def main(table_like_format):
# make data list
for m in models:
print(m)
data_set = prepare_data()
prev_model_root = None
if table_like_format:
print('model\tweights\tkey\t'
'precision\trecall\tf1\t'
'precision_c\trecall_C\tf1_c\t'
'd_by_doc\td_by_char\td_by_char_avg')
for model_root, model_weights in models:
if model_root != prev_model_root:
if not table_like_format:
print('model: ', model_root)
else:
print()
prev_model_root = model_root
if verbose:
print('evaluating weights: ', model_weights)
params_fn = Path(local_config.data_path) / model_root / 'param.txt'
if not params_fn.is_file():
params_fn = Path(local_config.data_path) / (
model_root + '.param.txt') # старый вариант
assert params_fn.is_file(), str(params_fn)
recognizer = infer_retinanet.BrailleInference(
params_fn=params_fn,
model_weights_fn=os.path.join(local_config.data_path, model_root,
model_weights),
create_script=None,
inference_width=inference_width,
verbose=verbose)
for key, data_list in data_set.items():
res = validate_model(recognizer,
data_list,
do_filter_lonely_rects=do_filter_lonely_rects,
metrics_for_lines=metrics_for_lines)
# print('{model_weights} {key} precision: {res[precision]:.4}, recall: {res[recall]:.4} f1: {res[f1]:.4} '
# 'precision_r: {res[precision_r]:.4}, recall_r: {res[recall_r]:.4} f1_r: {res[f1_r]:.4} '
# 'd_by_doc: {res[d_by_doc]:.4} d_by_char: {res[d_by_char]:.4} '
# 'd_by_char_avg: {res[d_by_char_avg]:.4}'.format(model_weights=model_weights, key=key, res=res))
if table_like_format:
print(
'{model}\t{weights}\t{key}\t'
'{res[precision_r]:.4}\t{res[recall_r]:.4}\t{res[f1_r]:.4}\t'
'{res[precision_c]:.4}\t{res[recall_c]:.4}\t{res[f1_c]:.4}\t'
'{res[d_by_doc]:.4}\t{res[d_by_char]:.4}\t'
'{res[d_by_char_avg]:.4}'.format(model=model_root,
weights=model_weights,
key=key,
res=res))
else:
print(
'{model_weights} {key} '
'precision_r: {res[precision_r]:.4}, recall_r: {res[recall_r]:.4} f1_r: {res[f1_r]:.4} '
'd_by_doc: {res[d_by_doc]:.4} d_by_char: {res[d_by_char]:.4} '
'd_by_char_avg: {res[d_by_char_avg]:.4}'.format(
model_weights=model_weights, key=key, res=res))
def get_recognizer(self):
global recognizer
if recognizer is None:
print("infer_retinanet.BrailleInference()")
t = timeit.default_timer()
recognizer = infer_retinanet.BrailleInference(verbose=1,
params_fn=os.path.join(MODEL_PATH, 'weights', 'param.txt'),
model_weights_fn=os.path.join(MODEL_PATH, 'weights', MODEL_WEIGHTS),
create_script=None)
print(timeit.default_timer() - t)
return recognizer
def evaluate_accuracy(params_fn,
model,
device,
data_list,
do_filter_lonely_rects=False,
metrics_for_lines=True):
"""
:param recognizer: infer_retinanet.BrailleInference instance
:param data_list: list of (image filename, groundtruth pseudotext)
:return: (<distance> avg. by documents, <distance> avg. by char, <<distance> avg. by char> avg. by documents>)
"""
# по символам
recognizer = infer_retinanet.BrailleInference(
params_fn=params_fn,
model_weights_fn=model,
create_script=None,
inference_width=inference_width,
device=device,
verbose=verbose)
tp_c = 0
fp_c = 0
fn_c = 0
for gt_dict in data_list:
img_fn, gt_text, gt_rects = gt_dict['image_fn'], gt_dict[
'gt_text'], gt_dict['gt_rects']
res_dict = recognizer.run(
img_fn,
lang=lang,
draw_refined=infer_retinanet.BrailleInference.DRAW_NONE,
find_orientation=False,
process_2_sides=False,
align_results=False,
repeat_on_aligned=False,
gt_rects=gt_rects)
lines = res_dict['lines']
if do_filter_lonely_rects:
lines, filtered_chars = postprocess.filter_lonely_rects_for_lines(
lines)
if metrics_for_lines:
boxes = []
labels = []
for ln in lines:
boxes += [ch.refined_box for ch in ln.chars]
labels += [ch.label for ch in ln.chars]
else:
boxes = res_dict['boxes']
labels = res_dict['labels']
tpi, fpi, fni = char_metrics_rects(
boxes=boxes,
labels=labels,
gt_rects=res_dict['gt_rects'],
image_wh=(res_dict['labeled_image'].width,
res_dict['labeled_image'].height),
img=None,
do_filter_lonely_rects=do_filter_lonely_rects)
tp_c += tpi
fp_c += fpi
fn_c += fni
precision_c = tp_c / (tp_c + fp_c) if tp_c + fp_c != 0 else 0.
recall_c = tp_c / (tp_c + fn_c) if tp_c + fn_c != 0 else 0.
return {
'precision':
precision_c,
'recall':
recall_c,
'f1':
2 * precision_c * recall_c /
(precision_c + recall_c) if precision_c + recall_c != 0 else 0.,
}
parser = argparse.ArgumentParser(description='Angelina Braille Reader: optical Braille text recognizer .')
parser.add_argument('input', type=str, help='File(s) to be processed: image, pdf or zip file or directory name')
parser.add_argument('results_dir', nargs='?', type=str, help='(Optional) output directory. If not specified, results are placed at input location')
parser.add_argument('-l', '--lang', type=str, default='RU', help='(Optional) Document language (RU, EN, LV, GR or UZ). If not specified, is RU')
parser.add_argument('-o', '--orient', action='store_false', help="Don't find orientation, use original file orientation")
parser.add_argument('-2', dest='two', action='store_true', help="Process 2 sides")
args = parser.parse_args()
if not Path(args.input).exists():
print('input file/path does not exist: ' + args.input)
exit()
recognizer = infer_retinanet.BrailleInference(
params_fn=os.path.join(local_config.data_path, 'weights', 'param.txt'),
model_weights_fn=os.path.join(local_config.data_path, 'weights', model_weights),
create_script=None)
if Path(args.input).is_dir():
results_dir = args.results_dir or args.input
recognizer.process_dir_and_save(str(Path(args.input)/'**'/'*.*'), results_dir,
lang=args.lang, extra_info=None,
draw_refined=recognizer.DRAW_NONE,
remove_labeled_from_filename=False,
find_orientation=args.orient,
align_results=True,
process_2_sides=args.two,
repeat_on_aligned=False,
save_development_info=False)
else:
results_dir = args.results_dir or Path(args.input).parent