machinevisiontoolbox.Image.peak2d

Image.peak2d(npeaks=2, scale=1, interp=False, positive=True)

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, 20, 30, 10, 10, 10],
       [10, 10, 10, 40, 50, 45, 10, 10, 10],
       [10, 10, 10, 15, 20, 30, 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]])
>>> 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