def load_image(path_img, img_type=TYPES_LOAD_IMAGE[0]):
""" load image and annotation according chosen type
:param str path_img:
:param str img_type:
:return ndarray:
"""
path_img = tl_data.update_path(path_img)
if not os.path.isfile(path_img):
raise FileNotFoundError('missing: "%s"' % path_img)
if img_type == '2d_split':
img, _ = tl_data.load_img_double_band_split(path_img)
if img.ndim != 2:
raise ImageDimensionError('image dims: %r' % img.shape)
# img = np.rollaxis(np.tile(img, (3, 1, 1)), 0, 3)
# if img.max() > 1:
# img = (img / 255.)
elif img_type == '2d_rgb':
img, _ = tl_data.load_image_2d(path_img)
# if img.max() > 1:
# img = (img / 255.)
elif img_type == '2d_segm':
img, _ = tl_data.load_image_2d(path_img)
if img.ndim == 3:
img = img[:, :, 0]
if ANNOT_RELABEL_SEQUENCE:
img, _, _ = segmentation.relabel_sequential(img)
else:
logging.error('not supported loading img_type: %s', img_type)
img = None
return img
def load_image_segm_center(idx_row, path_out=None, dict_relabel=None):
""" by paths load images and segmentation and weather centers exist,
load them if the path out is given redraw visualisation of inputs
:param (int, DF:row) idx_row: tuple of index and row
:param str path_out: path to output directory
:param dict dict_relabel: look-up table for relabeling
:return(str, ndarray, ndarray, [[int, int]]): idx_name, img_rgb, segm, centers
"""
idx, row_path = idx_row
for k in ['path_image', 'path_segm', 'path_centers']:
row_path[k] = tl_data.update_path(row_path[k])
if not os.path.exists(row_path[k]):
raise FileNotFoundError('missing %s' % row_path[k])
idx_name = get_idx_name(idx, row_path['path_image'])
img_struc, img_gene = tl_data.load_img_double_band_split(
row_path['path_image'], im_range=None)
# img_rgb = np.array(Image.open(row_path['path_img']))
img_rgb = tl_data.merge_image_channels(img_struc, img_gene)
if np.max(img_rgb) > 1:
img_rgb = img_rgb / float(np.max(img_rgb))
seg_ext = os.path.splitext(os.path.basename(row_path['path_segm']))[-1]
if seg_ext == '.npz':
with np.load(row_path['path_segm'], allow_pickle=True) as npzfile:
segm = npzfile[npzfile.files[0]]
if dict_relabel is not None:
segm = seg_lbs.merge_probab_labeling_2d(segm, dict_relabel)
else:
segm = tl_data.io_imread(row_path['path_segm'])
if dict_relabel is not None:
segm = seg_lbs.relabel_by_dict(segm, dict_relabel)
if row_path['path_centers'] is not None and os.path.isfile(
row_path['path_centers']):
ext = os.path.splitext(os.path.basename(row_path['path_centers']))[-1]
if ext == '.csv':
centers = tl_data.load_landmarks_csv(row_path['path_centers'])
centers = tl_data.swap_coord_x_y(centers)
elif ext == '.png':
centers = tl_data.io_imread(row_path['path_centers'])
# relabel loaded segm into relevant one
centers = np.array(LUT_ANNOT_CENTER_RELABEL)[centers]
else:
logging.warning('not supported file format %s', ext)
centers = None
else:
centers = None
if is_drawing(path_out):
export_visual_input_image_segm(path_out, idx_name, img_rgb, segm,
centers)
return idx_name, img_rgb, segm, centers
def load_image(path_img, img_type=TYPE_LOAD_IMAGE):
""" load image from given path according specification
:param str path_img:
:param str img_type:
:return ndarray:
"""
path_img = os.path.abspath(os.path.expanduser(path_img))
assert os.path.isfile(path_img), 'missing: "%s"' % path_img
if img_type == 'segm':
img = tl_data.io_imread(path_img)
elif img_type == '2d_struct':
img, _ = tl_data.load_img_double_band_split(path_img)
assert img.ndim == 2, 'image can be only single color'
else:
logging.error('not supported loading img_type: %s', img_type)
img = tl_data.io_imread(path_img)
logging.debug('image shape: %r, value range %f - %f', img.shape, img.min(),
img.max())
return img
