# 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)
# Adding new processor at the end to have a single function.
augment.add(pr.ShowImage())
for _ in range(5):
# everything compressed into a single function
image = augment(image_fullpath)
# We can also pop the last processor added.
augment.pop()
# We now create another processor for geometric augmentation.
if split == pr.TRAIN:
self.add(pr.ControlMap(self.augment_image, [0], [0]))
self.add(pr.ControlMap(self.augment_boxes, [0, 1], [0, 1]))
self.add(pr.ControlMap(self.preprocess_image, [0], [0]))
self.add(pr.ControlMap(self.preprocess_boxes, [1], [1]))
self.add(
pr.SequenceWrapper({0: {
'image': [size, size, 3]
}}, {1: {
'boxes': [len(prior_boxes), 4 + num_classes]
}}))
prior_boxes = create_prior_boxes()
draw_boxes.processors[0].processor.one_hot_encoded = True
draw_boxes.insert(0, pr.ControlMap(pr.DecodeBoxes(prior_boxes), [1], [1]))
draw_boxes.insert(
2, pr.ControlMap(pr.FilterClassBoxes2D(class_names[1:]), [1], [1]))
def deprocess_image(image):
image = (image + pr.BGR_IMAGENET_MEAN).astype('uint8')
return P.image.convert_color_space(image, pr.BGR2RGB)
augmentator = AugmentDetection(prior_boxes, num_classes=len(class_names))
print('Image and boxes augmentations')
for _ in range(10):
sample = {'image': image_fullpath, 'boxes': box_data.copy()}
data = augmentator(sample)
image, boxes = data['inputs']['image'], data['labels']['boxes']
