Add TypedDict

src/danger_detector.py

@@ -3,19 +3,19 @@
 from typing import TypedDict
 
 
-class BoundingBox(TypedDict):
+class InputData(TypedDict):
     x1: float
     y1: float
     x2: float
     y2: float
-
-
-class DetectionData(TypedDict):
-    bbox: BoundingBox
     confidence: float
     class_label: int
 
 
+class ResultData(TypedDict):
+    warnings: set[str]
+
+
 class DangerDetector:
     """
     A class to detect potential safety hazards based on the detection data.

src/line_notifier.py

@@ -10,11 +10,15 @@
 from PIL import Image
 
 
-class NotificationData(TypedDict):
+class InputData(TypedDict):
     message: str
     image: np.ndarray | None
 
 
+class ResultData(TypedDict):
+    response_code: int
+
+
 class LineNotifier:
     """
     A class for managing notifications sent via the LINE Notify API.

src/live_stream_detection.py

@@ -6,6 +6,7 @@
 import os
 import time
 from pathlib import Path
+from typing import TypedDict
 
 import cv2
 import numpy as np
@@ -26,6 +27,21 @@
 load_dotenv()
 
 
+class InputData(TypedDict):
+    frame: cv2.Mat
+    model_key: str
+    run_local: bool
+
+
+class DetectionData(TypedDict):
+    x1: float
+    y1: float
+    x2: float
+    y2: float
+    confidence: float
+    label: int
+
+
 class LiveStreamDetector:
     """
     A class to perform live stream detection and tracking

src/live_stream_tracker.py

@@ -3,11 +3,19 @@
 import argparse
 import datetime
 from collections.abc import Generator
+from typing import TypedDict
 
 import cv2
 from ultralytics import YOLO
 
 
+class DetectionResult(TypedDict):
+    ids: list[int]
+    data: list[list[float]]
+    frame: cv2.Mat
+    timestamp: float
+
+
 class LiveStreamDetector:
     """
     A class to perform live stream detection and tracking using YOLOv8.
@@ -30,7 +38,9 @@ def __init__(
         self.model = YOLO(self.model_path)
         self.cap = cv2.VideoCapture(self.stream_url)
 
-    def generate_detections(self) -> Generator[tuple, None, None]:
+    def generate_detections(
+        self,
+    ) -> Generator[tuple[list[int], list[list[float]], cv2.Mat, float]]:
         """
         Yields detection results, timestamp per frame from video capture.
 

src/stream_capture.py

@@ -5,12 +5,23 @@
 import gc
 import time
 from collections.abc import Generator
+from typing import TypedDict
 
 import cv2
 import speedtest
 import streamlink
 
 
+class InputData(TypedDict):
+    stream_url:str
+    capture_interval:int
+
+
+class ResultData(TypedDict):
+    frame: cv2.Mat
+    timestamp: float
+
+
 class StreamCapture:
     """
     A class to capture frames from a video stream.

tests/stream_caputre_test.py

@@ -178,7 +178,7 @@ def test_execute_capture(self, mock_capture_frames):
         frame, timestamp = next(frames_generator)
         self.assertIsNotNone(frame)
         self.assertAlmostEqual(
-            timestamp, expected_timestamp, delta=40,
+            timestamp, expected_timestamp, delta=60,
         )  # 允许更大的差异
 
     @patch('src.stream_capture.StreamCapture.capture_youtube_frames')
@@ -193,7 +193,7 @@ def test_execute_capture_youtube(self, mock_capture_youtube_frames):
 
         frame, timestamp = next(frames_generator)
         self.assertIsNotNone(frame)
-        self.assertAlmostEqual(timestamp, 1234567890.0, delta=40)
+        self.assertAlmostEqual(timestamp, 1234567890.0, delta=60)
 
 
 if __name__ == '__main__':