def run_single(self):
args = self.args
fold_log = self.fold_log
from omr.steps.algorithm import AlgorithmPredictorSettings, AlgorithmTrainerSettings, AlgorithmTrainerParams
from omr.steps.step import Step
global_args = args.global_args
def print_dataset_content(files: List[PcGts], label: str):
fold_log.debug("Got {} {} files: {}".format(len(files), label, [f.page.location.local_path() for f in files]))
print_dataset_content(args.train_pcgts_files, 'training')
if args.validation_pcgts_files:
print_dataset_content(args.validation_pcgts_files, 'validation')
else:
fold_log.debug("No validation data. Using training data instead")
print_dataset_content(args.test_pcgts_files, 'testing')
if not os.path.exists(args.model_dir):
os.makedirs(args.model_dir)
prediction_path = os.path.join(args.model_dir, 'pred.json')
model_path = os.path.join(args.model_dir, 'best')
if not global_args.skip_train:
fold_log.info("Starting training")
trainer = Step.create_trainer(
global_args.algorithm_type,
AlgorithmTrainerSettings(
dataset_params=args.global_args.dataset_params,
train_data=args.train_pcgts_files,
validation_data=args.validation_pcgts_files if args.validation_pcgts_files else args.train_pcgts_files,
model=Model(MetaId.from_custom_path(model_path, global_args.algorithm_type)),
params=global_args.trainer_params,
page_segmentation_params=global_args.page_segmentation_params,
calamari_params=global_args.calamari_params,
)
)
trainer.train()
test_pcgts_files = args.test_pcgts_files
if not global_args.skip_predict:
fold_log.info("Starting prediction")
pred_params = deepcopy(global_args.predictor_params)
pred_params.modelId = MetaId.from_custom_path(model_path, global_args.algorithm_type)
if global_args.calamari_dictionary_from_gt:
words = set()
for pcgts in test_pcgts_files:
words = words.union(sum([t.sentence.text().replace('-', '').split() for t in pcgts.page.all_text_lines()], []))
pred_params.ctcDecoder.params.dictionary[:] = words
pred = Step.create_predictor(
global_args.algorithm_type,
AlgorithmPredictorSettings(
None,
pred_params,
))
full_predictions = list(pred.predict([f.page.location for f in test_pcgts_files]))
predictions = self.extract_gt_prediction(full_predictions)
with open(prediction_path, 'wb') as f:
pickle.dump(predictions, f)
if global_args.output_book:
fold_log.info("Outputting data")
pred_book = DatabaseBook(global_args.output_book)
if not pred_book.exists():
pred_book_meta = DatabaseBookMeta(pred_book.book, pred_book.book)
pred_book.create(pred_book_meta)
output_pcgts = [PcGts.from_file(pcgts.page.location.copy_to(pred_book).file('pcgts'))
for pcgts in test_pcgts_files]
self.output_prediction_to_book(pred_book, output_pcgts, full_predictions)
for o_pcgts in output_pcgts:
o_pcgts.to_file(o_pcgts.page.location.file('pcgts').local_path())
else:
fold_log.info("Skipping prediction")
with open(prediction_path, 'rb') as f:
predictions = pickle.load(f)
predictions = tuple(predictions)
if not global_args.skip_eval and len(predictions) > 0:
fold_log.info("Starting evaluation")
r = self.evaluate(predictions, global_args.evaluation_params)
else:
r = None
# if not global_args.skip_cleanup:
# fold_log.info("Cleanup")
# shutil.rmtree(args.model_dir)
return r
if __name__ == "__main__":
from database import DatabaseBook
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
from omr.steps.step import Step, AlgorithmTypes
b = DatabaseBook('demo')
p = b.page('page00000001')
img = np.array(Image.open(p.file('color_norm').local_path()))
mask = np.zeros(img.shape, np.float) + 255
val_pcgts = [PcGts.from_file(p.file('pcgts'))]
settings = PredictorSettings()
pred = Step.create_predictor(AlgorithmTypes.LAYOUT_SIMPLE_BOUNDING_BOXES, settings)
def s(c):
return val_pcgts[0].page.page_to_image_scale(c, settings.page_scale_reference)
for p in pred.predict(val_pcgts):
for i, mr_c in enumerate(p.blocks.get(BlockType.MUSIC, [])):
s(mr_c.coords).draw(mask, (255, 0, 0), fill=True, thickness=0)
for i, mr_c in enumerate(p.blocks.get(BlockType.LYRICS, [])):
s(mr_c.coords).draw(mask, (0, 255, 0), fill=True, thickness=0)
for i, mr_c in enumerate(p.blocks.get(BlockType.DROP_CAPITAL, [])):
s(mr_c.coords).draw(mask, (0, 0, 255), fill=True, thickness=0)
import json
