def run(self,
n_train: int,
val_amount: float,
cross_folds: int,
single_folds: List[int],
train_books: List[str],
test_books: List[str],
train_books_extend: List[str]
):
from omr.steps.step import Step
global_args = self.global_args
logger.info("Finding PcGts files with valid ground truth")
train_args = generate_dataset(
lock_states=[LockState(Locks.STAFF_LINES, True), LockState(Locks.LAYOUT, True)],
n_train=n_train,
val_amount=val_amount,
cross_folds=cross_folds,
single_folds=single_folds,
train_books=train_books,
test_books=test_books,
train_books_extend=train_books_extend,
)
train_args = [SingleDataArgs(gd.fold,
os.path.join(global_args.model_dir, '{}_{}'.format(global_args.algorithm_type.value, gd.fold)),
gd.train_pcgts_files, gd.validation_pcgts_files,
gd.test_pcgts_files,
global_args) for gd in train_args]
experimenter_class = Step.meta(self.global_args.algorithm_type).experimenter()
results = [experimenter_class(args, logger).run_single() for args in train_args]
experimenter_class.print_results(self.global_args, results, logger)
def put(self, request, group, style):
meta = ModelMeta.from_json(request.body)
default_type = AlgorithmGroups(group).types()[0]
model = Model.from_id_str(meta.id)
target_meta = MetaId(
DatabaseAvailableModels.local_default_models(style, default_type),
Step.meta(default_type).model_dir())
target_model = Model(target_meta)
model.copy_to(target_model, override=True)
return Response()
def post(self, request, book, operation):
body = json.loads(request.body, encoding='utf-8')
book = DatabaseBook(book)
algorithm = Step.predictor(AlgorithmTypes(operation))
page_selection = PageSelection.from_params(PageSelectionParams.from_dict(body), book)
pages = page_selection.get_pages(algorithm.unprocessed)
return Response({
'pages': [p.page for p in pages],
'pageCount': page_selection.page_count.value,
'singlePage': page_selection.single_page,
'book': book.book,
'totalPages': len(book.pages()),
})
def __init__(self, settings: AlgorithmPredictorSettings):
super().__init__(settings)
meta = Step.meta(AlgorithmTypes.OCR_CALAMARI)
from ommr4all.settings import BASE_DIR
model = Model(
MetaId.from_custom_path(
BASE_DIR +
'/internal_storage/default_models/fraktur/text_calamari/',
meta.type()))
settings = AlgorithmPredictorSettings(model=model, )
settings.params.ctcDecoder.params.type = CTCDecoderParams.CTC_DEFAULT
self.ocr_predictor = meta.create_predictor(settings)
def __init__(self, settings: AlgorithmPredictorSettings):
super().__init__(settings)
self.document_id = settings.params.documentId
self.document_text = settings.params.documentText
self.document_similar_tester = SimilarDocumentChecker()
self.text_normalizer = LyricsNormalizationProcessor(
LyricsNormalizationParams(LyricsNormalization.WORDS))
meta = Step.meta(AlgorithmTypes.OCR_CALAMARI)
from ommr4all.settings import BASE_DIR
model = Model(
MetaId.from_custom_path(
BASE_DIR +
'/internal_storage/default_models/fraktur/text_calamari/',
meta.type()))
settings = AlgorithmPredictorSettings(model=model, )
settings.params.ctcDecoder.params.type = CTCDecoderParams.CTC_DEFAULT
self.ocr_predictor = meta.create_predictor(settings)
from ..dataset import SymbolDetectionDataset, Dataset
from omr.steps.step import Step
class Meta(AlgorithmMeta):
@staticmethod
def type() -> AlgorithmTypes:
return AlgorithmTypes.SYMBOLS_SEQUENCE_TO_SEQUENCE
@classmethod
def predictor(cls) -> Type[AlgorithmPredictor]:
from .predictor import OMRPredictor
return OMRPredictor
@classmethod
def trainer(cls) -> Type[AlgorithmTrainer]:
from .trainer import OMRTrainer
return OMRTrainer
@classmethod
