def closest_objects(labeled,n_cpu=1):
from ops.process import feature_table
from scipy.spatial import cKDTree
features = {
'i' : lambda r: r.centroid[0],
'j' : lambda r: r.centroid[1],
'label' : lambda r: r.label
}
df = feature_table(labeled,labeled,features)
kdt = cKDTree(df[['i','j']])
distances,indexes = kdt.query(df[['i','j']],3,n_jobs=n_cpu)
df['first_neighbor'],df['first_neighbor_distance'] = indexes[:,1],distances[:,1]
df['second_neighbor'],df['second_neighbor_distance'] = indexes[:,2],distances[:,2]
first_neighbors = df[['i','j']].values[df['first_neighbor'].values]
second_neighbors = df[['i','j']].values[df['second_neighbor'].values]
angles = [angle(v,p0,p1)
for v,p0,p1
in zip(df[['i','j']].values,first_neighbors,second_neighbors)]
df['angle_between_neighbors'] = np.array(angles)*(180/np.pi)
return df.drop(columns=['i','j']).set_index('label')
def _extract_features(data, labels, wildcards, features=None):
"""Extracts features in dictionary and combines with generic region
features.
"""
from ops.process import feature_table
from ops.features import features_basic
features = features.copy() if features else dict()
features.update(features_basic)
df = feature_table(data, labels, features)
for k, v in sorted(wildcards.items()):
df[k] = v
return df
def _extract_features_bare(data, labels, features=None, wildcards=None,**kwargs):
"""Extracts features in dictionary and combines with generic region
features.
"""
from ops.process import feature_table
features = features.copy() if features else dict()
features.update({'label': lambda r: r.label})
df = feature_table(data, labels, features)
if wildcards is not None:
for k,v in sorted(wildcards.items()):
df[k] = v
return df
filetypes=(("image files","*.tif"),
("all files","*.*")
)
)
)
root_2.destroy()
_,ext_2= splitext(filename_2)
if ext_2 not in image_ext:
raise Exception('intensity image file must be .tif or .tiff')
else:
img_file = filename_2
img_labels = read(label_file)
df = (feature_table(img_labels, img_labels, features_basic)
.assign(label_file=label_file,img_file=img_file)
)
else:
table_file = filename_1
raise Exception('input table file must be .csv or .hdf')
elif len(sys.argv) == 2:
filename_1 = sys.argv[1]
_,ext_1= splitext(filename_1)
if ext_1 not in table_ext:
raise Exception('input table file must be .csv or .hdf')
else:
table_file = filename_1
df = pd.read_hdf(table_file)
elif len(sys.argv) > 2: