Improve Performance of ElasticTransformation (aleju#624)

This patch applies various performance-related changes to
`ElasticTransformation`. These cover: (a) the re-use of
generated random samples for multiple images in the same
batch (with some adjustments so that they are not identical),
(b) the caching of generated and re-useable arrays,
(c) a performance-optimized smoothing method for the
underlying displacement maps and (d) the use of nearest
neighbour interpolation (`order=0`) instead of cubic
interpolation (`order=3`) as the new default parameter
for `order`.

These changes lead to a speedup of about 3x to 4x (more
for larger images) at a slight loss of visual
quality (mainly from `order=0`) and variety (due to the
re-use of random samples within each batch).
The new smoothing method leads to slightly stronger
displacements for larger `sigma` values.

changelogs/master/improved/20200223_faster_elastic_tf.md

@@ -0,0 +1,19 @@
+# Improved Performance of `ElasticTransformation` #624
+
+This patch applies various performance-related changes to
+`ElasticTransformation`. These cover: (a) the re-use of
+generated random samples for multiple images in the same
+batch (with some adjustments so that they are not identical),
+(b) the caching of generated and re-useable arrays,
+(c) a performance-optimized smoothing method for the
+underlying displacement maps and (d) the use of nearest
+neighbour interpolation (`order=0`) instead of cubic
+interpolation (`order=3`) as the new default parameter
+for `order`.
+
+These changes lead to a speedup of about 3x to 4x (more
+for larger images) at a slight loss of visual
+quality (mainly from `order=0`) and variety (due to the
+re-use of random samples within each batch).
+The new smoothing method leads to slightly stronger
+displacements for larger `sigma` values.

checks/check_elastic_transformation.py

@@ -25,6 +25,13 @@ def main():
     augs_cv2 = aug_cv2.augment_images([image] * 8)
     ia.imshow(ia.draw_grid(augs_scipy + augs_cv2, rows=2))
 
+    # check behaviour for multiple consecutive batches
+    aug = iaa.ElasticTransformation(alpha=(5, 100), sigma=(3, 5))
+    images1 = aug(images=[np.copy(image) for _ in range(10)])
+    images2 = aug(images=[np.copy(image) for _ in range(10)])
+    images3 = aug(images=[np.copy(image) for _ in range(10)])
+    ia.imshow(ia.draw_grid(images1 + images2 + images3, rows=3))
+
     print("alpha=vary, sigma=0.25")
     augs = [iaa.ElasticTransformation(alpha=alpha, sigma=0.25) for alpha in np.arange(0.0, 50.0, 0.1)]
     images_aug = [aug.augment_image(image) for aug in augs]

checks/check_segmentation_maps.py

@@ -106,6 +106,21 @@ def main():
         ])
     )
 
+    print("ElasticTransformation alpha=200, sig=20...")
+    aug = iaa.ElasticTransformation(alpha=200.0, sigma=20.0)
+    aug_det = aug.to_deterministic()
+    quokka_aug = aug_det.augment_image(quokka)
+    segmaps_aug = aug_det.augment_segmentation_maps(segmap)
+    segmaps_drawn = segmap.draw_on_image(quokka)[0]
+    segmaps_aug_drawn = segmaps_aug.draw_on_image(quokka_aug)[0]
+
+    ia.imshow(
+        np.hstack([
+            segmaps_drawn,
+            segmaps_aug_drawn
+        ])
+    )
+
     print("CopAndPad mode=constant...")
     aug = iaa.CropAndPad(px=(-10, 10, 15, -15), pad_mode="constant", pad_cval=128)
     aug_det = aug.to_deterministic()