def random_transform_group_entry(self, image, annotations, transform=None):
""" Randomly transforms image and annotation.
"""
# randomly transform both image and annotations
if transform or self.transform_generator:
if transform is None:
transform = adjust_transform_for_image(
next(self.transform_generator), image,
self.transform_parameters.relative_translation)
# apply transformation to image
image = apply_transform(transform, image,
self.transform_parameters)
# randomly transform the masks and expand so to have a fake channel dimension
for i, mask in enumerate(annotations['masks']):
annotations['masks'][i] = apply_transform(
transform, mask, self.transform_parameters)
annotations['masks'][i] = np.expand_dims(
annotations['masks'][i], axis=2)
# Transform the bounding boxes in the annotations.
annotations['bboxes'] = annotations['bboxes'].copy()
for index in range(annotations['bboxes'].shape[0]):
annotations['bboxes'][index, :] = transform_aabb(
transform, annotations['bboxes'][index, :])
return image, annotations
def random_transform_group_entry(self, image, annotations, masks):
# randomly transform both image and annotations
if self.transform_generator:
transform = adjust_transform_for_image(next(self.transform_generator), image, self.transform_parameters.relative_translation)
image = apply_transform(transform, image, self.transform_parameters)
# randomly transform the masks and expand so to have a fake channel dimension
for m in range(len(masks)):
masks[m] = apply_transform(transform, masks[m], self.transform_parameters)
masks[m] = np.expand_dims(masks[m], axis=2)
# randomly transform the bounding boxes
annotations = annotations.copy()
for index in range(annotations.shape[0]):
annotations[index, :4] = transform_aabb(transform, annotations[index, :4])
return image, annotations, masks
def random_transform_group_entry(self, image, annotations, transform=None):
""" Randomly transforms image and annotation.
"""
# randomly transform both image and annotations
if transform is not None or self.transform_generator:
if transform is None:
transform = adjust_transform_for_image(next(self.transform_generator), image,
self.transform_parameters.relative_translation)
# apply transformation to image
image = apply_transform(transform, image, self.transform_parameters)
# Transform the bounding boxes in the annotations.
annotations['bboxes'] = annotations['bboxes'].copy()
for index in range(annotations['bboxes'].shape[0]):
annotations['bboxes'][index, :] = transform_aabb(transform, annotations['bboxes'][index, :])
return image, annotations
from keras_retinanet.utils.transform import random_transform
from keras_retinanet.utils.image import apply_transform, TransformParameters, adjust_transform_for_image, read_image_bgr
from matplotlib.image import imsave
if __name__ == "__main__":
img = read_image_bgr('./TestFile/foo.jpg')
transform = random_transform(flip_x_chance=1, flip_y_chance=0)
print(transform)
transform_parameters = TransformParameters()
transform = adjust_transform_for_image(
transform, img, transform_parameters.relative_translation)
img_transformed = apply_transform(transform, img, transform_parameters)
imsave('./TestFile/foo_transformed.jpg', img_transformed)
exit(0)
