Implement alignment service

OCR/ocr/services/alignment/__init__.py

@@ -0,0 +1 @@
+from .image_alignment import ImageAligner as ImageAligner

OCR/alignment/four_point_transform.py → OCR/ocr/services/alignment/backends/four_point_transform.py

@@ -10,8 +10,15 @@
 
 
 class FourPointTransform:
-    def __init__(self, image: Path):
-        self.image = cv.imread(str(image), cv.IMREAD_GRAYSCALE)
+    def __init__(self, image: Path | np.ndarray):
+        if isinstance(image, np.ndarray):
+            self.image = image
+        else:
+            self.image = cv.imread(str(image))
+
+    @classmethod
+    def align(self, source_image, template_image):
+        return FourPointTransform(source_image).dewarp()
 
     @staticmethod
     def _order_points(quadrilateral: np.ndarray) -> np.ndarray:
@@ -28,7 +35,9 @@ def _order_points(quadrilateral: np.ndarray) -> np.ndarray:
 
     def find_largest_contour(self):
         """Compute contours for an image and find the biggest one by area."""
-        _, contours, _ = cv.findContours(self.image, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+        contours, _ = cv.findContours(
+            cv.cvtColor(self.image, cv.COLOR_BGR2GRAY), cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE
+        )
         return functools.reduce(lambda a, b: b if cv.contourArea(a) < cv.contourArea(b) else a, contours)
 
     def simplify_polygon(self, contour):
@@ -40,8 +49,8 @@ def dewarp(self) -> np.ndarray:
         biggest_contour = self.find_largest_contour()
         simplified = self.simplify_polygon(biggest_contour)
 
-        height, width = self.image.shape
+        height, width, _ = self.image.shape
         destination = np.array([[0, 0], [width, 0], [width, height], [0, height]], dtype=np.float32)
 
-        M = cv.getPerspectiveTransform(self.order_points(simplified), destination)
+        M = cv.getPerspectiveTransform(self._order_points(simplified), destination)
         return cv.warpPerspective(self.image, M, (width, height))

OCR/alignment/image_homography.py → OCR/ocr/services/alignment/backends/image_homography.py

@@ -5,15 +5,22 @@
 
 
 class ImageHomography:
-    def __init__(self, template: Path, match_ratio=0.3):
+    def __init__(self, template: Path | np.ndarray, match_ratio=0.3):
         """Initialize the image homography pipeline with a `template` image."""
         if match_ratio >= 1 or match_ratio <= 0:
             raise ValueError("`match_ratio` must be between 0 and 1")
 
-        self.template = cv.imread(template)
+        if isinstance(template, np.ndarray):
+            self.template = template
+        else:
+            self.template = cv.imread(template)
         self.match_ratio = match_ratio
         self._sift = cv.SIFT_create()
 
+    @classmethod
+    def align(self, source_image, template_image):
+        return ImageHomography(template_image).transform_homography(source_image)
+
     def estimate_self_similarity(self):
         """Calibrate `match_ratio` using a self-similarity metric."""
         raise NotImplementedError
@@ -48,9 +55,25 @@ def estimate_transform_matrix(self, other):
         M, _ = cv.findHomography(dst_pts, src_pts, cv.RANSAC, 5.0)
         return M
 
-    def transform_homography(self, other, matrix=None):
-        """Run the image homography pipeline against a query image."""
+    def transform_homography(self, other, min_axis=100, matrix=None):
+        """
+        Run the image homography pipeline against a query image.
+
+        Parameters:
+        min_axis: minimum x- and y-axis length, in pixels, to attempt to do a homography transform.
+            If the input image is under the axis limits, return the original input image unchanged.
+        matrix: if specified, a transformation matrix to warp the input image. Otherwise this will be
+            estimated with `estimate_transform_matrix`.
+        """
+
+        if other.shape[0] < min_axis and other.shape[1] < min_axis:
+            return other
+
         if matrix is None:
-            matrix = self.estimate_transform_matrix(other)
+            try:
+                matrix = self.estimate_transform_matrix(other)
+            except cv.error:
+                print("could not estimate transform matrix")
+                return other
 
         return cv.warpPerspective(other, matrix, (self.template.shape[1], self.template.shape[0]))

OCR/ocr/services/alignment/image_alignment.py

@@ -0,0 +1,19 @@
+import numpy as np
+
+from ocr.services.alignment.backends import ImageHomography
+
+
+class ImageAligner:
+    def __init__(self, aligner=ImageHomography):
+        self.aligner = aligner
+
+    def align(self, source_image: np.ndarray, template_image: np.ndarray) -> np.ndarray:
+        """
+        Aligns an image using the specified image alignment backend.
+
+        source_image: the image to be aligned, as a numpy ndarray.
+        template_image: the image that `source_image` will be aligned against, as a numpy ndarray.
+            May not be used for all image alignment backends.
+        """
+        aligned_image = self.aligner.align(source_image, template_image)
+        return aligned_image

OCR/tests/alignment_test.py

@@ -2,8 +2,10 @@
 
 import cv2 as cv
 import numpy as np
+import pytest
 
-from alignment import ImageHomography, RandomPerspectiveTransform
+from ocr.services.alignment.backends import FourPointTransform, ImageHomography, RandomPerspectiveTransform
+from ocr.services.alignment import ImageAligner
 
 
 path = os.path.dirname(__file__)
@@ -14,6 +16,15 @@
 
 
 class TestAlignment:
+    @pytest.mark.parametrize("align_class", [ImageHomography, FourPointTransform])
+    def test_align_implementation(self, align_class):
+        """Tests that the ImageAligner class backends implement the `align` method."""
+        template_image = cv.imread(template_image_path)
+        aligner = ImageAligner(aligner=align_class)
+        result = aligner.align(filled_image, template_image)
+        assert result.shape == template_image.shape, "Aliged image has wrong shape"
+        assert np.median(cv.absdiff(template_image, result)) <= 1, "Median difference too high"
+
     def test_random_warp(self):
         """Test that a random warp generates an image different from the template."""
         transformed = RandomPerspectiveTransform(filled_image_path).random_transform(distortion_scale=0.1)