61 lines
2.1 KiB
Python
61 lines
2.1 KiB
Python
import numpy as np
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from django.test import SimpleTestCase
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from processing.algorithms import (
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LAPLACIAN_MASK,
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ProcessingError,
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average_images,
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gamma,
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histogram_equalization,
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median_filter,
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negative,
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roberts,
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sobel,
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subtract_images,
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verify_registration,
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)
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class AlgorithmTests(SimpleTestCase):
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def test_negative_transform_uses_l_minus_one(self):
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image = np.array([[[0, 127, 255]]], dtype=np.uint8)
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result = negative(image, {})
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np.testing.assert_array_equal(result, np.array([[[255, 128, 0]]], dtype=np.uint8))
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def test_gamma_identity(self):
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image = np.array([[[0, 128, 255]]], dtype=np.uint8)
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result = gamma(image, {"gamma": 1, "c": 1})
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np.testing.assert_array_equal(result, image)
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def test_laplacian_mask_sums_to_zero(self):
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self.assertEqual(int(LAPLACIAN_MASK.sum()), 0)
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def test_histogram_equalization_spreads_two_levels(self):
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image = np.array([[0, 0], [255, 255]], dtype=np.uint8)
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result = histogram_equalization(image, {})
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np.testing.assert_array_equal(result, image)
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def test_median_removes_impulse_noise(self):
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image = np.full((3, 3, 3), 100, dtype=np.uint8)
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image[1, 1] = 255
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result = median_filter(image, {"size": 3})
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self.assertEqual(int(result[1, 1, 0]), 100)
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def test_gradient_outputs_are_display_normalized(self):
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image = np.zeros((5, 5, 3), dtype=np.uint8)
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image[:, 3:] = 255
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self.assertEqual(sobel(image, {}).dtype, np.uint8)
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self.assertEqual(roberts(image, {}).dtype, np.uint8)
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def test_arithmetic_requires_registered_shapes(self):
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left = np.zeros((2, 2, 3), dtype=np.uint8)
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right = np.zeros((3, 2, 3), dtype=np.uint8)
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with self.assertRaises(ProcessingError):
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verify_registration([left, right])
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def test_average_and_subtraction(self):
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left = np.zeros((2, 2, 3), dtype=np.uint8)
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right = np.full((2, 2, 3), 100, dtype=np.uint8)
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self.assertEqual(int(average_images([left, right])[0, 0, 0]), 50)
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self.assertEqual(int(subtract_images(left, right)[0, 0, 0]), 0)
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