def run(args):
# check if loading a json file
if len(args.files) == 1 and args.files[0].endswith("json"):
import json
with open(args.files[0], 'r') as f:
json_args = json.load(f)
for key, value in json_args.items():
setattr(args, key, value)
# checks
if args.extended_prediction_data_format not in ["pred", "json"]:
raise Exception(
"Only 'pred' and 'json' are allowed extended prediction data formats"
)
# add json as extension, resolve wildcard, expand user, ... and remove .json again
args.checkpoint = [(cp if cp.endswith(".json") else cp + ".json")
for cp in args.checkpoint]
args.checkpoint = glob_all(args.checkpoint)
args.checkpoint = [cp[:-5] for cp in args.checkpoint]
# create voter
voter_params = VoterParams()
voter_params.type = VoterParams.Type.Value(args.voter.upper())
voter = voter_from_proto(voter_params)
# load files
input_image_files = glob_all(args.files)
if args.text_files:
args.text_files = glob_all(args.text_files)
# skip invalid files and remove them, there wont be predictions of invalid files
dataset = create_dataset(
args.dataset,
DataSetMode.PREDICT,
input_image_files,
args.text_files,
skip_invalid=True,
remove_invalid=True,
args={'text_index': args.pagexml_text_index},
)
print("Found {} files in the dataset".format(len(dataset)))
if len(dataset) == 0:
raise Exception(
"Empty dataset provided. Check your files argument (got {})!".
format(args.files))
# predict for all models
predictor = MultiPredictor(checkpoints=args.checkpoint,
batch_size=args.batch_size,
processes=args.processes)
do_prediction = predictor.predict_dataset(
dataset, progress_bar=not args.no_progress_bars)
avg_sentence_confidence = 0
n_predictions = 0
# output the voted results to the appropriate files
for result, sample in do_prediction:
n_predictions += 1
for i, p in enumerate(result):
p.prediction.id = "fold_{}".format(i)
# vote the results (if only one model is given, this will just return the sentences)
prediction = voter.vote_prediction_result(result)
prediction.id = "voted"
sentence = prediction.sentence
avg_sentence_confidence += prediction.avg_char_probability
if args.verbose:
lr = "\u202A\u202B"
print("{}: '{}{}{}'".format(sample['id'],
lr[get_base_level(sentence)], sentence,
"\u202C"))
output_dir = args.output_dir
dataset.store_text(sentence,
sample,
output_dir=output_dir,
extension=".pred.txt")
if args.extended_prediction_data:
ps = Predictions()
ps.line_path = sample[
'image_path'] if 'image_path' in sample else sample['id']
ps.predictions.extend([prediction] +
[r.prediction for r in result])
output_dir = output_dir if output_dir else os.path.dirname(
ps.line_path)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
if args.extended_prediction_data_format == "pred":
with open(os.path.join(output_dir, sample['id'] + ".pred"),
'wb') as f:
f.write(ps.SerializeToString())
elif args.extended_prediction_data_format == "json":
with open(os.path.join(output_dir, sample['id'] + ".json"),
'w') as f:
# remove logits
for prediction in ps.predictions:
prediction.logits.rows = 0
prediction.logits.cols = 0
prediction.logits.data[:] = []
f.write(
MessageToJson(ps, including_default_value_fields=True))
else:
raise Exception("Unknown prediction format.")
print("Average sentence confidence: {:.2%}".format(
avg_sentence_confidence / n_predictions))
dataset.store()
print("All files written")
def run(args):
# checks
if args.extended_prediction_data_format not in ["pred", "json"]:
raise Exception(
"Only 'pred' and 'json' are allowed extended prediction data formats"
)
# add json as extension, resolve wildcard, expand user, ... and remove .json again
args.checkpoint = [(cp if cp.endswith(".json") else cp + ".json")
for cp in args.checkpoint]
args.checkpoint = glob_all(args.checkpoint)
args.checkpoint = [cp[:-5] for cp in args.checkpoint]
# create voter
voter_params = VoterParams()
voter_params.type = VoterParams.Type.Value(args.voter.upper())
voter = voter_from_proto(voter_params)
# load files
files = glob.glob(args.files)
dataset = AbbyyDataSet(files,
skip_invalid=True,
remove_invalid=False,
binary=args.binary)
dataset.load_samples(processes=args.processes,
progress_bar=not args.no_progress_bars)
print("Found {} files in the dataset".format(len(dataset)))
if len(dataset) == 0:
raise Exception(
"Empty dataset provided. Check your files argument (got {})!".
