def resolve_segmentation(cls, m, categories=None):
filename, tnumbers = m
result = {}
if wants('texture', categories):
result['texture'] = tnumbers
if wants('color', categories):
result['color'] = colorname.label_major_colors(
imread(filename)) + 1
arrs = [a for a in result.values() if numpy.shape(a) >= 2]
shape = arrs[0].shape[-2:] if arrs else (1, 1)
return result, shape
def resolve_segmentation(cls, m, categories=None):
result = {}
if wants('scene', categories):
result['scene'] = m['scene']
if wants('part', categories):
result['part'] = load_parts(m)
if wants('object', categories):
result['object'] = load_segmentation(m)
if wants('color', categories):
result['color'] = colorname.label_major_colors(load_image(m)) + 1
arrs = [a for a in result.values() if numpy.shape(a) >= 2]
shape = arrs[0].shape[-2:] if arrs else (1, 1)
return result, shape
def resolve_segmentation(cls, m, categories=None):
directory, version, partdir, contextdir, part_key, seg_fn = m
result = {}
if wants('color', categories):
jpeg_fn = os.path.join(directory, version, 'JPEGImages',
seg_fn.replace('.mat', '.jpg'))
result['color'] = colorname.label_major_colors(imread(jpeg_fn)) + 1
if wants('part', categories):
objs, parts = load_parts_segmentation(
os.path.join(directory, partdir, 'Annotations_Part', seg_fn),
part_key) # (ignore object layer the from parts segmentation)
result['part'] = parts
if wants('object', categories):
result['object'] = loadmat(
os.path.join(directory, contextdir, 'trainval',
seg_fn))['LabelMap']
arrs = [a for a in result.values() if numpy.shape(a) >= 2]
shape = arrs[0].shape[-2:] if arrs else (1, 1)
return result, shape
def resolve_segmentation(cls, m, categories=None):
directory, row, supply = m
fnjpg = os.path.join(directory, row['photo'])
result = {}
if wants('material', categories) and wants('material', supply):
mats = {}
for material, fn in row['segments']:
mask = imread(os.path.join(directory, fn)) != 0
if material not in mats:
mats[material] = mask
else:
mats[material] |= mask
allmats = numpy.concatenate([
material * fn[None,:,:]
for material, fn in sorted(mats.items(), key=lambda x: x[0])])
result['material'] = allmats
if wants('color', categories) and wants('color', supply):
result['color'] = colorname.label_major_colors(imread(fnjpg)) + 1
arrs = [a for a in result.values() if numpy.shape(a) >= 2]
shape = arrs[0].shape[-2:] if arrs else (1, 1)
return result, shape
def resolve_segmentation(cls, m, categories=None):
directory, row, scene, supply = m
photo_id = int(row['photo_id'])
fnpng = os.path.join(directory, 'photos-labels', '%d.png' % photo_id)
fnjpg = os.path.join(directory, 'photos', '%d.jpg' % photo_id)
if wants('material', categories) or wants('object', categories):
labels = imread(fnpng)
# Opensurfaces has some completely unlabled images; omit them.
# TODO: figure out how to do this nicely in the joiner instead.
# if labels.max() == 0:
# return {}
result = {}
if wants('scene', categories) and wants('scene', supply):
result['scene'] = scene
if wants('material', categories) and wants('material', supply):
result['material'] = labels[:, :, 0]
if wants('object', categories) and wants('object', supply):
result['object'] = labels[:, :, 1]
if wants('color', categories) and wants('color', supply):
result['color'] = colorname.label_major_colors(imread(fnjpg)) + 1
arrs = [a for a in result.values() if numpy.shape(a) >= 2]
shape = arrs[0].shape[-2:] if arrs else (1, 1)
return result, shape