Merge pull request #194 from geometrikal/master

Interpolation order for relevant prepro functions

tensorlayer/prepro.py

@@ -136,7 +136,7 @@ def apply_fn(results, i, data, kwargs):
 
 ## Image
 def rotation(x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2,
-                    fill_mode='nearest', cval=0.):
+                    fill_mode='nearest', cval=0., order=1):
     """Rotate an image randomly or non-randomly.
 
     Parameters
@@ -155,6 +155,8 @@ def rotation(x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=
         - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
     cval : scalar, optional
         Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0
+    order : int, optional
+        The order of interpolation. The order has to be in the range 0-5. See ``apply_transform``.
 
         - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
 
@@ -174,11 +176,11 @@ def rotation(x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=
 
     h, w = x.shape[row_index], x.shape[col_index]
     transform_matrix = transform_matrix_offset_center(rotation_matrix, h, w)
-    x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
+    x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval, order)
     return x
 
 def rotation_multi(x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2,
-                    fill_mode='nearest', cval=0.):
+                    fill_mode='nearest', cval=0., order=1):
     """Rotate multiple images with the same arguments, randomly or non-randomly.
     Usually be used for image segmentation which x=[X, Y], X and Y should be matched.
 
@@ -207,7 +209,7 @@ def rotation_multi(x, rg=20, is_random=False, row_index=0, col_index=1, channel_
     transform_matrix = transform_matrix_offset_center(rotation_matrix, h, w)
     results = []
     for data in x:
-        results.append( apply_transform(data, transform_matrix, channel_index, fill_mode, cval))
+        results.append( apply_transform(data, transform_matrix, channel_index, fill_mode, cval, order))
     return np.asarray(results)
 
 # crop
@@ -345,7 +347,7 @@ def flip_axis_multi(x, axis, is_random=False):
 
 # shift
 def shift(x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2,
-                 fill_mode='nearest', cval=0.):
+                 fill_mode='nearest', cval=0., order=1):
     """Shift an image randomly or non-randomly.
 
     Parameters
@@ -366,6 +368,8 @@ def shift(x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channe
         - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
     cval : scalar, optional
         Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0.
+    order : int, optional
+        The order of interpolation. The order has to be in the range 0-5. See ``apply_transform``.
 
         - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
     """
@@ -380,11 +384,11 @@ def shift(x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channe
                                    [0, 0, 1]])
 
     transform_matrix = translation_matrix  # no need to do offset
-    x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
+    x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval, order)
     return x
 
 def shift_multi(x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2,
-                 fill_mode='nearest', cval=0.):
+                 fill_mode='nearest', cval=0., order=1):
     """Shift images with the same arguments, randomly or non-randomly.
     Usually be used for image segmentation which x=[X, Y], X and Y should be matched.
 
@@ -407,12 +411,12 @@ def shift_multi(x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1,
     transform_matrix = translation_matrix  # no need to do offset
     results = []
     for data in x:
-        results.append( apply_transform(data, transform_matrix, channel_index, fill_mode, cval))
+        results.append( apply_transform(data, transform_matrix, channel_index, fill_mode, cval, order))
     return np.asarray(results)
 
 # shear
 def shear(x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2,
-                 fill_mode='nearest', cval=0.):
+                 fill_mode='nearest', cval=0., order=1):
     """Shear an image randomly or non-randomly.
 
     Parameters
@@ -432,6 +436,8 @@ def shear(x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_i
         - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
     cval : scalar, optional
         Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0.
+    order : int, optional
+        The order of interpolation. The order has to be in the range 0-5. See ``apply_transform``.
 
         - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
     """
@@ -445,11 +451,11 @@ def shear(x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_i
 
     h, w = x.shape[row_index], x.shape[col_index]
     transform_matrix = transform_matrix_offset_center(shear_matrix, h, w)
-    x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
+    x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval, order)
     return x
 
 def shear_multi(x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2,
-                 fill_mode='nearest', cval=0.):
+                 fill_mode='nearest', cval=0., order=1):
     """Shear images with the same arguments, randomly or non-randomly.
     Usually be used for image segmentation which x=[X, Y], X and Y should be matched.
 
