def _get_multiplier_for_color_randomness():
  """Returns a multiplier to get semi-random colors from successive indices.

  This function computes a prime number, p, in the range [2, 17] that:
  - is closest to len(STANDARD_COLORS) / 10
  - does not divide len(STANDARD_COLORS)

  If no prime numbers in that range satisfy the constraints, p is returned as 1.

  Once p is established, it can be used as a multiplier to select
  non-consecutive colors from STANDARD_COLORS:
  colors = [(p * i) % len(STANDARD_COLORS) for i in range(20)]
  """
  num_colors = len(STANDARD_COLORS)
  prime_candidates = [5, 7, 11, 13, 17]

  # Remove all prime candidates that divide the number of colors.
  prime_candidates = [p for p in prime_candidates if num_colors % p]
  if not prime_candidates:
    return 1

  # Return the closest prime number to num_colors / 10.
  abs_distance = [np.abs(num_colors / 10. - p) for p in prime_candidates]
  num_candidates = len(abs_distance)
  inds = [i for _, i in sorted(zip(abs_distance, range(num_candidates)))]
  return prime_candidates[inds[0]]
Returns a multiplier to get semi-random colors from successive indices.

  This function computes a prime number, p, in the range [2, 17] that:
  - is closest to len(STANDARD_COLORS) / 10
  - does not divide len(STANDARD_COLORS)

  If no prime numbers in that range satisfy the constraints, p is returned as 1.

  Once p is established, it can be used as a multiplier to select
  non-consecutive colors from STANDARD_COLORS:
  colors = [(p * i) % len(STANDARD_COLORS) for i in range(20)]
  
inference_api/src/main/object_detection/utils/visualization_utils.py