def perform_orientation_swap(path_img,
path_out,
img_template,
swap_type=SWAP_CONDITION):
""" compute the density in front adn back part of the egg rotate eventually
we split the egg into thirds instead half because the middle part variate
:param str path_img: path to input image
:param str path_out: path to output folder
:param ndarray img_template: template / mean image
:param str swap_type: used swap condition
"""
img, _ = tl_data.load_image_2d(path_img)
# cut the same image
img_size = img_template.shape
img = img[:img_size[0], :img_size[1]]
if swap_type == 'cc':
b_swap = condition_swap_correl(img, img_template)
else:
b_swap = condition_swap_density(img)
if b_swap:
img = img[::-1, ::-1, :]
path_img = os.path.join(path_out, os.path.basename(path_img))
tl_data.export_image(path_img, img)
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)