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

fake_img = paddle.randn((3, 2, 4)).astype(paddle.float32)
print(fake_img)

#Tensor(shape=[3, 2, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
#       [[[ 0.02169025, -0.97859967, -1.39175487, -1.07478464],
#         [ 0.20654772,  1.74624777,  0.32268861, -0.13857445]],
#
#        [[-0.14993843,  1.10793507, -0.40056887, -1.94395220],
#         [ 0.41686651,  0.44551995, -0.09356714, -0.60898107]],
#
#        [[-0.24998808, -1.47699273, -0.88838995,  0.42629015],
#         [ 0.56948012, -0.96200180,  0.53355658,  3.20450878]]])

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(gpu:0), stop_gradient=True,
#       [[[ 0.02169025,  0.        ,  0.        , -1.07478464],
#         [ 0.20654772,  1.74624777,  0.32268861, -0.13857445]],
#
#         [[-0.14993843,  0.        ,  0.        , -1.94395220],
#           [ 0.41686651,  0.44551995, -0.09356714, -0.60898107]],
#
#         [[-0.24998808,  0.        ,  0.        ,  0.42629015],
#          [ 0.56948012, -0.96200180,  0.53355658,  3.20450878]]])