@@ -471,7 +477,7 @@ def shear_multi(x, intensity=0.1, is_random=False, row_index=0, col_index=1, cha
     transform_matrix = transform_matrix_offset_center(shear_matrix, h, w)
     results = []
     for data in x:
-        results.append( apply_transform(data, transform_matrix, channel_index, fill_mode, cval))
+        results.append( apply_transform(data, transform_matrix, channel_index, fill_mode, cval, order))
     return np.asarray(results)
 
 # swirl
@@ -659,7 +665,7 @@ def elastic_transform_multi(x, alpha, sigma, mode="constant", cval=0, is_random=
 
 # zoom
 def zoom(x, zoom_range=(0.9, 1.1), is_random=False, row_index=0, col_index=1, channel_index=2,
-                fill_mode='nearest', cval=0.):
+                fill_mode='nearest', cval=0., order=1):
     """Zoom in and out of a single image, randomly or non-randomly.
 
     Parameters
@@ -680,6 +686,8 @@ def zoom(x, zoom_range=(0.9, 1.1), is_random=False, row_index=0, col_index=1, ch
         - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
     cval : scalar, optional
         Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0.
+    order : int, optional
+        The order of interpolation. The order has to be in the range 0-5. See ``apply_transform``.
 
         - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
     """
@@ -701,11 +709,11 @@ def zoom(x, zoom_range=(0.9, 1.1), is_random=False, row_index=0, col_index=1, ch
 
     h, w = x.shape[row_index], x.shape[col_index]
     transform_matrix = transform_matrix_offset_center(zoom_matrix, h, w)
-    x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
+    x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval, order)
     return x
 
 def zoom_multi(x, zoom_range=(0.9, 1.1), is_random=False,
-        row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.):
+        row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1):
     """Zoom in and out of images with the same arguments, randomly or non-randomly.
     Usually be used for image segmentation which x=[X, Y], X and Y should be matched.
 
@@ -738,7 +746,7 @@ def zoom_multi(x, zoom_range=(0.9, 1.1), is_random=False,
     # return x
     results = []
     for data in x:
-        results.append( apply_transform(data, transform_matrix, channel_index, fill_mode, cval))
+        results.append( apply_transform(data, transform_matrix, channel_index, fill_mode, cval, order))
     return np.asarray(results)
 
 # image = tf.image.random_brightness(image, max_delta=32. / 255.)
@@ -1074,7 +1082,7 @@ def transform_matrix_offset_center(matrix, x, y):
     return transform_matrix
 
 
-def apply_transform(x, transform_matrix, channel_index=2, fill_mode='nearest', cval=0.):
+def apply_transform(x, transform_matrix, channel_index=2, fill_mode='nearest', cval=0., order=1):
     """Return transformed images by given transform_matrix from ``transform_matrix_offset_center``.
 
     Parameters
@@ -1091,6 +1099,15 @@ def apply_transform(x, transform_matrix, channel_index=2, fill_mode='nearest', c
         - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
     cval : scalar, optional
         Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0
+    order : int, optional
+        The order of interpolation. The order has to be in the range 0-5:
+
+        - 0 Nearest-neighbor
+        - 1 Bi-linear (default)
+        - 2 Bi-quadratic
+        - 3 Bi-cubic
+        - 4 Bi-quartic
+        - 5 Bi-quintic   
 
         - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
 
@@ -1102,7 +1119,7 @@ def apply_transform(x, transform_matrix, channel_index=2, fill_mode='nearest', c
     final_affine_matrix = transform_matrix[:2, :2]
     final_offset = transform_matrix[:2, 2]
     channel_images = [ndi.interpolation.affine_transform(x_channel, final_affine_matrix,
-                      final_offset, order=0, mode=fill_mode, cval=cval) for x_channel in x]
+                      final_offset, order=order, mode=fill_mode, cval=cval) for x_channel in x]
     x = np.stack(channel_images, axis=0)
     x = np.rollaxis(x, 0, channel_index+1)
     return x