bigocrpdf/tests/test_perspective_geometry.py at main · biglinux/bigocrpdf · GitHub

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"""Tests for perspective geometry functions."""

import numpy as np

from bigocrpdf.services.perspective_document import four_point_transform, order_points


class TestOrderPoints:
    """Tests for order_points."""

    def test_already_ordered(self):
        pts = np.array([[0, 0], [100, 0], [100, 100], [0, 100]], dtype="float32")
        result = order_points(pts)
        np.testing.assert_array_almost_equal(result[0], [0, 0])  # top-left
        np.testing.assert_array_almost_equal(result[1], [100, 0])  # top-right
        np.testing.assert_array_almost_equal(result[2], [100, 100])  # bottom-right
        np.testing.assert_array_almost_equal(result[3], [0, 100])  # bottom-left

    def test_shuffled(self):
        pts = np.array([[100, 100], [0, 0], [0, 100], [100, 0]], dtype="float32")
        result = order_points(pts)
        np.testing.assert_array_almost_equal(result[0], [0, 0])
        np.testing.assert_array_almost_equal(result[1], [100, 0])
        np.testing.assert_array_almost_equal(result[2], [100, 100])
        np.testing.assert_array_almost_equal(result[3], [0, 100])

    def test_non_rectangular(self):
        pts = np.array([[10, 5], [90, 10], [85, 95], [5, 90]], dtype="float32")
        result = order_points(pts)
        # top-left: smallest sum = (5,90)→95 vs (10,5)→15 → (10,5)
        np.testing.assert_array_almost_equal(result[0], [10, 5])
        np.testing.assert_array_almost_equal(result[2], [85, 95])

    def test_output_shape(self):
        pts = np.array([[0, 0], [1, 0], [1, 1], [0, 1]], dtype="float32")
        result = order_points(pts)
        assert result.shape == (4, 2)
        assert result.dtype == np.float32


class TestFourPointTransform:
    """Tests for four_point_transform."""

    def test_identity_transform(self):
        # Create a 100x100 test image with a known pattern
        img = np.zeros((100, 100, 3), dtype=np.uint8)
        img[25:75, 25:75] = 255  # White square in center
        pts = np.array([[0, 0], [99, 0], [99, 99], [0, 99]], dtype="float32")
        result = four_point_transform(img, pts)
        assert result.shape[0] > 0
        assert result.shape[1] > 0

    def test_crop_region(self):
        img = np.zeros((200, 200, 3), dtype=np.uint8)
        img[50:150, 50:150] = 128
        pts = np.array([[50, 50], [150, 50], [150, 150], [50, 150]], dtype="float32")
        result = four_point_transform(img, pts)
        # Result should be approximately 100x100
        assert abs(result.shape[0] - 100) <= 1
        assert abs(result.shape[1] - 100) <= 1

    def test_output_is_ndarray(self):
        img = np.zeros((100, 100, 3), dtype=np.uint8)
        pts = np.array([[10, 10], [90, 10], [90, 90], [10, 90]], dtype="float32")
        result = four_point_transform(img, pts)
        assert isinstance(result, np.ndarray)