def _visualise_registration(self, df_row):
""" visualise the registration results according what landmarks were
estimated - in registration or moving frame
:param (int, dict) df_row: tow from iterated table
"""
_, dict_row = df_row
image_ref = tl_io.load_image(dict_row[COL_IMAGE_REF])
points_ref = tl_io.load_landmarks(dict_row[COL_POINTS_REF])
points_move = tl_io.load_landmarks(dict_row[COL_POINTS_MOVE])
# visualise particular experiment by idx
if COL_POINTS_REF_WARP in dict_row:
if COL_IMAGE_REF_WARP not in dict_row \
or not isinstance(dict_row[COL_IMAGE_REF_WARP], str):
logging.warning('missing registered image "%s"',
COL_IMAGE_REF_WARP)
return None
if COL_POINTS_REF_WARP not in dict_row \
or not isinstance(dict_row[COL_POINTS_REF_WARP], str):
logging.warning('missing registered landmarks "%s"',
COL_POINTS_REF_WARP)
return None
image_warp = tl_io.load_image(dict_row[COL_IMAGE_REF_WARP])
points_warp = tl_io.load_landmarks(dict_row[COL_POINTS_REF_WARP])
# draw image with landmarks
image = tl_visu.draw_image_points(image_warp, points_warp)
tl_io.save_image(os.path.join(dict_row[COL_REG_DIR],
NAME_IMAGE_MOVE_WARP_POINTS), image)
# visualise the landmarks move during registration
fig = tl_visu.draw_images_warped_landmarks(image_ref, image_warp,
points_move, points_ref,
points_warp)
elif COL_POINTS_MOVE_WARP in dict_row:
image_move = tl_io.load_image(dict_row[COL_IMAGE_MOVE])
# image_warp = tl_io.load_image(row['Moving image, Transf.'])
points_warp = tl_io.load_landmarks(dict_row[COL_POINTS_MOVE_WARP])
# draw image with landmarks
image = tl_visu.draw_image_points(image_move, points_warp)
tl_io.save_image(os.path.join(dict_row[COL_REG_DIR],
NAME_IMAGE_REF_POINTS_WARP), image)
# visualise the landmarks move during registration
fig = tl_visu.draw_images_warped_landmarks(image_ref, image_move,
points_ref, points_move,
points_warp)
else:
logging.error('not allowed scenario: no output image or landmarks')
fig, _ = tl_visu.create_figure((1, 1))
path_fig = os.path.join(dict_row[COL_REG_DIR], NAME_IMAGE_REGIST_VISUAL)
tl_visu.export_figure(path_fig, fig)
return path_fig
def _visualise_registration(self, df_row):
""" visualise the registration results according what landmarks were
estimated - in registration or moving frame
:param (int, dict) df_row: tow from iterated table
"""
_, row = df_row
path_img_ref, _, path_lnds_ref, path_lnds_move = self._get_paths(row)
image_ref = tl_io.load_image(path_img_ref)
points_ref = tl_io.load_landmarks(path_lnds_ref)
points_move = tl_io.load_landmarks(path_lnds_move)
# visualise particular experiment by idx
if COL_POINTS_MOVE_WARP in row:
fig = self.__visual_image_move_warp_lnds_move_warp(
row, image_ref, points_ref, points_move)
if fig is None:
return None
elif COL_POINTS_REF_WARP in row:
fig = self.__visual_image_ref_warp_lnds_move_warp(
row, image_ref, points_ref, points_move)
else:
logging.error('Visualisation: no output image or landmarks')
fig, _ = tl_visu.create_figure((1, 1))
path_fig = os.path.join(self._get_path_reg_dir(row),
NAME_IMAGE_WARPED_VISUAL)
tl_visu.export_figure(path_fig, fig)
return path_fig
def __visual_image_ref_warp_lnds_move_warp(self, record, image_ref,
points_ref, points_move):
""" visualise the case with warped reference landmarks to the move frame
:param {} record: row with the experiment
:param ndarray image_ref: reference image
:param ndarray points_ref: landmarks in reference frame
:param ndarray points_move: landmarks in moving frame
:return obj:
"""
image_move = tl_io.load_image(self._update_path(
record[COL_IMAGE_MOVE]))
