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- import unittest
- import numpy as np
- from cvdatasets.dataset.part.base import BasePart
- class PartCropTest(unittest.TestCase):
- def setUp(self):
- self.im = np.random.randn(300, 300, 3)
- def tearDown(self):
- pass
- def _check_crop(self, cropped_im, _should):
- self.assertIsNotNone(cropped_im,
- "method crop should return something!")
- self.assertIsInstance(cropped_im, type(self.im),
- "result should have the same type as the input image")
- crop_h, crop_w, _ = cropped_im.shape
- h, w, _ = _should.shape
- self.assertEqual(crop_h, h, "incorrect crop height")
- self.assertEqual(crop_w, w, "incorrect crop width")
- self.assertTrue((cropped_im == _should).all(),
- "crop was incorret")
- def test_bbox_part_crop(self):
- _id, x, y, w, h = annotation = (0, 20, 20, 100, 100)
- bbox = BasePart.new(self.im, annotation)
- cropped_im = bbox.crop(self.im)
- _should = self.im[y:y+h, x:x+w]
- self._check_crop(cropped_im, _should)
- def test_location_part_crop(self):
- _id, center_x, center_y, _vis = annotation = (0, 50, 50, 1)
- bbox = BasePart.new(self.im, annotation)
- h, w, c = self.im.shape
- for ratio in np.linspace(0.1, 0.3, num=9):
- _h, _w = int(h * ratio), int(w * ratio)
- cropped_im = bbox.crop(self.im, ratio=ratio)
- x, y = center_x - _h // 2, center_y - _w // 2
- _should = self.im[y : y + _h, x : x + _w]
- self._check_crop(cropped_im, _should)
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