def test_true_segments_1d_large(self):
# Arbitrary boolean array to get True and False runs from.
run_values = [False, True, False, True, False, True, False, True]
run_lengths = [3, 5, 2, 6, 4, 8, 7, 1]
values = tf.constant(np.repeat(run_values, run_lengths))
centers, lengths = segments.true_segments_1d(
values, mode=segments.SegmentsMode.CENTERS)
with self.test_session():
self.assertAllEqual(centers.eval(), [
sum(run_lengths[:1]) + (run_lengths[1] - 1) // 2,
sum(run_lengths[:3]) + (run_lengths[3] - 1) // 2,
sum(run_lengths[:5]) + (run_lengths[5] - 1) // 2,
sum(run_lengths[:7]) + (run_lengths[7] - 1) // 2
])
self.assertAllEqual(lengths.eval(), run_lengths[1::2])
starts, lengths = segments.true_segments_1d(
values, mode=segments.SegmentsMode.STARTS)
with self.test_session():
self.assertAllEqual(starts.eval(), [
sum(run_lengths[:1]),
sum(run_lengths[:3]),
sum(run_lengths[:5]),
sum(run_lengths[:7])
])
self.assertAllEqual(lengths.eval(), run_lengths[1::2])
def test_true_segments_1d_max_gap(self):
# Arbitrary boolean array to get True and False runs from.
values = tf.constant([
False, False,
True, True, True,
False, False,
True,
False, False, False, False, False, False,
True, True, True, True,
False,
True, True,
False, False,
True,
]) # pyformat: disable
centers, lengths = segments.true_segments_1d(values, max_gap=0)
with self.test_session():
self.assertAllEqual(centers.eval(), [3, 7, 15, 19, 23])
self.assertAllEqual(lengths.eval(), [3, 1, 4, 2, 1])
centers, lengths = segments.true_segments_1d(values, max_gap=1)
with self.test_session():
self.assertAllEqual(centers.eval(), [3, 7, 17, 23])
self.assertAllEqual(lengths.eval(), [3, 1, 7, 1])
for max_gap in range(2, 6):
centers, lengths = segments.true_segments_1d(values, max_gap=max_gap)
with self.test_session():
self.assertAllEqual(centers.eval(), [4, 18])
self.assertAllEqual(lengths.eval(), [6, 10])
centers, lengths = segments.true_segments_1d(values, max_gap=6)
with self.test_session():
self.assertAllEqual(centers.eval(), [12])
self.assertAllEqual(lengths.eval(), [22])
def test_true_segments_1d(self):
# Arbitrary boolean array to get True and False runs from.
values = tf.constant([True, True, True, False, True, False, True, True])
centers, lengths = segments.true_segments_1d(
values, mode=segments.SegmentsMode.CENTERS)
with self.test_session():
self.assertAllEqual(centers.eval(), [1, 4, 6])
self.assertAllEqual(lengths.eval(), [3, 1, 2])
starts, lengths = segments.true_segments_1d(
values, mode=segments.SegmentsMode.STARTS)
with self.test_session():
self.assertAllEqual(starts.eval(), [0, 4, 6])
self.assertAllEqual(lengths.eval(), [3, 1, 2])
def _verticals_in_column(self, max_gap, column):
"""Gets the verticals from a single column.
Args:
max_gap: The scalar max_gap value to use. int tensor.
column: The scalar column index. int tensor.
Returns:
int32 tensor of shape (num_lines_in_column, 2, 2). All start_x and end_x
values are equal to column.
"""
image_column = self.filtered_image[:, column]
run_starts, run_lengths = segments.true_segments_1d(
image_column,
mode=segments.SegmentsMode.STARTS,
max_gap=max_gap,
min_length=self.min_length)
num_runs = tf.shape(run_starts)[0]
# x is the same for all runs in the column.
x = tf.fill([num_runs], column)
y0 = run_starts
y1 = run_starts + run_lengths - 1
return tf.stack(
[
tf.stack([x, y0], axis=1),
tf.stack([x, y1], axis=1),
], axis=1)
def _test_true_segments_1d_min_length(self, min_length):
# Arbitrary boolean array to get True and False runs from.
values = tf.constant([
False,
False,
False,
True,
False,
True,
True,
False,
True,
False,
True,
True,
True,
True,
False,
True,
True,
]) # pyformat: disable
all_centers = np.asarray([3, 5, 8, 11, 15])
all_lengths = np.asarray([1, 2, 1, 4, 2])
expected_centers = all_centers[all_lengths >= min_length]
expected_lengths = all_lengths[all_lengths >= min_length]
centers, lengths = segments.true_segments_1d(values,
min_length=min_length)
with self.test_session():
self.assertAllEqual(expected_centers, centers.eval())
self.assertAllEqual(expected_lengths, lengths.eval())
def test_true_segments_1d_empty(self):
for mode in list(segments.SegmentsMode):
for max_gap in [0, 1]:
centers, lengths = segments.true_segments_1d(
[], mode=mode, max_gap=max_gap)
with self.test_session():
self.assertAllEqual(centers.eval(), [])
self.assertAllEqual(lengths.eval(), [])
def _process_column(i):
"""Removes staves from a single column of the image.
Args:
i: The index of the column to remove.
Returns:
The single column of the image with staff lines erased.
"""
column = image[:, i]
# Identify runs in the column that correspond to staff lines and can be
# erased.
runs, run_lengths = segments.true_segments_1d(
column < self.threshold)
column_staffline_ys = all_staffline_center_ys[:, :, i]
# The run center has to be within staffline_thickness of a staff line.
run_matches_staffline = tf.less_equal(
tf.reduce_min(tf.abs(runs[:, None, None] -
column_staffline_ys[None, :, :]),
axis=[1, 2]),
self.staff_detector.staffline_thickness)
keep_run = tf.logical_and(run_lengths < max_runlength,
run_matches_staffline)
keep_run.set_shape([None])
runs = tf.boolean_mask(runs, keep_run)
run_lengths = tf.boolean_mask(run_lengths, keep_run)
def do_process_column(runs, run_lengths):
"""Process the column if there are any runs matching staff lines.
Args:
runs: The center of each vertical run.
run_lengths: The length of each vertical run.
Returns:
The column of the image with staff lines erased.
"""
# Erase ys that belong to a run corresponding to a staff line.
y_run_pair_distance = tf.abs(ys[:, None] - runs[None, :])
y_runs = tf.argmin(y_run_pair_distance, axis=1)
y_run_distance = tf.reduce_min(y_run_pair_distance, axis=1)
y_run_lengths = tf.gather(run_lengths, y_runs)
erase_y = tf.less_equal(y_run_distance,
tf.floordiv(y_run_lengths, 2))
white_column = tf.fill(tf.shape(column),
tf.constant(255, tf.uint8))
return tf.where(erase_y, white_column, column)
return tf.cond(
tf.shape(runs)[0] > 0,
lambda: do_process_column(runs, run_lengths), lambda: column)
def _test_true_segments_1d_all_false(self, length):
centers, lengths = segments.true_segments_1d(tf.zeros(length, tf.bool))
with self.test_session():
self.assertAllEqual(centers.eval(), [])
self.assertAllEqual(lengths.eval(), [])
def _get_staff_ys(is_staff, staffline_thickness):
# Return the detected staves--segments in is_staff that are roughly not much
# bigger than staffline_thickness.
segment_ys, segment_sizes = segments.true_segments_1d(is_staff)
return tf.boolean_mask(segment_ys,
segment_sizes <= staffline_thickness * 2)
