test

python

import imgaug as ia
from imgaug import augmenters as iaa
ia.seed(1)
image = ia.quokka(size=(256, 256))
bbs = ia.BoundingBoxesOnImage([
ia.BoundingBox(x1=65, y1=100, x2=200, y2=150),
ia.BoundingBox(x1=150, y1=80, x2=200, y2=130)
], shape=image.shape)
seq = iaa.Sequential([
iaa.Multiply((1.2, 1.5)), # change brightness, doesn't affect BBs
iaa.Affine(
translate_px={"x": 40, "y": 60},
scale=(0.5, 0.7)
) # translate by 40/60px on x/y axis, and scale to 50-70%, affects BBs
])
# Make our sequence deterministic.
# We can now apply it to the image and then to the BBs and it will
# lead to the same augmentations.
# IMPORTANT: Call this once PER BATCH, otherwise you will always get the
# exactly same augmentations for every batch!
seq_det = seq.to_deterministic()
# Augment BBs and images.
# As we only have one image and list of BBs, we use
# [image] and [bbs] to turn both into lists (batches) for the
# functions and then [0] to reverse that. In a real experiment, your
# variables would likely already be lists.
image_aug = seq_det.augment_images([image])[0]
bbs_aug = seq_det.augment_bounding_boxes([bbs])[0]
# print coordinates before/after augmentation (see below)
# use .x1_int, .y_int, ... to get integer coordinates
for i in range(len(bbs.bounding_boxes)):
before = bbs.bounding_boxes[i]
after = bbs_aug.bounding_boxes[i]
print("BB %d: (%.4f, %.4f, %.4f, %.4f) -> (%.4f, %.4f, %.4f, %.4f)" % (
i,
before.x1, before.y1, before.x2, before.y2,
after.x1, after.y1, after.x2, after.y2)
)
# image with BBs before/after augmentation (shown below)
image_before = bbs.draw_on_image(image, thickness=2)
image_after = bbs_aug.draw_on_image(image_aug, thickness=2, color=[0, 0, 255])