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mindspore.ops.crop_and_resize

mindspore.ops.crop_and_resize(image, boxes, box_indices, crop_size, method='bilinear', extrapolation_value=0.0)[source]

Extracts crops from the input image Tensor and resizes them.

Note

In case that the output shape depends on crop_size, the crop_size must be constant. For now, the backward of the operator only support bilinear method, for other methods, will return 0.

Parameters

image (Tensor) – A 4-D Tensor representing a batch of images. It has shape $(b a t c h, i m a g e_h e i g h t, i m a g e_w i d t h, d e p t h)$ .
boxes (Tensor) – A 2-D Tensor with shape $(n u m_b o x e s, 4)$ representing the normalized coordinates of the boxes to be cropped. The coordinates are specified in the form $[y 1, x 1, y 2, x 2]$ , where $(y 1, x 1)$ is the first corner and $(y 2, x 2)$ is the second corner of the box. If $y 1 > y 2$ , the sampled crop is inverted upside down, the width dimensionis treated similarly when $x 1 > x 2$ . If normalized coordinates are not in range $[0, 1]$ , extrapolated input image values are used instead. Supported data type: float32.
box_indices (Tensor) – A 1-D Tensor of shape $(n u m_b o x e s)$ representing the batch index for each box. Supported type: int32.
crop_size (Tuple[int]) – A tuple of two elements: (crop_height, crop_width), representing the output size of the cropped and resized images. Only positive values are supported. Supported type: int32.
method (str, optional) – An optional string that specifies the sampling method for resizing. It can be “bilinear”, “nearest” or “bilinear_v2”. The option “bilinear” stands for standard bilinear interpolation algorithm, while “bilinear_v2” may result in better result in some cases. “nearest” is the nearest neighbor interpolation algorithm. Default: “bilinear”.
extrapolation_value (float, optional) – An optional float value used extrapolation, if applicable. Default: 0.0.

Returns

A 4-D tensor of shape $(n u m_{b} o x e s, c r o p_{h} e i g h t, c r o p_{w} i d t h, d e p t h)$ with type(float32).

Raises

TypeError – If image or boxes or box_indices is not a Tensor.
TypeError – If crop_size is not a Tuple with two int32 elements.
TypeError – If dtype of boxes is not float or that of box_indices is not int.
TypeError – If method is not a str.
TypeError – If extrapolation_value is not a float.
ValueError – If the shape rank of image is not 4.
ValueError – If the shape rank of boxes is not 2.
ValueError – If the second dim of boxes is not 4.
ValueError – If the shape rank of box_indices is not 1.
ValueError – If the first dim of box_indices is not equal to that of boxes.
ValueError – If existing element in box_indices is out of range [0, batch).
ValueError – If the data of crop_size is not positive.
ValueError – If method is not one of ‘bilinear’, ‘nearest’, ‘bilinear_v2’.

Supported Platforms:: Ascend GPU CPU

Examples

>>> BATCH_SIZE = 1
>>> NUM_BOXES = 5
>>> IMAGE_HEIGHT = 256
>>> IMAGE_WIDTH = 256
>>> CHANNELS = 3
>>> image = np.random.normal(size=[BATCH_SIZE, IMAGE_HEIGHT, IMAGE_WIDTH, CHANNELS]).astype(np.float32)
>>> boxes = np.random.uniform(size=[NUM_BOXES, 4]).astype(np.float32)
>>> box_indices = np.random.uniform(size=[NUM_BOXES], low=0, high=BATCH_SIZE).astype(np.int32)
>>> crop_size = (24, 24)
>>> output = ops.crop_and_resize(Tensor(image), Tensor(boxes), Tensor(box_indices), crop_size)
>>> print(output.shape)
 (5, 24, 24, 3)