Source code for mindspore.ops.operations.image_ops

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"""image_ops"""
from ... import context
from ..._checkparam import Validator as validator
from ..._checkparam import Rel
from ...common import dtype as mstype
from ..primitive import PrimitiveWithInfer, prim_attr_register, Primitive


[文档]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 must be constant. Args: 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) """ @prim_attr_register def __init__(self, method="bilinear", extrapolation_value=0.0): """Initialize 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, ["bilinear", "nearest", "bilinear_v2"], "method", self.name) self.method = method validator.check_value_type("extrapolation_value", extrapolation_value, [float], self.name) self.extrapolation_value = extrapolation_value self.is_ge = context.get_context("enable_ge") 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}', the 'crop_size' cannot be None, but got {crop_size['value']}.") 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_dtype_valid("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_dtype_valid("boxes", boxes_dtype, [mstype.float32], self.name) validator.check_tensor_dtype_valid("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, mstype.int64], Rel.IN, self.name) validator.check("crop_width dtype", crop_size_dtype[1], "expected", [mstype.int32, mstype.int64], Rel.IN, self.name) num_boxes = boxes_shape[0] crop_height = crop_size_value[0] crop_width = crop_size_value[1] depth = x_shape[3] out_shape = (num_boxes, crop_height, crop_width, depth) if self.is_ge: out_shape = (num_boxes, x_shape[1], crop_height, crop_width) return {'shape': out_shape, 'dtype': mstype.float32, 'value': None}
class NonMaxSuppressionV3(Primitive): r""" Greedily selects a subset of bounding boxes in descending order of score. .. warning:: When input "max_output_size" is negative, it will be treated as 0. Note: This algorithm is agnostic to where the origin is in the coordinate system. This algorithm is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. Inputs: - **boxes** (Tensor) - A 2-D Tensor of shape [num_boxes, 4]. - **scores** (Tensor) - A 1-D Tensor of shape [num_boxes] representing a single score corresponding to each box (each row of boxes), the num_boxes of "scores" must be equal to the num_boxes of "boxes". - **max_output_size** (Union[Tensor, Number.Int]) - A scalar integer Tensor representing the maximum number of boxes to be selected by non max suppression. - **iou_threshold** (Union[Tensor, Number.Float]) - A 0-D float tensor representing the threshold for deciding whether boxes overlap too much with respect to IOU, and iou_threshold must be equal or greater than 0 and be equal or smaller than 1. - **score_threshold** (Union[Tensor, Number.Float]) - A 0-D float tensor representing the threshold for deciding when to remove boxes based on score. Outputs: A 1-D integer Tensor of shape [M] representing the selected indices from the boxes tensor, where M <= max_output_size. Raises: TypeError: If the dtype of `boxes` and `scores` is different. TypeError: If the dtype of `iou_threshold` and `score_threshold` is different. TypeError: If `boxes` is not tensor or its dtype is not float16 or float32. TypeEroor: If `scores` is not tensor or its dtype is not float16 or float32. TypeError: If `max_output_size` is not tensor or scalar.If `max_output_size` is not int32 or int64. TypeError: If `iou_threshold` is not tensor or scalar. If its type is not float16 or float32. TypeError: If `score_threshold` is not tensor or scalar. If its type is not float16 or float32. ValueError: If the size of shape of `boxes` is not 2 or the second value of its shape is not 4. ValueError: If the size of shape of `scores` is not 1. ValueError: If each of the size of shape of `max_output_size`, `iou_threshold`, `score_threshold` is not 0. Supported Platforms: ``Ascend`` Examples: >>> boxes = Tensor(np.array([[1, 2, 3, 4], [1, 3, 3, 4], [1, 3, 4, 4], ... [1, 1, 4, 4], [1, 1, 3, 4]]), mstype.float32) >>> scores = Tensor(np.array([0.4, 0.5, 0.72, 0.9, 0.45]), mstype.float32) >>> max_output_size = Tensor(5, mstype.int32) >>> iou_threshold = Tensor(0.5, mstype.float32) >>> score_threshold = Tensor(0, mstype.float32) >>> nonmaxsuppression = ops.NonMaxSuppressionV3() >>> output = nonmaxsuppression(boxes, scores, max_output_size, iou_threshold, score_threshold) >>> print(output) [3 2 0] """ @prim_attr_register def __init__(self): """Initialize NonMaxSuppressionV3""" class HSVToRGB(Primitive): """ Convert one or more images from HSV to RGB. The format of the image(s) should be NHWC. Inputs: - **x** (Tensor) - The input image must be a 4-D tensor of shape [batch, image_height, image_width, channel]. Number of channel must be 3. Types allowed: float16, float32, float64. Outputs: A 4-D tensor of shape [batch, image_height, image_width, channel] with same type of input. Raises: TypeError: If `x` is not a Tensor. TypeError: If the dtype of `x` is not float16, float32, float64. ValueError: If rank of the `x` is not equal to 4. ValueError: If the last dimension of `x` is not equal to 3. Supported Platforms: ``CPU`` Examples: >>> image = np.array([0.5, 0.5, 0.5]).astype(np.float32).reshape([1, 1, 1, 3]) >>> hsv_to_rgb = P.HSVToRGB() >>> output = hsv_to_rgb(Tensor(image)) >>> print(output) [[[[0.25 0.5 0.5 ]]]] """ @prim_attr_register def __init__(self): pass