fix inference status sending and logging

Azaion.Inference/inference.pxd

@@ -37,6 +37,7 @@ cdef class Inference:
     cdef stop(self)
 
     cdef preprocess(self, frames)
+    cdef send_detection_status(self, client_id)
     cdef remove_overlapping_detections(self, list[Detection] detections, float confidence_threshold=?)
     cdef postprocess(self, output, ai_config)
     cdef split_list_extend(self, lst, chunk_size)

Azaion.Inference/inference.pyx

@@ -51,6 +51,8 @@ else:
     from onnx_engine import OnnxEngine
 
 
+
+
 cdef class Inference:
     def __init__(self, loader_client, remote_handler):
         self.loader_client = loader_client
@@ -295,10 +297,12 @@ cdef class Inference:
                 batch_frames.clear()
                 batch_timestamps.clear()
         v_input.release()
+        self.send_detection_status(cmd.client_id)
 
     cdef on_annotation(self, RemoteCommand cmd, Annotation annotation):
         cdef RemoteCommand response = RemoteCommand(CommandType.INFERENCE_DATA, annotation.serialize())
         self.remote_handler.send(cmd.client_id, response)
+        self.detection_counts[annotation.original_media_name] = self.detection_counts.get(annotation.original_media_name, 0) + 1
 
     cdef _process_images(self, RemoteCommand cmd, AIRecognitionConfig ai_config, list[str] image_paths):
         cdef list frame_data
@@ -319,10 +323,23 @@ cdef class Inference:
             if len(frame_data) > self.engine.get_batch_size():
                 for chunk in self.split_list_extend(frame_data, self.engine.get_batch_size()):
                     self._process_images_inner(cmd, ai_config, chunk)
+                self.send_detection_status(cmd.client_id)
 
         for chunk in self.split_list_extend(frame_data, self.engine.get_batch_size()):
             self._process_images_inner(cmd, ai_config, chunk)
+        self.send_detection_status(cmd.client_id)
 
+    cdef send_detection_status(self, client_id):
+        for media_name in self.detection_counts.keys():
+            try:
+                status = {
+                    "mn": media_name,
+                    "dc": self.detection_counts[media_name]
+                }
+                self.remote_handler.send(client_id, <RemoteCommand>RemoteCommand(CommandType.INFERENCE_STATUS, msgpack.packb(status)))
+            except Exception:
+                pass
+        self.detection_counts.clear()
 
     cdef split_to_tiles(self, frame, path, tile_size, overlap_percent):
         constants_inf.log(<str>f'splitting image {path} to tiles...')
@@ -369,18 +386,6 @@ cdef class Inference:
                 _, image = cv2.imencode('.jpg', frames[i])
                 annotation.image = image.tobytes()
                 self.on_annotation(cmd, annotation)
-                self.detection_counts[original_media_names[i]] = self.detection_counts.get(original_media_names[i], 0) + 1
-
-        # Send detecting status for each original media name
-        for media_name in self.detection_counts.keys():
-            try:
-                status = {
-                    "mn": media_name,
-                    "dc": self.detection_counts[media_name]
-                }
-                self.remote_handler.send(cmd.client_id, <RemoteCommand>RemoteCommand(CommandType.INFERENCE_STATUS, msgpack.packb(status)))
-            except Exception:
-                pass
 
     cdef stop(self):
         self.stop_signal = True