text: import numpy as np
  import unittest
  from skimage._shared.testing import assert_array_almost_equal
  from skimage._shared.testing import assert_equal
  from scipy import ndimage as ndi
  from skimage.feature import peak
  
  
  np.random.seed(21)
  
  class TestPeakLocalMax():
      def test_trivial_case(self):
          trivial = np.zeros((25, 25))
          peak_indices = peak.peak_local_max(trivial, min_distance=1, indices=True)
          assert type(peak_indices) is np.ndarray
          assert not peak_indices     # inherent boolean-ness of empty list
          peaks = peak.peak_local_max(trivial, min_distance=1, indices=False)
          assert (peaks.astype(np.bool) == trivial).all()
  
      def test_noisy_peaks(self):
          peak_locations = [(7, 7), (7, 13), (13, 7), (13, 13)]
  
          # image with noise of amplitude 0.8 and peaks of amplitude 1
          image = 0.8 * np.random.rand(20, 20)
          for r, c in peak_locations:
              image[r, c] = 1
  
          peaks_detected = peak.peak_local_max(image, min_distance=5)
  
          assert len(peaks_detected) == len(peak_locations)
          for loc in peaks_detected:
              assert tuple(loc) in peak_locations
  
      def test_relative_threshold(self):
          image = np.zeros((5, 5), dtype=np.uint8)
          image[1, 1] = 10
          image[3, 3] = 20
          peaks = peak.peak_local_max(image, min_distance=1, threshold_rel=0.5)
          assert len(peaks) == 1
          assert_array_almost_equal(peaks, [(3, 3)])
  
      def test_absolute_threshold(self):
          image = np.zeros((5, 5), dtype=np.uint8)
          image[1, 1] = 10
          image[3, 3] = 20
          peaks = peak.peak_local_max(image, min_distance=1, threshold_abs=10)
          assert len(peaks) == 1
          assert_array_almost_equal(peaks, [(3, 3)])
  
      def test_constant_image(self):
          image = 128 * np.ones((20, 20), dtype=np.uint8)
          peaks = peak.peak_local_max(image, min_distance=1)
          assert len(peaks) == 0
  
      def test_flat_peak(self):
          image = np.zeros((5, 5), dtype=np.uint8)
          image[1:3, 1:3] = 10
          peaks = peak.peak_local_max(image, min_distance=1)
          assert len(peaks) == 4
  
      def test_sorted_peaks(self):
          image = np.zeros((5, 5), dtype=np.uint8)
          image[1, 1] = 20
          image[3, 3] = 10
          peaks = peak.peak_local_max(image, min_distance=1)
          assert peaks.tolist() == [[3, 3], [1, 1]]
  
          image = np.zeros((3, 10))
          image[1, (1, 3, 5, 7)] = (1, 3, 2, 4)
          peaks = peak.peak_local_max(image, min_distance=1)
          assert peaks.tolist() == [[1, 7], [1, 5], [1, 3], [1, 1]]
  
      def test_num_peaks(self):
          image = np.zeros((7, 7), dtype=np.uint8)
          image[1, 1] = 10
          image[1, 3] = 11
          image[1, 5] = 12
          image[3, 5] = 8
          image[5, 3] = 7
          assert len(peak.peak_local_max(image, min_distance=1, threshold_abs=0)) == 5
          peaks_limited = peak.peak_local_max(
              image, min_distance=1, threshold_abs=0, num_peaks=2)
          assert len(peaks_limited) == 2
          assert (1, 3) in peaks_limited
          assert (1, 5) in peaks_limited
          peaks_limited = peak.peak_local_max(
              image, min_distance=1, threshold_abs=0, num_peaks=4)
          assert len(peaks_limited) == 4
          assert (1, 3) in peaks_limited
          assert (1, 5) in peaks_limited
          assert (1, 1) in peaks_limited
          assert (3, 5) in peaks_limited
  
      def test_num_peaks_and_labels(self):
          image = np.zeros((7, 7), dtype=np.uint8)
          labels = np.zeros((7, 7), dtype=np.uint8) + 20
          image[1, 1] = 10
          image[1, 3] = 11
          image[1, 5] = 12
          image[3, 5] = 8
          image[5, 3] = 7
          peaks_limited = peak.peak_local_max(
              image, min_distance=1, threshold_abs=0, labels=labels)
          assert len(peaks_limited) == 5
          peaks_limited = peak.peak_local_max(
              image, min_distance=1, threshold_abs=0, labels=labels, num_peaks=2)
          assert len(peaks_limited) == 2
  
