feature: custom creation date

Allow to set custom creation date everywhere

learning_loop_node/detector/detector_logic.py

@@ -46,7 +46,7 @@ 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_all_info(self, image: np.ndarray, tags: List[str], source: Optional[str] = None) -> ImageMetadata:  # pylint: disable=unused-argument
+    def evaluate_with_all_info(self, image: np.ndarray, tags: List[str], source: Optional[str] = None, creation_date: Optional[str] = None) -> ImageMetadata:  # pylint: disable=unused-argument
         """Called by the detector node when an image should be evaluated (REST or SocketIO).
         Tags, source come from the caller and may be used in this function. 
         By default, this function simply calls `evaluate`"""

learning_loop_node/detector/detector_node.py

@@ -186,10 +186,11 @@ async def upload(sid, data: Dict) -> Optional[Dict]:
             tags.append('picked_by_system')
 
             source = data.get('source', None)
+            creation_date = data.get('creation_date', None)
 
             loop = asyncio.get_event_loop()
             try:
-                await loop.run_in_executor(None, self.outbox.save, data['image'], detections, tags, source)
+                await loop.run_in_executor(None, self.outbox.save, data['image'], detections, tags, source, creation_date)
             except Exception as e:
                 self.log.exception('could not upload via socketio')
                 return {'error': str(e)}
@@ -343,15 +344,16 @@ async def get_detections(self,
                              camera_id: Optional[str],
                              tags: List[str],
                              source: Optional[str] = None,
-                             autoupload: Optional[str] = None) -> ImageMetadata:
+                             autoupload: Optional[str] = None,
+                             creation_date: Optional[str] = None) -> ImageMetadata:
         """ 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 uploading to the loop and returns the detections as a dictionary.
         Note: raw_image is a numpy array of type uint8, but not in the correct 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_with_all_info, raw_image, tags, source)
+        detections = await loop.run_in_executor(None, self.detector_logic.evaluate_with_all_info, raw_image, tags, source, creation_date)
         self.detection_lock.release()
 
         fix_shape_detections(detections)
@@ -361,19 +363,19 @@ async def get_detections(self,
 
         if autoupload is None or autoupload == 'filtered':  # NOTE default is filtered
             Thread(target=self.relevance_filter.may_upload_detections,
-                   args=(detections, camera_id, raw_image, tags, source)).start()
+                   args=(detections, camera_id, raw_image, tags, source, creation_date)).start()
         elif autoupload == 'all':
-            Thread(target=self.outbox.save, args=(raw_image, detections, tags, source)).start()
+            Thread(target=self.outbox.save, args=(raw_image, detections, tags, source, creation_date)).start()
         elif autoupload == 'disabled':
             pass
         else:
             self.log.error('unknown autoupload value %s', autoupload)
         return detections
 
-    async def upload_images(self, images: List[bytes]):
+    async def upload_images(self, images: List[bytes], source: Optional[str], creation_date: Optional[str]):
         loop = asyncio.get_event_loop()
         for image in images:
-            await loop.run_in_executor(None, self.outbox.save, image, ImageMetadata(), ['picked_by_system'])
+            await loop.run_in_executor(None, self.outbox.save, image, ImageMetadata(), ['picked_by_system'], source, creation_date)
 
     def add_category_id_to_detections(self, model_info: ModelInformation, detections: ImageMetadata):
         def find_category_id_by_name(categories: List[Category], category_name: str):

learning_loop_node/detector/inbox_filter/relevance_filter.py

@@ -16,7 +16,8 @@ def may_upload_detections(self,
                               cam_id: str,
                               raw_image: bytes,
                               tags: List[str],
-                              source: Optional[str] = None
+                              source: Optional[str] = None,
+                              creation_date: Optional[str] = None
                               ) -> List[str]:
         for group in self.cam_histories.values():
             group.forget_old_detections()
@@ -29,5 +30,5 @@ def may_upload_detections(self,
         if len(causes) > 0:
             tags = tags if tags is not None else []
             tags.extend(causes)
-            self.outbox.save(raw_image, dets, tags, source)
+            self.outbox.save(raw_image, dets, tags, source, creation_date)
         return causes

learning_loop_node/detector/rest/upload.py

@@ -1,6 +1,6 @@
-from typing import TYPE_CHECKING, List
+from typing import TYPE_CHECKING, List, Optional
 
-from fastapi import APIRouter, File, Request, UploadFile
+from fastapi import APIRouter, File, Query, Request, UploadFile
 
 if TYPE_CHECKING:
     from ..detector_node import DetectorNode
@@ -9,13 +9,21 @@
 
 
 @router.post("/upload")
-async def upload_image(request: Request, files: List[UploadFile] = File(...)):
+async def upload_image(request: Request,
+                       files: List[UploadFile] = File(...),
+                       source: Optional[str] = Query(None, description='Source of the image'),
+                       creation_date: Optional[str] = Query(None, description='Creation date of the image')):
     """
+    Upload an image or multiple images to the learning loop.
+
+    The image source and the image creation date are optional query parameters.
+    Images are automatically tagged with 'picked_by_system'.
+
     Example Usage
 
-        curl -X POST -F '[email protected]' "http://localhost:/upload"
+        curl -X POST -F '[email protected]' "http://localhost:/upload?source=test&creation_date=2024-01-01T00:00:00"
     """
     raw_files = [await file.read() for file in files]
     node: DetectorNode = request.app
-    await node.upload_images(raw_files)
+    await node.upload_images(raw_files, source, creation_date)
     return 200, "OK"