InnovArul · January 1, 2020 11:22 · InnovArul · Jan 1, 2020
diff --git a/imgaug_segfault.py b/imgaug_segfault.py
 import imgaug as ia 
 import imgaug.augmenters  as iaa 
 print("loaded") 
 prob = 0.5 
 sometimes = lambda aug: iaa.Sometimes(prob, aug) 
 output_shape=(473,473) 
 seq = iaa.Sequential([ 
    # apply the following augmenters to most images 
    iaa.Fliplr(0.5),
    sometimes(iaa.Affine(
         scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}, 
         translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)}, 
         order=[0, 1], 
         cval=(0, 255), 
         mode=ia.ALL 
    )),

    iaa.Resize({"height": output_shape[0], "width": "keep-aspect-ratio"}),
    iaa.CropToFixedSize(width=output_shape[1], height=output_shape[0], position="center"), 
    iaa.PadToFixedSize(width=output_shape[1], height=output_shape[0], pad_mode=ia.ALL), 
    # incase if the resizing is missed, resize the images explicity 
    iaa.Resize({"height": output_shape[0], "width": output_shape[1]})
 ])

 # creating dummy images
 import numpy as np 

 for i in range(100):
    img = np.random.rand(709,450,3) 
    seg = np.ones((1,709,450,1))
    print("augmenting", i) 
    iaug, saug = seq(image=img, segmentation_maps=seg.astype(np.int32))
	import imgaug as ia
	import imgaug.augmenters as iaa
	print("loaded")
	prob = 0.5
	sometimes = lambda aug: iaa.Sometimes(prob, aug)
	output_shape=(473,473)
	seq = iaa.Sequential([
	# apply the following augmenters to most images
	iaa.Fliplr(0.5),
	sometimes(iaa.Affine(
	scale={"x": (0.8, 1.2), "y": (0.8, 1.2)},
	translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)},
	order=[0, 1],
	cval=(0, 255),
	mode=ia.ALL
	)),

	iaa.Resize({"height": output_shape[0], "width": "keep-aspect-ratio"}),
	iaa.CropToFixedSize(width=output_shape[1], height=output_shape[0], position="center"),
	iaa.PadToFixedSize(width=output_shape[1], height=output_shape[0], pad_mode=ia.ALL),
	# incase if the resizing is missed, resize the images explicity
	iaa.Resize({"height": output_shape[0], "width": output_shape[1]})
	])

	# creating dummy images
	import numpy as np

	for i in range(100):
	img = np.random.rand(709,450,3)
	seg = np.ones((1,709,450,1))
	print("augmenting", i)
	iaug, saug = seq(image=img, segmentation_maps=seg.astype(np.int32))