DAQ: added face dtecetion correction in Y

daq/src/aaredaq/daq.py

@@ -34,7 +34,7 @@ from aaredaqlib.models import (
     PuckLoadedInfo,
     SampleShortInfoList,
     DAQStatusModel, BeamlineStatus, SessionStatus, SampleCameraSettings, AutofocusSettings, BoundingBoxModel,
-    ZoomModeEnum, CrystalSize, SimpleScanParameters)
+    ZoomModeEnum, CrystalSize, SimpleScanParameters, MLBoxModel)
 from aaredaqlib.raster_grid import RasterGridRequest, CompletedRasterGrid, CompletedRasterGridElem
 from aaredaqlib.rotation_scan import RotationScanRequest, CompletedRotationScan
 from aaredaqlib.sample_geometry import SampleGeometryModel
@@ -825,7 +825,6 @@ class AareDAQ:
 
         if boxes is None:
             if filename is not None:
-                #cv2.imwrite(f"{filename}_no_detection.jpg", bgr_image)
                 self.__aare.upload_image(sample_id, f"{filename}_no_detection", bgr_image)
             return None, None, None
 
@@ -897,60 +896,6 @@ class AareDAQ:
             self.__cfg.state_busy = False
             raise
 
-    def __loop_center(self, s, filename: str | None = "", iteration: int = 0) -> SmargonCoordinate:
-        # TODO: tidy up!
-        curr_image = self.camera_image
-
-        gray_without_feature = cv2.cvtColor(
-            self.__cfg.get_alc_bkg(self.zoom, s.exposure, s.gain), cv2.COLOR_RGB2GRAY
-        )
-        gray_with_feature = cv2.cvtColor(curr_image, cv2.COLOR_RGB2GRAY)
-
-        # Subtract the "without feature" image from the "with feature" image
-        diff_image = cv2.absdiff(gray_with_feature, gray_without_feature)
-
-        thresh_value, thresh = cv2.threshold(diff_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-
-        print(f'Thresh value: {thresh_value}')
-        if filename is not None:
-            #cv2.imwrite(f"{filename}_curr_image_colour.jpg", curr_image)
-            cv2.imwrite(f"curr_image.jpg", gray_with_feature)
-            cv2.imwrite(f"diff.jpg", diff_image)
-            cv2.imwrite(f"thresh.jpg", thresh)
-            #cv2.imwrite(f"{filename}_adaptive.tiff", adapt_thresh)
-
-        # # Find contours of the detected feature
-        contours, _ = cv2.findContours(
-            thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
-        )
-
-        leftmost_point = None
-
-        # Loop through each contour
-        for contour in contours:
-            # Loop through each point in the current contour
-            for point in contour:
-                x, y = point[0]  # Point is a nested array [ [x, y] ]
-
-                # Check if this is the leftmost point
-                if leftmost_point is None or x < leftmost_point[0]:
-                    leftmost_point = (x, y)
-
-        if leftmost_point is None:
-            raise LoopCenteringFailed()
-
-        geom = self.sample_geometry
-        if iteration == 0:
-            coord = geom.picture_to_smargon(
-                Coordinate(x=leftmost_point[0], y=leftmost_point[1])
-            )
-        else:
-            #TODO only move x after first iteration
-            coord = geom.picture_to_smargon(
-                Coordinate(x=leftmost_point[0], y=leftmost_point[1])
-            )
-        return SmargonCoordinate(sh_mm=coord)
-
     def face_detection(self, steps: int = 14, step_size: int = 15) -> dict:
         self.__cfg.try_set_busy(timeout=360)
         try:
@@ -966,6 +911,25 @@ class AareDAQ:
         self.__cfg.state_busy = False
         return result
 
+    def face_detection_centre_correction(self, m:MLBoxModel, tolerance: float = 0.2):
+        geom = self.sample_geometry
+        beam_y = geom.beam_location_pxl.y
+        beam_x = geom.beam_location_pxl.x
+
+        x1 = m.box.top_x
+        y1 = m.box.top_y
+        y2 = m.box.bottom_y
+
+        centre_y = y1 + (y2 - y1) / 2
+        centre_x = x1
+
+        if beam_y !=0 and abs(centre_y-beam_y)/abs(beam_y) > tolerance:
+            coord = geom.picture_to_smargon(Coordinate(x=beam_x, y=centre_y))
+            self.__devs.smargon.target = SmargonCoordinate(sh_mm=coord)
+            self.__devs.smargon.wait(60)
+
+        return
+
     def __face_detection_sequence(self, steps: int = 14, step_size: int = 15) -> dict:
         self.__set_state(BeamlineStateEnum.SampleAlignment)
         self.__devs.lamp_light = 2.5
@@ -1004,6 +968,8 @@ class AareDAQ:
             cls_id = int(m.cls.value)
             x1, y1, x2, y2 = m.box.top_x, m.box.top_y, m.box.bottom_x, m.box.bottom_y
 
+            self.face_detection_centre_correction(m, tolerance=0.2)
+
             if cls_id == 3:
                 boxes[angle] = (x1, y1, x2, y2)
                 logger.info(f"accepted box at angle {angle}, cls={cls_id}, box={(x1, y1, x2, y2)}")