refactor: rewrite depthai node management

camera/depthai.py

@@ -1,6 +1,7 @@
 """
 Camera event loop
 """
+import abc
 import datetime
 import logging
 import typing
@@ -117,18 +118,105 @@ class FrameProcessor:
         return frame_msg.id
 
 
+class Source(abc.ABC):
+    @abc.abstractmethod
+    def get_stream_name(self) -> str:
+        pass
+
+    @abc.abstractmethod
+    def link_preview(self, input_node: dai.Node.Input):
+        pass
+
+
+class ObjectDetectionNN:
+    """
+    Node to detect objects into image
+
+    Read image as input and apply resize transformation before to run NN on it
+    Result is available with 'get_stream_name()' stream
+    """
+
+    def __init__(self, pipeline: dai.Pipeline):
+        # Define a neural network that will make predictions based on the source frames
+        detection_nn = pipeline.createNeuralNetwork()
+        detection_nn.setBlobPath(_NN_PATH)
+        detection_nn.setNumPoolFrames(4)
+        detection_nn.input.setBlocking(False)
+        detection_nn.setNumInferenceThreads(2)
+        self._detection_nn = detection_nn
+        self._xout = self._configure_xout_nn(pipeline)
+        self._detection_nn.out.link(self._xout.input)
+        self._manip_image = self._configure_manip(pipeline)
+
+    @staticmethod
+    def _configure_manip(pipeline: dai.Pipeline) -> dai.node.ImageManip:
+        # Resize image
+        manip = pipeline.createImageManip()
+        manip.initialConfig.setResize(_NN_WIDTH, _NN_HEIGHT)
+        manip.initialConfig.setFrameType(dai.ImgFrame.Type.RGB888p)
+        manip.initialConfig.setKeepAspectRatio(False)
+        return manip
+
+    @staticmethod
+    def _configure_xout_nn(pipeline: dai.Pipeline) -> dai.node.XLinkOut:
+        xout_nn = pipeline.createXLinkOut()
+        xout_nn.setStreamName("nn")
+        xout_nn.input.setBlocking(False)
+        return xout_nn
+
+    def get_stream_name(self) -> str:
+        return self._xout.getStreamName()
+
+    def get_input(self) -> dai.Node.Input:
+        return self._manip_image.inputImage
+
+
+class CameraSource(Source):
+    """Image source based on camera preview"""
+
+    def __init__(self, pipeline: dai.Pipeline, img_width: int, img_height: int):
+        cam_rgb = pipeline.createColorCamera()
+        xout_rgb = pipeline.createXLinkOut()
+        xout_rgb.setStreamName("rgb")
+
+        self._cam_rgb = cam_rgb
+        self._xout_rgb = xout_rgb
+
+        # Properties
+        cam_rgb.setBoardSocket(dai.CameraBoardSocket.RGB)
+        cam_rgb.setPreviewSize(width=img_width, height=img_height)
+        cam_rgb.setInterleaved(False)
+        cam_rgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.RGB)
+        cam_rgb.setFps(30)
+
+        # link camera preview to output
+        cam_rgb.preview.link(xout_rgb.input)
+
+    def link_preview(self, input_node: dai.Node.Input):
+        self._cam_rgb.preview.link(input_node)
+
+    def get_stream_name(self) -> str:
+        return self._xout_rgb.getStreamName()
+
+
+
+    @staticmethod
+
+
 class PipelineController:
     """
     Pipeline controller that drive camera device
     """
 
     def __init__(self, img_width: int, img_height: int, frame_processor: FrameProcessor,
-                 object_processor: ObjectProcessor):
+                 object_processor: ObjectProcessor, camera: Source, object_node: ObjectDetectionNN):
         self._img_width = img_width
         self._img_height = img_height
         self._pipeline = self._configure_pipeline()
         self._frame_processor = frame_processor
         self._object_processor = object_processor
+        self._camera = camera
+        self._object_node = object_node
         self._stop = False
 
     def _configure_pipeline(self) -> dai.Pipeline:
@@ -137,54 +225,12 @@ class PipelineController:
 
         pipeline.setOpenVINOVersion(version=dai.OpenVINO.VERSION_2021_4)
 
-        detection_nn = self._configure_detection_nn(pipeline)
-        xout_nn = self._configure_xout_nn(pipeline)
-
-        # Resize image
-        manip = pipeline.create(dai.node.ImageManip)
-        manip.initialConfig.setResize(_NN_WIDTH, _NN_HEIGHT)
-        manip.initialConfig.setFrameType(dai.ImgFrame.Type.RGB888p)
-        manip.initialConfig.setKeepAspectRatio(False)
-
-        cam_rgb = pipeline.create(dai.node.ColorCamera)
-        xout_rgb = pipeline.create(dai.node.XLinkOut)
-        xout_rgb.setStreamName("rgb")
-
-        # Properties
-        cam_rgb.setBoardSocket(dai.CameraBoardSocket.RGB)
-        cam_rgb.setPreviewSize(width=self._img_width, height=self._img_height)
-        cam_rgb.setInterleaved(False)
-        cam_rgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.RGB)
-        cam_rgb.setFps(30)
-
         # Link preview to manip and manip to nn
-        cam_rgb.preview.link(manip.inputImage)
-        manip.out.link(detection_nn.input)
-
-        # Linking to output
-        cam_rgb.preview.link(xout_rgb.input)
-        detection_nn.out.link(xout_nn.input)
+        self._camera.link_preview(self._object_node.get_input())
 
         logger.info("pipeline configured")
         return pipeline
 
-    @staticmethod
-    def _configure_xout_nn(pipeline: dai.Pipeline) -> dai.node.XLinkOut:
-        xout_nn = pipeline.create(dai.node.XLinkOut)
-        xout_nn.setStreamName("nn")
-        xout_nn.input.setBlocking(False)
-        return xout_nn
-
-    @staticmethod
-    def _configure_detection_nn(pipeline: dai.Pipeline) -> dai.node.NeuralNetwork:
-        # Define a neural network that will make predictions based on the source frames
-        detection_nn = pipeline.create(dai.node.NeuralNetwork)
-        detection_nn.setBlobPath(_NN_PATH)
-        detection_nn.setNumPoolFrames(4)
-        detection_nn.input.setBlocking(False)
-        detection_nn.setNumInferenceThreads(2)
-        return detection_nn
-
     def run(self) -> None:
         """
         Start event loop
@@ -200,8 +246,8 @@ class PipelineController:
             device.startPipeline()
             # Queues
             queue_size = 4
-            q_rgb = device.getOutputQueue(name="rgb", maxSize=queue_size, blocking=False)
-            q_nn = device.getOutputQueue(name="nn", maxSize=queue_size, blocking=False)
+            q_rgb = device.getOutputQueue(name=self._camera.get_stream_name(), maxSize=queue_size, blocking=False)
+            q_nn = device.getOutputQueue(name=self._object_node.get_stream_name(), maxSize=queue_size, blocking=False)
 
             self._stop = False
             while True:
@@ -223,6 +269,7 @@ class PipelineController:
             frame_ref = self._frame_processor.process(in_rgb)
         except FrameProcessError as ex:
             logger.error("unable to process frame: %s", str(ex))
+            return
         # Read NN result
         in_nn: dai.NNData = q_nn.get()
         self._object_processor.process(in_nn, frame_ref)