Add SomeOf; Add OneOf

README.md

@@ -71,38 +71,19 @@ images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)
 
 # Sometimes(0.5, ...) applies the given augmenter in 50% of all cases,
 # e.g. Sometimes(0.5, GaussianBlur(0.3)) would blur roughly every second image.
-rarely = lambda aug: iaa.Sometimes(0.1, aug)
-sometimes = lambda aug: iaa.Sometimes(0.25, aug)
-often = lambda aug: iaa.Sometimes(0.5, aug)
+sometimes = lambda aug: iaa.Sometimes(0.5, aug)
 
 # Define our sequence of augmentation steps that will be applied to every image
 # All augmenters with per_channel=0.5 will sample one value _per image_
 # in 50% of all cases. In all other cases they will sample new values
 # _per channel_.
-seq = iaa.Sequential([
+seq = iaa.Sequential(
+    [
+        # apply the following augmenters to most images
         iaa.Fliplr(0.5), # horizontally flip 50% of all images
-        iaa.Flipud(0.5), # vertically flip 50% of all images
-        rarely(iaa.Superpixels(p_replace=(0, 1.0), n_segments=(20, 200))), # convert images into their superpixel representation
-        often(iaa.Crop(percent=(0, 0.1))), # crop images by 0-10% of their height/width
-        rarely(iaa.GaussianBlur((0, 3.0))), # blur images with a sigma between 0 and 3.0
-        rarely(iaa.AverageBlur(k=(2, 7))), # blur image using local means with kernel sizes between 2 and 7
-        rarely(iaa.MedianBlur(k=(3, 11))), # blur image using local medians with kernel sizes between 2 and 7
-        sometimes(iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5))), # sharpen images
-        sometimes(iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0))), # emboss images
-        # search either for all edges or for directed edges
-        rarely(iaa.Sometimes(0.5,
-            iaa.EdgeDetect(alpha=(0, 0.7)),
-            iaa.DirectedEdgeDetect(alpha=(0, 0.7), direction=(0.0, 1.0)),
-        )),
-        often(iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5)), # add gaussian noise to images
-        often(iaa.Dropout((0.0, 0.1), per_channel=0.5)), # randomly remove up to 10% of the pixels
-        often(iaa.CoarseDropout((0.0, 0.05), size_percent=(0.02, 0.25), per_channel=True)),
-        rarely(iaa.Invert(0.25, per_channel=True)), # invert color channels
-        often(iaa.Add((-10, 10), per_channel=0.5)), # change brightness of images (by -10 to 10 of original value)
-        often(iaa.Multiply((0.5, 1.5), per_channel=0.5)), # change brightness of images (50-150% of original value)
-        often(iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5)), # improve or worsen the contrast
-        sometimes(iaa.Grayscale(alpha=(0.0, 1.0))),
-        often(iaa.Affine(
+        iaa.Flipud(0.2), # vertically flip 20% of all images
+        sometimes(iaa.Crop(percent=(0, 0.1))), # crop images by 0-10% of their height/width
+        sometimes(iaa.Affine(
             scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}, # scale images to 80-120% of their size, individually per axis
             translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)}, # translate by -20 to +20 percent (per axis)
             rotate=(-45, 45), # rotate by -45 to +45 degrees
@@ -111,7 +92,37 @@ seq = iaa.Sequential([
             cval=(0, 255), # if mode is constant, use a cval between 0 and 255
             mode=ia.ALL # use any of scikit-image's warping modes (see 2nd image from the top for examples)
         )),
-        rarely(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)) # move pixels locally around (with random strengths)
+        # execute 0 to 5 of the following (less important) augmenters per image
+        # don't execute all of them, as that would often be way too strong
+        iaa.SomeOf((0, 5),
+            [
+                sometimes(iaa.Superpixels(p_replace=(0, 1.0), n_segments=(20, 200))), # convert images into their superpixel representation
+                iaa.OneOf([
+                    iaa.GaussianBlur((0, 3.0)), # blur images with a sigma between 0 and 3.0
+                    iaa.AverageBlur(k=(2, 7)), # blur image using local means with kernel sizes between 2 and 7
+                    iaa.MedianBlur(k=(3, 11)), # blur image using local medians with kernel sizes between 2 and 7
+                ]),
+                iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)), # sharpen images
+                iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)), # emboss images
+                # search either for all edges or for directed edges
+                sometimes(iaa.OneOf([
+                    iaa.EdgeDetect(alpha=(0, 0.7)),
+                    iaa.DirectedEdgeDetect(alpha=(0, 0.7), direction=(0.0, 1.0)),
+                ])),
+                iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5), # add gaussian noise to images
+                iaa.OneOf([
+                    iaa.Dropout((0.01, 0.1), per_channel=0.5), # randomly remove up to 10% of the pixels
+                    iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05), per_channel=0.2),
+                ]),
+                iaa.Invert(0.05, per_channel=True), # invert color channels
+                iaa.Add((-10, 10), per_channel=0.5), # change brightness of images (by -10 to 10 of original value)
+                iaa.Multiply((0.5, 1.5), per_channel=0.5), # change brightness of images (50-150% of original value)
+                iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5), # improve or worsen the contrast
+                iaa.Grayscale(alpha=(0.0, 1.0)),
+                sometimes(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)) # move pixels locally around (with random strengths)
+            ],
+            random_order=True
+        )
     ],
     random_order=True
 )