  
      def test_num_peaks_tot_vs_labels_4quadrants(self):
          np.random.seed(21)
          image = np.random.uniform(size=(20, 30))
          i, j = np.mgrid[0:20, 0:30]
          labels = 1 + (i >= 10) + (j >= 15) * 2
          result = peak.peak_local_max(image, labels=labels,
                                       min_distance=1, threshold_rel=0,
                                       indices=True,
                                       num_peaks=np.inf,
                                       num_peaks_per_label=2)
          assert len(result) == 8
          result = peak.peak_local_max(image, labels=labels,
                                       min_distance=1, threshold_rel=0,
                                       indices=True,
                                       num_peaks=np.inf,
                                       num_peaks_per_label=1)
          assert len(result) == 4
          result = peak.peak_local_max(image, labels=labels,
                                       min_distance=1, threshold_rel=0,
                                       indices=True,
                                       num_peaks=2,
                                       num_peaks_per_label=2)
          assert len(result) == 2
  
  
      def test_num_peaks3D(self):
          # Issue 1354: the old code only hold for 2D arrays
          # and this code would die with IndexError
          image = np.zeros((10, 10, 100))
          image[5,5,::5] = np.arange(20)
          peaks_limited = peak.peak_local_max(image, min_distance=1, num_peaks=2)
          assert len(peaks_limited) == 2
  
      def test_reorder_labels(self):
          image = np.random.uniform(size=(40, 60))
          i, j = np.mgrid[0:40, 0:60]
          labels = 1 + (i >= 20) + (j >= 30) * 2
          labels[labels == 4] = 5
          i, j = np.mgrid[-3:4, -3:4]
          footprint = (i * i + j * j <= 9)
          expected = np.zeros(image.shape, float)
          for imin, imax in ((0, 20), (20, 40)):
              for jmin, jmax in ((0, 30), (30, 60)):
                  expected[imin:imax, jmin:jmax] = ndi.maximum_filter(
                                  image[imin:imax, jmin:jmax], footprint=footprint)
          expected = (expected == image)
          result = peak.peak_local_max(image, labels=labels, min_distance=1,
                                       threshold_rel=0, footprint=footprint,
                                       indices=False, exclude_border=False)
          assert (result == expected).all()
  
      def test_indices_with_labels(self):
          image = np.random.uniform(size=(40, 60))
          i, j = np.mgrid[0:40, 0:60]
          labels = 1 + (i >= 20) + (j >= 30) * 2
          i, j = np.mgrid[-3:4, -3:4]
          footprint = (i * i + j * j <= 9)
          expected = np.zeros(image.shape, float)
          for imin, imax in ((0, 20), (20, 40)):
              for jmin, jmax in ((0, 30), (30, 60)):
                  expected[imin:imax, jmin:jmax] = ndi.maximum_filter(
                      image[imin:imax, jmin:jmax], footprint=footprint)
          expected = np.transpose(np.nonzero(expected == image))
          expected = expected[np.argsort(image[tuple(expected.T)])[::-1]]
          result = peak.peak_local_max(image, labels=labels, min_distance=1,
                                       threshold_rel=0, footprint=footprint,
                                       indices=True, exclude_border=False)
          result = result[np.argsort(image[tuple(result.T)])[::-1]]
          assert (result == expected).all()
  
      def test_ndarray_indices_false(self):
          nd_image = np.zeros((5, 5, 5))
          nd_image[2, 2, 2] = 1
          peaks = peak.peak_local_max(nd_image, min_distance=1, indices=False)
          assert (peaks == nd_image.astype(np.bool)).all()
  
      def test_ndarray_exclude_border(self):
          nd_image = np.zeros((5, 5, 5))
          nd_image[[1, 0, 0], [0, 1, 0], [0, 0, 1]] = 1
          nd_image[3, 0, 0] = 1
          nd_image[2, 2, 2] = 1
          expected = np.zeros_like(nd_image, dtype=np.bool)
          expected[2, 2, 2] = True
          expectedNoBorder = nd_image > 0
          result = peak.peak_local_max(nd_image, min_distance=2,
              exclude_border=2, indices=False)
          assert_equal(result, expected)
          # Check that bools work as expected
          assert_equal(
              peak.peak_local_max(nd_image, min_distance=2,
                  exclude_border=2, indices=False),
              peak.peak_local_max(nd_image, min_distance=2,
                  exclude_border=True, indices=False)
              )
          assert_equal(
              peak.peak_local_max(nd_image, min_distance=2,
                  exclude_border=0, indices=False),
              peak.peak_local_max(nd_image, min_distance=2,
                  exclude_border=False, indices=False)
              )
          # Check both versions with  no border
          assert_equal(
              peak.peak_local_max(nd_image, min_distance=2,
                  exclude_border=0, indices=False),
              expectedNoBorder,
              )
          assert_equal(
              peak.peak_local_max(nd_image,
                  exclude_border=False, indices=False),
              expectedNoBorder,
              )
  