# image_warp = tl_io.load_image(row['Moving image, Transf.'])
path_points_warp = self._update_path(record[COL_POINTS_REF_WARP],
'expt')
points_warp = tl_io.load_landmarks(path_points_warp)
# draw image with landmarks
image = tl_visu.draw_image_points(image_move, points_warp)
tl_io.save_image(
os.path.join(self._get_path_reg_dir(record),
NAME_IMAGE_REF_POINTS_WARP), image)
# visualise the landmarks move during registration
fig = tl_visu.draw_images_warped_landmarks(image_ref, image_move,
points_ref, points_move,
points_warp)
return fig
def __compute_landmarks_statistic(self, df_row):
""" after successful registration load initial nad estimated landmarks
afterwords compute various statistic for init, and final alignment
:param (int, dict) df_row: tow from iterated table
"""
idx, row = df_row
path_img_ref, _, path_lnds_ref, path_lnds_move = self._get_paths(row)
# load initial landmarks
points_ref = tl_io.load_landmarks(path_lnds_ref)
points_move = tl_io.load_landmarks(path_lnds_move)
points_warped = []
img_size = tl_io.load_image(path_img_ref).shape[:2]
# compute statistic
self.__compute_landmarks_inaccuracy(idx, points_ref, points_move,
'init', img_size)
# load transformed landmarks
if COL_POINTS_REF_WARP in row:
points_target = points_move
path_landmarks = self._update_path(row[COL_POINTS_REF_WARP],
'expt')
elif COL_POINTS_MOVE_WARP in row:
points_target = points_ref
path_landmarks = self._update_path(row[COL_POINTS_MOVE_WARP],
'expt')
else:
logging.error('Statistic: no output landmarks')
points_target, path_landmarks = [], None
# load landmarks
if isinstance(path_landmarks, str):
points_warped = tl_io.load_landmarks(path_landmarks)
# compute statistic
img_size = tl_io.load_image(path_img_ref).shape[:2]
if list(points_target) and list(points_warped):
self.__compute_landmarks_inaccuracy(idx, points_target,
points_warped, 'final',
img_size)
def __visual_image_move_warp_lnds_move_warp(self, record, image_ref,
points_ref, points_move):
""" visualise the case with warped moving image and landmarks
to the reference frame so they are simple to overlap
:param {} record: row with the experiment
:param ndarray image_ref:
:param ndarray points_move:
:param ndarray points_ref:
:return obj | None:
"""
if COL_IMAGE_MOVE_WARP not in record \
or not isinstance(record[COL_IMAGE_MOVE_WARP], str):
logging.warning('Missing registered image "%s"',
COL_IMAGE_MOVE_WARP)
return None
if COL_POINTS_MOVE_WARP not in record \
or not isinstance(record[COL_POINTS_MOVE_WARP], str):
logging.warning('Missing registered landmarks "%s"',
COL_POINTS_MOVE_WARP)
return None
path_image_warp = self._update_path(record[COL_IMAGE_MOVE_WARP],
'expt')
image_warp = tl_io.load_image(path_image_warp)
path_points_warp = self._update_path(record[COL_POINTS_MOVE_WARP],
'expt')
points_warp = tl_io.load_landmarks(path_points_warp)
# draw image with landmarks
image = tl_visu.draw_image_points(image_warp, points_warp)
tl_io.save_image(
os.path.join(self._get_path_reg_dir(record),
NAME_IMAGE_MOVE_WARP_POINTS), image)
# visualise the landmarks move during registration
fig = tl_visu.draw_images_warped_landmarks(image_ref, image_warp,
points_move, points_ref,
points_warp)
return fig