# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Defines image operators with functional form."""
from mindspore.ops import operations as P
from mindspore.ops.operations import image_ops as IMG
import mindspore.common.dtype as mstype
from mindspore.common.tensor import Tensor
from mindspore._c_expression import Tensor as Tensor_
from .._primitive_cache import _get_cache_prim
[docs]def bounding_box_decode(anchor_box, deltas, max_shape, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0),
wh_ratio_clip=0.016):
r"""
Decode the bounding box locations, calculate the offset, and convert the offset into a Bbox, which is used to mark
the target in the subsequent images, etc.
Args:
anchor_box (Tensor): Anchor boxes. The shape of `anchor_box` must be :math:`(n, 4)`.
deltas (Tensor): Delta of boxes. Which has the same shape with `anchor_box`.
max_shape (tuple): The max size limit for decoding box calculation.
means (tuple, optional): The means of `deltas` calculation. Default: ``(0.0, 0.0, 0.0, 0.0)`` .
stds (tuple, optional): The standard deviations of `deltas` calculation. Default: ``(1.0, 1.0, 1.0, 1.0)`` .
wh_ratio_clip (float, optional): The limit of width and height ratio for decoding box calculation.
Default: ``0.016`` .
Returns:
Tensor, decoded boxes. It has the same data type and shape as `anchor_box`.
Raises:
TypeError: If `means`, `stds` or `max_shape` is not a tuple.
TypeError: If `wh_ratio_clip` is not a float.
TypeError: If `anchor_box` or `deltas` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> anchor_box = Tensor([[4, 1, 2, 1], [2, 2, 2, 3]], mindspore.float32)
>>> deltas = Tensor([[3, 1, 2, 2], [1, 2, 1, 4]], mindspore.float32)
>>> output = ops.bounding_box_decode(anchor_box, deltas, max_shape=(768, 1280), means=(0.0, 0.0, 0.0, 0.0),
... stds=(1.0, 1.0, 1.0, 1.0), wh_ratio_clip=0.016)
>>> print(output)
[[ 4.1953125 0. 0. 5.1953125]
[ 2.140625 0. 3.859375 60.59375 ]]
"""
bounding_box_decode_op = _get_cache_prim(P.BoundingBoxDecode)(max_shape, means, stds, wh_ratio_clip)
return bounding_box_decode_op(anchor_box, deltas)
[docs]def bounding_box_encode(anchor_box, groundtruth_box, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0)):
r"""
Encode the bounding box locations, calculate the offset between the predicted bounding boxes and
the real bounding boxes, and the offset will be used as a variable for the loss.
Args:
anchor_box (Tensor): Anchor boxes. The shape of `anchor_box` must be :math:`(n, 4)`.
groundtruth_box (Tensor): Ground truth boxes. Which has the same shape with `anchor_box`.
means (tuple, optional): Means for encoding bounding boxes calculation. Default: ``(0.0, 0.0, 0.0, 0.0)`` .
stds (tuple, optional): The standard deviations of deltas calculation. Default: (``1.0, 1.0, 1.0, 1.0)`` .
Returns:
Tensor, encoded bounding boxes. It has the same data type and shape as input `anchor_box`.
Raises:
TypeError: If `means` or `stds` is not a tuple.
TypeError: If `anchor_box` or `groundtruth_box` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> anchor_box = Tensor([[2, 2, 2, 3], [2, 2, 2, 3]], mindspore.float32)
>>> groundtruth_box = Tensor([[1, 2, 1, 4], [1, 2, 1, 4]], mindspore.float32)
>>> output = ops.bounding_box_encode(anchor_box, groundtruth_box, means=(0.0, 0.0, 0.0, 0.0),
... stds=(1.0, 1.0, 1.0, 1.0))
>>> print(output)
[[ -1. 0.25 0. 0.40551758]
[ -1. 0.25 0. 0.40551758]]
"""
bounding_box_encode_op = _get_cache_prim(P.BoundingBoxEncode)(means, stds)
return bounding_box_encode_op(anchor_box, groundtruth_box)
[docs]def check_valid(bboxes, img_metas):
r"""
Checks whether the bounding box is in the image.
`bboxes` contain several sets of bounding boxes, each represented by two abscissa points :math:`(x0, x1)` and
two ordinate points :math:`(y0, y1)` .
`img_metas` provides information about the original image, including three parameters
:math:`(height, width, ratio)` , which specify the valid boundary of the image.
when the following conditions are met:
:math:`x0 >= 0`
:math:`y0 >= 0`
:math:`x1 <= width * ratio - 1`
:math:`y1 <= height * ratio - 1`
the bounding box is considered to be within the image.
.. warning::
The bounding box specified by `bboxes` and the image information specified by `img_metas` need to be valid,
i.e.:
:math:`x0 <= x1` , :math:`y0 <= y1` , and :math:`(height, width, ratio)` are all positive.