def experimenter(cls) -> Type[Experimenter]:
from ..experimenter import SymbolsExperimenter
return SymbolsExperimenter
@staticmethod
def dataset_class() -> Type[Dataset]:
return SymbolDetectionDataset
Step.register(Meta)
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
from omr.dataset.datafiles import dataset_by_locked_pages, LockState
random.seed(1)
np.random.seed(1)
if False:
train_pcgts, val_pcgts = dataset_by_locked_pages(
0.8,
[LockState(Locks.SYMBOLS, True),
LockState(Locks.LAYOUT, True)],
True,
[
# DatabaseBook('Graduel_Part_1'),
# DatabaseBook('Graduel_Part_2'),
# DatabaseBook('Graduel_Part_3'),
])
book = DatabaseBook('Gothic_Test')
meta = Step.meta(AlgorithmTypes.OCR_CALAMARI)
# model = meta.newest_model_for_book(book)
model = Model(
MetaId.from_custom_path(
BASE_DIR +
'/internal_storage/pretrained_models/text_calamari/fraktur_historical',
meta.type()))
settings = AlgorithmPredictorSettings(model=model, )
pred = meta.create_predictor(settings)
ps: List[PredictionResult] = list(pred.predict(book.pages()[0:1]))
for i, p in enumerate(ps):
canvas = PcGtsCanvas(p.pcgts.page,
p.text_lines[0].line.operation.scale_reference)
for j, s in enumerate(p.text_lines):
canvas.draw(s)
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
from shared.pcgtscanvas import PcGtsCanvas
from omr.dataset.datafiles import dataset_by_locked_pages, LockState
random.seed(1)
np.random.seed(1)
if False:
train_pcgts, val_pcgts = dataset_by_locked_pages(
0.8,
[LockState(Locks.SYMBOLS, True),
LockState(Locks.LAYOUT, True)],
True,
[
# DatabaseBook('Graduel_Part_1'),
# DatabaseBook('Graduel_Part_2'),
# DatabaseBook('Graduel_Part_3'),
])
book = DatabaseBook('Paper_New_York')
meta = Step.meta(AlgorithmTypes.SYLLABLES_FROM_TEXT)
model = meta.best_model_for_book(book)
settings = AlgorithmPredictorSettings(model=model, )
pred = meta.create_predictor(settings)
ps: List[PredictionResult] = list(pred.predict(book.pages()[:1]))
for i, p in enumerate(ps):
pmr = p.page_match_result
canvas = PcGtsCanvas(pmr.pcgts.page, PageScaleReference.NORMALIZED_X2)
canvas.draw(pmr.text_prediction_result.text_lines[4],
color=(25, 150, 25),
background=True)
# canvas.draw(pmr.match_results)
# canvas.draw(p.annotations)
canvas.show()
def algorithm_meta(self) -> Type[AlgorithmMeta]:
return Step.create_meta(self.algorithm_type)
os.environ['DJANGO_SETTINGS_MODULE'] = 'ommr4all.settings'
django.setup()
from ommr4all.settings import BASE_DIR
import random
import matplotlib.pyplot as plt
from shared.pcgtscanvas import PcGtsCanvas
from omr.dataset.datafiles import dataset_by_locked_pages, LockState
from database.file_formats.pcgts import PageScaleReference
random.seed(1)
np.random.seed(1)
if False:
train_pcgts, val_pcgts = dataset_by_locked_pages(0.8, [LockState(Locks.SYMBOLS, True), LockState(Locks.LAYOUT, True)], True, [
# DatabaseBook('Graduel_Part_1'),
# DatabaseBook('Graduel_Part_2'),
# DatabaseBook('Graduel_Part_3'),
])
book = DatabaseBook('Paper_New_York')
meta = Step.meta(AlgorithmTypes.SYLLABLES_IN_ORDER)
model = meta.best_model_for_book(book)
settings = AlgorithmPredictorSettings(
model=model,
)
pred = meta.create_predictor(settings)
ps: List[PredictionResult] = list(pred.predict(book.pages()[:1]))
for i, p in enumerate(ps):
pmr = p.page_match_result
canvas = PcGtsCanvas(pmr.pcgts.page, PageScaleReference.NORMALIZED_X2)
canvas.draw(pmr.match_results)
canvas.draw(p.annotations)
canvas.show()
def get(self, request, group, style):
default_type = AlgorithmGroups(group).types()[0]
return Response(
Step.meta(default_type).list_available_models_for_style(
style).to_dict())
