def __init__(self, class_names, prior_boxes, variances=[.1, .2]):
super(ShowBoxes, self).__init__()
self.deprocess_boxes = SequentialProcessor([
pr.DecodeBoxes(prior_boxes, variances),
pr.ToBoxes2D(class_names, True),
pr.FilterClassBoxes2D(class_names[1:])
])
self.denormalize_boxes2D = pr.DenormalizeBoxes2D()
self.draw_boxes2D = pr.DrawBoxes2D(class_names)
self.show_image = pr.ShowImage()
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']