def export_cut_objects(df_row,
path_out,
padding,
use_mask=True,
bg_color=None):
""" cut and expert objects in image according given segmentation
:param df_row:
:param str path_out: path for exporting image
:param int padding: set padding around segmented object
"""
annot, _ = tl_data.load_image_2d(df_row['path_1'])
img, name = tl_data.load_image_2d(df_row['path_2'])
if annot.shape[:2] != img.shape[:2]:
raise ImageDimensionError('image sizes not match %r vs %r' %
(annot.shape, img.shape))
uq_objects = np.unique(annot)
if len(uq_objects) == 1:
return
for idx in uq_objects[1:]:
img_new = tl_data.cut_object(img, annot == idx, padding, use_mask,
bg_color)
path_img = os.path.join(path_out, '%s_%i.png' % (name, idx))
logging.debug('saving image "%s"', path_img)
tl_data.io_imsave(path_img, img_new)
def extract_ellipse_object(idx_row, path_images, path_out, norm_size):
""" cut the image selection according ellipse parameters
and scale it into given size to have all image in the end the same sizes
:param (int, row) idx_row: index and row with ellipse parameters
:param str path_images: path to the image folder
:param str path_out: path to output folder
:param (int, int) norm_size: output image size
"""
_, row = idx_row
# select image with this name and any extension
list_imgs = glob.glob(os.path.join(path_images, row['image_name'] + '.*'))
path_img = sorted(list_imgs)[0]
img, _ = tl_data.load_image_2d(path_img)
# create mask according to chosen ellipse
ell_params = row[COLUMNS_ELLIPSE].tolist()
mask = ell_fit.add_overlap_ellipse(np.zeros(img.shape[:2], dtype=int),
ell_params, 1)
# cut the particular image
img_cut = tl_data.cut_object(img, mask, 0, use_mask=True, bg_color=None)
# scaling according to the normal size
img_norm = transform.resize(img_cut, norm_size)
path_img = os.path.join(path_out, os.path.basename(path_img))
tl_data.export_image(path_img, img_norm)