def test_corpus_image(self):
filename = os.path.join(tf.resource_loader.get_data_files_path(),
'../testdata/IMSLP00747-000.png')
image_t = omr_image.decode_music_score_png(tf.read_file(filename))
remover = removal.StaffRemover(staves.StaffDetector(image_t))
with self.test_session() as sess:
removed, image = sess.run([remover.remove_staves, image_t])
self.assertFalse(np.allclose(removed, image))
# If staff removal runs successfully, we should be unable to estimate the
# staffline distance from the staves-removed image.
est_staffline_distance, est_staffline_thickness = sess.run(
staffline_distance.estimate_staffline_distance_and_thickness(removed))
print(est_staffline_distance)
self.assertAllEqual([], est_staffline_distance)
self.assertEqual(-1, est_staffline_thickness)
def test_corpus_image(self):
# Test only the default staff detector (because projection won't detect all
# staves).
filename = os.path.join(tf.resource_loader.get_data_files_path(),
'../testdata/IMSLP00747-000.png')
image_t = omr_image.decode_music_score_png(tf.read_file(filename))
detector = staves.StaffDetector(image_t)
with self.test_session() as sess:
staves_arr, staffline_distances = sess.run(
[detector.staves, detector.staffline_distance])
self.assertAllClose(
np.mean(staves_arr[:, :, 1], axis=1), # average y position
[
413, 603, 848, 1040, 1286, 1476, 1724, 1915, 2162, 2354, 2604,
2795
],
atol=5)
self.assertAllEqual(staffline_distances, [16] * 12)
def __init__(self,
num_sections=DEFAULT_NUM_SECTIONS,
patch_height=15,
patch_width=12,
run_options=None):
self.num_sections = num_sections
self.patch_height = patch_height
self.patch_width = patch_width
self.run_options = run_options
self.graph = tf.Graph()
with self.graph.as_default():
# Identifying information for the patch.
self.filename = tf.placeholder(tf.string, name='filename')
self.staff_index = tf.placeholder(tf.int64, name='staff_index')
self.y_position = tf.placeholder(tf.int64, name='y_position')
image = image_module.decode_music_score_png(
tf.read_file(self.filename))
staff_detector = staves_module.StaffDetector(image)
staff_remover = removal.StaffRemover(staff_detector)
extractor = StafflineExtractor(staff_remover.remove_staves,
staff_detector,
num_sections=num_sections,
target_height=patch_height)
# Index into the staff strips array, where a y position of 0 is the center
# element. Positive positions count up (towards higher notes, towards the
# top of the image, and smaller indices into the array).
position_index = num_sections // 2 - self.y_position
self.all_stafflines = extractor.extract_staves()
# The entire extracted horizontal strip of the image.
self.staffline = self.all_stafflines[self.staff_index,
position_index]
# Determine the scale for converting image x coordinates to the scaled
# staff strip from which the patch is extracted.
extracted_staff_strip_height = tf.shape(self.all_stafflines)[2]
unscaled_staff_strip_heights = tf.multiply(
DEFAULT_STAFFLINE_DISTANCE_MULTIPLE,
staff_detector.staffline_distance)
self.all_staffline_scales = tf.divide(
tf.to_float(extracted_staff_strip_height),
tf.to_float(unscaled_staff_strip_heights))
self.staffline_scale = self.all_staffline_scales[self.staff_index]
def pipeline_graph(png_path, staffline_height, patch_width, num_stafflines):
"""Constructs the graph for the staffline patches pipeline.
Args:
png_path: Path to the input png. String scalar tensor.
staffline_height: Height of a staffline. int.
patch_width: Width of a patch. int.
num_stafflines: Number of stafflines to extract around each staff. int.
Returns:
A tensor representing the staffline patches. float32 with shape
(num_patches, staffline_height, patch_width).
"""
image_t = image.decode_music_score_png(tf.read_file(png_path))
staff_detector = staves.StaffDetector(image_t)
staff_remover = removal.StaffRemover(staff_detector)
stafflines = tf.identity(staffline_extractor.StafflineExtractor(
staff_remover.remove_staves,
staff_detector,
target_height=staffline_height,
num_sections=num_stafflines).extract_staves(),
name='stafflines')
return _extract_patches(stafflines, patch_width)
