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No commits in common. "f7f5db9f41533434e72c09d6f7f16332d5d91fc4" and "494708a37664a6b44642c3595ec2fb9b97a36886" have entirely different histories.
f7f5db9f41
...
494708a376
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@ -1 +1 @@
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3.10.12
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3.11.5
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@ -10,7 +10,7 @@
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"request": "launch",
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"module": "wyzely_detect",
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"args": [
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"--run-scale", "0.25", "--view-scale", "0.5", "--no-remove-representations", "--fake-second-source"
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"--run-scale", "0.25", "--view-scale", "0.5", "--no-remove-representations"
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],
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"justMyCode": true
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},
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@ -1,7 +1,8 @@
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# import face_recognition
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from pathlib import Path
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import cv2
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import sys
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import os
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from prettytable import PrettyTable
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# import hjson as json
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@ -16,15 +17,17 @@ args = None
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def main():
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global objects_and_peoples
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global args
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args = argparser.parse_args()
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# Check if a CUDA GPU is available. If it is, set it via torch. If not, set it to cpu
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# https://github.com/ultralytics/ultralytics/issues/3084#issuecomment-1732433168
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# Currently, I have been unable to set up Poetry to use GPU for Torch
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for i in range(torch.cuda.device_count()):
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print(f"Using {torch.cuda.get_device_properties(i).name} for pytorch")
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print(f'Using {torch.cuda.get_device_properties(i).name} for pytorch')
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if torch.cuda.is_available():
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torch.cuda.set_device(0)
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print("Set CUDA device")
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@ -35,10 +38,9 @@ def main():
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if args.force_disable_tensorflow_gpu:
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print("Forcing tensorflow to use CPU")
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import tensorflow as tf
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tf.config.set_visible_devices([], "GPU")
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if tf.config.experimental.list_logical_devices("GPU"):
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print("GPU disabled unsuccessfully")
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tf.config.set_visible_devices([], 'GPU')
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if tf.config.experimental.list_logical_devices('GPU'):
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print('GPU disabled unsuccessfully')
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else:
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print("GPU disabled successfully")
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@ -48,24 +50,15 @@ def main():
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# Set the video capture to the appropriate source
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if not args.rtsp_url and not args.capture_device:
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print("No stream or capture device set, defaulting to capture device 0")
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video_sources = {"devices": [cv2.VideoCapture(0)]}
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video_sources = {
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"devices": [cv2.VideoCapture(0)]
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}
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else:
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video_sources = {
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"streams": [cv2.VideoCapture(url) for url in args.rtsp_url],
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"devices": [cv2.VideoCapture(device) for device in args.capture_device],
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}
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if args.fake_second_source:
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try:
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video_sources["devices"].append(video_sources["devices"][0])
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except KeyError:
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print("No capture device to use as second source. Trying stream.")
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try:
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video_sources["devices"].append(video_sources["devices"][0])
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except KeyError:
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print("No stream to use as a second source")
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# When the code tries to resize the nonexistent capture device 1, the program will fail
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# Eliminate lag by setting the buffer size to 1
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# This makes it so that the video capture will only grab the most recent frame
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# However, this means that the video may be choppy
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@ -81,54 +74,40 @@ def main():
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pretty_table = PrettyTable(field_names=["Source Type", "Resolution"])
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for source_type, sources in video_sources.items():
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for source in sources:
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if (
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source.get(cv2.CAP_PROP_FRAME_WIDTH) == 0
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or source.get(cv2.CAP_PROP_FRAME_HEIGHT) == 0
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):
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message = "Capture for a source failed as resolution is 0x0.\n"
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if source_type == "streams":
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message += "Check if the stream URL is correct and if the stream is online."
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else:
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message += "Check if the capture device is connected, working, and not in use by another program."
