call the restart function and handle errors better in the detection process

Dockerfile

@@ -38,9 +38,9 @@ RUN apt -qq update && apt -qq install --no-install-recommends -y \
     && apt -qq install --no-install-recommends -y \
         libedgetpu1-max \
     ## Tensorflow lite (python 3.7 only)
-    && wget -q https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
-    && python3.7 -m pip install tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
-    && rm tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
+    && wget -q https://dl.google.com/coral/python/tflite_runtime-2.1.0-cp37-cp37m-linux_x86_64.whl \
+    && python3.7 -m pip install tflite_runtime-2.1.0-cp37-cp37m-linux_x86_64.whl \
+    && rm tflite_runtime-2.1.0-cp37-cp37m-linux_x86_64.whl \
     && rm -rf /var/lib/apt/lists/* \
     && (apt-get autoremove -y; apt-get autoclean -y)
 

detect_objects.py

@@ -9,7 +9,7 @@ import multiprocessing as mp
 import subprocess as sp
 import numpy as np
 import logging
-from flask import Flask, Response, make_response, jsonify, request
+from flask import Flask, Response, make_response, jsonify
 import paho.mqtt.client as mqtt
 
 from frigate.video import track_camera
@@ -218,26 +218,21 @@ def main():
 
     @app.route('/<camera_name>')
     def mjpeg_feed(camera_name):
-        fps = int(request.args.get('fps', '3'))
-        height = int(request.args.get('h', '360'))
         if camera_name in CONFIG['cameras']:
             # return a multipart response
-            return Response(imagestream(camera_name, fps, height),
+            return Response(imagestream(camera_name),
                             mimetype='multipart/x-mixed-replace; boundary=frame')
         else:
             return "Camera named {} not found".format(camera_name), 404
 
-    def imagestream(camera_name, fps, height):
+    def imagestream(camera_name):
         while True:
-            # max out at specified FPS
-            time.sleep(1/fps)
+            # max out at 1 FPS
+            time.sleep(1)
             frame = object_processor.get_current_frame(camera_name)
             if frame is None:
-                frame = np.zeros((height,int(height*16/9),3), np.uint8)
-
-            frame = cv2.resize(frame, dsize=(int(height*16/9), height), interpolation=cv2.INTER_LINEAR)
+                frame = np.zeros((720,1280,3), np.uint8)
             frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
-
             ret, jpg = cv2.imencode('.jpg', frame)
             yield (b'--frame\r\n'
                 b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')

frigate/edgetpu.py

@@ -75,6 +75,7 @@ def run_detector(detection_queue, avg_speed, start):
         input_frame = plasma_client.get(object_id, timeout_ms=0)
 
         if input_frame is plasma.ObjectNotAvailable:
+            plasma_client.put(np.zeros((20,6), np.float32), object_id_out)
             continue
 
         # detect and put the output in the plasma store
@@ -123,11 +124,7 @@ class RemoteObjectDetector():
         object_id_detections = plasma.ObjectID(hashlib.sha1(str.encode(f"out-{now}")).digest())
         self.plasma_client.put(tensor_input, object_id_frame)
         self.detection_queue.put(now)
-        raw_detections = self.plasma_client.get(object_id_detections, timeout_ms=10000)
-
-        if raw_detections is plasma.ObjectNotAvailable:
-            self.plasma_client.delete([object_id_frame])
-            return detections
+        raw_detections = self.plasma_client.get(object_id_detections)
 
         for d in raw_detections:
             if d[1] < threshold: