def voxel_instance_size(target: np.ndarray, ds_name: str) -> pd.DataFrame:
''' Calculate the voxel based size of each instance in an instance segmentation map.
Args:
target: The target data as numpy ndarray
ds_name: Name of the dataset, saved in pd column
Return
A single column Panda data frame that contains the voxel-based instance sizes.
'''
def distance_nn(target: np.ndarray, ds_name: str, iso=[1, 1, 1]) -> pd.DataFrame:
''' Caculate the distance to the NN for each instance in the target matrix.
Args:
target: The target data as numpy ndarray
iso: Axis scaling factor in case of anisotropy
ds_name: Name of the dataset, saved in pd column
Return
A single column Panda data frame that contains the distance of each instance to its NN
'''
def pixel_intensity(source: np.ndarray, target: np.ndarray, bOrF: str, ds_name: str) -> pd.DataFrame:
''' Retrives the intesity of each pixel. Writes them to a Pandas data frame.
Can handle background for foreground.
Args:
source: Source numpy ndarray
target: Target numpy ndarray
bOrF: Either 'Foreground' or 'Background', indicates which intensities to estimate
ds_name: Name of the dataset, saved in pd column
Return
A pandas frame with the intensity of each pixel, if the pixel belongs to the background
or foreground, and the dataset it belongs to.
'''
Added three functions for dataset analysis: