def main(args):
logging.info('Starting main...')
_start_time = time.clock()
########################################################
# Load gt image.
logging.info('Loading staffline image.')
# - Initialize Dataset. This checks for the root.
if args.staff_imfile is None:
cvc_dataset = CvcMuscimaDataset(root=args.root)
args.staff_imfile = cvc_dataset.imfile(page=args.number,
writer=args.writer,
distortion='ideal',
mode='staff_only')
# - Load the image.
gt = (imread(args.staff_imfile, as_grey=True) * 255).astype('uint8')
# - Cast as binary mask.
gt[gt > 0] = 1
########################################################
# Locate stafflines in gt image.
logging.info('Getting staffline connected components.')
# - Get connected components in gt image.
connected_components, labels, bboxes = compute_connected_components(gt)
# - Use vertical dimension of CCs to determine which ones belong together
# to form stafflines. (Criterion: row overlap.)
n_rows, n_cols = gt.shape
intervals = [[] for _ in range(n_rows)
] # For each row: which CCs have pxs on that row?
for label, (t, l, b, r) in list(bboxes.items()):
if label == 0:
continue
# Ignore very short staffline segments that can easily be artifacts
# and should not affect the vertical range of the staffline anyway.
if (r - l) < 8:
continue
for row in range(t, b):
intervals[row].append(label)
logging.info('Grouping staffline connected components into stafflines.')
staffline_components = [
] # For each staffline, we collect the CCs that it is made of
_in_staffline = False
_current_staffline_components = []
for r_labels in intervals:
if not _in_staffline:
# Last row did not contain staffline components.
if len(r_labels) == 0:
# No staffline component on current row
continue
else:
_in_staffline = True
_current_staffline_components += r_labels
else:
# Last row contained staffline components.
if len(r_labels) == 0:
# Current staffline has no more rows.
staffline_components.append(set(_current_staffline_components))
_current_staffline_components = []
_in_staffline = False
continue
else:
# Current row contains staffline components: the current
# staffline continues.
_current_staffline_components += r_labels
logging.info('No. of stafflines, with component groups: {0}'
''.format(len(staffline_components)))
# Now: merge the staffline components into one bbox/mask.
logging.info(
'Merging staffline components into staffline bboxes and masks.')
staffline_bboxes = []
staffline_masks = []
for sc in sorted(staffline_components,
key=lambda c: min([bboxes[cc][0]
for cc in c])): # Sorted top-down
st, sl, sb, sr = n_rows, n_cols, 0, 0
for component in sc:
t, l, b, r = bboxes[component]
st, sl, sb, sr = min(t, st), min(l, sl), max(b, sb), max(r, sr)
_sm = gt[st:sb, sl:sr]
staffline_bboxes.append((st, sl, sb, sr))
staffline_masks.append(_sm)
# Check if n. of stafflines is divisible by 5
n_stafflines = len(staffline_bboxes)
logging.info('\tTotal stafflines: {0}'.format(n_stafflines))
if n_stafflines % 5 != 0:
import matplotlib.pyplot as plt
stafllines_mask_image = numpy.zeros(gt.shape)
for i, (_sb, _sm) in enumerate(zip(staffline_bboxes, staffline_masks)):
t, l, b, r = _sb
stafllines_mask_image[t:b, l:r] = min(255, (i * 333) % 255 + 40)
plt.imshow(stafllines_mask_image, cmap='jet', interpolation='nearest')
plt.show()
raise ValueError('No. of stafflines is not divisible by 5!')
logging.info('Creating staff bboxes and masks.')
# - Go top-down and group the stafflines by five to get staves.
# (The staffline bboxes are already sorted top-down.)
staff_bboxes = []
staff_masks = []
for i in range(n_stafflines // 5):
_sbb = staffline_bboxes[5 * i:5 * (i + 1)]
_st = min([bb[0] for bb in _sbb])
_sl = min([bb[1] for bb in _sbb])
_sb = max([bb[2] for bb in _sbb])
_sr = max([bb[3] for bb in _sbb])
staff_bboxes.append((_st, _sl, _sb, _sr))
staff_masks.append(gt[_st:_sb, _sl:_sr])
logging.info('Total staffs: {0}'.format(len(staff_bboxes)))
##################################################################
# (Optionally fill in missing pixels, based on full image.)
logging.info('SKIP: fill in missing pixels based on full image.')
