def __init__(self, shape, bkg_paths, mean=pr.BGR_IMAGENET_MEAN):
super(AugmentImage, self).__init__()
# self.add(LoadImage(4))
self.add(pr.ResizeImage(shape))
self.add(pr.BlendRandomCroppedBackground(bkg_paths))
self.add(pr.RandomContrast())
self.add(pr.RandomBrightness())
self.add(pr.RandomSaturation(0.7))
self.add(pr.RandomHue())
self.add(pr.ConvertColorSpace(pr.RGB2BGR))
# let's download a test image and put it inside our PAZ directory
IMAGE_URL = ('https://github.com/oarriaga/altamira-data/releases/download'
'/v0.9/image_augmentation.png')
filename = os.path.basename(IMAGE_URL)
image_fullpath = get_file(filename, IMAGE_URL, cache_subdir='paz/tutorials')
# we load the original image and display it
image = load_image(image_fullpath)
show_image(image)
# We construct a data augmentation pipeline using the built-in PAZ processors:
augment = SequentialProcessor()
augment.add(pr.RandomContrast())
augment.add(pr.RandomBrightness())
augment.add(pr.RandomSaturation())
# We can now apply our pipeline as a normal function:
for _ in range(5):
image = load_image(image_fullpath)
# use it as a normal function
image = augment(image)
show_image(image)
# We can add to our sequential pipeline other function anywhere i.e. arg 0:
augment.insert(0, pr.LoadImage())
for _ in range(5):
# now we don't load the image every time.
image = augment(image_fullpath)
show_image(image)
def __init__(self):
super(AugmentImage, self).__init__()
self.add(pr.RandomContrast())
self.add(pr.RandomBrightness())
self.add(pr.RandomSaturation())
self.add(pr.RandomHue())