import argparse import os import dotenv from pathlib import Path argparser = None def set_argparse(): global argparser if Path(".env").is_file(): dotenv.load_dotenv() print("Loaded .env file") else: print("No .env file found") # One important thing to consider is that most function parameters are optional and have a default value # However, with argparse, those are never used since a argparse always passes something, even if it's None argparser = argparse.ArgumentParser( prog="Wyzely Detect", description="Recognize faces/objects in a video stream (from a webcam or a security camera) and send notifications to your devices", # noqa: E501 epilog="For env bool options, setting them to anything except for an empty string will enable them." ) video_options = argparser.add_argument_group("Video Options") stream_source = video_options.add_mutually_exclusive_group() stream_source.add_argument( "--rtsp-url", action="append", # If RTSP_URL is in the environment, use it, otherwise just use a blank list # This may cause problems down the road, but if it does, env for this can be removed default=[os.environ["RTSP_URL"]] if "RTSP_URL" in os.environ and os.environ["RTSP_URL"] != "" else [], type=str, help="RTSP camera URL", ) stream_source.add_argument( "--capture-device", action="append", # If CAPTURE_DEVICE is in the environment, use it, otherwise just use a blank list # If __main__.py detects that no capture device or remote stream is set, it will default to 0 default=[int(os.environ["CAPTURE_DEVICE"])] if "CAPTURE_DEVICE" in os.environ and os.environ["CAPTURE_DEVICE"] != "" else [], type=int, help="Capture device number", ) video_options.add_argument( "--run-scale", # Set it to the env RUN_SCALE if it isn't blank, otherwise set it to 0.25 default=os.environ["RUN_SCALE"] if "RUN_SCALE" in os.environ and os.environ["RUN_SCALE"] != "" # else 0.25, else 1, type=float, help="The scale to run the detection at, default is 0.25", ) video_options.add_argument( "--view-scale", # Set it to the env VIEW_SCALE if it isn't blank, otherwise set it to 0.75 default=os.environ["VIEW_SCALE"] if "VIEW_SCALE" in os.environ and os.environ["VIEW_SCALE"] != "" # else 0.75, else 1, type=float, help="The scale to view the detection at, default is 0.75", ) video_options.add_argument( "--no-display", default=os.environ["NO_DISPLAY"] if "NO_DISPLAY" in os.environ and os.environ["NO_DISPLAY"] != "" and os.environ["NO_DISPLAY"].lower() != "false" else False, action="store_true", help="Don't display the video feed", ) video_options.add_argument( '-c', '--force-disable-tensorflow-gpu', default=os.environ["FORCE_DISABLE_TENSORFLOW_GPU"] if "FORCE_DISABLE_TENSORFLOW_GPU" in os.environ and os.environ["FORCE_DISABLE_TENSORFLOW_GPU"] != "" and os.environ["FORCE_DISABLE_TENSORFLOW_GPU"].lower() != "false" else False, action="store_true", help="Force disable tensorflow GPU through env since sometimes it's not worth it to install cudnn and whatnot", ) notifcation_services = argparser.add_argument_group("Notification Services") notifcation_services.add_argument( "--ntfy-url", default=os.environ["NTFY_URL"] if "NTFY_URL" in os.environ and os.environ["NTFY_URL"] != "" else None, type=str, help="The URL to send notifications to", ) # Various timers timers = argparser.add_argument_group("Timers") timers.add_argument( "--detection-duration", default=os.environ["DETECTION_DURATION"] if "DETECTION_DURATION" in os.environ and os.environ["DETECTION_DURATION"] != "" else 2, type=int, help="The duration (in seconds) that an object must be detected for before sending a notification", ) timers.add_argument( "--detection-window", default=os.environ["DETECTION_WINDOW"] if "DETECTION_WINDOW" in os.environ and os.environ["DETECTION_WINDOW"] != "" else 15, type=int, help="The time (seconds) before the detection duration resets", ) timers.add_argument( "--notification-window", default=os.environ["NOTIFICATION_WINDOW"] if "NOTIFICATION_WINDOW" in os.environ and os.environ["NOTIFICATION_WINDOW"] != "" else 30, type=int, help="The time (seconds) before another notification can be sent", ) face_recognition = argparser.add_argument_group("Face Recognition options") face_recognition.add_argument( "--faces-directory", default=os.environ["FACES_DIRECTORY"] if "FACES_DIRECTORY" in os.environ and os.environ["FACES_DIRECTORY"] != "" else "faces", type=str, help="The directory to store the faces. Can either contain images or subdirectories with images, the latter being the preferred method", # noqa: E501 ) face_recognition.add_argument( "--face-confidence-threshold", default=os.environ["FACE_CONFIDENCE_THRESHOLD"] if "FACE_CONFIDENCE_THRESHOLD" in os.environ and os.environ["FACE_CONFIDENCE_THRESHOLD"] != "" else 0.3, type=float, help="The confidence (currently cosine similarity) threshold to use for face recognition", ) face_recognition.add_argument( "--no-remove-representations", default=os.environ["NO_REMOVE_REPRESENTATIONS"] if "NO_REMOVE_REPRESENTATIONS" in os.environ and os.environ["NO_REMOVE_REPRESENTATIONS"] != "" and os.environ["NO_REMOVE_REPRESENTATIONS"].lower() != "false" else False, action="store_true", help="Don't remove representations_.pkl at the start of the program. Greatly improves startup time, but doesn't take into account changes to the faces directory since it was created", # noqa: E501 ) object_detection = argparser.add_argument_group("Object Detection options") object_detection.add_argument( "--detect-object", action="append", # Stuff is appended to default, as far as I can tell default=[], type=str, help="The object(s) to detect. Must be something the model is trained to detect", ) object_detection.add_argument( "--object-confidence-threshold", default=os.environ["OBJECT_CONFIDENCE_THRESHOLD"] if "OBJECT_CONFIDENCE_THRESHOLD" in os.environ and os.environ["OBJECT_CONFIDENCE_THRESHOLD"] != "" # I think this should always be a str so using lower shouldn't be a problem. # Also, if the first check fails the rest shouldn't be run and os.environ["OBJECT_CONFIDENCE_THRESHOLD"].lower() != "false" else 0.6, type=float, help="The confidence threshold to use", ) debug = argparser.add_argument_group("Debug options") debug.add_argument( "--fake-second-source", help="Duplicate the first source and use it as a second source. Capture device takes priority.", action="store_true", default=os.environ["FAKE_SECOND_SOURCE"] if "FAKE_SECOND_SOURCE" in os.environ and os.environ["FAKE_SECOND_SOURCE"] != "" and os.environ["FAKE_SECOND_SOURCE"].lower() != "false" else False, ) # return argparser # This will run when this file is imported set_argparse()