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))
def __init__(self,
phase,
rotation_range=30,
delta_scales=[0.2, 0.2],
num_keypoints=15):
super(AugmentKeypoints, self).__init__()
self.add(pr.UnpackDictionary(['image', 'keypoints']))
if phase == 'train':
self.add(pr.ControlMap(pr.RandomBrightness()))
self.add(pr.ControlMap(pr.RandomContrast()))
self.add(pr.RandomKeypointRotation(rotation_range))
self.add(pr.RandomKeypointTranslation(delta_scales))
self.add(pr.ControlMap(pr.NormalizeImage(), [0], [0]))
self.add(pr.ControlMap(pr.ExpandDims(-1), [0], [0]))
self.add(pr.ControlMap(pr.NormalizeKeypoints((96, 96)), [1], [1]))
self.add(
pr.SequenceWrapper({0: {
'image': [96, 96, 1]
}}, {1: {
'keypoints': [num_keypoints, 2]
}}))
def __init__(self,
phase,
rotation_range=30,
delta_scales=[0.2, 0.2],
with_partition=False,
num_keypoints=15):
super(AugmentKeypoints, self).__init__()
self.add(pr.UnpackDictionary(['image', 'keypoints']))
if phase == 'train':
self.add(pr.ControlMap(pr.RandomBrightness()))
self.add(pr.ControlMap(pr.RandomContrast()))
self.add(pr.RandomKeypointRotation(rotation_range))
self.add(pr.RandomKeypointTranslation(delta_scales))
self.add(pr.ControlMap(pr.NormalizeImage(), [0], [0]))
self.add(pr.ControlMap(pr.ExpandDims(-1), [0], [0]))
self.add(pr.ControlMap(pr.NormalizeKeypoints((96, 96)), [1], [1]))
labels_info = {1: {'keypoints': [num_keypoints, 2]}}
if with_partition:
outro_indices = list(range(1, 16))
self.add(pr.ControlMap(PartitionKeypoints(), [1], outro_indices))
labels_info = {}
for arg in range(num_keypoints):
labels_info[arg + 1] = {'keypoint_%s' % arg: [2]}
self.add(pr.SequenceWrapper({0: {'image': [96, 96, 1]}}, labels_info))
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.utils import get_file
# 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)
def __init__(self):
super(AugmentImage, self).__init__()
self.add(pr.RandomContrast())
self.add(pr.RandomBrightness())
self.add(pr.RandomSaturation())
self.add(pr.RandomHue())