Removed scipy dependency from image_data_generator.

keras_preprocessing/image/image_data_generator.py

@@ -4,15 +4,6 @@
 
 import numpy as np
 
-try:
-    import scipy
-    # scipy.linalg cannot be accessed until explicitly imported
-    from scipy import linalg
-
-    # scipy.ndimage cannot be accessed until explicitly imported
-except ImportError:
-    scipy = None
-
 from .affine_transformations import (apply_affine_transform,
                                      apply_brightness_shift,
                                      apply_channel_shift, flip_axis)
@@ -315,7 +306,7 @@ def __init__(self,
 
         self.mean = None
         self.std = None
-        self.principal_components = None
+        self.zca_whitening_matrix = None
 
         if isinstance(zoom_range, (float, int)):
             self.zoom_range = [1 - zoom_range, 1 + zoom_range]
@@ -731,10 +722,10 @@ def standardize(self, x):
                               'been fit on any training data. Fit it '
                               'first by calling `.fit(numpy_data)`.')
         if self.zca_whitening:
-            if self.principal_components is not None:
-                flatx = np.reshape(x, (-1, np.prod(x.shape[-3:])))
-                whitex = np.dot(flatx, self.principal_components)
-                x = np.reshape(whitex, x.shape)
+            if self.zca_whitening_matrix is not None:
+                flat_x = x.reshape(-1, np.prod(x.shape[-3:]))
+                white_x = flat_x @ self.zca_whitening_matrix
+                x = np.reshape(white_x, x.shape)
             else:
                 warnings.warn('This ImageDataGenerator specifies '
                               '`zca_whitening`, but it hasn\'t '
@@ -977,12 +968,9 @@ def fit(self, x,
             x /= (self.std + 1e-6)
 
         if self.zca_whitening:
-            if scipy is None:
-                raise ImportError('Using zca_whitening requires SciPy. '
-                                  'Install SciPy.')
-            flat_x = np.reshape(
-                x, (x.shape[0], x.shape[1] * x.shape[2] * x.shape[3]))
-            sigma = np.dot(flat_x.T, flat_x) / flat_x.shape[0]
-            u, s, _ = linalg.svd(sigma)
-            s_inv = 1. / np.sqrt(s[np.newaxis] + self.zca_epsilon)
-            self.principal_components = (u * s_inv).dot(u.T)
+            n = len(x)
+            flat_x = np.reshape(x, (n, -1))
+
+            u, s, _ = np.linalg.svd(flat_x.T, full_matrices=False)
+            s_inv = np.sqrt(n) / (s + self.zca_epsilon)
+            self.zca_whitening_matrix = (u * s_inv).dot(u.T)