Merge branch 'master' into auto-exposure-gain-awb

openflexure_microscope/api/app.py

@@ -218,10 +218,16 @@ def cleanup():
 
 atexit.register(cleanup)
 
-# Start the app
-if __name__ == "__main__":
+
+def ofm_serve():
+    # Start a debug server
     from labthings import Server
 
     logging.info("Starting OpenFlexure Microscope Server...")
     server: Server = Server(app)
     server.run(host="0.0.0.0", port=5000, debug=debug_app, zeroconf=True)
+
+
+# Start the app if the module is run directly
+if __name__ == "__main__":
+    ofm_serve()

openflexure_microscope/api/default_extensions/autofocus.py

@@ -127,20 +127,20 @@ def monitor_sharpness(microscope: Microscope):
         m.stop()
 
 
-def sharpness_sum_lap2(rgb_image: np.ndarray) -> np.float:
+def sharpness_sum_lap2(rgb_image: np.ndarray) -> float:
     """Return an image sharpness metric: sum(laplacian(image)**")"""
-    image_bw: np.float = np.mean(rgb_image, 2)
-    image_lap: np.float = ndimage.filters.laplace(image_bw)
-    return np.mean(image_lap.astype(np.float) ** 4)
+    image_bw = np.mean(rgb_image, 2)
+    image_lap = ndimage.filters.laplace(image_bw)
+    return float(np.mean(image_lap.astype(float) ** 4))
 
 
-def sharpness_edge(image: np.ndarray) -> np.float:
+def sharpness_edge(image: np.ndarray) -> float:
     """Return a sharpness metric optimised for vertical lines"""
-    gray: np.float = np.mean(image.astype(float), 2)
+    gray = np.mean(image.astype(float), 2)
     n: int = 20
     edge: np.ndarray = np.array([[-1] * n + [1] * n])
-    return np.sum(
-        [np.sum(ndimage.filters.convolve(gray, W) ** 2) for W in [edge, edge.T]]
+    return float(
+        np.sum([np.sum(ndimage.filters.convolve(gray, W) ** 2) for W in [edge, edge.T]])
     )
 
 
@@ -169,7 +169,7 @@ class AutofocusExtension(BaseExtension):
 
     def measure_sharpness(
         self, microscope: Microscope, metric_fn: Callable = sharpness_sum_lap2
-    ) -> np.float:
+    ) -> float:
         """Measure the sharpness of the camera's current view."""
 
         if hasattr(microscope.camera, "array") and callable(
@@ -185,7 +185,7 @@ class AutofocusExtension(BaseExtension):
         dz: List[int],
         settle: float = 0.5,
         metric_fn: Callable = sharpness_sum_lap2,
-    ) -> Tuple[List[int], List[np.float]]:
+    ) -> Tuple[List[int], List[float]]:
         """Perform a simple autofocus routine.
         The stage is moved to z positions (relative to current position) in dz,
         and at each position an image is captured and the sharpness function 
@@ -197,7 +197,7 @@ class AutofocusExtension(BaseExtension):
         stage: BaseStage = microscope.stage
 
         with set_properties(stage, backlash=256), stage.lock, camera.lock:
-            sharpnesses: List[np.float] = []
+            sharpnesses: List[float] = []
             positions: List[int] = []
 
             # Some cameras may not have annotate_text. Reset if it does

openflexure_microscope/api/default_extensions/picamera_autocalibrate/recalibrate_utils.py

@@ -205,7 +205,7 @@ def lst_from_channels(channels: np.ndarray) -> np.ndarray:
 
     logging.info("Generating a lens shading table at %sx%s", *lst_resolution)
     lens_shading: np.ndarray = np.zeros(
-        [channels.shape[0]] + lst_resolution, dtype=np.float
+        [channels.shape[0]] + lst_resolution, dtype=float
     )
     for i in range(lens_shading.shape[0]):
         image_channel: np.ndarray = channels[i, :, :]
@@ -297,7 +297,7 @@ def recalibrate_camera(camera: PiCamera):
     _ = rgb_image(camera)
 
     # Fix the AWB gains so the image is neutral
-    channel_means = np.mean(np.mean(rgb_image(camera), axis=0, dtype=np.float), axis=0)
+    channel_means = np.mean(np.mean(rgb_image(camera), axis=0, dtype=float), axis=0)
     old_gains = camera.awb_gains
     camera.awb_gains = (
         channel_means[1] / channel_means[0] * old_gains[0],

openflexure_microscope/api/static/src/components/tabContentComponents/slideScanComponents/paneSlideScan.vue

@@ -160,6 +160,7 @@ export default {
       stride_size: [800, 600, 10],
       fast_autofocus: true,
       autofocus_dz: 2000,
+      style: "raster",
       use_video_port: false
     };
   },

openflexure_microscope/json.py

@@ -21,8 +21,8 @@ class JSONEncoder(LabThingsJSONEncoder):
         # Numpy integers
         elif isinstance(o, np.integer):
             return int(o)
-        # Numpy floats
-        elif isinstance(o, np.float):
+        # Numpy floats are just Python floats
+        elif isinstance(o, float):
             return float(o)
         # Numpy arrays
         elif isinstance(o, np.ndarray):

openflexure_microscope/stage/mock.py

@@ -89,7 +89,7 @@ class MissingStage(BaseStage):
             )
             displacement = move
 
-        initial_move = np.array(displacement, dtype=np.int)
+        initial_move = np.array(displacement, dtype=np.integer)
 
         self._position = list(np.array(self._position) + np.array(initial_move))
         logging.debug(np.array(self._position) + np.array(initial_move))

openflexure_microscope/stage/sanga.py

@@ -277,13 +277,11 @@ class SangaDeltaStage(SangaStage):
         logging.debug(self.R_camera)
 
         # Transformation matrix converting delta into cartesian
-        x_fac: np.float = -1 * np.multiply(
+        x_fac: float = -1 * np.multiply(
             np.divide(2, np.sqrt(3)), np.divide(self.flex_b, self.flex_h)
         )
-        y_fac: np.float = -1 * np.divide(self.flex_b, self.flex_h)
-        z_fac: np.float = np.multiply(
-            np.divide(1, 3), np.divide(self.flex_b, self.flex_a)
-        )
+        y_fac: float = -1 * np.divide(self.flex_b, self.flex_h)
+        z_fac: float = np.multiply(np.divide(1, 3), np.divide(self.flex_b, self.flex_a))
 
         self.Tvd: np.ndarray = np.array(
             [