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import random
from math import sqrt
from unittest import TestCase
import cv2
import numpy as np
import torch
from mmengine.testing import assert_allclose
from mmdet.structures.bbox import HorizontalBoxes
from mmdet.structures.mask import BitmapMasks, PolygonMasks
class TestHorizontalBoxes(TestCase):
def test_init(self):
th_boxes = torch.Tensor([10, 10, 20, 20]).reshape(1, 1, 4)
th_boxes_cxcywh = torch.Tensor([15, 15, 10, 10]).reshape(1, 1, 4)
boxes = HorizontalBoxes(th_boxes)
assert_allclose(boxes.tensor, th_boxes)
boxes = HorizontalBoxes(th_boxes, in_mode='xyxy')
assert_allclose(boxes.tensor, th_boxes)
boxes = HorizontalBoxes(th_boxes_cxcywh, in_mode='cxcywh')
assert_allclose(boxes.tensor, th_boxes)
with self.assertRaises(ValueError):
boxes = HorizontalBoxes(th_boxes, in_mode='invalid')
def test_cxcywh(self):
th_boxes = torch.Tensor([10, 10, 20, 20]).reshape(1, 1, 4)
th_boxes_cxcywh = torch.Tensor([15, 15, 10, 10]).reshape(1, 1, 4)
boxes = HorizontalBoxes(th_boxes)
assert_allclose(
HorizontalBoxes.xyxy_to_cxcywh(th_boxes), th_boxes_cxcywh)
assert_allclose(th_boxes,
HorizontalBoxes.cxcywh_to_xyxy(th_boxes_cxcywh))
assert_allclose(boxes.cxcywh, th_boxes_cxcywh)
def test_propoerty(self):
th_boxes = torch.Tensor([10, 10, 20, 20]).reshape(1, 1, 4)
boxes = HorizontalBoxes(th_boxes)
# Centers
centers = torch.Tensor([15, 15]).reshape(1, 1, 2)
assert_allclose(boxes.centers, centers)
# Areas
areas = torch.Tensor([100]).reshape(1, 1)
assert_allclose(boxes.areas, areas)
# widths
widths = torch.Tensor([10]).reshape(1, 1)
assert_allclose(boxes.widths, widths)
# heights
heights = torch.Tensor([10]).reshape(1, 1)
assert_allclose(boxes.heights, heights)
def test_flip(self):
img_shape = [50, 85]
# horizontal flip
th_boxes = torch.Tensor([10, 10, 20, 20]).reshape(1, 1, 4)
flipped_boxes_th = torch.Tensor([65, 10, 75, 20]).reshape(1, 1, 4)
boxes = HorizontalBoxes(th_boxes)
boxes.flip_(img_shape, direction='horizontal')
assert_allclose(boxes.tensor, flipped_boxes_th)
# vertical flip
th_boxes = torch.Tensor([10, 10, 20, 20]).reshape(1, 1, 4)
flipped_boxes_th = torch.Tensor([10, 30, 20, 40]).reshape(1, 1, 4)
boxes = HorizontalBoxes(th_boxes)
boxes.flip_(img_shape, direction='vertical')
assert_allclose(boxes.tensor, flipped_boxes_th)
# diagonal flip
th_boxes = torch.Tensor([10, 10, 20, 20]).reshape(1, 1, 4)
flipped_boxes_th = torch.Tensor([65, 30, 75, 40]).reshape(1, 1, 4)
boxes = HorizontalBoxes(th_boxes)
boxes.flip_(img_shape, direction='diagonal')
assert_allclose(boxes.tensor, flipped_boxes_th)
def test_translate(self):
th_boxes = torch.Tensor([10, 10, 20, 20]).reshape(1, 1, 4)
boxes = HorizontalBoxes(th_boxes)
boxes.translate_([23, 46])
translated_boxes_th = torch.Tensor([33, 56, 43, 66]).reshape(1, 1, 4)
assert_allclose(boxes.tensor, translated_boxes_th)
def test_clip(self):
th_boxes = torch.Tensor([10, 10, 20, 20]).reshape(1, 1, 4)
img_shape = [13, 14]
boxes = HorizontalBoxes(th_boxes)
boxes.clip_(img_shape)
cliped_boxes_th = torch.Tensor([10, 10, 14, 13]).reshape(1, 1, 4)
assert_allclose(boxes.tensor, cliped_boxes_th)
def test_rotate(self):
th_boxes = torch.Tensor([10, 10, 20, 20]).reshape(1, 1, 4)
center = (15, 15)
angle = -45
boxes = HorizontalBoxes(th_boxes)
boxes.rotate_(center, angle)
