Source code for mindspore.ops.operations.image_ops

# Copyright 2020 Huawei Technologies Co., Ltd
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# http://www.apache.org/licenses/LICENSE-2.0
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"""image_ops"""
from ..._checkparam import Validator as validator
from ..._checkparam import Rel
from ...common import dtype as mstype
from ..primitive import PrimitiveWithInfer, prim_attr_register


[docs]class CropAndResize(PrimitiveWithInfer): """ Extracts crops from the input image tensor and resizes them. Note: In case that the output shape depends on crop_size, the crop_size should be constant. Args: method (str): An optional string specifying the sampling method for resizing. It can be either "bilinear" or "nearest" and default to "bilinear" extrapolation_value (float): An optional float defaults to 0. Value used for extrapolation, when applicable. 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 allowd: 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 allowd: 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. Examples: >>> class CropAndResizeNet(nn.Cell): >>> def __init__(self, crop_size): >>> super(CropAndResizeNet, self).__init__() >>> self.crop_and_resize = P.CropAndResize() >>> self.crop_size = crop_size >>> @ms_function >>> 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.asnumpy()) """ @prim_attr_register def __init__(self, method="bilinear", extrapolation_value=0.0): """init CropAndResize""" self.init_prim_io_names(inputs=['x', 'boxes', 'box_index', 'crop_size'], outputs=['y']) validator.check_value_type("method", method, [str], self.name) validator.check_string("method", method, ["bilinear", "nearest"], self.name) self.method = method validator.check_value_type("extrapolation_value", extrapolation_value, [float], self.name) self.extrapolation_value = extrapolation_value def __infer__(self, x, boxes, box_index, crop_size): # get shape x_shape = list(x['shape']) boxes_shape = list(boxes['shape']) box_index_shape = list(box_index['shape']) # get value if crop_size['value'] is None: raise ValueError(f"For {self.name}, crop_size must be constant.") crop_size_value = crop_size['value'] # get dtype x_dtype = x['dtype'] boxes_dtype = boxes['dtype'] box_index_dtype = box_index['dtype'] crop_size_dtype = crop_size['dtype'] # check dytpe validator.check_tensor_type_same({"x": x_dtype}, [mstype.int8, mstype.int16, mstype.int32, mstype.int64, mstype.float16, mstype.float32, mstype.float64, mstype.uint8, mstype.uint16], self.name) validator.check_tensor_type_same({"boxes": boxes_dtype}, [mstype.float32], self.name) validator.check_tensor_type_same({"box_index": box_index_dtype}, [mstype.int32], self.name) validator.check_value_type("crop_size", crop_size_value, [tuple], self.name) # check input shape rank validator.check("x rank", len(x_shape), "expected", 4, Rel.EQ, self.name) validator.check("boxes rank", len(boxes_shape), "expected", 2, Rel.EQ, self.name) validator.check("box_index rank", len(box_index_shape), "expected", 1, Rel.EQ, self.name) validator.check("crop_size size", len(crop_size_value), "expected", 2, Rel.EQ, self.name) validator.check("boxes dim_0", boxes_shape[0], "box_index dim_0", box_index_shape[0], Rel.EQ, self.name) validator.check("boxes dim_1", boxes_shape[1], "expected", 4, Rel.EQ, self.name) # check crop_size_value validator.check("crop_height", crop_size_value[0], "minimum", 0, Rel.GT, self.name) validator.check("crop_width", crop_size_value[1], "minimum", 0, Rel.GT, self.name) # check crop_size element type validator.check("crop_height dtype", crop_size_dtype[0], "expected", mstype.int32, Rel.EQ, self.name) validator.check("crop_width dtype", crop_size_dtype[1], "expected", mstype.int32, Rel.EQ, self.name) num_boxes = boxes_shape[0] crop_height = crop_size_value[0] crop_width = crop_size_value[1] depth = x_shape[3] return {'shape': (num_boxes, crop_height, crop_width, depth), 'dtype': mstype.float32, 'value': None}