# - Load full image
# - Find gap regions
# - Obtain gap region masks from full image
# - Add gap region mask to staffline mask.
# Create the Nodes for stafflines and staffs:
# - Load corresponding annotation, to which the stafflines and
# staves should be added. (This is needed to correctly set docname
# and node_ids.)
if not args.annot:
nodes = []
next_node_id = 0
dataset = 'FCNOMR'
document = os.path.splitext(os.path.basename(args.staff_imfile))[0]
else:
if not os.path.isfile(args.annot):
raise ValueError('Annotation file {0} does not exist!'.format(
args.annot))
logging.info('Creating Nodes...')
nodes = read_nodes_from_file(args.annot)
logging.info('Non-staffline Nodes: {0}'.format(len(nodes)))
next_node_id = max([c.id for c in nodes]) + 1
dataset = nodes[0].dataset
document = nodes[0].document
# - Create the staffline Nodes
stafflines = []
for sl_bb, sl_m in zip(staffline_bboxes, staffline_masks):
t, l, b, r = sl_bb
c = Node(id_=next_node_id,
class_name=_CONST.STAFFLINE_CLASS_NAME,
top=t,
left=l,
height=b - t,
width=r - l,
mask=sl_m,
dataset=dataset,
document=document)
stafflines.append(c)
next_node_id += 1
if not args.stafflines_only:
# - Create the staff Nodes
staffs = []
for s_bb, s_m in zip(staff_bboxes, staff_masks):
t, l, b, r = s_bb
c = Node(id_=next_node_id,
class_name=_CONST.STAFF_CLASS_NAME,
top=t,
left=l,
height=b - t,
width=r - l,
mask=s_m,
dataset=dataset,
document=document)
staffs.append(c)
next_node_id += 1
# - Add the inlinks/outlinks
for i, sc in enumerate(staffs):
sl_from = 5 * i
sl_to = 5 * (i + 1)
for sl in stafflines[sl_from:sl_to]:
sl.inlinks.append(sc.id)
sc.outlinks.append(sl.id)
# Add the staffspaces.
staffspaces = []
for i, staff in enumerate(staffs):
current_stafflines = [
sc for sc in stafflines if sc.id in staff.outlinks
]
sorted_stafflines = sorted(current_stafflines, key=lambda x: x.top)
current_staffspaces = []
# Percussion single-line staves do not have staffspaces.
if len(sorted_stafflines) == 1:
continue
# Internal staffspace
for s1, s2 in zip(sorted_stafflines[:-1], sorted_stafflines[1:]):
# s1 is the UPPER staffline, s2 is the LOWER staffline
# Left and right limits: to simplify things, we take the column
# *intersection* of (s1, s2). This gives the invariant that
# the staffspace is limited from top and bottom in each of its columns.
l = max(s1.left, s2.left)
r = min(s1.right, s2.right)
# Shift s1, s2 to the right by this much to have the cols. align
# All of these are non-negative.
dl1, dl2 = l - s1.left, l - s2.left
dr1, dr2 = s1.right - r, s2.right - r
# The stafflines are not necessarily straight,
# so top is given for the *topmost bottom edge* of the top staffline + 1
# First create mask
canvas = numpy.zeros((s2.bottom - s1.top, r - l),
dtype='uint8')
# Paste masks into canvas.
# This assumes that the top of the bottom staffline is below
# the top of the top staffline... and that the bottom
# of the top staffline is above the bottom of the bottom
# staffline. This may not hold in very weird situations,
# but it's good for now.
logging.debug(s1.bounding_box, s1.mask.shape)
logging.debug(s2.bounding_box, s2.mask.shape)
logging.debug(canvas.shape)
logging.debug(
'l={0}, dl1={1}, dl2={2}, r={3}, dr1={4}, dr2={5}'
''.format(l, dl1, dl2, r, dr1, dr2))
