feat(v1): add basic backend and frontend

backend/processing/tests/test_algorithms.py

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