Args:
bboxes (Tensor): Bounding boxes tensor with shape :math:`(N, 4)` . :math:`N` indicates the number of
bounding boxes, the value `4` indicates four coordinate points :math:`(x0, y0, x1, y1)` . Data type must
be float16 or float32.
img_metas (Tensor): Raw image size information with the format of :math:`(height, width, ratio)` , specifying
the valid boundary :math:`(height * ratio - 1, width * ratio - 1)` . Data type must be float16 or float32.
Returns:
Tensor, with shape of :math:`(N,)` and dtype of bool, specifying whether the bounding boxes is in the image.
`True` indicates valid, while `False` indicates invalid.
Raises:
TypeError: If `bboxes` or `img_metas` is not a Tensor.
TypeError: If dtype of `bboxes` or `img_metas` is neither float16 nor float32.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> bboxes = Tensor(np.linspace(0, 6, 12).reshape(3, 4), mindspore.float32)
>>> img_metas = Tensor(np.array([2, 1, 3]), mindspore.float32)
>>> output = ops.check_valid(bboxes, img_metas)
>>> print(output)
[ True False False]
"""
check_valid_op = _get_cache_prim(P.CheckValid)()
return check_valid_op(bboxes, img_metas)
[docs]def crop_and_resize(image, boxes, box_indices, crop_size, method="bilinear", extrapolation_value=0.0):
r"""
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.
Args:
image (Tensor): A 4-D Tensor representing a batch of images. It has shape
:math:`(batch, image\_height, image\_width, depth)`.
boxes (Tensor): A 2-D Tensor with shape :math:`(num\_boxes, 4)` representing the normalized
coordinates of the boxes to be cropped. The coordinates are specified in the
form :math:`[y1, x1, y2, x2]`, where :math:`(y1, x1)` is the first corner
and :math:`(y2, x2)` is the second corner of the box.
If :math:`y1 > y2`, the sampled crop is inverted upside down, the width dimensionis treated
similarly when :math:`x1 > x2`. If normalized coordinates are not in range :math:`[0, 1]`,
extrapolated input image values are used instead. Supported data type: float32.
box_indices (Tensor): A 1-D Tensor of shape :math:`(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 extrapolation, if applicable.
Default: ``0.0`` .
Returns:
A 4-D tensor of shape :math:`(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 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:
>>> 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)
"""
if not isinstance(image, (Tensor, Tensor_)):
raise TypeError("For crop_and_resize, the input image must be a tensor")
if not isinstance(boxes, (Tensor, Tensor_)):
raise TypeError("For crop_and_resize, the input boxes must be a tensor")
if not isinstance(box_indices, (Tensor, Tensor_)):
raise TypeError("For crop_and_resize, the input box_indices must be a tensor")
if not isinstance(crop_size, tuple):
raise TypeError("For crop_and_resize, the input crop_size must be a tuple, but got {}".format(type(crop_size)))
if len(crop_size) != 2:
raise ValueError("For crop_and_resize, the crop_size's length must be 2, bot got {}".format(len(crop_size)))
if not isinstance(crop_size[0], int) or not isinstance(crop_size[1], int):
raise TypeError("For crop_and_resize, the crop_size's value must be int.")
if crop_size[0] <= 0 or crop_size[1] <= 0:
raise ValueError("For crop_and_resize, the crop_size's value must be positive.")
image_shape = image.shape
if len(image_shape) != 4:
raise ValueError(
"For crop_and_resize, the input image must be 4D Tensor, but got is {}D".format(len(image_shape)))
boxes_dtype = _get_cache_prim(P.DType)()(boxes)
if boxes_dtype not in [mstype.float32]:
raise TypeError(
"For crop_and_resize, the input boxes must be {}, but got {}".format(mstype.float32, boxes_dtype))
boxes_shape = boxes.shape
if len(boxes_shape) != 2 or boxes_shape[-1] != 4:
raise ValueError("For crop_and_resize, the input boxes must be 2D and the second-dim must be 4, "
"but got {}".format(boxes_shape))
box_indices_dtype = _get_cache_prim(P.DType)()(box_indices)
if box_indices_dtype not in [mstype.int32]:
raise TypeError(
"For crop_and_resize, the input box_indices must be {}, but got {}".format(mstype.int32, box_indices_dtype))
box_indices_shape = box_indices.shape
if len(box_indices_shape) != 1:
raise ValueError("For crop_and_resize, the input box_indices must be 1D, but got {}".format(box_indices_shape))
if boxes_shape[0] != box_indices_shape[0]:
raise ValueError("For crop_and_resize, the first dim of input box_indices must be equal to that of input boxes"
", but got {} vs {}".format(box_indices_shape[0], boxes_shape[0]))
_crop_and_resize = _get_cache_prim(IMG.CropAndResize)(method, extrapolation_value)
return _crop_and_resize(image, boxes, box_indices, crop_size)
__all__ = [
'bounding_box_decode',
'bounding_box_encode',
'check_valid',
'crop_and_resize'
]
__all__.sort()