def _visual_image_ref_warp_lnds_move_warp(record, path_dataset=None,
path_experiment=None):
""" visualise the case with warped reference landmarks to the move frame
:param {} record: row with the experiment
:param str|None path_dataset: path to the dataset folder
:param str|None path_experiment: path to the experiment folder
:return obj|None:
"""
assert COL_POINTS_REF_WARP in record and isinstance(record[COL_POINTS_REF_WARP], str), \
'Missing registered image "%s"' % COL_POINTS_REF_WARP
path_points_warp = update_path_(record[COL_POINTS_REF_WARP], path_experiment)
if not os.path.isfile(path_points_warp):
logging.warning('missing warped landmarks for: %r', dict(record))
return
points_ref, points_move, path_img_ref = _load_landmarks(record, path_dataset)
points_warp = load_landmarks(path_points_warp)
if not list(points_warp):
return
# draw image with landmarks
image_move = load_image(update_path_(record[COL_IMAGE_MOVE], path_dataset))
# image_warp = tl_io.load_image(row['Moving image, Transf.'])
image = draw_image_points(image_move, points_warp)
save_image(os.path.join(update_path_(record[COL_REG_DIR], path_experiment),
NAME_IMAGE_REF_POINTS_WARP), image)
del image
# visualise the landmarks move during registration
image_ref = load_image(path_img_ref)
fig = draw_images_warped_landmarks(image_ref, image_move, points_ref,
points_move, points_warp)
del image_ref, image_move
return fig
def __images_preprocessing(self, item):
""" create some pre-process images, convert to gray scale and histogram matching
:param dict item: the input record
:return dict: updated item with optionally added pre-process images
"""
path_dir = self._get_path_reg_dir(item)
def __path_img(path_img, pproc):
img_name, img_ext = os.path.splitext(os.path.basename(path_img))
return os.path.join(path_dir, img_name + '_' + pproc + img_ext)
def __save_img(col, path_img_new, img):
col_temp = col + self.COL_IMAGE_EXT_TEMP
if isinstance(item.get(col_temp), str):
path_img = self._absolute_path(item[col_temp],
destination='expt')
os.remove(path_img)
save_image(path_img_new, img)
return self._relativize_path(path_img_new,
destination='path_exp'), col
def __convert_gray(path_img_col):
path_img, col = path_img_col
path_img_new = __path_img(path_img, 'gray')
__save_img(col, path_img_new, rgb2gray(load_image(path_img)))
return self._relativize_path(path_img_new,
destination='path_exp'), col
for pproc in self.params.get('preprocessing', []):
path_img_ref, path_img_move, _, _ = self._get_paths(
item, prefer_pproc=True)
if pproc.startswith('match'):
color_space = pproc.split('-')[-1]
path_img_new = __path_img(path_img_move, pproc)
img = image_histogram_matching(
load_image(path_img_move),
load_image(path_img_ref),
use_color=color_space,
)
path_img_new, col = __save_img(self.COL_IMAGE_MOVE,
path_img_new, img)
item[col + self.COL_IMAGE_EXT_TEMP] = path_img_new
elif pproc in ('gray', 'grey'):
argv_params = [(path_img_ref, self.COL_IMAGE_REF),
(path_img_move, self.COL_IMAGE_MOVE)]
# IDEA: find a way how to convert images in parallel inside mproc pool
# problem is in calling class method inside the pool which is ot static
for path_img, col in iterate_mproc_map(__convert_gray,
argv_params,
nb_workers=1,
desc=None):
item[col + self.COL_IMAGE_EXT_TEMP] = path_img
else:
logging.warning('unrecognized pre-processing: %s', pproc)
return item
def _visual_image_move_warp_lnds_ref_warp(cls,
item,
path_dataset=None,
path_experiment=None):
""" visualise the case with warped reference landmarks to the move frame
:param dict item: row with the experiment
:param str|None path_dataset: path to the dataset folder
:param str|None path_experiment: path to the experiment folder
