Merged in bugfix/RAM-3338_window_size (pull request jrkerns#347)

RAM-3338 fix window rolling over sample edge

Approved-by: Randy Taylor

docs/source/changelog.rst

@@ -36,6 +36,10 @@ CT
 Image Metrics
 ^^^^^^^^^^^^^
 
+* Disk-finding metrics, such as Winston-Lutz, had a bug that would cause disks to not be found
+  if the image size was smaller than the search window. This happened if the image size was ~<=3x the
+  BB size. I.e. if the image was 200x200 pixels and the BB was 70 pixels, the search window sampler would
+  not correctly size the window. This was only found to affect users of small pieces of film.
 * The ``SizedDiskRegion`` and ``SizedDiskLocator`` classes now have a ``min_number``, ``max_number``, and ``min_separation_<pixels|mm>`` parameters,
   as the ``GlobalSizedDiskLocator`` class does. This allows the user to specify the minimum and maximum number of disks.
   Previously, the ``SizedDisk<Region|Locator>`` classes would only find one disk.

pylinac/metrics/image.py

@@ -368,9 +368,9 @@ def calculate(self) -> list[RegionProperties]:
             self.expected_position.x += self.image.shape[1] / 2
             self.expected_position.y += self.image.shape[0] / 2
         # sample the image in the search window; need to convert to mm
-        left = math.floor(self.expected_position.x - self.search_window[0] / 2)
+        left = max(math.floor(self.expected_position.x - self.search_window[0] / 2), 0)
         right = math.ceil(self.expected_position.x + self.search_window[0] / 2)
-        top = math.floor(self.expected_position.y - self.search_window[1] / 2)
+        top = max(math.floor(self.expected_position.y - self.search_window[1] / 2), 0)
         bottom = math.ceil(self.expected_position.y + self.search_window[1] / 2)
         sample = self.image[top:bottom, left:right]
         # we might need to invert the image so that the BB pixel intensity is higher than the background

tests_basic/test_winstonlutz.py

@@ -1231,21 +1231,21 @@ class TIFFImages(WinstonLutzMixin, TestCase):
     file_name = ["AQA.zip"]
     num_images = 2
     sid = 1000
-    dpi = 200
+    dpi = 300
     # I don't know the actual axis values, this is just to get a result
     axis_mapping = {
         "AQA_A_03082023.tif": (0, 0, 0),
         "AQA_B_03082023.tif": (90, 0, 0),
     }
-    bb_size = 30
+    bb_size = 20
     gantry_iso_size = 0.15
     collimator_iso_size = None
     couch_iso_size = None
-    cax2bb_max_distance = 1.14
-    cax2bb_median_distance = 0.82
-    cax2bb_mean_distance = 0.8
+    cax2bb_max_distance = 0.75
+    cax2bb_median_distance = 0.5
+    cax2bb_mean_distance = 0.5
     axis_of_rotation = {-1: Axis.GANTRY}
-    bb_shift_vector = Vector(x=1.1, y=0.34, z=-0.30)
+    bb_shift_vector = Vector(x=0.74, y=0.16, z=-0.14)
 
 
 class VarianBBkV(WinstonLutzMixin, TestCase):