def put(self, request, book):
book = DatabaseBook(book)
if not book.exists():
return Response(status=status.HTTP_400_BAD_REQUEST)
file = book.local_path("virtual_keyboard.json")
json.dump(json.loads(request.body, encoding="utf-8"),
open(file, 'w'),
indent=4)
return Response()
def get(self, request, book):
book = DatabaseBook(book)
if not book.exists():
return Response(status=status.HTTP_400_BAD_REQUEST)
file = book.local_path("virtual_keyboard.json")
if not os.path.exists(file):
file = book.local_default_virtual_keyboards_path('default.json')
with open(file) as f:
return Response(json.load(f))
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
Symbols = 3
NoteComponents = 4
Clefs = 5
Accidentals = 6
Size = 7
global_counts = np.zeros(Counts.Size, dtype=np.uint32)
table = PrettyTable(["Dataset"] +
[str(Counts(i))[7:] for i in range(Counts.Size)])
for book_name in args.datasets:
book = DatabaseBook(book_name)
counts = np.zeros(Counts.Size, dtype=np.uint32)
if not book.exists():
raise ValueError("Dataset '{}' does not exist at '{}'".format(
book.book, book.local_path()))
for page in book.pages():
pcgts = PcGts.from_file(page.file('pcgts'))
counts[Counts.Pages] += 1
for mr in pcgts.page.music_regions:
for ml in mr.staffs:
counts[Counts.Staves] += 1
counts[Counts.StaffLines] += len(ml.staff_lines)
for s in ml.symbols:
if isinstance(s, Neume):
n: Neume = s