Image.peak2d#

Image.peak2d(npeaks: int = 2, scale: int = 1, interp: bool = False, positive: bool = True) → tuple[ndarray, ndarray][source]#

Find local maxima in image

Parameters:

  • npeaks (int, optional) – number of peaks to return, defaults to 2

  • scale (int) – scale of peaks to consider

  • interp (bool, optional) – interpolate the peak positions, defaults to False

  • positive (bool, optional) – only select peaks that are positive, defaults to False

Returns:

peak magnitude and positions

Return type:

ndarray(npeaks), ndarray(2,npeaks)

Find the positions of the local maxima in the image. A local maxima is the largest value within a sliding window of width \(2 \mathtt{scale}+1\).

Example:

>>> from machinevisiontoolbox import Image
>>> import numpy as np
>>> peak = np.array([[10, 20, 30], [40, 50, 45], [15, 20, 30]])
>>> img = Image(np.pad(peak, 3, constant_values=10))
>>> img.A
array([[10, 10, 10, ..., 10, 10, 10],
       [10, 10, 10, ..., 10, 10, 10],
       [10, 10, 10, ..., 10, 10, 10],
       ...,
       [10, 10, 10, ..., 10, 10, 10],
       [10, 10, 10, ..., 10, 10, 10],
       [10, 10, 10, ..., 10, 10, 10]], shape=(9, 9), dtype=uint8)
>>> img.peak2d(interp=True)
(array([7.5]), array([[4.1667],
       [4.    ]]))

Note

  • Edges elements will never be returned as maxima.

  • To find minima, use peak2d(-image).

  • The interp option fits points in the neighbourhood about the peak with a paraboloid and its peak position is returned.

Seealso:

findpeaks2d