# canvas[:s1.height, :] += s1.mask[:, dl1:s1.width-dr1]
# canvas[-s2.height:, :] += s2.mask[:, dl2:s2.width-dr2]
# We have to deal with staffline interruptions.
# One way to do this
# is watershed fill: put markers along the bottom and top
# edge, use mask * 10000 as elevation
s1_above, s1_below = staffline_surroundings_mask(s1)
s2_above, s2_below = staffline_surroundings_mask(s2)
# Get bounding boxes of the individual stafflines' masks
# that intersect with the staffspace bounding box, in terms
# of the staffline bounding box.
s1_t, s1_l, s1_b, s1_r = 0, dl1, \
s1.height, s1.width - dr1
s1_h, s1_w = s1_b - s1_t, s1_r - s1_l
s2_t, s2_l, s2_b, s2_r = canvas.shape[0] - s2.height, dl2, \
canvas.shape[0], s2.width - dr2
s2_h, s2_w = s2_b - s2_t, s2_r - s2_l
logging.debug(s1_t, s1_l, s1_b, s1_r, (s1_h, s1_w))
# We now take the intersection of s1_below and s2_above.
# If there is empty space in the middle, we fill it in.
staffspace_mask = numpy.ones(canvas.shape)
staffspace_mask[s1_t:s1_b, :] -= (
1 - s1_below[:, dl1:s1.width - dr1])
staffspace_mask[s2_t:s2_b, :] -= (
1 - s2_above[:, dl2:s2.width - dr2])
ss_top = s1.top
ss_bottom = s2.bottom
ss_left = l
ss_right = r
staffspace = Node(next_node_id,
_CONST.STAFFSPACE_CLASS_NAME,
top=ss_top,
left=ss_left,
height=ss_bottom - ss_top,
width=ss_right - ss_left,
mask=staffspace_mask,
dataset=dataset,
document=document)
staffspace.inlinks.append(staff.id)
staff.outlinks.append(staffspace.id)
current_staffspaces.append(staffspace)
next_node_id += 1
# Add top and bottom staffspace.
# These outer staffspaces will have the width
# of their bottom neighbor, and height derived
# from its mask columns.
# This is quite approximate, but it should do.
# Upper staffspace
tsl = sorted_stafflines[0]
tsl_heights = tsl.mask.sum(axis=0)
tss = current_staffspaces[0]
tss_heights = tss.mask.sum(axis=0)
uss_top = max(0, tss.top - max(tss_heights))
uss_left = tss.left
uss_width = tss.width
# We use 1.5, so that large noteheads
# do not "hang out" of the staffspace.
uss_height = int(tss.height / 1.2)
# Shift because of height downscaling:
uss_top += tss.height - uss_height
uss_mask = tss.mask[:uss_height, :] * 1
staffspace = Node(next_node_id,
_CONST.STAFFSPACE_CLASS_NAME,
top=uss_top,
left=uss_left,
height=uss_height,
width=uss_width,
mask=uss_mask,
dataset=dataset,
document=document)
current_staffspaces.append(staffspace)
staff.outlinks.append(staffspace.id)
staffspace.inlinks.append(staff.id)
next_node_id += 1
# Lower staffspace
bss = current_staffspaces[-1]
bss_heights = bss.mask.sum(axis=0)
bsl = sorted_stafflines[-1]
bsl_heights = bsl.mask.sum(axis=0)
lss_top = bss.bottom # + max(bsl_heights)
lss_left = bss.left
lss_width = bss.width
lss_height = int(bss.height / 1.2)
lss_mask = bss.mask[:lss_height, :] * 1
staffspace = Node(next_node_id,
_CONST.STAFFSPACE_CLASS_NAME,
top=lss_top,
left=lss_left,
height=lss_height,
width=lss_width,
mask=lss_mask,
dataset=dataset,
document=document)
current_staffspaces.append(staffspace)
staff.outlinks.append(staffspace.id)
staffspace.inlinks.append(staff.id)
next_node_id += 1
# ################ End of dealing with upper/lower staffspace ######
# Add to current list
staffspaces += current_staffspaces
# - Join the lists together
nodes_with_staffs = nodes \
+ stafflines \
+ staffspaces \
+ staffs
else:
nodes_with_staffs = nodes + stafflines
logging.info('Exporting the new Node list: {0} objects'
''.format(len(nodes_with_staffs)))
