o # i@s`ddlmZddlZddlmZddgZ      ddddZ      ddddZdS)) annotationsN) rescale_arraycreate_test_image_2dcreate_test_image_3d heightintwidthnum_objsrad_maxrad_min noise_maxfloatnum_seg_classes channel_dim int | None random_statenp.random.RandomState | Nonereturntuple[np.ndarray, np.ndarray]c Cs||krtd|d|d|dkrtd|dt||} | d|kr1td| d|dt||f} |d urAtjjjn|} t|D]Q} | |||} | |||}| ||}tj| || | ||f\}}||||||k}|dkrt | || |<qG| d d | |<qGt | j tj d d }| j d ||| j d}tt| |}|d urt|tr|dvstd|d kr|d }|d }||fS|d}|d}||fS)a Return a noisy 2D image with `num_objs` circles and a 2D mask image. The maximum and minimum radii of the circles are given as `rad_max` and `rad_min`. The mask will have `num_seg_classes` number of classes for segmentations labeled sequentially from 1, plus a background class represented as 0. If `noise_max` is greater than 0 then noise will be added to the image taken from the uniform distribution on range `[0,noise_max)`. If `channel_dim` is None, will create an image without channel dimension, otherwise create an image with channel dimension as first dim or last dim. Args: height: height of the image. The value should be larger than `2 * rad_max`. width: width of the image. The value should be larger than `2 * rad_max`. num_objs: number of circles to generate. Defaults to `12`. rad_max: maximum circle radius. Defaults to `30`. rad_min: minimum circle radius. Defaults to `5`. noise_max: if greater than 0 then noise will be added to the image taken from the uniform distribution on range `[0,noise_max)`. Defaults to `0`. num_seg_classes: number of classes for segmentations. Defaults to `5`. channel_dim: if None, create an image without channel dimension, otherwise create an image with channel dimension as first dim or last dim. Defaults to `None`. random_state: the random generator to use. Defaults to `np.random`. Returns: Randomised Numpy array with shape (`height`, `width`) `rad_min`  should be less than `rad_max` .z should be no less than 1.the minimal size 4 of the image should be larger than `2 * rad_max` 2xN?Fcopyrsize)rrinvalid channel dim..N ValueErrorminnpzerosrandom__self__rangerandintogridceilastypeint32uniformshapermaximum isinstancer AssertionError)r r r rrrrrrmin_sizeimagers_xyradspyspxcirclelabelsnorm noisyimagerGV/home/dell461/cl/sdc2/last_ska_mid/HISourceFinder-master-l/src/monai/data/synthetic.pyrs>#    &depthc Cs||krtd|d|d|dkrtdt|||} | d|kr.td| d|dt|||f} | d ur?tjjjn| } t|D]d} | |||}| |||}| |||}| ||}tj| ||| ||| ||f\}}}||||||||k}|dkrt | || |<qE| d d | |<qEt | j tj d d }| j d ||| j d}tt| |}|d urt|tr|dvstd|d kr|d |d fn|d|df\}}||fS)an Return a noisy 3D image and segmentation. Args: height: height of the image. The value should be larger than `2 * rad_max`. width: width of the image. The value should be larger than `2 * rad_max`. depth: depth of the image. The value should be larger than `2 * rad_max`. num_objs: number of circles to generate. Defaults to `12`. rad_max: maximum circle radius. Defaults to `30`. rad_min: minimum circle radius. Defaults to `5`. noise_max: if greater than 0 then noise will be added to the image taken from the uniform distribution on range `[0,noise_max)`. Defaults to `0`. num_seg_classes: number of classes for segmentations. Defaults to `5`. channel_dim: if None, create an image without channel dimension, otherwise create an image with channel dimension as first dim or last dim. Defaults to `None`. random_state: the random generator to use. Defaults to `np.random`. Returns: Randomised Numpy array with shape (`height`, `width`, `depth`) See also: :py:meth:`~create_test_image_2d` rrrrz.f`rad_min` {rad_min} should be no less than 1.rrrNr Fr!rr#)r%rr&r'r()r r rIr rrrrrrr:r;r<r=r>r?zr@rArBZspzrCrDrErFrGrGrHras8$    4 &)rrrr rNN)r r r r r r rr rr rrrr rrrrrr)r r r r rIr r r rr rr rrrr rrrrrr) __future__rnumpyr+monai.transforms.utilsr__all__rrrGrGrGrHs(   P