Add a Gaussian blur filter

bob/learn/tensorflow/image/__init__.py

+from .filter import gaussian_kernel, GaussianFilter
+
+# gets sphinx autodoc done right - don't remove it
+def __appropriate__(*args):
+    """Says object was actually declared here, an not on the import module.
+
+  Parameters:
+
+    *args: An iterable of objects to modify
+
+  Resolves `Sphinx referencing issues
+  <https://github.com/sphinx-doc/sphinx/issues/3048>`
+  """
+    for obj in args:
+        obj.__module__ = __name__
+
+
+__appropriate__(GaussianFilter)
+__all__ = [_ for _ in dir() if not _.startswith("_")]

bob/learn/tensorflow/image/filter.py

+import tensorflow as tf
+
+
+def gaussian_kernel(size: int, mean: float, std: float):
+    """Makes 2D gaussian Kernel for convolution.
+    Code adapted from: https://stackoverflow.com/a/52012658/1286165"""
+
+    d = tf.distributions.Normal(mean, std)
+
+    vals = d.prob(tf.range(start=-size, limit=size + 1, dtype=tf.float32))
+
+    gauss_kernel = tf.einsum("i,j->ij", vals, vals)
+
+    return gauss_kernel / tf.reduce_sum(gauss_kernel)
+
+
+class GaussianFilter:
+    """A class for blurring images"""
+
+    def __init__(self, size=13, mean=0.0, std=3.0, **kwargs):
+        super().__init__(**kwargs)
+        self.size = size
+        self.mean = mean
+        self.std = std
+        self.gauss_kernel = gaussian_kernel(size, mean, std)[:, :, None, None]
+
+    def __call__(self, image):
+        shape = tf.shape(image)
+        image = tf.reshape(image, [-1, shape[-3], shape[-2], shape[-1]])
+        input_channels = shape[-1]
+        gauss_kernel = tf.tile(self.gauss_kernel, [1, 1, input_channels, 1])
+        return tf.nn.depthwise_conv2d(
+            image,
+            gauss_kernel,
+            strides=[1, 1, 1, 1],
+            padding="SAME",
+            data_format="NHWC",
+        )