# - Export the combined list.
nodes_string = export_node_list(nodes_with_staffs)
if args.export is not None:
with open(args.export, 'w') as hdl:
hdl.write(nodes_string)
else:
print(nodes_string)
_end_time = time.clock()
logging.info('add_staffline_symbols.py done in {0:.3f} s'
''.format(_end_time - _start_time))
def staffline_bboxes_and_masks_from_horizontal_merge(mask):
"""Returns a list of staff_line bboxes and masks
computed from the input mask, with
each set of connected components in the mask that has at least
one pixel in a neighboring or overlapping row is assigned to
the same label. Intended for finding staffline masks from individual
components of the stafflines (for this purpose, you have to assume
that the stafflines are straight)."""
logging.info('Getting staffline connected components.')
cc, labels, bboxes = compute_connected_components(mask)
logging.info('Getting staffline component vertical projections')
# - Use vertical dimension of CCs to determine which ones belong together
# to form stafflines. (Criterion: row overlap.)
n_rows, n_cols = mask.shape
# For each row of the image: which CCs have pxs on that row?
intervals = [[] for _ in range(n_rows)]
for label, (t, l, b, r) in list(bboxes.items()):
if label == 0:
continue
# Ignore very short staffline segments that can easily be artifacts
# and should not affect the vertical range of the staffline anyway.
# The "8" is a magic number, no good reason for specifically 8.
# (It should be some proportion of the image width, like 0.01.)
if (r - l) < 8:
continue
for row in range(t, b):
intervals[row].append(label)
logging.warning('Grouping staffline connected components into stafflines.')
# For each staffline, we collect the CCs that it is made of. We assume stafflines
# are separated from each other by at least one empty row.
staffline_components = []
_in_staffline = False
_current_staffline_components = []
for r_labels in intervals:
if not _in_staffline:
# Last row did not contain staffline components.
if len(r_labels) == 0:
# No staffline component on current row
continue
else:
_in_staffline = True
_current_staffline_components += r_labels
else:
# Last row contained staffline components.
if len(r_labels) == 0:
# Current staffline has no more rows.
staffline_components.append(set(_current_staffline_components))
_current_staffline_components = []
_in_staffline = False
continue
else:
# Current row contains staffline components: the current
# staffline continues.
_current_staffline_components += r_labels
logging.info('No. of stafflines, with component groups: {0}'
''.format(len(staffline_components)))
# Now: merge the staffline components into one bbox/mask.
logging.info(
'Merging staffline components into staffline bboxes and masks.')
staffline_bboxes = []
staffline_masks = []
for sc in sorted(staffline_components,
key=lambda c: min([bboxes[cc][0]
for cc in c])): # Sorted top-down
st, sl, sb, sr = n_rows, n_cols, 0, 0
for component in sc:
t, l, b, r = bboxes[component]
st, sl, sb, sr = min(t, st), min(l, sl), max(b, sb), max(r, sr)
# Here, again, we assume that no two "real" stafflines have overlapping
# bounding boxes.
_sm = mask[st:sb, sl:sr]
staffline_bboxes.append((st, sl, sb, sr))
staffline_masks.append(_sm)
# Check if n. of stafflines is divisible by 5
n_stafflines = len(staffline_bboxes)
logging.warning('\tTotal stafflines: {0}'.format(n_stafflines))
if n_stafflines % 5 != 0:
# try:
# import matplotlib.pyplot as plt
# stafllines_mask_image = numpy.zeros(mask.shape)
# for i, (_sb, _sm) in enumerate(zip(staffline_bboxes, staffline_masks)):
# t, l, b, r = _sb
# stafllines_mask_image[t:b, l:r] = min(255, (i * 333) % 255 + 40)
# plt.imshow(stafllines_mask_image, cmap='jet', interpolation='nearest')
# plt.show()
# except ImportError:
# pass
raise ValueError('No. of stafflines is not divisible by 5!')
return staffline_bboxes, staffline_masks