Start adding background detect tests

src/openflexure_microscope_server/background_detect.py

@@ -14,6 +14,10 @@ from scipy.stats import norm
 from labthings_fastapi.thing_description import type_to_dataschema
 
 
+class MissingBackgroundData(RuntimeError):
+    """An error raised if checking for sample without background data set."""
+
+
 class BackgroundDetectorStatus(BaseModel):
     """The status information about a background detector instance needed for the GUI.
 
@@ -39,9 +43,9 @@ class BackgroundDetectorStatus(BaseModel):
 class BackgroundDetectAlgorithm:
     """The base class for defining background detect algorithms."""
 
-    background_data_model: BaseModel
+    background_data_model: BaseModel = BaseModel
     """The data model of the background data. This must be set by child classes"""
-    settings_data_model: BaseModel
+    settings_data_model: BaseModel = BaseModel
     """The data model of algorithm settings. This must be set by child classes"""
 
     def __init__(self):
@@ -76,6 +80,7 @@ class BackgroundDetectAlgorithm:
 
     @background_data.setter
     def background_data(self, value: Optional[BaseModel | dict]) -> None:
+        try:
             if value is None:
                 self._background_data = None
             elif isinstance(value, self.background_data_model):
@@ -86,6 +91,10 @@ class BackgroundDetectAlgorithm:
                 raise TypeError(
                     f"Cannot set background_data with an object of type {type(value)}"
                 )
+        except PydanticUserError as e:
+            raise NotImplementedError(
+                "BackgroundDetectAlgorithms must set their own background data model."
+            ) from e
 
     @property
     def settings(self) -> BaseModel:
@@ -168,10 +177,9 @@ class ColourChannelDetectLUV(BackgroundDetectAlgorithm):
         """
         d = self.background_data
         if not d:
-            raise RuntimeError(
+            raise MissingBackgroundData(
                 "Background is not set: you need to calibrate background detection."
             )
-        print(d)
         # Only use the U and V channels of as brightness (L) often changes as the
         # height of the sample changes.
         return np.all(
@@ -192,9 +200,7 @@ class ColourChannelDetectLUV(BackgroundDetectAlgorithm):
         """
         image_luv = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)
         mask = self.background_mask(image_luv)
-        print(np.count_nonzero(mask))
-        print(np.prod(mask.shape))
-        print((1 - np.count_nonzero(mask) / np.prod(mask.shape)) * 100)
+
         return (1 - np.count_nonzero(mask) / np.prod(mask.shape)) * 100
 
     def image_is_sample(self, image: np.ndarray) -> tuple[bool, str]:
@@ -206,7 +212,8 @@ class ColourChannelDetectLUV(BackgroundDetectAlgorithm):
         """
         sample_coverage = self.get_sample_coverage(image)
 
-        is_sample = sample_coverage > self.settings.min_sample_coverage
+        # Use bool otherwise get numpy variants of True and False.
+        is_sample = bool(sample_coverage > self.settings.min_sample_coverage)
         message = f"{sample_coverage:0.1f}% sample"
         if not is_sample:
             message = "only " + message

tests/test_background_detectors.py

@@ -0,0 +1,106 @@
+"""Test the scan planning algorithms of the Microscope.
+
+As well as low level function by function tests, this test suite also provides tests
+that simulate scanning a sample, checking that the expected path is followed.
+"""
+
+import re
+
+import pytest
+from pydantic import BaseModel
+import numpy as np
+
+# Just for testing importing as
+from openflexure_microscope_server.background_detect import (
+    MissingBackgroundData,
+    BackgroundDetectorStatus,
+    BackgroundDetectAlgorithm,
+    ChannelDistributions,
+    ColourChannelDetectSettings,
+    ColourChannelDetectLUV
+)
+
+RNG = np.random.default_rng()
+IMG_SHAPE = (820, 616, 3)
+BG_COLOR = [220, 215, 217]
+
+PERC_REGEX = re.compile(r"(\d+\.+\d)+%")
+
+@pytest.fixture
+def background_image():
+    image = np.ones(IMG_SHAPE, dtype=np.int16)
+    image[:, :, 0] *= BG_COLOR[0]
+    image[:, :, 1] *= BG_COLOR[1]
+    image[:, :, 2] *= BG_COLOR[2]
+    image += RNG.normal(scale=3, size=IMG_SHAPE).astype("int16")
+    image[image < 0] = 0
+    image[image > 255] = 255
+    return image.astype("uint8")
+
+@pytest.fixture
+def sample_image(background_image):
+    image = background_image.copy()
+    x_cent = IMG_SHAPE[0]//2
+    image[:x_cent,:,0] -= 180
+    return image
+
+
+def test_bg_detect_base_class():
+    """Test the base class for background detect.
+
+    If initialised as is it should raise not implemented error.
+    """
+    with pytest.raises(NotImplementedError):
+        BackgroundDetectAlgorithm()
+
+def test_partial_base_class(background_image):
+    """Create a partial class so to initialise the base class and test other methods.
+
+    This test is to check that if the necessary methods are not set, that an
+    appropriate error is raise.
+    """
+    class BadAlgo1(BackgroundDetectAlgorithm):
+        """Only has a settings model so it can initialise."""
+
+        settings_data_model: BaseModel = ColourChannelDetectSettings
+
+    bad_algo1 = BadAlgo1()
+    status = bad_algo1.status
+    assert not status.ready
+    assert isinstance(status.settings, ColourChannelDetectSettings)
+
+    with pytest.raises(NotImplementedError):
+        # Should error on any dictionary input. This simulates loading settings from
+        # disk
+        bad_algo1.background_data = {"key": 1}
+
+    with pytest.raises(NotImplementedError):
+        bad_algo1.set_background(background_image)
+
+    with pytest.raises(NotImplementedError):
+        bad_algo1.image_is_sample(background_image)
+
+def test_colour_channel_luv(background_image, sample_image):
+    cc_luv = ColourChannelDetectLUV()
+
+    # No background data so it is not ready and will error if image_is_sample is called.
+    assert not cc_luv.status.ready
+    with pytest.raises(MissingBackgroundData):
+        cc_luv.image_is_sample(background_image)
+
+    # Set the background
+    cc_luv.set_background(background_image)
+    # Now it is ready
+    assert cc_luv.status.ready
+    sample, message = cc_luv.image_is_sample(background_image)
+    assert not sample
+    assert "0.0%" in message
+    sample, message = cc_luv.image_is_sample(sample_image)
+    assert sample
+    match = PERC_REGEX.search(message)
+    assert match is not None
+    # Should be 50% background. Allowing 49.8-50.2% due to noise.
+    assert 49.8 < float(match.group(1)) < 50.2
+
+
+# TODO Save data and reload it.