def init(self, img: ImageT, bbox: np.ndarray) -> None:
self.model.eval()
bbox = assure_int_bbox(bbox)
self.target_bbox = BBox(*bbox)
self.curr_instance_side_size = calc_bbox_side_size_with_context(
self.target_bbox)
size_ratio = self.cfg.exemplar_size / self.cfg.instance_size
exemplar_side_size = int(
round(self.curr_instance_side_size * size_ratio))
exemplar_bbox = BBox.build_from_center_and_size(
self.target_bbox.center,
np.asarray((exemplar_side_size, exemplar_side_size)))
exemplar_img = center_crop_and_resize(
img, exemplar_bbox,
(self.cfg.exemplar_size, self.cfg.exemplar_size))
if self.on_exemplar_img_extract:
self.on_exemplar_img_extract(exemplar_img)
exemplar_img_tensor = torch.unsqueeze(pil_to_tensor(exemplar_img), 0)
exemplar_img_tensor = exemplar_img_tensor.to(self.device)
self.kernel_reg, self.kernel_cls = self.model.learn_kernels(
exemplar_img_tensor)
def test_output_shape(self):
bbox = BBox(0, 0, 100, 100)
width, height = 127, 255
patch = center_crop_and_resize(self.img, bbox, (width, height))
patch = np.asarray(patch)
self.assertEqual(patch.shape, (height, width, 3))
def test_bbox_outsize_image_needs_padding_constant_border(self):
bbox = BBox(-50, -10, 200, 300)
border = 127 # Value to fill the border with.
patch = center_crop_and_resize(self.img, bbox, (127, 255), border)
patch_flat = np.asarray(patch).flatten()
values = np.unique(patch_flat)
self.assertTrue(len(values) > 1)
self.assertTrue(border in values)
self.assertTrue(self.PIX_VALUE in values)
def generate_bbox(self):
valid_x_range = 0, self.img_size[0] - self.width_range[1] - 1
valid_y_range = 0, self.img_size[1] - self.height_range[1] - 1
x = rand_between(*valid_x_range, as_int=True)
y = rand_between(*valid_y_range, as_int=True)
width = rand_between(*self.width_range, as_int=True)
height = rand_between(*self.height_range, as_int=True)
return BBox(x, y, width, height)
def _read_image_and_transform(
img_path: str,
anno: np.ndarray,
output_side_size: int,
transform: Callable[[Image.Image], torch.Tensor],
size_with_context_scale: float = 1.0) -> torch.Tensor:
bbox = BBox(*anno)
side_size_with_context = calc_bbox_side_size_with_context(bbox)
side_size_scaled = side_size_with_context * size_with_context_scale
new_size = (side_size_scaled, side_size_scaled)
bbox.size = np.asarray(new_size).round().astype(np.int) # !!!
img = Image.open(img_path)
output_side = (output_side_size, output_side_size)
patch = center_crop_and_resize(img, bbox, output_side)
if patch.mode == 'L':
patch = patch.convert('RGB')
patch_tensor = transform(patch)
return patch_tensor
def test_bbox_within_image_no_padding(self):
bbox = BBox(0, 0, 100, 100)
patch = center_crop_and_resize(self.img, bbox, (127, 255))
patch_flat = np.asarray(patch).flatten()
self.assertTrue(np.all(patch_flat == patch_flat[0]))
def test_negative_height(self):
with self.assertRaises(AssertionError):
BBox(100, 100, 10, -200)
def test_negative_width(self):
with self.assertRaises(AssertionError):
BBox(100, 100, -10, 200)
def setUp(self) -> None:
self.bbox = BBox(10, 20, 300, 400)