]
app_param_frame_mchin = result_chin.appearance_parameters[-1]
feat_shape.append(shape_param_frame_mchin)
feat_app.append(app_param_frame_mchin)
feat_shape_app.append(np.concatenate((shape_param_frame_mchin, app_param_frame_mchin)))
else:
raise Exception('Unknown shape model, currently supported are: face, lips, chin')
else: # we did not detect any face
zero_feat_shape = np.zeros(process_opts['shape_components'][-1])
zero_feat_app = np.zeros(process_opts['appearance_components'][-1])
zero_feat_shape_app = np.zeros(
process_opts['shape_components'][-1] + process_opts['appearance_components'][-1])
feat_shape.append(zero_feat_shape)
feat_app.append(zero_feat_app)
feat_shape_app.append(zero_feat_shape_app)
npfeat_shape = np.array(feat_shape)
npfeat_app = np.array(feat_app)
npfeat_shape_app = np.array(feat_shape_app)
npfeat_app_delta = vsrmath.accurate_derivative(npfeat_app, 'delta')
npfeat_shape_app_delta = vsrmath.accurate_derivative(npfeat_shape_app, 'delta')
return {'shape': npfeat_shape,
'app': npfeat_app,
'shape_app': npfeat_shape_app,
'app_delta': npfeat_app_delta,
'shape_app_delta': npfeat_shape_app_delta}
def test_importing_imageio_avi_no_frames(is_file, mock_image):
mock_image.return_value.get_length.return_value = 0
is_file.return_value = True
ll = mio.import_video('fake_image_being_mocked.avi')
assert len(ll) == 0
p_out = '/vol/bitbucket/ml9915/landmarksLeft/'
bboxes = {}
for cnt, im in enumerate(ims):
if im.n_channels == 3:
im = im.as_greyscale()
# ffld2 detector returns bounding_boxes
lns = ffld2_detector(im)
if im.landmarks.n_groups == 0:
# there are no detections
print 'error'
continue
name = '{0:06d}'.format(cnt)
bboxes[name] = lns
# export the boundingbox
mio.export_pickle(bboxes, p_out + filename +'.pkl', overwrite=True)
# decide which detector to use:
ffld2_detector = load_ffld2_frontal_face_detector()
# input path
pbLeft = ('/vol/bitbucket/ml9915/annotateVideosLeft/')
filesLeft = os.listdir(pbLeft)
filesLeft = filter(lambda x: not x.startswith("._"), filesLeft)
for filename in filesLeft:
if(filename == '.DS_Store'):
continue
print filename
# import the video
ims = mio.import_video(join(pbLeft, filename))
storeBB(ims, filename.split('.')[0])
def test_importing_imageio_avi_no_frames(is_file, mock_image):
mock_image.return_value.get_length.return_value = 0
is_file.return_value = True
ll = mio.import_video('fake_image_being_mocked.avi')
assert len(ll) == 0