      def test_empty(self):
          image = np.zeros((10, 20))
          labels = np.zeros((10, 20), int)
          result = peak.peak_local_max(image, labels=labels,
                                       footprint=np.ones((3, 3), bool),
                                       min_distance=1, threshold_rel=0,
                                       indices=False, exclude_border=False)
          assert np.all(~ result)
  
      def test_one_point(self):
          image = np.zeros((10, 20))
          labels = np.zeros((10, 20), int)
          image[5, 5] = 1
          labels[5, 5] = 1
          result = peak.peak_local_max(image, labels=labels,
                                       footprint=np.ones((3, 3), bool),
                                       min_distance=1, threshold_rel=0,
                                       indices=False, exclude_border=False)
          assert np.all(result == (labels == 1))
  
      def test_adjacent_and_same(self):
          image = np.zeros((10, 20))
          labels = np.zeros((10, 20), int)
          image[5, 5:6] = 1
          labels[5, 5:6] = 1
          result = peak.peak_local_max(image, labels=labels,
                                       footprint=np.ones((3, 3), bool),
                                       min_distance=1, threshold_rel=0,
                                       indices=False, exclude_border=False)
          assert np.all(result == (labels == 1))
  
      def test_adjacent_and_different(self):
          image = np.zeros((10, 20))
          labels = np.zeros((10, 20), int)
          image[5, 5] = 1
          image[5, 6] = .5
          labels[5, 5:6] = 1
          expected = (image == 1)
          result = peak.peak_local_max(image, labels=labels,
                                       footprint=np.ones((3, 3), bool),
                                       min_distance=1, threshold_rel=0,
                                       indices=False, exclude_border=False)
          assert np.all(result == expected)
          result = peak.peak_local_max(image, labels=labels,
                                       min_distance=1, threshold_rel=0,
                                       indices=False, exclude_border=False)
          assert np.all(result == expected)
  
      def test_not_adjacent_and_different(self):
          image = np.zeros((10, 20))
          labels = np.zeros((10, 20), int)
          image[5, 5] = 1
          image[5, 8] = .5
          labels[image > 0] = 1
          expected = (labels == 1)
          result = peak.peak_local_max(image, labels=labels,
                                       footprint=np.ones((3, 3), bool),
                                       min_distance=1, threshold_rel=0,
                                       indices=False, exclude_border=False)
          assert np.all(result == expected)
  
      def test_two_objects(self):
          image = np.zeros((10, 20))
          labels = np.zeros((10, 20), int)
          image[5, 5] = 1
          image[5, 15] = .5
          labels[5, 5] = 1
          labels[5, 15] = 2
          expected = (labels > 0)
          result = peak.peak_local_max(image, labels=labels,
                                       footprint=np.ones((3, 3), bool),
                                       min_distance=1, threshold_rel=0,
                                       indices=False, exclude_border=False)
          assert np.all(result == expected)
  
      def test_adjacent_different_objects(self):
          image = np.zeros((10, 20))
          labels = np.zeros((10, 20), int)
          image[5, 5] = 1
          image[5, 6] = .5
          labels[5, 5] = 1
          labels[5, 6] = 2
          expected = (labels > 0)
          result = peak.peak_local_max(image, labels=labels,
                                       footprint=np.ones((3, 3), bool),
                                       min_distance=1, threshold_rel=0,
                                       indices=False, exclude_border=False)
          assert np.all(result == expected)
  
      def test_four_quadrants(self):
          image = np.random.uniform(size=(20, 30))
          i, j = np.mgrid[0:20, 0:30]
          labels = 1 + (i >= 10) + (j >= 15) * 2
          i, j = np.mgrid[-3:4, -3:4]
          footprint = (i * i + j * j <= 9)
          expected = np.zeros(image.shape, float)
          for imin, imax in ((0, 10), (10, 20)):
              for jmin, jmax in ((0, 15), (15, 30)):
                  expected[imin:imax, jmin:jmax] = ndi.maximum_filter(
                      image[imin:imax, jmin:jmax], footprint=footprint)
          expected = (expected == image)
          result = peak.peak_local_max(image, labels=labels, footprint=footprint,
                                       min_distance=1, threshold_rel=0,
                                       indices=False, exclude_border=False)
          assert np.all(result == expected)
  
      def test_disk(self):
          '''regression test of img-1194, footprint = [1]
          Test peak.peak_local_max when every point is a local maximum
          '''
          image = np.random.uniform(size=(10, 20))
          footprint = np.array([[1]])
          result = peak.peak_local_max(image, labels=np.ones((10, 20)),
                                       footprint=footprint,
                                       min_distance=1, threshold_rel=0,
                                       threshold_abs=-1, indices=False,
                                       exclude_border=False)
          assert np.all(result)
          result = peak.peak_local_max(image, footprint=footprint, threshold_abs=-1,
                                       indices=False, exclude_border=False)
          assert np.all(result)
  
