191 lines
7.3 KiB
Python
191 lines
7.3 KiB
Python
# import face_recognition
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from pathlib import Path
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import cv2
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# import hjson as json
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import torch
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from ultralytics import YOLO
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from .utils import notify, utils
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from .utils.cli_args import argparser
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DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
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args = None
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objects_and_peoples = {
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"objects": {},
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"peoples": {},
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}
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def main():
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global objects_and_peoples
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global args
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# RUN_BY_COMPOSE = os.getenv("RUN_BY_COMPOSE") # Replace this with code to check for gpu
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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(torch.cuda.get_device_properties(i).name)
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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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else:
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print("No CUDA device available, using CPU")
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model = YOLO("yolov8n.pt")
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# Depending on if the user wants to use a stream or a capture device,
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# Set the video capture to the appropriate source
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if args.rtsp_url is not None:
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video_capture = cv2.VideoCapture(args.rtsp_url)
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else:
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video_capture = cv2.VideoCapture(args.capture_device)
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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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video_capture.set(cv2.CAP_PROP_BUFFERSIZE, 1)
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# Print the resolution of the video
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print(
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f"Video resolution: {video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)}x{video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)}" # noqa: E501
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)
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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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ret, frame = video_capture.read()
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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=args.run_scale, fy=args.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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results = model(run_frame, verbose=False)
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path_to_faces = Path(args.faces_directory)
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path_to_faces_exists = path_to_faces.is_dir()
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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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# The following is stuff for people
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# This is still in the for loop as each result, no matter if anything is detected, will be present.
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# Thus, there will always be one result (r)
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# Only run if path_to_faces exists
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# May be better to check every iteration, but this also works
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if path_to_faces_exists:
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if face_details := utils.recognize_face(
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path_to_directory=path_to_faces,
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run_frame=run_frame,
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min_confidence=args.face_confidence_threshold,
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no_remove_representations=args.no_remove_representations,
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):
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plot_boxes.append(face_details)
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objects_and_peoples = notify.thing_detected(
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thing_name=face_details["label"],
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objects_and_peoples=objects_and_peoples,
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detection_type="peoples",
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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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)
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# The following is stuff for objects
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# Setup dictionary of object names
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if (
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objects_and_peoples["objects"] == {}
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or objects_and_peoples["objects"] is None
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):
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for name in r.names.values():
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objects_and_peoples["objects"][name] = {
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"last_detection_time": None,
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"detection_duration": None,
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# "first_detection_time": None,
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"last_notification_time": None,
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}
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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 args.detect_object:
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if obj not in objects_and_peoples:
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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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for box in r.boxes:
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# Get the name of the object
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class_id = r.names[box.cls[0].item()]
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# Get the coordinates of the object
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cords = box.xyxy[0].tolist()
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cords = [round(x) for x in cords]
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# Get the confidence
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conf = round(box.conf[0].item(), 2)
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# Print it out, adding a spacer between each object
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# print("Object type:", class_id)
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# print("Coordinates:", cords)
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# print("Probability:", conf)
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# print("---")
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# Now do stuff (if conf > 0.5)
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if conf < args.object_confidence_threshold or (
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class_id not in args.detect_object and args.detect_object != []
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):
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# If the confidence is too low
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# or if the object is not in the list of objects to detect and the list of objects to detect is not empty
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# then skip this iteration
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continue
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# Add the object to the list of objects to plot
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plot_boxes.append(
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{
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"label": class_id,
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"x1": cords[0],
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"y1": cords[1],
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"x2": cords[2],
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"y2": cords[3],
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}
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)
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objects_and_peoples = notify.thing_detected(
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thing_name=class_id,
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objects_and_peoples=objects_and_peoples,
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detection_type="objects",
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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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)
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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 = utils.plot_label(
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boxes=plot_boxes,
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full_frame=frame,
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# full_frame=r.plot(),
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run_scale=args.run_scale,
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view_scale=args.view_scale,
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)
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# Display the resulting frame
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# cv2.imshow("", r)
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if not args.no_display:
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cv2.imshow(f"Video{i}", 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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break
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# Release handle to the webcam
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print("Releasing video capture")
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video_capture.release()
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cv2.destroyAllWindows()
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if __name__ == "__main__":
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main()
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