import numpy as np
import os
import scipy.signal
[docs]
def rescale_values_array(arr, minv=0.0, maxv=1.0, dtype=np.float32):
"""Rescale the values of numpy array `arr` to be from `minv` to `maxv`.
Args:
arr (ndarry): Array whose values will be rescaled.
minv (float): Minimum value of the output array.
maxv (float): Maximum value of the output array.
dtype (type): Cast array to this type before performing the rescaling.
"""
if dtype is not None:
arr = arr.astype(dtype)
mina = np.min(arr)
maxa = np.max(arr)
if mina == maxa:
return arr * minv
norm = (arr - mina) / (maxa - mina) # normalize the array first
return (norm * (maxv - minv)) + minv # rescale by minv and maxv, which is the normalized array by default
[docs]
def gaussian_kernel(kernlen=10):
"""
Create a 2D gaussian kernel with user-defined size.
Args:
kernlen (int): size of kernel
Returns:
ndarray: a 2D array of size (kernlen,kernlen)
"""
x = np.linspace(-1, 1, kernlen + 1)
kern1d = np.diff(scipy.stats.norm.cdf(x))
kern2d = np.outer(kern1d, kern1d)
return rescale_values_array(kern2d / kern2d.sum())
[docs]
def heatmap_generation(image, kernel_size):
"""
Generate heatmap from image containing sing voxel label using
convolution with gaussian kernel
Args:
image (ndarray): 2D array containing single voxel label
kernel_size (int): size of gaussian kernel
Returns:
ndarray: 2D array heatmap matching the label.
"""
kernel = gaussian_kernel(kernel_size)
map = scipy.signal.convolve(image, kernel, mode='same')
return rescale_values_array(map)