Disable detection

xeonqq · Mar 13, 2024 · 13a4631 · 13a4631
1 parent 881145a
commit 13a4631
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Showing 3 changed files with 61 additions and 56 deletions.
diff --git a/requirements.txt b/requirements.txt
@@ -1,6 +1,6 @@
-scipy==1.6.3
-filterpy==1.4.5
-pygame==2.0.1
-pytest==6.2.3
-PyYAML==5.4.1
-transitions==0.8.10
+scipy
+filterpy
+pygame
+pytest
+PyYAML
+transitions
diff --git a/src/copilot.py b/src/copilot.py
@@ -3,10 +3,10 @@
 
 from PIL import Image
 
-from pycoral.adapters import common
-from pycoral.adapters import detect
-from pycoral.adapters import classify
-from pycoral.utils.dataset import read_label_file
+# from pycoral.adapters import common
+# from pycoral.adapters import detect
+# from pycoral.adapters import classify
+# from pycoral.utils.dataset import read_label_file
 
 # from .button import Button
 from .utils import (
@@ -56,7 +56,7 @@ def __init__(
 
         self._classfication_interpreter = traffic_light_classifier_interpreter
         self._classfication_interpreter.allocate_tensors()
-        self._traffic_light_size = common.input_size(self._classfication_interpreter)
+        # self._traffic_light_size = common.input_size(self._classfication_interpreter)
 
         self._speaker = speaker
 
@@ -66,12 +66,12 @@ def __init__(
         assert tile_size == input_shape[1]
         self._tile_config = TileConfig(tile_size, tile_w_overlap, tile_h_overlap)
 
-        self._ssd_labels = read_label_file(self._args.label) if self._args.label else {}
-        self._traffic_light_labels = (
-            read_label_file(self._args.traffic_light_label)
-            if self._args.traffic_light_label
-            else {}
-        )
+        # self._ssd_labels = read_label_file(self._args.label) if self._args.label else {}
+        # self._traffic_light_labels = (
+            # read_label_file(self._args.traffic_light_label)
+            # if self._args.traffic_light_label
+            # else {}
+        # )
 
         self._h_crop_keep_percentage = 0.6
         self._led = led
@@ -97,7 +97,7 @@ def run(self):
             )
             prev_cycle_time = current_cycle_time
             logging.debug("recv image from: {}".format(image_time))
-            self.process(image)
+            # self.process(image)
             logging.debug(
                 "process time: %.2f ms"
                 % ((time.perf_counter() - current_cycle_time) * 1000)
@@ -142,7 +142,7 @@ def classify(self, traffic_light_thumbnail):
         traffic_light_resized = traffic_light_thumbnail.resize(
             self._traffic_light_size, Image.ANTIALIAS
         )
-        common.set_input(self._classfication_interpreter, traffic_light_resized)
+        # common.set_input(self._classfication_interpreter, traffic_light_resized)
         start = time.perf_counter()
         self._classfication_interpreter.invoke()
         classes = classify.get_classes(
@@ -179,7 +179,7 @@ def detect(self, img):
         ):
             # print(tile_location)
             tile = img.crop(tile_location)
-            common.set_input(self._ssd_interpreter, tile)
+            # common.set_input(self._ssd_interpreter, tile)
             start = time.perf_counter()
             self._ssd_interpreter.invoke()
             inference_time += time.perf_counter() - start

diff --git a/src/main.py b/src/main.py
@@ -4,10 +4,10 @@
 import time
 import threading
 
-import picamera
+from picamera2 import Picamera2
 
 from src.os_utils import generate_recording_postfix
-from .led import Led
+from .abc import ILed
 from .camera_capturer import CameraCapturer
 from .camera_recorder import CameraRecorder
 from .pubsub import PubSub
@@ -109,43 +109,48 @@ def main():
 
         blackbox = BlackBox(image_saver)
 
-        with picamera.PiCamera() as camera:
-            # fps for recording
-            camera.framerate = 20
-            camera.resolution = camera_info.resolution
-            camera.exposure_mode = "sports"
-
-            led_pin = 10
-            led = Led(led_pin)
-            camera_recorder = CameraRecorder(camera, led, args.blackbox_path)
-            camera_capturer = CameraCapturer(
-                camera, 5, camera_recorder.is_recording, pubsub, inference_config
-            )
+        camera = Picamera2()
+
+
+        main_stream = {"size": camera_info.resolution}
+        lores_stream = {"size": inference_config.inference_resolution}
+        video_config = camera.create_video_configuration(main_stream, lores_stream, encode="main")
+        camera.configure(video_config)
+
+        #camera.framerate = 20
+        #camera.exposure_mode = "sports"
 
-            if args.cpu:
-                from tflite_runtime.interpreter import Interpreter as make_interpreter
-            else:
-                from pycoral.utils.edgetpu import make_interpreter
-
-            try:
-                copilot = CoPilot(
-                    args,
-                    pubsub,
-                    blackbox,
-                    camera_info,
-                    inference_config,
-                    led,
-                    Speaker(args.lang),
-                    make_interpreter(args.ssd_model),
-                    make_interpreter(args.traffic_light_classification_model),
-                )
-            except ValueError as e:
-                print(str(e) + "Use --cpu if you do not have a coral tpu")
-                return
-            copilot.run()
+        led_pin = 10
+        led = ILed()
+        camera_recorder = CameraRecorder(camera, led, args.blackbox_path)
+        camera_capturer = CameraCapturer(
+            camera, 5, camera_recorder.is_recording, pubsub, inference_config
+        )
+
+        if args.cpu:
+            from tflite_runtime.interpreter import Interpreter as make_interpreter
+        else:
+            from pycoral.utils.edgetpu import make_interpreter
+
+        try:
+            copilot = CoPilot(
+                args,
+                pubsub,
+                blackbox,
+                camera_info,
+                inference_config,
+                led,
+                Speaker(args.lang),
+                make_interpreter(args.ssd_model),
+                make_interpreter(args.traffic_light_classification_model),
+            )
+        except ValueError as e:
+            print(str(e) + "Use --cpu if you do not have a coral tpu")
+            return
+        copilot.run()
 
     except Exception as e:
-        Speaker(args.lang).play_sound("dead", is_blocking=True)
+        # Speaker(args.lang).play_sound("dead", is_blocking=True)
         logging.critical(str(e))
         print(str(e))
         exit(1)