def __init__(self, feature_names, gallery_dtype, colors, size):
self.gallery = None
self.bbox = None
self.features = None
self.images = None
self.contours = defaultdict(list)
self.colors = colors
self.image_size = size
self._images = list()
self._treatment = list()
self.feature_names = feature_names
self._dt_gallery = gallery_dtype
self._dt_bbox = [('treatment', "S128"), ('label', np.uint32),
('x', np.uint16), ('y', np.uint16), ('top', np.uint16),
('bottom', np.uint16), ('left', np.uint16),
('right', np.uint16)]
self._dt_features = [(str(n), np.float32) for n in feature_names]
self._dt_contours = h5py.special_dtype(vlen=np.uint16)
self._dt_groups = [('feature', "S64")] + \
[(str(g), "S64") for g in FeatureGroups.keys()]
# setup the table of feature groups
self.fgroups = np.empty((len(feature_names), ), dtype=self._dt_groups)
for i, fname in enumerate(feature_names):
line = [fname]
for v in FeatureGroups.values():
fname2 = cn.splitFeatureName(fname)[1]
line.append(v.inverse[fname2])
line = tuple([str(l) for l in line])
self.fgroups[i] = np.array(line, dtype=self._dt_groups)
def __init__(self, feature_names, gallery_dtype, colors, size):
self.gallery = None
self.bbox = None
self.features = None
self.images = None
self.contours = defaultdict(list)
self.colors = colors
self.image_size = size
self._images = list()
self._treatment = list()
self.feature_names = feature_names
self._dt_gallery = gallery_dtype
self._dt_bbox = [('file', 'S128'),
('treatment', "S128"), ('label', np.uint32),
('x', np.uint16), ('y', np.uint16), ('top', np.uint16),
('bottom', np.uint16), ('left', np.uint16),
('right', np.uint16),
('angle', np.float32), ('eccentricity', np.float32)]
self._dt_features = [(str(n), np.float32) for n in feature_names]
self._dt_contours = h5py.special_dtype(vlen=np.uint16)
self._dt_groups = [('feature', "S64")] + \
[(str(g), "S64") for g in FeatureGroups.keys()]
# setup the table of feature groups
self.fgroups = np.empty((len(feature_names), ), dtype=self._dt_groups)
for i, fname in enumerate(feature_names):
line = [fname]
for v in FeatureGroups.values():
fname2 = cn.splitFeatureName(fname)[1]
line.append(v.inverse[fname2])
line = tuple([str(l) for l in line])
self.fgroups[i] = np.array(line, dtype=self._dt_groups)
def featureGroups(self, region=None):
"""Return the feature groups as nested dictionaries.
e.g. {channel: {meta_group: { group: (feature_list)}}}"""
fg = self[self.dmodel.feature_groups]
ftrs = fg.dtype.names[0]
groups = fg.dtype.names[1:]
channels = self.channelNames
ch_groups = OrderedDict()
for channel in channels:
meta_group = OrderedDict()
for group in groups:
fgroup = FGroup(group, [(g, []) for g in np.unique(fg[group])])
meta_group[group] = fgroup
ch_groups[channel] = meta_group
for line in fg:
channel, fname = cn.splitFeatureName(line[0])
channel = cn.validFromShort(channel)
for i, group in enumerate(groups):
ch_groups[channel][group][line[i+1]].append(fname)
return ch_groups
def featureGroups(self, region=None):
"""Return the feature groups as nested dictionaries.
e.g. {channel: {meta_group: { group: (feature_list)}}}"""
fg = self[self.dmodel.feature_groups]
ftrs = fg.dtype.names[0]
groups = fg.dtype.names[1:]
channels = self.channelNames
ch_groups = OrderedDict()
for channel in channels:
meta_group = OrderedDict()
for group in groups:
fgroup = FGroup(group, [(g, []) for g in np.unique(fg[group])])
meta_group[group] = fgroup
ch_groups[channel] = meta_group
for line in fg:
channel, fname = cn.splitFeatureName(line[0])
channel = cn.validFromShort(channel)
for i, group in enumerate(groups):
ch_groups[channel][group][line[i + 1]].append(fname)
return ch_groups
