def main(args: EvalArgs):
# Local imports (imports that require tensorflow)
from calamari_ocr.ocr.scenario import CalamariScenario
from calamari_ocr.ocr.dataset.data import Data
from calamari_ocr.ocr.evaluator import Evaluator
if args.checkpoint:
saved_model = SavedCalamariModel(args.checkpoint, auto_update=True)
trainer_params = CalamariScenario.trainer_cls().params_cls().from_dict(saved_model.dict)
data_params = trainer_params.scenario.data
else:
data_params = Data.default_params()
data = Data(data_params)
pred_data = args.pred if args.pred is not None else args.gt.to_prediction()
evaluator = Evaluator(args.evaluator, data=data)
evaluator.preload_gt(gt_dataset=args.gt)
r = evaluator.run(gt_dataset=args.gt, pred_dataset=pred_data)
# TODO: More output
print("Evaluation result")
print("=================")
print("")
print(
"Got mean normalized label error rate of {:.2%} ({} errs, {} total chars, {} sync errs)".format(
r["avg_ler"], r["total_char_errs"], r["total_chars"], r["total_sync_errs"]
)
)
# sort descending
print_confusions(r, args.n_confusions)
samples = data.create_pipeline(evaluator.params.setup, args.gt).reader().samples()
print_worst_lines(r, samples, args.n_worst_lines)
if args.xlsx_output:
write_xlsx(
args.xlsx_output,
[
{
"prefix": "evaluation",
"results": r,
"gt_files": [s["id"] for s in samples],
}
],
)
return r
def get_preproc_image():
data_params = Data.default_params()
data_params.skip_invalid_gt = False
data_params.pre_proc.run_parallel = False
data_params.pre_proc.processors = data_params.pre_proc.processors[:-1]
for p in data_params.pre_proc.processors_of_type(FinalPreparationProcessorParams):
p.pad = 0
post_init(data_params)
pl = Data(data_params).create_pipeline(DataPipelineParams, None)
pl.mode = PipelineMode.PREDICTION
preproc = data_params.pre_proc.create(pl)
def pp(image):
its = InputSample(
image, None, SampleMeta("001", fold_id="01")
).to_input_target_sample()
s = preproc.apply_on_sample(its)
return s.inputs
return pp
def get_preproc_text(rtl=False):
data_params = Data.default_params()
data_params.skip_invalid_gt = False
data_params.pre_proc.run_parallel = False
if rtl:
for p in data_params.pre_proc.processors_of_type(BidiTextProcessorParams):
p.bidi_direction = BidiDirection.RTL
post_init(data_params)
pl = Data(data_params).create_pipeline(DataPipelineParams, None)
pl.mode = PipelineMode.TARGETS
preproc = data_params.pre_proc.create(pl)
def pp(text):
its = InputSample(
None, text, SampleMeta("001", fold_id="01")
).to_input_target_sample()
s = preproc.apply_on_sample(its)
return s.targets
return pp