Merge pull request #7 from slashtechno/improve-argparse-organization

Improve argparse organization

wyzely_detect/__main__.py

@@ -1,17 +1,14 @@
 # import face_recognition
-import cv2
-import dotenv
 from pathlib import Path
-import os
+
+import cv2
 
 # import hjson as json
 import torch
 from ultralytics import YOLO
 
-import argparse
+from .utils import notify, utils
-
+from .utils.cli_args import argparser
-from .utils import notify
-from .utils import utils
 
 DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
 args = None
@@ -27,137 +24,6 @@ def main():
     global args
     # RUN_BY_COMPOSE = os.getenv("RUN_BY_COMPOSE") # Replace this with code to check for gpu
 
-    if Path(".env").is_file():
-        dotenv.load_dotenv()
-        print("Loaded .env file")
-    else:
-        print("No .env file found")
-
-    # TODO: If possible, move the argparse stuff to a separate file
-    # It's taking up too many lines in this file
-    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=":)",
-    )
-
-    # required='RUN_SCALE' not in os.environ,
-
-    argparser.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",
-    )
-    argparser.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",
-    )
-
-    argparser.add_argument(
-        "--no-display",
-        default=os.environ["NO_DISPLAY"]
-        if "NO_DISPLAY" in os.environ and os.environ["NO_DISPLAY"] != ""
-        else False,
-        action="store_true",
-        help="Don't display the video feed",
-    )
-
-    argparser.add_argument(
-        "--confidence-threshold",
-        default=os.environ["CONFIDENCE_THRESHOLD"]
-        if "CONFIDENCE_THRESHOLD" in os.environ
-        and os.environ["CONFIDENCE_THRESHOLD"] != ""
-        else 0.6,
-        type=float,
-        help="The confidence threshold to use",
-    )
-
-    argparser.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
-    )
-    argparser.add_argument(
-        "--detect-object",
-        nargs="*",
-        default=[],
-        type=str,
-        help="The object(s) to detect. Must be something the model is trained to detect",
-    )
-
-    stream_source = argparser.add_mutually_exclusive_group()
-    stream_source.add_argument(
-        "--url",
-        default=os.environ["URL"]
-        if "URL" in os.environ and os.environ["URL"] != ""
-        else None,  # noqa: E501
-        type=str,
-        help="The URL of the stream to use",
-    )
-    stream_source.add_argument(
-        "--capture-device",
-        default=os.environ["CAPTURE_DEVICE"]
-        if "CAPTURE_DEVICE" in os.environ and os.environ["CAPTURE_DEVICE"] != ""
-        else 0,  # noqa: E501
-        type=int,
-        help="The capture device to use. Can also be a url.",
-    )
-
-    # Defaults for the stuff here and down are already set in notify.py.
-    # Setting them here just means that argparse will display the default values as defualt
-    # TODO: Perhaps just remove the default parameter and just add to the help message that the default is set is x
-    # TODO: Make ntfy optional in ntfy.py. Currently, unless there is a local or LAN instance of ntfy, this can't run offline
-    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 "https://ntfy.sh/wyzely-detect",
-        type=str,
-        help="The URL to send notifications to",
-    )
-
-    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",
-    )
-
     args = argparser.parse_args()
 
     # Check if a CUDA GPU is available. If it is, set it via torch. If not, set it to cpu
@@ -175,8 +41,8 @@ def main():
 
     # Depending on if the user wants to use a stream or a capture device,
     # Set the video capture to the appropriate source
-    if args.url:
+    if args.rtsp_url is not None:
-        video_capture = cv2.VideoCapture(args.url)
+        video_capture = cv2.VideoCapture(args.rtsp_url)
     else:
         video_capture = cv2.VideoCapture(args.capture_device)
 