rotated_boxes_th = torch.Tensor([
15 - 5 * sqrt(2), 15 - 5 * sqrt(2), 15 + 5 * sqrt(2),
15 + 5 * sqrt(2)
]).reshape(1, 1, 4)
assert_allclose(boxes.tensor, rotated_boxes_th)
def test_project(self):
th_boxes = torch.Tensor([10, 10, 20, 20]).reshape(1, 1, 4)
boxes1 = HorizontalBoxes(th_boxes)
boxes2 = boxes1.clone()
matrix = np.zeros((3, 3), dtype=np.float32)
center = [random.random() * 80, random.random() * 80]
angle = random.random() * 180
matrix[:2, :3] = cv2.getRotationMatrix2D(center, angle, 1)
x_translate = random.random() * 40
y_translate = random.random() * 40
matrix[0, 2] = matrix[0, 2] + x_translate
matrix[1, 2] = matrix[1, 2] + y_translate
scale_factor = random.random() * 2
matrix[2, 2] = 1 / scale_factor
boxes1.project_(matrix)
boxes2.rotate_(center, -angle)
boxes2.translate_([x_translate, y_translate])
boxes2.rescale_([scale_factor, scale_factor])
assert_allclose(boxes1.tensor, boxes2.tensor)
# test empty boxes
empty_boxes = HorizontalBoxes(torch.zeros((0, 4)))
empty_boxes.project_(matrix)
def test_rescale(self):
scale_factor = [0.4, 0.8]
# rescale
th_boxes = torch.Tensor([10, 10, 20, 20]).reshape(1, 1, 4)
boxes = HorizontalBoxes(th_boxes)
boxes.rescale_(scale_factor)
rescaled_boxes_th = torch.Tensor([4, 8, 8, 16]).reshape(1, 1, 4)
assert_allclose(boxes.tensor, rescaled_boxes_th)
def test_resize(self):
scale_factor = [0.4, 0.8]
th_boxes = torch.Tensor([10, 10, 20, 20]).reshape(1, 1, 4)
boxes = HorizontalBoxes(th_boxes)
boxes.resize_(scale_factor)
resized_boxes_th = torch.Tensor([13, 11, 17, 19]).reshape(1, 1, 4)
assert_allclose(boxes.tensor, resized_boxes_th)
def test_is_inside(self):
th_boxes = torch.Tensor([[10, 10, 20, 20], [-5, -5, 15, 15],
[45, 45, 55, 55]]).reshape(1, 3, 4)
img_shape = [30, 30]
boxes = HorizontalBoxes(th_boxes)
index = boxes.is_inside(img_shape)
index_th = torch.BoolTensor([True, True, False]).reshape(1, 3)
assert_allclose(index, index_th)
def test_find_inside_points(self):
th_boxes = torch.Tensor([10, 10, 20, 20]).reshape(1, 4)
boxes = HorizontalBoxes(th_boxes)
points = torch.Tensor([[0, 0], [0, 15], [15, 0], [15, 15]])
index = boxes.find_inside_points(points)
index_th = torch.BoolTensor([False, False, False, True]).reshape(4, 1)
assert_allclose(index, index_th)
# is_aligned
boxes = boxes.expand(4, 4)
index = boxes.find_inside_points(points, is_aligned=True)
index_th = torch.BoolTensor([False, False, False, True])
assert_allclose(index, index_th)
def test_from_instance_masks(self):
bitmap_masks = BitmapMasks.random()
boxes = HorizontalBoxes.from_instance_masks(bitmap_masks)
self.assertIsInstance(boxes, HorizontalBoxes)
self.assertEqual(len(boxes), len(bitmap_masks))
polygon_masks = PolygonMasks.random()
boxes = HorizontalBoxes.from_instance_masks(polygon_masks)
self.assertIsInstance(boxes, HorizontalBoxes)
self.assertEqual(len(boxes), len(bitmap_masks))
# zero length masks
bitmap_masks = BitmapMasks.random(num_masks=0)
boxes = HorizontalBoxes.from_instance_masks(bitmap_masks)
self.assertIsInstance(boxes, HorizontalBoxes)
self.assertEqual(len(boxes), 0)
polygon_masks = PolygonMasks.random(num_masks=0)
boxes = HorizontalBoxes.from_instance_masks(polygon_masks)
self.assertIsInstance(boxes, HorizontalBoxes)
self.assertEqual(len(boxes), 0)
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