format(args.files))
# predict for all models
predictor = MultiPredictor(checkpoints=args.checkpoint,
batch_size=args.batch_size,
processes=args.processes)
do_prediction = predictor.predict_dataset(
dataset, progress_bar=not args.no_progress_bars)
# output the voted results to the appropriate files
input_image_files = []
# creat input_image_files list for next loop
for page in dataset.book.pages:
for fo in page.getFormats():
input_image_files.append(page.imgFile)
for (result, sample), filepath in zip(do_prediction, input_image_files):
for i, p in enumerate(result):
p.prediction.id = "fold_{}".format(i)
# vote the results (if only one model is given, this will just return the sentences)
prediction = voter.vote_prediction_result(result)
prediction.id = "voted"
sentence = prediction.sentence
if args.verbose:
lr = "\u202A\u202B"
print("{}: '{}{}{}'".format(sample['id'],
lr[get_base_level(sentence)], sentence,
"\u202C"))
output_dir = args.output_dir if args.output_dir else os.path.dirname(
filepath)
sample["format"].text = sentence
if args.extended_prediction_data:
ps = Predictions()
ps.line_path = filepath
ps.predictions.extend([prediction] +
[r.prediction for r in result])
if args.extended_prediction_data_format == "pred":
with open(os.path.join(output_dir, sample['id'] + ".pred"),
'wb') as f:
f.write(ps.SerializeToString())
elif args.extended_prediction_data_format == "json":
with open(os.path.join(output_dir, sample['id'] + ".json"),
'w') as f:
# remove logits
for prediction in ps.predictions:
prediction.logits.rows = 0
prediction.logits.cols = 0
prediction.logits.data[:] = []
f.write(
MessageToJson(ps, including_default_value_fields=True))
else:
raise Exception("Unknown prediction format.")
w = XMLWriter(output_dir, os.path.dirname(filepath), dataset.book)
w.write()
print("All files written")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--files",
nargs="+",
required=True,
default=[],
help="List all image files that shall be processed")
parser.add_argument("--checkpoint",
type=str,
nargs="+",
default=[],
help="Path to the checkpoint without file extension")
parser.add_argument("-j",
"--processes",
type=int,
default=1,
help="Number of processes to use")
parser.add_argument(
"--batch_size",
type=int,
default=1,
help=
"The batch size during the prediction (number of lines to process in parallel)"
)
parser.add_argument("--verbose",
action="store_true",
help="Print additional information")
parser.add_argument(
"--voter",
type=str,
default="confidence_voter_default_ctc",
help=
"The voting algorithm to use. Possible values: confidence_voter_default_ctc (default), "
"confidence_voter_fuzzy_ctc, sequence_voter")
parser.add_argument(
"--output_dir",
type=str,
help=
"By default the prediction files will be written to the same directory as the given files. "
"You can use this argument to specify a specific output dir for the prediction files."
)
parser.add_argument(
"--extended_prediction_data",
action="store_true",
help=
"Write: Predicted string, labels; position, probabilities and alternatives of chars to a .pred (protobuf) file"
)
parser.add_argument(
"--extended_prediction_data_format",
type=str,
default="json",
help=
"Extension format: Either pred or json. Note that json will not print logits."
)
parser.add_argument("--no_progress_bars",
action="store_true",
help="Do not show any progress bars")
args = parser.parse_args()
# checks
if args.extended_prediction_data_format not in ["pred", "json"]:
raise Exception(
"Only 'pred' and 'json' are allowed extended prediction data formats"
)
# add json as extension, resolve wildcard, expand user, ... and remove .json again
args.checkpoint = [(cp if cp.endswith(".json") else cp + ".json")
for cp in args.checkpoint]
args.checkpoint = glob_all(args.checkpoint)
args.checkpoint = [cp[:-5] for cp in args.checkpoint]
# create voter
voter_params = VoterParams()
voter_params.type = VoterParams.Type.Value(args.voter.upper())
voter = voter_from_proto(voter_params)
# load files
input_image_files = sorted(glob_all(args.files))
# skip invalid files, but keep then so that empty predictions are created
dataset = FileDataSet(input_image_files,
skip_invalid=True,
remove_invalid=False)
print("Found {} files in the dataset".format(len(dataset)))
if len(dataset) == 0:
raise Exception(
"Empty dataset provided. Check your files argument (got {})!".
