Move api_url from config.json to .env

examples/Model-Server/app.py

@@ -63,4 +63,4 @@ def create_tables():
 
 if __name__ == '__main__':
     # Run the Flask application on all available IPs at port 5000
-    app.run(host='0.0.0.0', port=5000)
+    app.run(Thread = True, host='0.0.0.0', port=5000)

main.py

@@ -6,14 +6,16 @@
 import time
 import gc
 from typing import NoReturn, Dict
+import os
+from dotenv import load_dotenv
 
 from src.stream_capture import StreamCapture
 from src.line_notifier import LineNotifier
 from src.monitor_logger import LoggerConfig
 from src.live_stream_detection import LiveStreamDetector
 from src.danger_detector import DangerDetector
 
-def main(logger, video_url: str, api_url: str = 'http://localhost:5000', model_key: str = 'yolov8x', label: str = None, image_name: str = 'prediction_visual.png', line_token: str = None, output_path: str = None) -> NoReturn:
+def main(logger, video_url: str, model_key: str = 'yolov8x', label: str = None, image_name: str = 'prediction_visual.png', line_token: str = None, output_path: str = None) -> NoReturn:
     """
     Main execution function that detects hazards, sends notifications, logs warnings, and optionally saves output images.
 
@@ -24,6 +26,9 @@ def main(logger, video_url: str, api_url: str = 'http://localhost:5000', model_k
         image_name (str, optional): The file name of the image to send with notifications. Defaults to 'demo_data/{label}/prediction_visual.png'.
         output_path (str, optional): The file path where output images should be saved. If not specified, images are not saved.
     """
+    # Load environment variables
+    load_dotenv()
+    api_url = os.getenv('API_URL', 'http://localhost:5000') 
 
     steaming_capture = StreamCapture(stream_url = video_url)
 
@@ -55,7 +60,7 @@ def main(logger, video_url: str, api_url: str = 'http://localhost:5000', model_k
         if output_path:
             # Here you could modify or extend the output filename based on timestamp or other criteria
             output_file = output_path.format(timestamp=timestamp)
-            live_stream_detector.save_frame(frame, output_file)
+            live_stream_detector.save_frame(frame)
 
         # Check for warnings and send notifications if necessary
         warnings = danger_detector.detect_danger(datas)

src/live_stream_detection.py

@@ -184,9 +184,9 @@ def save_frame(self, frame: cv2.Mat) -> None:
         # Define the complete output path including the filename
         output_path = output_dir / self.output_filename
 
-        # Check if the file exists and remove it if it does
-        if output_path.exists():
-            output_path.unlink() 
+        # # Check if the file exists and remove it if it does
+        # if output_path.exists():
+        #     output_path.unlink() 
 
         # Save the image to the specified path
         cv2.imwrite(str(output_path), frame)

src/stream_capture.py

@@ -73,15 +73,15 @@ def capture_frames(self) -> Generator[Tuple[cv2.Mat, float], None, None]:
                 continue
 
             current_time = datetime.datetime.now()
-            if (current_time - last_process_time).total_seconds() >= 5:
+            if (current_time - last_process_time).total_seconds() >= 15:
                 last_process_time = current_time
                 timestamp = current_time.timestamp()
 
                 yield frame, timestamp
 
-            # Skip frames if necessary
-            for _ in range(5):  # Skip 5 frames
-                self.cap.grab()
+            # # Skip frames if necessary
+            # for _ in range(5):  # Skip 5 frames
+            #     self.cap.grab()
 
             if cv2.waitKey(1) & 0xFF == ord('q'):
                 break
@@ -169,9 +169,9 @@ def capture_youtube_frames(self) -> Generator[Tuple[cv2.Mat, float], None, None]
                     timestamp = current_time.timestamp()
                     yield frame, timestamp
 
-                # Skip frames if necessary to manage frame rate
-                for _ in range(5):  # Skip 5 frames
-                    self.cap.grab()
+                # # Skip frames if necessary to manage frame rate
+                # for _ in range(5):  # Skip 5 frames
+                #     self.cap.grab()
 
                 if cv2.waitKey(1) & 0xFF == ord('q'):
                     break