Small refactoring using `black`

examples/python-dataflow/dataflow.yml

@@ -4,7 +4,7 @@ nodes:
       source: ./webcam.py
       inputs:
         tick:
-          source: dora/timer/millis/100
+          source: dora/timer/millis/50
           queue_size: 1000
       outputs:
         - image

examples/python-dataflow/plot.py

@@ -13,6 +13,8 @@ from utils import LABELS
 CI = os.environ.get("CI")
 
 font = cv2.FONT_HERSHEY_SIMPLEX
+
+
 class Plotter:
     """
     Plot image and bounding box
@@ -77,7 +79,6 @@ class Plotter:
         return DoraStatus.CONTINUE
 
 
-
 plotter = Plotter()
 node = Node()
 

examples/python-operator-dataflow/dataflow.yml

@@ -3,7 +3,7 @@ nodes:
     operator:
       python: webcam.py
       inputs:
-        tick: dora/timer/millis/100
+        tick: dora/timer/millis/50
       outputs:
         - image
 

examples/python-operator-dataflow/object_detection.py

@@ -41,23 +41,17 @@ class Operator:
         """Handle image
         Args:
             dora_input (dict): Dict containing the "id", "data", and "metadata"
-            send_output Callable[[str, bytes | pa.UInt8Array, Optional[dict]], None]: 
+            send_output Callable[[str, bytes | pa.UInt8Array, Optional[dict]], None]:
                 Function for sending output to the dataflow:
                 - First argument is the `output_id`
-                - Second argument is the data as either bytes or `pa.UInt8Array` 
+                - Second argument is the data as either bytes or `pa.UInt8Array`
                 - Third argument is dora metadata dict
                 e.g.: `send_output("bbox", pa.array([100], type=pa.uint8()), dora_event["metadata"])`
         """
 
-        frame = (
-            dora_input["value"]
-            .to_numpy()
-            .reshape((CAMERA_HEIGHT, CAMERA_WIDTH, 3))
-        )
+        frame = dora_input["value"].to_numpy().reshape((CAMERA_HEIGHT, CAMERA_WIDTH, 3))
         frame = frame[:, :, ::-1]  # OpenCV image (BGR to RGB)
         results = self.model(frame)  # includes NMS
-        arrays = pa.array(
-            np.array(results.xyxy[0].cpu()).ravel().view(np.uint8)
-        )
+        arrays = pa.array(np.array(results.xyxy[0].cpu()).ravel().view(np.uint8))
         send_output("bbox", arrays, dora_input["metadata"])
         return DoraStatus.CONTINUE

examples/python-operator-dataflow/plot.py

@@ -51,18 +51,19 @@ class Operator:
         Args:
             dora_input["id"] (str): Id of the dora_input declared in the yaml configuration
             dora_input["data"] (bytes): Bytes message of the dora_input
-            send_output Callable[[str, bytes | pa.UInt8Array, Optional[dict]], None]: 
+            send_output Callable[[str, bytes | pa.UInt8Array, Optional[dict]], None]:
                 Function for sending output to the dataflow:
                 - First argument is the `output_id`
-                - Second argument is the data as either bytes or `pa.UInt8Array` 
+                - Second argument is the data as either bytes or `pa.UInt8Array`
                 - Third argument is dora metadata dict
-                e.g.: `send_output("bbox", pa.array([100], type=pa.uint8()), dora_event["metadata"])`        """
+                e.g.: `send_output("bbox", pa.array([100], type=pa.uint8()), dora_event["metadata"])`
+        """
         if dora_input["id"] == "image":
             frame = (
                 dora_input["value"]
                 .to_numpy()
                 .reshape((CAMERA_HEIGHT, CAMERA_WIDTH, 3))
-                .copy() # copy the image because we want to modify it below
+                .copy()  # copy the image because we want to modify it below
             )
             self.image = frame
 
@@ -74,11 +75,7 @@ class Operator:
             self.bboxs = np.reshape(bboxs, (-1, 6))
 
             self.bounding_box_messages += 1
-            print(
-                "received "
-                + str(self.bounding_box_messages)
-                + " bounding boxes"
-            )
+            print("received " + str(self.bounding_box_messages) + " bounding boxes")
 
         for bbox in self.bboxs:
             [