generate_example_images.py

@@ -2,7 +2,6 @@
 import imgaug as ia
 from imgaug import augmenters as iaa
 from imgaug import parameters as iap
-#from skimage import
 import numpy as np
 from scipy import ndimage, misc
 from skimage import data
@@ -11,13 +10,17 @@
 import six
 import six.moves as sm
 
+np.random.seed(44)
+ia.seed(44)
+
 def main():
     draw_single_sequential_images()
     draw_per_augmenter_images()
 
 def draw_single_sequential_images():
     image = misc.imresize(ndimage.imread("quokka.jpg")[0:643, 0:643], (128, 128))
 
+    """
     rarely = lambda aug: iaa.Sometimes(0.1, aug)
     sometimes = lambda aug: iaa.Sometimes(0.25, aug)
     often = lambda aug: iaa.Sometimes(0.5, aug)
@@ -57,6 +60,58 @@ def draw_single_sequential_images():
         ],
         random_order=True
     )
+    """
+
+    sometimes = lambda aug: iaa.Sometimes(0.5, aug)
+    seq = iaa.Sequential(
+        [
+            # apply the following augmenters to most images
+            iaa.Fliplr(0.5), # horizontally flip 50% of all images
+            iaa.Flipud(0.2), # vertically flip 20% of all images
+            sometimes(iaa.Crop(percent=(0, 0.1))), # crop images by 0-10% of their height/width
+            sometimes(iaa.Affine(
+                scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}, # scale images to 80-120% of their size, individually per axis
+                translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)}, # translate by -20 to +20 percent (per axis)
+                rotate=(-45, 45), # rotate by -45 to +45 degrees
+                shear=(-16, 16), # shear by -16 to +16 degrees
+                order=[0, 1], # use nearest neighbour or bilinear interpolation (fast)
+                cval=(0, 255), # if mode is constant, use a cval between 0 and 255
+                mode=ia.ALL # use any of scikit-image's warping modes (see 2nd image from the top for examples)
+            )),
+            # execute 0 to 5 of the following (less important) augmenters per image
+            # don't execute all of them, as that would often be way too strong
+            iaa.SomeOf((0, 5),
+                [
+                    sometimes(iaa.Superpixels(p_replace=(0, 1.0), n_segments=(20, 200))), # convert images into their superpixel representation
+                    iaa.OneOf([
+                        iaa.GaussianBlur((0, 3.0)), # blur images with a sigma between 0 and 3.0
+                        iaa.AverageBlur(k=(2, 7)), # blur image using local means with kernel sizes between 2 and 7
+                        iaa.MedianBlur(k=(3, 11)), # blur image using local medians with kernel sizes between 2 and 7
+                    ]),
+                    iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)), # sharpen images
+                    iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)), # emboss images
+                    # search either for all edges or for directed edges
+                    sometimes(iaa.OneOf([
+                        iaa.EdgeDetect(alpha=(0, 0.7)),
+                        iaa.DirectedEdgeDetect(alpha=(0, 0.7), direction=(0.0, 1.0)),
+                    ])),
+                    iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5), # add gaussian noise to images
+                    iaa.OneOf([
+                        iaa.Dropout((0.01, 0.1), per_channel=0.5), # randomly remove up to 10% of the pixels
+                        iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05), per_channel=0.2),
+                    ]),
+                    iaa.Invert(0.05, per_channel=True), # invert color channels
+                    iaa.Add((-10, 10), per_channel=0.5), # change brightness of images (by -10 to 10 of original value)
+                    iaa.Multiply((0.5, 1.5), per_channel=0.5), # change brightness of images (50-150% of original value)
+                    iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5), # improve or worsen the contrast
+                    iaa.Grayscale(alpha=(0.0, 1.0)),
+                    sometimes(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)) # move pixels locally around (with random strengths)
+                ],
+                random_order=True
+            )
+        ],
+        random_order=True
+    )
 
     grid = seq.draw_grid(image, cols=8, rows=8)
     misc.imsave("examples_grid.jpg", grid)