Fix Contrast feature

pyradiomics_radler/myngtdm.py

-from .mybase import MyRadiomicsFeaturesBase
+import numpy
+
 from radiomics.ngtdm import RadiomicsNGTDM
 
+from .mybase import MyRadiomicsFeaturesBase
+
 
 class MyRadiomicsNGTDM(RadiomicsNGTDM, MyRadiomicsFeaturesBase):
     def __init__(self, inputImage, inputMask, **kwargs):
         super().__init__(inputImage, inputMask, **kwargs)
 
+    def getContrastFeatureValue(self):
+        r"""
+        Calculate and return the contrast.
+
+        :math:`Contrast = \left(\frac{1}{N_{g,p}(N_{g,p}-1)}\displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}{p_{i}p_{j}(i-j)^2}\right)
+        \left(\frac{1}{N_{v,p}}\displaystyle\sum^{N_g}_{i=1}{s_i}\right)\text{, where }p_i \neq 0, p_j \neq 0`
+
+        Contrast is a measure of the spatial intensity change, but is also dependent on the overall gray level dynamic range.
+        Contrast is high when both the dynamic range and the spatial change rate are high, i.e. an image with a large range
+        of gray levels, with large changes between voxels and their neighbourhood.
+
+        N.B. In case of a completely homogeneous image, :math:`N_{g,p} = 1`, which would result in a division by 0. In this
+        case, an arbitray value of 0 is returned.
+        """
+        Ngp = self.coefficients['Ngp']
+        Nvp = self.coefficients['Nvp']
+        p_i = self.coefficients['p_i']
+        s_i = self.coefficients['s_i']
+        i = self.coefficients['ivector']
+
+        div = Ngp * (Ngp - 1)
+
+        if div != 0:
+            contrast = (
+                numpy.sum(p_i[:, None] * p_i[None, :] * (i[:, None] - i[None, :]) ** 2)
+                / div
+            ) * ((numpy.sum(s_i)) / Nvp)
+        else:
+            contrast = 0
+
+        return contrast