adds well centroid algorithm selection

src/chimpflow_api/constants.py

@@ -0,0 +1,6 @@
+class WELL_CENTROID_ALGORITHMS:
+    MIDDLE_PIXEL = "middle_pixel"
+    # Adapted from matlab code used by Texrank?
+    # Requires prepared background images on plate type.
+    # Doesn't work very well, sensitive to crystal density.
+    TEXRANK_LIKE = "textrank_like"

src/chimpflow_lib/chimp_adapter.py

@@ -11,6 +11,8 @@
 )
 from xchembku_api.models.crystal_well_model import CrystalWellModel
 
+from chimpflow_api.constants import WELL_CENTROID_ALGORITHMS
+
 with warnings.catch_warnings():
     warnings.filterwarnings("ignore", category=DeprecationWarning)
     from xchem_chimp.detector.chimp_detector import ChimpDetector
@@ -47,6 +49,21 @@ def __init__(self, specification: Dict):
             "num_classes",
         )
 
+        # Caller specifies the well centroid algorithm they want to use.
+        # None means don't calculate well centroid.
+        self.__well_centroid_algorithm = specification.get("well_centroid_algorithm")
+
+        if self.__well_centroid_algorithm == WELL_CENTROID_ALGORITHMS.TEXRANK_LIKE:
+            pass
+        elif self.__well_centroid_algorithm == WELL_CENTROID_ALGORITHMS.MIDDLE_PIXEL:
+            pass
+        elif self.__well_centroid_algorithm is None:
+            pass
+        else:
+            raise RuntimeError(
+                "configuration error: invalid well_centroid_algorithm '{self.__well_centroid_algorithm}'"
+            )
+
         self.__is_activated = False
         self.__detector: Optional[ChimpDetector] = None
 
@@ -110,9 +127,10 @@ def detect(
         with self.__profiler.context("coord_generator.extract_coordinates()"):
             coord_generator.extract_coordinates()
 
-        # Calculate well centers.
-        with self.__profiler.context("coord_generator.calculate_well_centres()"):
-            coord_generator.calculate_well_centres()
+        if self.__well_centroid_algorithm == WELL_CENTROID_ALGORITHMS.TEXRANK_LIKE:
+            # Calculate well centers.
+            with self.__profiler.context("coord_generator.calculate_well_centres()"):
+                coord_generator.calculate_well_centres()
 
         # Get the output stucture for the first (only) image.
         # TODO: Store the chimp detector output structure as json in the database.
@@ -123,17 +141,36 @@ def detect(
             crystal_well_uuid=crystal_well_model.uuid,
         )
         model.drop_detected = output_dict["drop_detected"]
-        target_position = output_dict["echo_coordinate"]
+        target_position = require(
+            "coord_generator output_dict",
+            output_dict,
+            "echo_coordinate",
+        )
         if len(target_position) > 0:
             # The target position is a list of (np.int64, np.int64), so have to convert to int.
             # Coordinate pairs are vertical-first.
             # TODO: Change the CrystalWellAutolocationModel to do type checking on field assignment.
             model.auto_target_x = int(target_position[0][1])
             model.auto_target_y = int(target_position[0][0])
-        well_centroid = output_dict["well_centroid"]
-        if well_centroid is not None:
+
+        if self.__well_centroid_algorithm == WELL_CENTROID_ALGORITHMS.TEXRANK_LIKE:
+            well_centroid = require(
+                "coord_generator output_dict",
+                output_dict,
+                "well_centroid",
+            )
             model.well_centroid_x = int(well_centroid[1])
             model.well_centroid_y = int(well_centroid[0])
+        # Anything else is assumed MIDDLE_PIXEL.
+        else:
+            original_image_shape = require(
+                "coord_generator output_dict",
+                output_dict,
+                "original_image_shape",
+            )
+            model.well_centroid_x = int(original_image_shape[1] / 2.0)
+            model.well_centroid_y = int(original_image_shape[0] / 2.0)
+
         model.number_of_crystals = len(output_dict["xtal_coordinates"])
 
         # TODO: Store the chimp detected crystal coordinates in the model too.

tests/test_chimp_adapter.py

@@ -10,6 +10,8 @@
 )
 from xchembku_api.models.crystal_well_model import CrystalWellModel
 
+from chimpflow_api.constants import WELL_CENTROID_ALGORITHMS
+
 # Base class for the tester.
 from tests.base import Base
 
@@ -45,22 +47,49 @@ async def _main_coroutine(self, constants, output_directory):
         self.__specification = {
             "model_path": constants["model_path"],
             "num_classes": 3,
+            "well_centroid_algorithm": WELL_CENTROID_ALGORITHMS.TEXRANK_LIKE,
         }
 
         # Do the work in a separate process since the torchvision won't release unless its process quits.
         # If it doesn't release, then subsequent pytest cases wait forever.
         # TODO: Figure out how to release resources from torchvision within a process.
-        p = multiprocessing.Process(target=self.__process, args=[self.__run_97wo_01A_1])
+        p = multiprocessing.Process(
+            target=self.__process1,
+        )
+        p.start()
+        p.join()
+        assert p.exitcode == 0
+
+        # ------------------------------------------------------------------
+        # Make a specification for the chimp adapter, this time with no centroid algorithm.
+        self.__specification = {
+            "model_path": constants["model_path"],
+            "num_classes": 3,
+            # No specified algorithm.
+            # "well_centroid_algorithm": WELL_CENTROID_ALGORITHMS.TEXRANK_LIKE,
+        }
+
+        p = multiprocessing.Process(
+            target=self.__process2,
+        )
         p.start()
         p.join()
         assert p.exitcode == 0
 
     # ----------------------------------------------------------------------------------------
-    def __process(self, run):
+    def __process1(self):
         chimp_adapter = ChimpAdapter(self.__specification)
 
         self.__run_97wo_01A_1(chimp_adapter)
         self.__run_97wo_01A_2(chimp_adapter)
+
+        # Display the profiler's end results.
+        logger.debug(f"profile\n{dls_utilpack_global_profiler()}")
+
+    # ----------------------------------------------------------------------------------------
+    def __process2(self):
+        chimp_adapter = ChimpAdapter(self.__specification)
+
         self.__run_97wo_01A_3(chimp_adapter)
 
         # Display the profiler's end results.
@@ -138,5 +167,6 @@ def __run_97wo_01A_3(self, chimp_adapter: ChimpAdapter) -> None:
         assert well_model_autolocation.auto_target_x == pytest.approx(417, 3)
         assert well_model_autolocation.auto_target_y == pytest.approx(672, 3)
 
-        assert well_model_autolocation.well_centroid_x == 638
-        assert well_model_autolocation.well_centroid_y == 494
+        # Centroid in this test comes from image central pixel.
+        assert well_model_autolocation.well_centroid_x == 612
+        assert well_model_autolocation.well_centroid_y == 512