:return obj|None:
"""
assert isinstance(item.get(cls.COL_POINTS_REF_WARP, None), str), \
'Missing registered points in "%s"' % cls.COL_POINTS_REF_WARP
path_points_warp = update_path(item[cls.COL_POINTS_REF_WARP],
pre_path=path_experiment)
if not os.path.isfile(path_points_warp):
logging.warning('missing warped landmarks for: %r', dict(item))
return
points_ref, points_move, path_img_ref = cls._load_landmarks(
item, path_dataset)
points_warp = load_landmarks(path_points_warp)
if not list(points_warp):
return
# draw image with landmarks
image_move = load_image(
update_path(item[cls.COL_IMAGE_MOVE], pre_path=path_dataset))
image = draw_image_points(image_move, points_warp)
save_image(
os.path.join(
update_path(item[cls.COL_REG_DIR], pre_path=path_experiment),
cls.NAME_IMAGE_REF_POINTS_WARP), image)
del image
image_ref = load_image(path_img_ref)
image_warp = cls._load_warped_image(item, path_experiment)
image = overlap_two_images(image_ref, image_warp)
save_image(
os.path.join(
update_path(item[cls.COL_REG_DIR], pre_path=path_experiment),
cls.NAME_IMAGE_REF_WARP), image)
del image, image_warp
# visualise the landmarks move during registration
fig = draw_images_warped_landmarks(image_ref, image_move, points_ref,
points_move, points_warp)
del image_ref, image_move
return fig
def _load_warped_image(cls, item, path_experiment=None):
"""load the wapted image if it exists
:param dict item: row with the experiment
:param str|None path_experiment: path to the experiment folder
:return ndarray:
"""
name_img = item.get(cls.COL_IMAGE_MOVE_WARP, None)
if not isinstance(name_img, str):
logging.warning('Missing registered image in "%s"', cls.COL_IMAGE_MOVE_WARP)
image_warp = None
else:
path_img_warp = update_path(name_img, pre_path=path_experiment)
if os.path.isfile(path_img_warp):
image_warp = load_image(path_img_warp)
else:
logging.warning('Define image is missing: %s', path_img_warp)
image_warp = None
return image_warp
def _visual_image_move_warp_lnds_move_warp(cls,
item,
path_dataset=None,
path_experiment=None):
""" visualise the case with warped moving image and landmarks
to the reference frame so they are simple to overlap
:param dict item: row with the experiment
:param str|None path_dataset: path to the dataset folder
:param str|None path_experiment: path to the experiment folder
:return obj|None:
"""
if not isinstance(item.get(cls.COL_POINTS_MOVE_WARP), str):
raise ValueError('Missing registered points in "%s"' %
cls.COL_POINTS_MOVE_WARP)
path_points_warp = update_path(item[cls.COL_POINTS_MOVE_WARP],
pre_path=path_experiment)
if not os.path.isfile(path_points_warp):
logging.warning('missing warped landmarks for: %r', dict(item))
return
points_ref, points_move, path_img_ref = cls._load_landmarks(
item, path_dataset)
image_warp = cls._load_warped_image(item, path_experiment)
points_warp = load_landmarks(path_points_warp)
if not list(points_warp):
return
# draw image with landmarks
image = draw_image_points(image_warp, points_warp)
_path = update_path(item[cls.COL_REG_DIR], pre_path=path_experiment)
save_image(os.path.join(_path, cls.NAME_IMAGE_MOVE_WARP_POINTS), image)
del image
# visualise the landmarks move during registration
image_ref = load_image(path_img_ref)
fig = draw_images_warped_landmarks(image_ref, image_warp, points_move,
points_ref, points_warp)
return fig
def __convert_gray(path_img_col):
path_img, col = path_img_col
path_img_new = __path_img(path_img, 'gray')
__save_img(col, path_img_new, rgb2gray(load_image(path_img)))
return self._relativize_path(path_img_new,
destination='path_exp'), col
def setUpClass(cls):
cls.img_ref = load_image(PATH_IMAGE_REF)
cls.img_src = load_image(PATH_IMAGE_SRC)
