def point_creator(refer_dir:str, name:str, dst:str, point_label=True, direct_mask=False, limit_pkl:str=None) -> None:
"""the refer_dir is the directory where all refer are;
the name is the name of the created .pkl file;
the dst is where the .pkl file will be saved;
if the limit_pkl is provided, only the files in limit_pkl will be used,
this .pkl files should follow the format of metric_path_collector.py"""
containor = {}
refers = path_list_creator(refer_dir)
if limit_pkl:
target = load_pickle(limit_pkl)
refers = [refer for refer in refers if get_name(refer) in target['labels']]
if point_label:
for label_p in refers:
points = point_detect(cv2.cvtColor(cv2.imread(label_p), cv2.COLOR_BGR2HSV))
containor[get_name(label_p)] = points
elif direct_mask:
for mask_p in refers:
containor[get_name(mask_p)] = mask_p
else:
for mask_p in refers:
mask = cv2.imread(mask_p, 0)
cnts = mask_to_cnts_watershed(mask, min_distance=14, for_real_mask=True)
points = [middle(c) for c in cnts]
containor[get_name(mask_p)] = points
save_dict(containor, name, dst)
def _temp_init(self):
img_dir = filedialog.askdirectory(
title='Select the directory where your slides are ...',
mustexist=True)
self.dst = join(dirname(img_dir), 'temp')
os.makedirs(self.dst, exist_ok=True)
self.path_list = path_list_creator(img_dir)
self.cur = 0
self.length = len(self.path_list)
self.loc = self.path_list[self.cur]
self.ID = get_name(self.loc)
def make_gif_from_dir(metric_dir_path, dst):
maid = defaultdict(dict)
path_list = path_list_creator(metric_dir_path)
for fp in path_list:
name = get_name(fp)
if '_pred' in fp:
maid[name]['predicted'] = fp
elif '_label' in fp:
maid[name]['labeled'] = fp
# elif '_fit' in fp:
# maid[name]['fit'] = fp
for f_name, fp_dict in maid.items():
if len(fp_dict) == 2:
out_p = join(dst, f_name + '.gif')
make_gif(fp_dict, out_p)
def count_from_dir(self, assist=False):
if assist:
self.is_assist = True
else:
self.is_assist = False
img_dir = filedialog.askdirectory(
title='Select the directory where your slides are ...',
mustexist=True)
self.img_p_list = path_list_creator(img_dir)
amount = len(self.img_p_list)
time_needed = str(datetime.timedelta(seconds=amount * 30))
self._message_prompt(title='The below is what we know ...',
text=f'\n\
The directory you selected is\n\
{img_dir}\n\
{amount} possible slices are there\n\
Depending on your computer, about {time_needed} is needed.\n\
If it is OK, please PRESS "Continue" and then\n\
select a directory to save results.',
normal=False)
def test_dict(img_dir:str) -> list:
return {get_name(fp):fp for fp in path_list_creator(img_dir)}
def find_models(dir_p:str) -> list:
files = [x for x in path_list_creator(dir_p) if x[-3:]=='.h5']
return files
if show_mask:
cv2.imwrite(os.path.join(dst, name + '_mask.jpg'), pred_mask_img)
values = stats(label_num, pred_cnts, possible_right_cnts)
self.metric_record[name] = values
if show_info:
print_dict = {}
print_dict.update(dict(zip(self.metric_keys.split(', '), values)))
print(print_dict)
def metric_from_dir(self, img_dir_path, label_dir_path, dst):
for img_p, label_p, name in path_matcher(img_dir_path, label_dir_path):
self.metric(img_p, label_p, show_info=False, dst=dst, name=name)
show_result(self.metric_record,
self.metric_keys.split(', '),
title='Metric',
dst=dst)
if __name__ == "__main__":
model_p = sys.argv[1]
img_dir = sys.argv[2]
dst = sys.argv[3]
actor = Unet_predictor(model_p)
os.makedirs(dst, exist_ok=True)
try:
label_dir = sys.argv[4]
actor.metric_from_dir(img_dir, label_dir, dst)
except IndexError:
img_p_list = path_list_creator(img_dir)
actor.predict_from_imgs(img_p_list, dst)