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.

Warning

This interface is deprecated and will be removed after version 2.9.0.

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 supports bilinear method. For other methods, it will return 0.

Parameters

  • image (Tensor) – A 4-D Tensor representing a batch of images. It has shape \((batch, image\_height, image\_width, depth)\).

  • boxes (Tensor) – A 2-D Tensor with shape \((num\_boxes, 4)\) representing the normalized coordinates of the boxes to be cropped. The coordinates are specified in the form \([y1, x1, y2, x2]\), where \((y1, x1)\) is the first corner and \((y2, x2)\) is the second corner of the box. If \(y1 > y2\), the sampled crop is inverted upside down, the width dimension is treated similarly when \(x1 > x2\). 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 \((num\_boxes)\) 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" . Default: "bilinear" .

    • "nearest": Nearest neighbor interpolation. Each output pixel is assigned the value of the nearest input pixel. This method is simple and fast but can result in blocky or pixelated outputs.

    • "bilinear": Bilinear interpolation. Each output pixel is a weighted average of the four nearest input pixels, computed using bilinear interpolation. This method produces smoother results compared to nearest neighbor interpolation.

    • "bilinear_v2": The optimized variant of "bilinear", it may achieve better result(higher precision and speed) in some cases.

  • extrapolation_value (float, optional) – An optional float value used for extrapolation, if applicable. Default: 0.0 .

Returns

A 4-D tensor of shape \((num\_boxes, crop\_height, crop\_width, depth)\) 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 int32.

  • 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

>>> import numpy as np
>>> from mindspore import ops, Tensor
>>> 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)