def inverse(self):
if not self._can_invert():
raise InvertibleError('Cannot invert a transformation before it is applied'
' (size before the cropping is unknown).')
crop_h, crop_w = self.size
pad_w = self._img_w - crop_w
pad_h = self._img_h - crop_h
pad_tl = (0, 0, pad_w, pad_h)
pad_tr = (pad_w, 0, 0, pad_h)
pad_bl = (0, pad_h, pad_w, 0)
pad_br = (pad_w, pad_h, 0, 0)
half_w = (self._img_w - crop_w) // 2
half_h = (self._img_h - crop_h) // 2
pad_center = (half_w, half_h, self._img_w - crop_w - half_w, self._img_h - crop_h - half_h)
tfs = []
for padding in [pad_tl, pad_tr, pad_bl, pad_br, pad_center]:
tf = T.Pad(padding)
tf._img_h = crop_h
tf._img_w = crop_w
tfs.append(tf)
def invert_crops(crops):
tl, tr, bl, br, center = crops
return [tf(img) for tf, img in zip(tfs, [tl, tr, bl, br, center])]
def invert_invert_crops(crops):
tl, tr, bl, br, center = crops
return [tf.inverse()(img) for tf, img in zip(tfs, [tl, tr, bl, br, center])]
return T.Lambda(
lambd=invert_crops,
tf_inv=invert_invert_crops,
repr_str=f'FiveCropInverse()',
)
def test_padding_int(self):
padding = 100
tf = T.Pad(padding=padding)
img_inv = tf(self.img_pil)
self.assertEqual(img_inv.size, (self.w + 2 * padding, self.h + 2 * padding))
self.assertEqual(self.img_pil.size, tf.invert(img_inv).size)
def test_padding_mismatch(self):
tf = T.Pad(0)
tf.padding = None
tf._img_h, tf._img_w = self.img_size
with self.assertRaises(Exception):
tf.inverse()
def test_invert_before_call(self):
with self.assertRaises(InvertibleError):
T.Pad(0).inverse()
def test_padding_tuple(self):
pad_lr, pad_tb = 100, 50
tf = T.Pad(padding=(pad_lr, pad_tb))
img_inv = tf(self.img_pil)
self.assertEqual(img_inv.size, (self.w + 2 * pad_lr, self.h + 2 * pad_tb))
self.assertEqual(self.img_pil.size, tf.invert(img_inv).size)