def gen_crop_transform_with_instance(crop_size,
image_size,
instances,
crop_box=True):
"""
Generate a CropTransform so that the cropping region contains
the center of the given instance.
Args:
crop_size (tuple): h, w in pixels
image_size (tuple): h, w
instance (dict): an annotation dict of one instance, in Detectron2's
dataset format.
"""
instance = (np.random.choice(instances), )
instance = instance[0]
crop_size = np.asarray(crop_size, dtype=np.int32)
bbox = BoxMode.convert(instance["bbox"], instance["bbox_mode"],
BoxMode.XYXY_ABS)
center_yx = (bbox[1] + bbox[3]) * 0.5, (bbox[0] + bbox[2]) * 0.5
# print(image_size[0], center_yx[0], image_size[1], center_yx[1])
# if not (image_size[0] >= center_yx[0] and image_size[1] >= center_yx[1]):
# center_yx = (bbox[1] + bbox[3]) * 0.1, (bbox[0] + bbox[2]) * 0.1
assert (image_size[0] >= center_yx[0] and image_size[1] >= center_yx[1]
), "The annotation bounding box is outside of the image!"
assert (image_size[0] >= crop_size[0] and image_size[1] >= crop_size[1]
), "Crop size is larger than image size!"
min_yx = np.maximum(np.floor(center_yx).astype(np.int32) - crop_size, 0)
max_yx = np.maximum(np.asarray(image_size, dtype=np.int32) - crop_size, 0)
max_yx = np.minimum(max_yx, np.ceil(center_yx).astype(np.int32))
y0 = np.random.randint(min_yx[0], max_yx[0] + 1)
x0 = np.random.randint(min_yx[1], max_yx[1] + 1)
# if some instance is cropped extend the box
if not crop_box:
num_modifications = 0
modified = True
# convert crop_size to float
crop_size = crop_size.astype(np.float32)
while modified:
modified, x0, y0, crop_size = adjust_crop(x0, y0, crop_size,
instances)
num_modifications += 1
if num_modifications > 100:
raise ValueError(
"Cannot finished cropping adjustment within 100 tries (#instances {})."
.format(len(instances)))
return T.CropTransform(0, 0, image_size[1], image_size[0])
return T.CropTransform(*map(int, (x0, y0, crop_size[1], crop_size[0])))
def test_register(self):
"""
Test register.
"""
dtype = "int"
def add1(t, x):
return x + 1
def flip_sub_width(t, x):
return x - t.width
T.Transform.register_type(dtype, add1)
T.HFlipTransform.register_type(dtype, flip_sub_width)
transforms = T.TransformList([
T.ScaleTransform(0, 0, 0, 0, 0),
T.CropTransform(0, 0, 0, 0),
T.HFlipTransform(3),
])
self.assertEqual(transforms.apply_int(3), 2)
# Testing __add__, __iadd__, __radd__, __len__.
transforms = transforms + transforms
transforms += transforms
transforms = T.NoOpTransform() + transforms
self.assertEqual(len(transforms), 13)
with self.assertRaises(AssertionError):
T.HFlipTransform.register_type(dtype, lambda x: 1)
with self.assertRaises(AttributeError):
transforms.no_existing
def test_crop_invalid_polygons(self):
# Ensure that invalid polygons are skipped.
transform = T.CropTransform(3, 4, 20, 20)
polygon = [
(0, 0),
(0, 3),
(3, 3),
(3, 0),
(2, 0),
(2, 2),
(1, 2),
(1, 1),
(2, 1),
(2, 0),
(0, 0),
]
cropped_polygons = transform.apply_polygons([polygon])
self.assertEqual(0, len(cropped_polygons))
def test_register(self):
"""
Test register.
"""
dtype = "int"
def add1(t, x):
return x + 1
def flip_sub_width(t, x):
return x - t.width
T.Transform.register_type(dtype, add1)
T.HFlipTransform.register_type(dtype, flip_sub_width)
transforms = T.TransformList([
T.ScaleTransform(0, 0, 0, 0, 0),
T.CropTransform(0, 0, 0, 0),
T.HFlipTransform(3),
])
self.assertEqual(transforms.apply_int(3), 2)
with self.assertRaises(AssertionError):
T.HFlipTransform.register_type(dtype, lambda x: 1)
def test_transformlist_flatten(self):
t0 = T.HFlipTransform(width=100)
t1 = T.ScaleTransform(3, 4, 5, 6)
t2 = T.CropTransform(4, 5, 6, 7)
t = T.TransformList([T.TransformList([t0, t1]), t2])
self.assertEqual(len(t.transforms), 3)