format(args.files))
# predict for all models
predictor = MultiPredictor(checkpoints=args.checkpoint)
do_prediction = predictor.predict_dataset(
dataset,
batch_size=args.batch_size,
processes=args.processes,
progress_bar=not args.no_progress_bars)
# output the voted results to the appropriate files
for (result, sample), filepath in zip(do_prediction, input_image_files):
for i, p in enumerate(result):
p.prediction.id = "fold_{}".format(i)
# vote the results (if only one model is given, this will just return the sentences)
prediction = voter.vote_prediction_result(result)
prediction.id = "voted"
sentence = prediction.sentence
if args.verbose:
print("{}: '{}'".format(sample['id'], sentence))
output_dir = args.output_dir if args.output_dir else os.path.dirname(
filepath)
with codecs.open(os.path.join(output_dir, sample['id'] + ".pred.txt"),
'w', 'utf-8') as f:
f.write(sentence)
if args.extended_prediction_data:
ps = Predictions()
ps.line_path = filepath
ps.predictions.extend([prediction] +
[r.prediction for r in result])
if args.extended_prediction_data_format == "pred":
with open(os.path.join(output_dir, sample['id'] + ".pred"),
'wb') as f:
f.write(ps.SerializeToString())
elif args.extended_prediction_data_format == "json":
with open(os.path.join(output_dir, sample['id'] + ".json"),
'w') as f:
# remove logits
for prediction in ps.predictions:
prediction.logits.rows = 0
prediction.logits.cols = 0
prediction.logits.data[:] = []
f.write(
MessageToJson(ps, including_default_value_fields=True))
else:
raise Exception("Unknown prediction format.")
print("All files written")
def run(args):
# check if loading a json file
if len(args.files) == 1 and args.files[0].endswith("json"):
import json
with open(args.files[0], 'r') as f:
json_args = json.load(f)
for key, value in json_args.items():
setattr(args, key, value)
# checks
if args.extended_prediction_data_format not in ["pred", "json"]:
raise Exception("Only 'pred' and 'json' are allowed extended prediction data formats")
# add json as extension, resolve wildcard, expand user, ... and remove .json again
args.checkpoint = [(cp if cp.endswith(".json") else cp + ".json") for cp in args.checkpoint]
args.checkpoint = glob_all(args.checkpoint)
args.checkpoint = [cp[:-5] for cp in args.checkpoint]
# create voter
voter_params = VoterParams()
voter_params.type = VoterParams.Type.Value(args.voter.upper())
voter = voter_from_proto(voter_params)
# load files
input_image_files = glob_all(args.files)
if args.text_files:
args.text_files = glob_all(args.text_files)
# skip invalid files and remove them, there wont be predictions of invalid files
dataset = create_dataset(
args.dataset,
DataSetMode.PREDICT,
input_image_files,
args.text_files,
skip_invalid=True,
remove_invalid=True,
args={'text_index': args.pagexml_text_index},
)
print("Found {} files in the dataset".format(len(dataset)))
if len(dataset) == 0:
raise Exception("Empty dataset provided. Check your files argument (got {})!".format(args.files))
# predict for all models
predictor = MultiPredictor(checkpoints=args.checkpoint, batch_size=args.batch_size, processes=args.processes)
do_prediction = predictor.predict_dataset(dataset, progress_bar=not args.no_progress_bars)
avg_sentence_confidence = 0
n_predictions = 0
# output the voted results to the appropriate files
for result, sample in do_prediction:
n_predictions += 1
for i, p in enumerate(result):
p.prediction.id = "fold_{}".format(i)
# vote the results (if only one model is given, this will just return the sentences)
prediction = voter.vote_prediction_result(result)
prediction.id = "voted"
sentence = prediction.sentence
avg_sentence_confidence += prediction.avg_char_probability
if args.verbose:
lr = "\u202A\u202B"
print("{}: '{}{}{}'".format(sample['id'], lr[get_base_level(sentence)], sentence, "\u202C" ))
output_dir = args.output_dir
dataset.store_text(sentence, sample, output_dir=output_dir, extension=".pred.txt")
if args.extended_prediction_data:
ps = Predictions()
ps.line_path = sample['image_path'] if 'image_path' in sample else sample['id']
ps.predictions.extend([prediction] + [r.prediction for r in result])
output_dir = output_dir if output_dir else os.path.dirname(ps.line_path)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
if args.extended_prediction_data_format == "pred":
with open(os.path.join(output_dir, sample['id'] + ".pred"), 'wb') as f:
f.write(ps.SerializeToString())
elif args.extended_prediction_data_format == "json":
with open(os.path.join(output_dir, sample['id'] + ".json"), 'w') as f:
# remove logits
for prediction in ps.predictions:
prediction.logits.rows = 0
prediction.logits.cols = 0
prediction.logits.data[:] = []
f.write(MessageToJson(ps, including_default_value_fields=True))
else:
raise Exception("Unknown prediction format.")
print("Average sentence confidence: {:.2%}".format(avg_sentence_confidence / n_predictions))
dataset.store()
print("All files written")