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 load(book: DatabaseBook):
path = book.local_path('book_dictionary.json')
try:
with open(path) as f:
d = DatabaseDictionary.from_book_json(book, json.load(f))
except FileNotFoundError:
try:
path = os.path.join(settings.BASE_DIR,
'internal_storage',
'default_dictionary',
'default_dictionary.json')
with open(path) as f:
d = DatabaseDictionary.from_book_json(book, json.load(f))
except FileNotFoundError:
d = DatabaseDictionary(b_id=book.book)
return d
def to_file(self, book: DatabaseBook):
self.b_id = book.book
s = self.to_json()
with open(book.local_path('book_dictionary.json'), 'w') as f:
js = json.dumps(s, indent=2)
f.write(js)
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
counts[Counts.Symbols] += len(n.notes)
counts[Counts.NoteComponents] += len(n.notes)
cm.plot_confusion_matrix(normalize=True)
return f_metrics.mean(axis=0), counts.sum(
axis=0), acc_counts, acc_acc, total_diffs
if __name__ == '__main__':
from omr.symboldetection.predictor import SymbolDetectionPredictor, create_predictor, PredictorTypes, SymbolDetectionPredictorParameters
from prettytable import PrettyTable
from database import DatabaseBook
b = DatabaseBook('Graduel')
eval_pcgts = [PcGts.from_file(p.file('pcgts')) for p in b.pages()[12:13]]
print([e.page.location.local_path() for e in eval_pcgts])
predictor = create_predictor(
PredictorTypes.PIXEL_CLASSIFIER,
SymbolDetectionPredictorParameters(
[b.local_path(os.path.join('pc_symbol_detection', 'model'))]))
gt_symbols, pred_symbols = [], []
for p in predictor.predict(eval_pcgts):
pred_symbols.append(p.symbols)
gt_symbols.append(p.line.operation.music_line.symbols)
evaluator = SymbolDetectionEvaluator()
metrics, counts, acc_counts, acc_acc, diffs = evaluator.evaluate(
gt_symbols, pred_symbols)
at = PrettyTable()
at.add_column("Type", ["All", "All", "Notes", "Clefs", "Accids"])
at.add_column("TP", counts[:, Counts.TP])
at.add_column("FP", counts[:, Counts.FP])
at.add_column("FN", counts[:, Counts.FN])