feature: detector

add an intermediate abstraction level that can allows the logic to use the tags when doing the inference.

learning_loop_node/detector/detector_logic.py

@@ -1,6 +1,6 @@
 import logging
 from abc import abstractmethod
-from typing import Optional
+from typing import List, Optional
 
 import numpy as np
 
@@ -46,6 +46,12 @@ def load_model(self):
     def init(self):
         """Called when a (new) model was loaded. Initialize the model. Model information available via `self.model_info`"""
 
+    def evaluate_with_tags(self, image: np.ndarray, tags: List[str]) -> Detections:  # pylint: disable=unused-argument
+        """Called by the detector node when an image should be evaluated (REST or SocketIO).
+        Tags come from the caller and may be used in this function. 
+        By default, this function simply calls `evaluate`"""
+        return self.evaluate(image)
+
     @abstractmethod
     def evaluate(self, image: np.ndarray) -> Detections:
         """Evaluate the image and return the detections.

learning_loop_node/detector/detector_node.py

@@ -326,12 +326,14 @@ def reload(self, reason: str):
             self.log.error('could not reload app')
 
     async def get_detections(self, raw_image: np.ndarray, camera_id: Optional[str], tags: List[str], autoupload: Optional[str] = None) -> Optional[Dict]:
-        """Note: raw_image is a numpy array of type uint8, but not in the correrct shape!
+        """ Main processing function for the detector node when an image is received via REST or SocketIO.
+        This function infers the detections from the image, cares about upload ing to the loop and returns the detections as a dictionary.
+        Note: raw_image is a numpy array of type uint8, but not in the correrct shape!
         It can be converted e.g. using cv2.imdecode(raw_image, cv2.IMREAD_COLOR)"""
 
         await self.detection_lock.acquire()
         loop = asyncio.get_event_loop()
-        detections = await loop.run_in_executor(None, self.detector_logic.evaluate, raw_image)
+        detections = await loop.run_in_executor(None, self.detector_logic.evaluate_with_tags, raw_image, tags)
         self.detection_lock.release()
 
         fix_shape_detections(detections)