def segment_image_model(imgs_idx_path,
params,
model,
path_out=None,
path_visu=None,
show_debug_imgs=SHOW_DEBUG_IMAGES):
""" segment image with already estimated model
:param (int, str) imgs_idx_path:
:param {str: ...} params: segmentation parameters
:param obj scaler:
:param obj pca:
:param obj model:
:param str path_out: path to dir with segmentation
:param str path_visu: path to dir with debug images
:param bool show_debug_imgs: whether show debug images
:return (str, ndarray):
"""
idx, path_img = parse_imgs_idx_path(imgs_idx_path)
logging.debug('segmenting image: "%s"', path_img)
idx_name = get_idx_name(idx, path_img)
img = load_image(path_img, params['img_type'])
path_img = os.path.join(params['path_exp'], FOLDER_IMAGE,
idx_name + '.png')
tl_data.io_imsave(path_img, img.astype(np.uint8))
debug_visual = dict() if show_debug_imgs else None
try:
segm, segm_soft = seg_pipe.segment_color2d_slic_features_model_graphcut(
img,
model,
sp_size=params['slic_size'],
sp_regul=params['slic_regul'],
dict_features=params['features'],
gc_regul=params['gc_regul'],
gc_edge_type=params['gc_edge_type'],
debug_visual=debug_visual)
path_npz = os.path.join(path_out, idx_name + '.npz')
np.savez_compressed(path_npz, segm_soft)
except Exception:
logging.error(traceback.format_exc())
segm = np.zeros(img.shape[:2])
boundary_size = int(np.sqrt(np.prod(segm.shape)) * 0.01)
segm = seg_lbs.assume_bg_on_boundary(segm,
bg_label=0,
boundary_size=boundary_size)
export_visual(idx_name, img, segm, debug_visual, path_out, path_visu)
# gc.collect(), time.sleep(1)
return idx_name, segm
def segment_image_independent(img_idx_path,
params,
path_out,
path_visu=None,
show_debug_imgs=SHOW_DEBUG_IMAGES):
""" segment image indecently (estimate model just for this model)
:param (int, str) img_idx_path:
:param dict params: segmentation parameters
:param str path_out: path to dir with segmentation
:param str path_visu: path to dir with debug images
:return (str, ndarray):
"""
idx, path_img = parse_imgs_idx_path(img_idx_path)
logging.debug('segmenting image: "%s"', path_img)
idx_name = get_idx_name(idx, path_img)
img = load_image(path_img, params['img_type'])
path_img = os.path.join(params['path_exp'], FOLDER_IMAGE,
idx_name + '.png')
tl_data.io_imsave(path_img, img.astype(np.uint8))
debug_visual = dict() if show_debug_imgs else None
try:
segm, segm_soft = seg_pipe.pipe_color2d_slic_features_model_graphcut(
img,
nb_classes=params['nb_classes'],
sp_size=params['slic_size'],
sp_regul=params['slic_regul'],
dict_features=params['features'],
estim_model=params['estim_model'],
pca_coef=params['pca_coef'],
gc_regul=params['gc_regul'],
gc_edge_type=params['gc_edge_type'],
debug_visual=debug_visual)
path_npz = os.path.join(path_out, idx_name + '.npz')
np.savez_compressed(path_npz, segm_soft)
except Exception:
logging.exception('pipe_color2d_slic_features_model_graphcut(...)')
segm = np.zeros(img.shape[:2])
boundary_size = int(params['slic_size'] * 3)
segm = seg_lbs.assume_bg_on_boundary(segm,
bg_label=0,
boundary_size=boundary_size)
export_visual(idx_name, img, segm, debug_visual, path_out, path_visu)
# gc.collect(), time.sleep(1)
return idx_name, segm
