# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Extracts non-empty patches of extracted stafflines.
Extracts vertical slices of the image where glyphs are expected
(see `staffline_extractor.py`), and takes horizontal windows of the slice which
will be clustered. Some patches will have a glyph roughly in their center, and
the corresponding cluster centroids will be labeled as such.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import apache_beam as beam
from apache_beam import metrics
from moonlight.staves import staffline_extractor
from moonlight.util import more_iter_tools
import numpy as np
from six.moves import filter
import tensorflow as tf
def _filter_patch(patch, min_num_dark_pixels=10):
unused_patch_name, patch = patch
return np.greater_equal(np.sum(np.less(patch, 0.5)), min_num_dark_pixels)
class StafflinePatchesDoFn(beam.DoFn):
"""Runs the staffline patches graph."""
def __init__(self, patch_height, patch_width, num_stafflines, timeout_ms,
max_patches_per_page):
self.patch_height = patch_height
self.patch_width = patch_width
self.num_stafflines = num_stafflines
self.timeout_ms = timeout_ms
self.max_patches_per_page = max_patches_per_page
self.total_pages_counter = metrics.Metrics.counter(self.__class__,
'total_pages')
self.failed_pages_counter = metrics.Metrics.counter(self.__class__,
'failed_pages')
self.successful_pages_counter = metrics.Metrics.counter(
self.__class__, 'successful_pages')
self.empty_pages_counter = metrics.Metrics.counter(self.__class__,
'empty_pages')
self.total_patches_counter = metrics.Metrics.counter(
self.__class__, 'total_patches')
self.emitted_patches_counter = metrics.Metrics.counter(
self.__class__, 'emitted_patches')
def start_bundle(self):
self.extractor = staffline_extractor.StafflinePatchExtractor(
patch_height=self.patch_height,
patch_width=self.patch_width,
run_options=tf.RunOptions(timeout_in_ms=self.timeout_ms))
self.session = tf.Session(graph=self.extractor.graph)
def process(self, png_path):
self.total_pages_counter.inc()
try:
with self.session.as_default():
patches_iter = self.extractor.page_patch_iterator(png_path)
# pylint: disable=broad-except
except Exception:
logging.exception('Skipping failed music score (%s)', png_path)
self.failed_pages_counter.inc()
return
patches_iter = filter(_filter_patch, patches_iter)
if 0 < self.max_patches_per_page:
# Subsample patches.
patches = more_iter_tools.iter_sample(patches_iter,
self.max_patches_per_page)
else:
patches = list(patches_iter)
if not patches:
self.empty_pages_counter.inc()
self.total_patches_counter.inc(len(patches))
# Serialize each patch as an Example.
for patch_name, patch in patches:
example = tf.train.Example()
example.features.feature['name'].bytes_list.value.append(
patch_name.encode('utf-8'))
example.features.feature['features'].float_list.value.extend(
patch.ravel())
example.features.feature['height'].int64_list.value.append(patch.shape[0])
example.features.feature['width'].int64_list.value.append(patch.shape[1])
yield example
self.successful_pages_counter.inc()
# Patches are sub-sampled by this point.
self.emitted_patches_counter.inc(len(patches))
def finish_bundle(self):
self.session.close()
del self.extractor
del self.session
