mindspore.ops.CropAndResize
- class mindspore.ops.CropAndResize(method='bilinear', extrapolation_value=0.0)[源代码]
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.
- Parameters
method (str) – 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. Default: “bilinear”
extrapolation_value (float) – An optional float value used extrapolation, if applicable. Default: 0.0.
- Inputs:
x (Tensor) - The input image must be a 4-D tensor of shape [batch, image_height, image_width, depth]. Types allowed: int8, int16, int32, int64, float16, float32, float64, uint8, uint16.
boxes (Tensor) - A 2-D tensor of shape [num_boxes, 4]. The i-th row of the tensor specifies the coordinates of a box in the box_ind[i] image and is specified in normalized coordinates [y1, x1, y2, x2]. A normalized coordinate value of y is mapped to the image coordinate at y * (image_height - 1), so as the [0, 1] interval of normalized image height is mapped to [0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled crop is an up-down flipped version of the original image. The width dimension is treated similarly. Normalized coordinates outside the [0, 1] range are allowed, in which case we use extrapolation_value to extrapolate the input image values. Types allowed: float32.
box_index (Tensor) - A 1-D tensor of shape [num_boxes] with int32 values in [0, batch). The value of box_ind[i] specifies the image that the i-th box refers to. Types allowed: int32.
crop_size (Tuple[int]) - A tuple of two int32 elements: (crop_height, crop_width). Only constant value is allowed. All cropped image patches are resized to this size. The aspect ratio of the image content is not preserved. Both crop_height and crop_width need to be positive.
- Outputs:
A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth] with type: float32.
- Raises
TypeError – If method is not a str.
TypeError – If extrapolation_value is not a float.
ValueError – If method is not one of ‘bilinear’, ‘nearest’, ‘bilinear_v2’.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> class CropAndResizeNet(nn.Cell): ... def __init__(self, crop_size): ... super(CropAndResizeNet, self).__init__() ... self.crop_and_resize = ops.CropAndResize() ... self.crop_size = crop_size ... ... def construct(self, x, boxes, box_index): ... return self.crop_and_resize(x, boxes, box_index, self.crop_size) ... >>> 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_index = np.random.uniform(size=[NUM_BOXES], low=0, high=BATCH_SIZE).astype(np.int32) >>> crop_size = (24, 24) >>> crop_and_resize = CropAndResizeNet(crop_size=crop_size) >>> output = crop_and_resize(Tensor(image), Tensor(boxes), Tensor(box_index)) >>> print(output.shape) (5, 24, 24, 3)