Fix --affine CLI parameter (#244)

* Fix --affine CLI parameter

* add tests and validations for the fix so this can no happen unintentionally again

* make all too long errors and warnings one liner. Line breaks mess up with tests and are not nice for the user

---------

Co-authored-by: Lukas Weber <l.alex.w@web.de>

Author: mstenta

stitching/cli/stitch.py

@@ -46,7 +46,8 @@ def create_parser():
         "--affine",
         action="store_true",
         help="Overwrites multiple parameters to optimize the stitching for "
-        "scans and images captured by specialized devices.",
+        "scans and images captured by specialized devices. The follwing parameters "
+        "are set: " + str(AffineStitcher.AFFINE_DEFAULTS),
     )
     parser.add_argument(
         "--medium_megapix",
@@ -315,6 +316,7 @@ def main():
     affine_mode = args_dict.pop("affine")
 
     if affine_mode:
+        args_dict.update(AffineStitcher.AFFINE_DEFAULTS)
         stitcher = AffineStitcher(**args_dict)
     else:
         stitcher = Stitcher(**args_dict)

stitching/cropper.py

@@ -95,9 +95,7 @@ def estimate_largest_interior_rectangle(self, mask):
         contours, hierarchy = cv.findContours(mask, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)
         if not hierarchy.shape == (1, 1, 4) or not np.all(hierarchy == -1):
             raise StitchingError(
-                """Invalid Contour. Run with --no-crop (using the stitch interface),
-                                 crop=false (using the stitcher class) or Cropper(False)
-                                 (using the cropper class)"""
+                "Invalid Contour. Run with --no-crop (using the stitch interface), crop=false (using the stitcher class) or Cropper(False) (using the cropper class)"  # noqa: E501
             )
         contour = contours[0][:, 0, :]
 

stitching/seam_finder.py

@@ -98,8 +98,7 @@ def blend_seam_masks(
 def colored_img_generator(sizes, colors):
     if len(sizes) + 1 > len(colors):
         warnings.warn(
-            """Without additional colors,
-            there will be seam masks with identical colors""",
+            "Without additional colors, there will be seam masks with identical colors",  # noqa: E501
             StitchingWarning,
         )
 

stitching/stitcher.py

@@ -1,3 +1,4 @@
+import warnings
 from types import SimpleNamespace
 
 from .blender import Blender
@@ -10,7 +11,7 @@
 from .feature_matcher import FeatureMatcher
 from .images import Images
 from .seam_finder import SeamFinder
-from .stitching_error import StitchingError
+from .stitching_error import StitchingError, StitchingWarning
 from .subsetter import Subsetter
 from .timelapser import Timelapser
 from .verbose import verbose_stitching
@@ -275,3 +276,12 @@ class AffineStitcher(Stitcher):
 
     DEFAULT_SETTINGS = Stitcher.DEFAULT_SETTINGS.copy()
     DEFAULT_SETTINGS.update(AFFINE_DEFAULTS)
+
+    def initialize_stitcher(self, **kwargs):
+        for key, value in kwargs.items():
+            if key in self.AFFINE_DEFAULTS and value != self.AFFINE_DEFAULTS[key]:
+                warnings.warn(
+                    f"You are overwriting an affine default ({key}={self.AFFINE_DEFAULTS[key]}) with another value ({value}). Make sure this is intended",  # noqa: E501
+                    StitchingWarning,
+                )
+        super().initialize_stitcher(**kwargs)

stitching/subsetter.py

@@ -30,11 +30,7 @@ def subset(self, img_names, features, matches):
 
         if len(indices) < len(img_names):
             warnings.warn(
-                """Not all images are included in the final panorama.
-                          If this is not intended, use the 'matches_graph_dot_file'
-                          parameter to analyze your matches. You might want to
-                          lower the 'confidence_threshold' or try another 'detector'
-                          to include all your images.""",
+                "Not all images are included in the final panorama. If this is not intended, use the 'matches_graph_dot_file' parameter to analyze your matches. You might want to lower the 'confidence_threshold' or try another 'detector' to include all your images.",  # noqa: E501
                 StitchingWarning,
             )
 
@@ -66,11 +62,7 @@ def get_indices_to_keep(self, features, pairwise_matches):
 
         if len(indices) < 2:
             raise StitchingError(
-                """No match exceeds the given confidence threshold.
-                                 Do your images have enough overlap and common
-                                 features? If yes, you might want to lower the
-                                 'confidence_threshold' or try another
-                                 'detector'."""
+                "No match exceeds the given confidence threshold. Do your images have enough overlap and common features? If yes, you might want to lower the 'confidence_threshold' or try another 'detector'."  # noqa: E501
             )
 
         return indices

tests/test_stitch_cli.py

@@ -58,6 +58,27 @@ def test_main_verbose(self):
                 img.shape[:2], (150, 590), atol=max_image_shape_derivation
             )
 
+    def test_main_affine(self):
+        output = test_output("budapest_from_cli.jpg")
+        test_args = [
+            "stitch.py",
+            test_input("budapest?.jpg"),
+            "--affine",
+            "--detector",
+            "sift",
+            "--no-crop",
+            "--output",
+            output,
+        ]
+        with patch.object(sys, "argv", test_args):
+            main()
+
+            img = cv.imread(output)
+            max_image_shape_derivation = 50
+            np.testing.assert_allclose(
+                img.shape[:2], (1155, 2310), atol=max_image_shape_derivation
+            )
+
     def test_main_feature_masks(self):
         output = test_output("features_with_mask_from_cli.jpg")
         test_args = [

tests/test_stitcher.py

@@ -161,6 +161,15 @@ def test_stitcher_boat_aquaduct_subset(self):
             graph_content = file.read()
             self.assertTrue(graph_content.startswith("graph matches_graph{"))
 
+    def test_affine_stitcher_warning(self):
+        with self.assertWarns(StitchingWarning) as cm:
+            AffineStitcher(estimator="homography")
+        self.assertTrue(
+            str(cm.warning).startswith(
+                "You are overwriting an affine default (estimator=affine)"
+            )
+        )
+
     def test_affine_stitcher_budapest(self):
         settings = {
             "detector": "sift",