def __init__(self, glyph_classifier_fn=None):
"""Creates the engine and TF graph for running OMR.
Args:
glyph_classifier_fn: Callable that loads the glyph classifier into the
graph. Accepts a `Structure` as the single argument, and returns an
instance of `BaseGlyphClassifier`. The function typically loads a TF
saved model or other external data, and wraps the classification in a
concrete glyph classifier subclass. If the classifier uses a
`StafflineExtractor` for classification, it must set the
`staffline_extractor` attribute of the `Structure`. Otherwise, glyph x
coordinates will not be scaled back to image coordinates.
"""
glyph_classifier_fn = (glyph_classifier_fn
or saved_classifier_fn.build_classifier_fn())
self.graph = tf.Graph()
self.session = tf.Session(graph=self.graph)
with self.graph.as_default():
with self.session.as_default():
with tf.name_scope('OMREngine'):
self.png_path = tf.placeholder(tf.string,
name='png_path',
shape=())
self.image = image.decode_music_score_png(
tf.read_file(self.png_path, name='page_image'))
self.structure = structure_module.create_structure(
self.image)
# Loading saved models happens outside of the name scope, because scopes
# can rename tensors from the model and cause dangling references.
# TODO(ringw): TF should be able to load models gracefully within a
# name scope.
self.glyph_classifier = glyph_classifier_fn(self.structure)
def run(input_pngs, glyphs_saved_model=None, output_notesequence=False):
"""Runs OMR over a list of input images.
Args:
input_pngs: A list of PNG filenames to process.
glyphs_saved_model: Optional saved model dir to override the included model.
output_notesequence: Whether to return a NoteSequence, as opposed to a Score
containing Pages with Glyphs.
Returns:
A NoteSequence message, or a Score message holding Pages for each input
image (with their detected Glyphs).
"""
return engine.OMREngine(
saved_classifier_fn.build_classifier_fn(glyphs_saved_model)).run(
input_pngs, output_notesequence=output_notesequence)