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