This transform can support to normalize ND spatial (channel-first) data. It also supports pseudo ND spatial data (e.g., (C,H,W) is a

This is an extra instance to allow for defining new mask generators. For creating other mask transforms, define a new class and simpl

Args: kspace: The input k-space data. The shape is (...,num_coils,H,W,2) for complex 2D inputs and (...,num_coils

Args: kspace: The input k-space data. The shape is (...,num_coils,H,W,2) for complex 2D inputs and (...,num_coils

data["label_prompt"] should not contain 0

Select positives and hard negatives from list samples per image. Hard negative sampler will be applied to each image independently.

Compute matches for a single image Args: boxes: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assu

Compute metric. See :func:`compute` for more information. Args: *args: positional arguments passed to :func:`compute`

Args: boxes: source bounding boxes, Nx4 or Nx6 or 0xM torch tensor or ndarray.

Converts the boxes in src_mode to the dst_mode. Args: boxes: source bounding boxes, Nx4 or Nx6 torch tensor or ndarray.

Convert given boxes to standard mode. Standard mode is "xyxy" or "xyzxyz", representing box format of [xmin, ymin, xmax, ymax

Args: boxes: source bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``

Args: boxes: source bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``

Args: boxes: source bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``

Args: boxes: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode`` spati

Args: boxes: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode`` label

Args: boxes: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``. labe

Args: boxes_mask: int16 array, sized (num_box, H, W). Each channel represents a box. The foreground region in cha

Args: boxes: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode`` label

Args: img: channel first array, must have shape: (num_channels, H[, W, ..., ]),

Callable that Optuna will use to optimize the hyper-parameters Args: obj_filename: the serialized Algo object.

Use the ensembled model to predict result. Args: pred_param: prediction parameter dictionary. The key has two groups: th

probs_map: the input probabilities map, it must have shape (H[, W, ...]). resolution_level: the level at which the probabilities map

Perform stain extraction. Args: image: uint8 RGB image to extract stain from return: target_he: H&E absorban

Perform stain normalization. Args: image: uint8 RGB image/patch to be stain normalized, pixel values between 0 and 255 R

Args: image: image where the lowest value points are labeled first. Shape must be [1, H, W, [D]]. mask: optional, the

Args: prob_map: probability map of segmentation, shape must be [C, H, W, [D]]

Args: mask: binary segmentation map, the output of :py:class:`GenerateWatershedMask`. Shape must be [1, H, W] or

Args: mask: binary segmentation map, the output of :py:class:`GenerateWatershedMask`. Shape must be [1, H, W] or

Args: mask: binary segmentation map, the output of :py:class:`GenerateWatershedMask`. Shape must be [1, H, W] or

Args: contours: list of (n, 2)-ndarrays, scipy-style clockwise line segments, with lines separating foreground/background.

Args: inst_mask: segmentation mask for a single instance. Shape should be [1, H, W, [D]] offset: optional offset of s

Args: inst_mask: segmentation mask for a single instance. Shape should be [1, H, W, [D]] offset: optional offset of s

Args: type_pred: pixel-level type prediction map after activation function. seg_pred: pixel-level segmentation predic