def measure_projections(page, title=None, p=0, m=0):
all_measures = []
cols = []
measure = m
if not hasattr(page, 'systems'):
return pd.DataFrame()
for i, system in enumerate(page.systems):
nb = len(system['barlines']) - 1
measures = [[] for _ in xrange(nb)]
cols += [(title, measure + k, p, i, k) for k in xrange(nb)]
for j, staffnum in enumerate(xrange(system['start'], system['stop'] + 1)):
staff = bitimage.as_hostimage(page.staves.extract_staff(staffnum)).astype(bool)
for k in xrange(nb):
measure_img = staff[:, int(system['barlines'][k,:,0].mean())
: int(system['barlines'][k+1,:,0].mean())]
scaled_measure = scipy.misc.imresize(measure_img, (5*6, int(30.0 * measure_img.shape[1] / measure_img.shape[0])))
measures[k] += list(scaled_measure.sum(1))
all_measures += measures
measure += nb
if len(cols):
cols = pd.MultiIndex.from_tuples(cols, names='score measure page staff staffmeasure'.split())
all_measures = pd.DataFrame(all_measures, index=cols)
return all_measures
else:
return pd.DataFrame()
def measure_projections(page, title=None, p=0, m=0):
all_measures = []
cols = []
measure = m
if not hasattr(page, 'systems'):
return pd.DataFrame()
for i, system in enumerate(page.systems):
nb = len(system['barlines']) - 1
measures = [[] for _ in xrange(nb)]
cols += [(title, measure + k, p, i, k) for k in xrange(nb)]
for j, staffnum in enumerate(
xrange(system['start'], system['stop'] + 1)):
staff = bitimage.as_hostimage(
page.staves.extract_staff(staffnum)).astype(bool)
for k in xrange(nb):
measure_img = staff[:,
int(system['barlines'][k, :, 0].mean()
):int(system['barlines'][k + 1, :,
0].mean())]
scaled_measure = scipy.misc.imresize(
measure_img,
(5 * 6,
int(30.0 * measure_img.shape[1] / measure_img.shape[0])))
measures[k] += list(scaled_measure.sum(1))
all_measures += measures
measure += nb
if len(cols):
cols = pd.MultiIndex.from_tuples(
cols, names='score measure page staff staffmeasure'.split())
all_measures = pd.DataFrame(all_measures, index=cols)
return all_measures
else:
return pd.DataFrame()
class_num = []
for score in CORPUS:
try:
score = metaomr.open(score)
except Exception:
continue
while len(score):
page = score[0]
preprocessing.process(page)
if type(
page.staff_dist
) is not tuple and page.staff_dist is not None and page.staff_dist >= 8:
image, scale = forest.scale_img(page)
pred = forest.predict(forest.classifier, image, get_classes=False)
images.append(bitimage.as_hostimage(image))
class_num.append(pred.get())
del image
del page
del score[0]
opencl.q.finish()
gc.collect()
if len(images) > 25:
break
import numpy as np
nums = np.concatenate([c.ravel() for c in class_num])
NUM_PATCHES = 100
CUTOFF = np.percentile(nums, 100.0 * NUM_PATCHES / len(nums))
images = []
class_num = []
for score in CORPUS:
try:
score = metaomr.open(score)
except Exception:
continue
while len(score):
page = score[0]
preprocessing.process(page)
if type(page.staff_dist) is not tuple and page.staff_dist is not None and page.staff_dist >= 8:
image, scale = forest.scale_img(page)
pred = forest.predict(forest.classifier, image, get_classes=False)
images.append(bitimage.as_hostimage(image))
class_num.append(pred.get())
del image
del page
del score[0]
opencl.q.finish()
gc.collect()
if len(images) > 25:
break
import numpy as np
nums=np.concatenate([c.ravel() for c in class_num])
NUM_PATCHES = 100
CUTOFF = np.percentile(nums, 100.0*NUM_PATCHES/len(nums))
if type(page.staff_dist) is not int:
raise Exception('staffsize failed')
page.process()
except Exception, e:
print imslpid, p, '-', e
pages[p] = None
for movement in xrange(len(mvmt_start) - 1):
mvmt_path = 'mvmt%d' % movement
for p in xrange(
mvmt_start[movement, 0], mvmt_start[movement + 1, 0]
if mvmt_start[movement + 1,
1] == 0 else mvmt_start[movement + 1, 0] + 1):
page = pages[p]
if page is None:
continue
img = bitimage.as_hostimage(page.img)[:, :page.orig_size[1]]
if p == mvmt_start[movement, 0] and mvmt_start[movement, 1] > 0:
new_system = page.systems[mvmt_start[movement, 1]]['start']
b = page.boundaries[new_system]
boundary = np.repeat(b[:, 1], b[1, 0] - b[0, 0])
img = img[:, :min(page.orig_size[1], len(boundary))]
boundary = boundary[:img.shape[1]]
img[np.arange(img.shape[0])[:, None] < boundary[None, :]] = 0
elif (p == mvmt_start[movement + 1, 0]
and 0 < mvmt_start[movement + 1, 1] < len(page.systems)):
new_system = page.systems[mvmt_start[movement + 1, 1]]['start']
b = page.boundaries[new_system]
boundary = np.repeat(b[:, 1], b[1, 0] - b[0, 0])
img = img[:, :min(page.orig_size[1], len(boundary))]
boundary = boundary[:img.shape[1]]
img[np.arange(img.shape[0])[:, None] >= boundary[None, :]] = 0
if type(page.staff_dist) is not int:
raise Exception('staffsize failed')
page.process()
except Exception, e:
print imslpid, p, '-', e
pages[p] = None
for movement in xrange(len(mvmt_start) - 1):
mvmt_path = 'mvmt%d' % movement
for p in xrange(mvmt_start[movement, 0],
mvmt_start[movement+1, 0]
if mvmt_start[movement+1, 1] == 0
else mvmt_start[movement+1, 0] + 1):
page = pages[p]
if page is None:
continue
img = bitimage.as_hostimage(page.img)[:, :page.orig_size[1]]
if p == mvmt_start[movement, 0] and mvmt_start[movement, 1] > 0:
new_system = page.systems[mvmt_start[movement,1]]['start']
b = page.boundaries[new_system]
boundary = np.repeat(b[:,1], b[1,0] - b[0,0])
img = img[:, :min(page.orig_size[1], len(boundary))]
boundary = boundary[:img.shape[1]]
img[np.arange(img.shape[0])[:, None] < boundary[None, :]] = 0
elif (p == mvmt_start[movement+1, 0]
and 0 < mvmt_start[movement+1, 1] < len(page.systems)):
new_system = page.systems[mvmt_start[movement+1,1]]['start']
b = page.boundaries[new_system]
boundary = np.repeat(b[:,1], b[1,0] - b[0,0])
img = img[:, :min(page.orig_size[1], len(boundary))]
boundary = boundary[:img.shape[1]]
img[np.arange(img.shape[0])[:, None] >= boundary[None, :]] = 0
