erase

paddle.vision.transforms. erase ( img, i, j, h, w, v, inplace=False ) [source]

Erase the pixels of selected area in input image with given value.

Parameters

  • img (paddle.Tensor | np.array | PIL.Image) – input Tensor image. For Tensor input, the shape should be (C, H, W). For np.array input, the shape should be (H, W, C).

  • i (int) – y coordinate of the top-left point of erased region.

  • j (int) – x coordinate of the top-left point of erased region.

  • h (int) – Height of the erased region.

  • w (int) – Width of the erased region.

  • v (paddle.Tensor | np.array) – value used to replace the pixels in erased region. It should be np.array when img is np.array or PIL.Image.

  • inplace (bool, optional) – Whether this transform is inplace. Default: False.

Returns

Erased image. The type is same with input image.

Return type

paddle.Tensor | np.array | PIL.Image

Examples

>>> import paddle
>>> paddle.seed(2023)
>>> fake_img = paddle.randn((3, 2, 4)).astype(paddle.float32)
>>> print(fake_img)
Tensor(shape=[3, 2, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[ 0.06132207,  1.11349595,  0.41906244, -0.24858207],
  [-1.85169315, -1.50370061,  1.73954511,  0.13331604]],
[[ 1.66359663, -0.55764782, -0.59911072, -0.57773495],
 [-1.03176904, -0.33741450, -0.29695082, -1.50258386]],
[[ 0.67233968, -1.07747352,  0.80170447, -0.06695852],
 [-1.85003340, -0.23008066,  0.65083790,  0.75387722]]])

>>> values = paddle.zeros((1,1,1), dtype=paddle.float32)
>>> result = paddle.vision.transforms.erase(fake_img, 0, 1, 1, 2, values)
>>> print(result)
Tensor(shape=[3, 2, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[ 0.06132207,  0.        ,  0.        , -0.24858207],
  [-1.85169315, -1.50370061,  1.73954511,  0.13331604]],
[[ 1.66359663,  0.        ,  0.        , -0.57773495],
 [-1.03176904, -0.33741450, -0.29695082, -1.50258386]],
[[ 0.67233968,  0.        ,  0.        , -0.06695852],
 [-1.85003340, -0.23008066,  0.65083790,  0.75387722]]])