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- import numpy as np
- __all__ = ["label_points"]
- def label_points(coords, output_shape):
- """Assign unique integer labels to coordinates on an image mask
- Parameters
- ----------
- coords : ndarray
- An array of N coordinates with dimension D
- output_shape : tuple
- The shape of the mask on which `coords` are labelled
- Returns
- -------
- labels: ndarray
- A mask of zeroes containing unique integer labels at the `coords`
- Examples
- --------
- >>> import numpy as np
- >>> from skimage.util._label import label_points
- >>> coords = np.array([[0, 1], [2, 2]])
- >>> output_shape = (5, 5)
- >>> mask = label_points(coords, output_shape)
- >>> mask
- array([[0, 1, 0, 0, 0],
- [0, 0, 0, 0, 0],
- [0, 0, 2, 0, 0],
- [0, 0, 0, 0, 0],
- [0, 0, 0, 0, 0]], dtype=uint64)
- Notes
- -----
- - The labels are assigned to coordinates that are converted to
- integer and considered to start from 0.
- - Coordinates that are out of range of the mask raise an IndexError.
- - Negative coordinates raise a ValueError
- """
- if coords.shape[1] != len(output_shape):
- raise ValueError("Dimensionality of points should match the " "output shape")
- if np.any(coords < 0):
- raise ValueError("Coordinates should be positive and start from 0")
- np_indices = tuple(np.transpose(np.round(coords).astype(int, copy=False)))
- labels = np.zeros(output_shape, dtype=np.uint64)
- labels[np_indices] = np.arange(1, coords.shape[0] + 1)
- return labels
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