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print(message)
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sys.exit(1)
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pretty_table.add_row(
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[
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source_type,
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f"{source.get(cv2.CAP_PROP_FRAME_WIDTH)}x{source.get(cv2.CAP_PROP_FRAME_HEIGHT)}",
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]
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[source_type, f"{source.get(cv2.CAP_PROP_FRAME_WIDTH)}x{source.get(cv2.CAP_PROP_FRAME_HEIGHT)}"]
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)
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print(pretty_table)
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print
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print("Beginning video capture...")
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while True:
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# Grab a single frame of video
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frames = []
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# frames = [source.read() for sources in video_sources.values() for source in sources]
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for list_of_sources in video_sources.values():
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frames.extend([source.read()[1] for source in list_of_sources])
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frames_to_show = []
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for frame in frames:
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frames_to_show.append(
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utils.process_footage(
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frame=frame,
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run_scale=args.run_scale,
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view_scale=args.view_scale,
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ret, frame = video_capture.read()
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frame_to_show = utils.process_footage(
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frame = frame,
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run_scale = args.run_scale,
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view_scale = args.view_scale,
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faces_directory=Path(args.faces_directory),
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face_confidence_threshold=args.face_confidence_threshold,
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no_remove_representations=args.no_remove_representations,
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detection_window=args.detection_window,
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detection_duration=args.detection_duration,
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notification_window=args.notification_window,
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ntfy_url=args.ntfy_url,
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model=model,
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detect_object=args.detect_object,
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object_confidence_threshold=args.object_confidence_threshold,
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)
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)
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# Display the resulting frame
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# TODO: When multi-camera support is added, this needs to be changed to allow all feeds
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if not args.no_display:
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for i, frame_to_show in enumerate(frames_to_show):
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cv2.imshow(f"Video {i}", frame_to_show)
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cv2.imshow("Video", frame_to_show)
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# Hit 'q' on the keyboard to quit!
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if cv2.waitKey(1) & 0xFF == ord("q"):
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@ -136,7 +115,7 @@ def main():
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# Release handle to the webcam
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print("Releasing video capture")
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[source.release() for sources in video_sources.values() for source in sources]
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video_capture.release()
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cv2.destroyAllWindows()
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@ -15,14 +15,16 @@ def set_argparse():
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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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epilog=":)",
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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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@ -30,9 +32,7 @@ def set_argparse():
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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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default=[os.environ["RTSP_URL"]] if "RTSP_URL" in os.environ and os.environ["RTSP_URL"] != "" else [],
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type=str,
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help="RTSP camera URL",
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)
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@ -41,9 +41,7 @@ def set_argparse():
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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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default=[int(os.environ["CAPTURE_DEVICE"])] if "CAPTURE_DEVICE" in os.environ and os.environ["CAPTURE_DEVICE"] != "" else [],
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type=int,
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help="Capture device number",
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)
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@ -71,20 +69,16 @@ def set_argparse():
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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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if "NO_DISPLAY" in os.environ and os.environ["NO_DISPLAY"] != ""
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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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'-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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if "FORCE_DISABLE_TENSORFLOW_GPU" in os.environ and os.environ["FORCE_DISABLE_TENSORFLOW_GPU"] != ""
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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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@ -128,6 +122,7 @@ def set_argparse():
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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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@ -151,12 +146,13 @@ def set_argparse():
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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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@ -171,25 +167,11 @@ def set_argparse():
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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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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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@ -2,9 +2,8 @@ import cv2
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import os
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import numpy as np
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from pathlib import Path
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# https://stackoverflow.com/a/42121886/18270659
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
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os.environ['TF_CPP_MIN_LOG_LEVEL']='3'
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from deepface import DeepFace # noqa: E402
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@ -12,7 +11,7 @@ from . import notify # noqa: E402
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first_face_try = True
