code: def hough_line_peaks(
      hspace,
      angles,
      dists,
      min_distance=9,
      min_angle=10,
      threshold=None,
      num_peaks=np.inf,
  ):
      """Return peaks in a straight line Hough transform.
  
      Identifies most prominent lines separated by a certain angle and distance
      in a Hough transform. Non-maximum suppression with different sizes is
      applied separately in the first (distances) and second (angles) dimension
      of the Hough space to identify peaks.
  
      Parameters
      ----------
      hspace : ndarray, shape (M, N)
          Hough space returned by the `hough_line` function.
      angles : array, shape (N,)
          Angles returned by the `hough_line` function. Assumed to be continuous.
          (`angles[-1] - angles[0] == PI`).
      dists : array, shape (M,)
          Distances returned by the `hough_line` function.
      min_distance : int, optional
          Minimum distance separating lines (maximum filter size for first
          dimension of hough space).
      min_angle : int, optional
          Minimum angle separating lines (maximum filter size for second
          dimension of hough space).
      threshold : float, optional
          Minimum intensity of peaks. Default is `0.5 * max(hspace)`.
      num_peaks : int, optional
          Maximum number of peaks. When the number of peaks exceeds `num_peaks`,
          return `num_peaks` coordinates based on peak intensity.
  
      Returns
      -------
      accum, angles, dists : tuple of array
          Peak values in Hough space, angles and distances.
  
      Examples
      --------
      >>> from skimage.transform import hough_line, hough_line_peaks
      >>> from skimage.draw import line
      >>> img = np.zeros((15, 15), dtype=bool)
      >>> rr, cc = line(0, 0, 14, 14)
      >>> img[rr, cc] = 1
      >>> rr, cc = line(0, 14, 14, 0)
      >>> img[cc, rr] = 1
      >>> hspace, angles, dists = hough_line(img)
      >>> hspace, angles, dists = hough_line_peaks(hspace, angles, dists)
      >>> len(angles)
      2
  
      """
      from ..feature.peak import _prominent_peaks
  
      min_angle = min(min_angle, hspace.shape[1])
      h, a, d = _prominent_peaks(
          hspace,
          min_xdistance=min_angle,
          min_ydistance=min_distance,
          threshold=threshold,
          num_peaks=num_peaks,
      )
      if a.size > 0:
          return (h, angles[a], dists[d])
      else:
          return (h, np.array([]), np.array([]))
  
file_path: scikit-image/skimage/transform/hough_transform.py
imports: import numpy
  from scipy.spatial import cKDTree
  from ._hough_transform import _hough_circle, _hough_ellipse, _hough_line
  from ._hough_transform import _probabilistic_hough_line
label_desc: Use image node_id 1 for calling a global function with example usage: hough_line_peaks(hspace, angles, dists, min_distance, min_angle, threshold, num_peaks) and returns: h,  angles,  dists, h,  np,  np