      def test_3D(self):
          image = np.zeros((30, 30, 30))
          image[15, 15, 15] = 1
          image[5, 5, 5] = 1
          assert_equal(peak.peak_local_max(image, min_distance=10, threshold_rel=0),
                       [[15, 15, 15]])
          assert_equal(peak.peak_local_max(image, min_distance=6, threshold_rel=0),
                       [[15, 15, 15]])
          assert sorted(peak.peak_local_max(image, min_distance=10, threshold_rel=0,
                                            exclude_border=False).tolist()) == \
              [[5, 5, 5], [15, 15, 15]]
          assert sorted(peak.peak_local_max(image, min_distance=5,
                                            threshold_rel=0).tolist()) == \
              [[5, 5, 5], [15, 15, 15]]
  
      def test_4D(self):
          image = np.zeros((30, 30, 30, 30))
          image[15, 15, 15, 15] = 1
          image[5, 5, 5, 5] = 1
          assert_equal(peak.peak_local_max(image, min_distance=10, threshold_rel=0),
                       [[15, 15, 15, 15]])
          assert_equal(peak.peak_local_max(image, min_distance=6, threshold_rel=0),
                       [[15, 15, 15, 15]])
          assert sorted(peak.peak_local_max(image, min_distance=10, threshold_rel=0,
                                            exclude_border=False).tolist()) == \
              [[5, 5, 5, 5], [15, 15, 15, 15]]
          assert sorted(peak.peak_local_max(image, min_distance=5,
                                            threshold_rel=0).tolist()) == \
              [[5, 5, 5, 5], [15, 15, 15, 15]]
  
  
      def test_threshold_rel_default(self):
          image = np.ones((5, 5))
  
          image[2, 2] = 1
          assert len(peak.peak_local_max(image)) == 0
  
          image[2, 2] = 2
          assert_equal(peak.peak_local_max(image), [[2, 2]])
  
          image[2, 2] = 0
          assert len(peak.peak_local_max(image, min_distance=0)) == image.size - 1
  
  class TestProminentPeaks(unittest.TestCase):
      def test_isolated_peaks(self):
          image = np.zeros((15, 15))
          x0, y0, i0 = (12, 8, 1)
          x1, y1, i1 = (2, 2, 1)
          x2, y2, i2 = (5, 13, 1)
          image[y0, x0] = i0
          image[y1, x1] = i1
          image[y2, x2] = i2
          out = peak._prominent_peaks(image)
          assert len(out[0]) == 3
          for i, x, y in zip (out[0], out[1], out[2]):
              self.assertTrue(i in (i0, i1, i2))
              self.assertTrue(x in (x0, x1, x2))
              self.assertTrue(y in (y0, y1, y2))
  
      def test_threshold(self):
          image = np.zeros((15, 15))
          x0, y0, i0 = (12, 8, 10)
          x1, y1, i1 = (2, 2, 8)
          x2, y2, i2 = (5, 13, 10)
          image[y0, x0] = i0
          image[y1, x1] = i1
          image[y2, x2] = i2
          out = peak._prominent_peaks(image, threshold=None)
          assert len(out[0]) == 3
          for i, x, y in zip (out[0], out[1], out[2]):
              self.assertTrue(i in (i0, i1, i2))
              self.assertTrue(x in (x0, x1, x2))
          out = peak._prominent_peaks(image, threshold=9)
          assert len(out[0]) == 2
          for i, x, y in zip (out[0], out[1], out[2]):
              self.assertTrue(i in (i0, i2))
              self.assertTrue(x in (x0, x2))
              self.assertTrue(y in (y0, y2))
  
      def test_peaks_in_contact(self):
          image = np.zeros((15, 15))
          x0, y0, i0 = (8, 8, 1)
          x1, y1, i1 = (7, 7, 1) # prominent peak
          x2, y2, i2 = (6, 6, 1)
          image[y0, x0] = i0
          image[y1, x1] = i1
          image[y2, x2] = i2
          out = peak._prominent_peaks(image, min_xdistance=3,
                                     min_ydistance=3,)
          assert_equal(out[0], np.array((i1,)))
          assert_equal(out[1], np.array((x1,)))
          assert_equal(out[2], np.array((y1,)))
  
      def test_input_labels_unmodified(self):
          image = np.zeros((10, 20))
          labels = np.zeros((10, 20), int)
          image[5, 5] = 1
          labels[5, 5] = 1
          labelsin = labels.copy()
          result = peak.peak_local_max(image, labels=labels,
                                       footprint=np.ones((3, 3), bool),
                                       min_distance=1, threshold_rel=0,
                                       indices=False, exclude_border=False)
          assert np.all(labels == labelsin)
  