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# TODO: When multi-camera support is ~~added~~ improved, this will need to be changed so that each camera has its own dict
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# TODO: When multi-camera support is added, this will need to be changed so that each camera has its own dict
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objects_and_peoples = {
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"objects": {},
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"peoples": {},
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@ -22,27 +21,36 @@ objects_and_peoples = {
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def process_footage(
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# Frame
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frame: np.ndarray = None,
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# scale
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run_scale: float = None,
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view_scale: float = None,
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# Face stuff
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faces_directory: str = None,
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face_confidence_threshold: float = None,
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no_remove_representations: bool = False,
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# Timer stuff
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detection_window: int = None,
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detection_duration: int = None,
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notification_window: int = None,
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ntfy_url: str = None,
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# Object stuff
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# YOLO object
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model=None,
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model = None,
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detect_object: list = None,
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object_confidence_threshold=None,
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object_confidence_threshold = None
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) -> np.ndarray:
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"""Takes in a frame and processes it"""
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"""
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Takes in a frame and processes it
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"""
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global objects_and_peoples
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# Resize frame of video to a smaller size for faster recognition processing
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run_frame = cv2.resize(frame, (0, 0), fx=run_scale, fy=run_scale)
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# view_frame = cv2.resize(frame, (0, 0), fx=args.view_scale, fy=args.view_scale)
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@ -52,7 +60,7 @@ def process_footage(
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path_to_faces = Path(faces_directory)
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path_to_faces_exists = path_to_faces.is_dir()
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for r in results:
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for i, r in enumerate(results):
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# list of dicts with each dict containing a label, x1, y1, x2, y2
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plot_boxes = []
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@ -97,8 +105,7 @@ def process_footage(
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# Also, make sure that the objects to detect are in the list of objects_and_peoples
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# If it isn't, print a warning
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for obj in detect_object:
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# .keys() shouldn't be needed
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if obj not in objects_and_peoples["objects"]:
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if obj not in objects_and_peoples["objects"].keys():
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print(
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f"Warning: {obj} is not in the list of objects the model can detect!"
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)
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@ -147,6 +154,7 @@ def process_footage(
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ntfy_url=ntfy_url,
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)
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# To debug plotting, use r.plot() to cross reference the bounding boxes drawn by the plot_label() and r.plot()
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frame_to_show = plot_label(
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boxes=plot_boxes,
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@ -230,8 +238,7 @@ def recognize_face(
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Returns a single dictonary as currently only 1 face can be detected in each frame
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Cosine threshold is 0.3, so if the confidence is less than that, it will return None
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dict conta # Maybe use os.exit() instead?
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ins the following keys: label, x1, y1, x2, y2
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dict contains the following keys: label, x1, y1, x2, y2
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The directory should be structured as follows:
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faces/
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name/
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|
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@ -278,11 +285,8 @@ def recognize_face(
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model_name="ArcFace",
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detector_backend="opencv",
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||||
)
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'''
|
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Example dataframe, for reference
|
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identity (path to image) | source_x | source_y | source_w | source_h | VGG-Face_cosine (pretty much the confidence \\_('_')_/)
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'''
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||||
except ValueError as e:
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||||
|
||||
except (ValueError) as e:
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||||
if (
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||||
str(e)
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||||
== "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False." # noqa: E501
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||||
|
|
@ -291,8 +295,7 @@ def recognize_face(
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return None
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||||
elif (
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||||
# Check if the error message contains "Validate .jpg or .png files exist in this path."
|
||||
"Validate .jpg or .png files exist in this path."
|
||||
in str(e)
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||||
"Validate .jpg or .png files exist in this path." in str(e)
|
||||
):
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||||
# If a verbose/silent flag is added, this should be changed to print only if verbose is true
|
||||
# print("No faces found in database")
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||||
|
|
@ -335,3 +338,8 @@ def recognize_face(
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|||
)
|
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return to_return
|
||||
return None
|
||||
|
||||
"""
|
||||
Example dataframe, for reference
|
||||
identity (path to image) | source_x | source_y | source_w | source_h | VGG-Face_cosine (pretty much the confidence \_('_')_/)
|
||||
"""
|
||||
|
|
|
|||
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