@@ -216,7 +82,10 @@ def main():
             # May be better to check every iteration, but this also works
             if path_to_faces_exists:
                 if face_details := utils.recognize_face(
-                    path_to_directory=path_to_faces, run_frame=run_frame
+                    path_to_directory=path_to_faces,
+                    run_frame=run_frame,
+                    min_confidence=args.face_confidence_threshold,
+                    no_remove_representations=args.no_remove_representations,
                 ):
                     plot_boxes.append(face_details)
                     objects_and_peoples = notify.thing_detected(
@@ -265,7 +134,7 @@ def main():
                 # print("---")
 
                 # Now do stuff (if conf > 0.5)
-                if conf < args.confidence_threshold or (
+                if conf < args.object_confidence_threshold or (
                     class_id not in args.detect_object and args.detect_object != []
                 ):
                     # If the confidence is too low

wyzely_detect/utils/cli_args.py

@@ -0,0 +1,167 @@
+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=":)",
+    )
+
+
+    video_options = argparser.add_argument_group("Video Options")
+    stream_source = video_options.add_mutually_exclusive_group()
+    stream_source.add_argument(
+        "--rtsp-url",
+        default=os.environ["RTSP_URL"]
+        if "RTSP_URL" in os.environ and os.environ["RTSP_URL"] != ""
+        else None,  # noqa: E501
+        type=str,
+        help="RTSP camera URL",
+    )
+    stream_source.add_argument(
+        "--capture-device",
+        default=os.environ["CAPTURE_DEVICE"]
+        if "CAPTURE_DEVICE" in os.environ and os.environ["CAPTURE_DEVICE"] != ""
+        else 0,  # noqa: E501
+        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"] != ""
+        else False,
+        action="store_true",
+        help="Don't display the video feed",
+    )
+
+
+    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",
+    )
+
+    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"] != ""
+        else False,
+        action="store_true",
+        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
+    )
+
+
+
+    object_detection = argparser.add_argument_group("Object Detection options")
+    object_detection.add_argument(
+        "--detect-object",
+        nargs="*",
+        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"] != ""
+        else 0.6,
+        type=float,
+        help="The confidence threshold to use",
+    )
+
+    # return argparser
+
+
+# This will run when this file is imported
+set_argparse()

wyzely_detect/utils/notify.py

@@ -104,6 +104,11 @@ def thing_detected(
         ):
             respective_type[thing_name]["last_notification_time"] = time.time()
             print(f"Detected {thing_name} for {detection_duration} seconds")
+            if ntfy_url is None:
+                print(
+                    "ntfy_url is None. Not sending notification. Set ntfy_url to send notifications"
+                )
+            else:
                 headers = construct_ntfy_headers(
                     title=f"{thing_name} detected",
                     tag="rotating_light",

wyzely_detect/utils/utils.py

@@ -68,6 +68,8 @@ def recognize_face(
     path_to_directory: Path = Path("faces"),
     # opencv image
     run_frame: np.ndarray = None,
+    min_confidence: float = 0.3,
+    no_remove_representations: bool = False,
 ) -> np.ndarray:
     """
     Accepts a path to a directory of images of faces to be used as a refference
@@ -94,13 +96,16 @@ def recognize_face(
     global first_face_try
 
     # If it's the first time the function is being run, remove representations_arcface.pkl, if it exists
-    if first_face_try:
+    if first_face_try and not no_remove_representations:
         try:
             path_to_directory.joinpath("representations_arcface.pkl").unlink()
             print("Removing representations_arcface.pkl")
         except FileNotFoundError:
             print("representations_arcface.pkl does not exist")
         first_face_try = False
+    elif first_face_try and no_remove_representations:
+        print("Not attempting to remove representations_arcface.pkl")
+        first_face_try = False
 
     # face_dataframes is a vanilla list of dataframes
     # It seems face_dataframes is empty if the face database (directory) doesn't exist. Seems to work if it's empty though
@@ -134,7 +139,7 @@ def recognize_face(
         # So we can just grab the path from there
         # iloc = Integer LOCation
         path_to_image = Path(df.iloc[-1]["identity"])
-        # If the parent name is the same as the path to the database, then set label to the image name instead of the parent directory name
+        # If the parent name is the same as the path to the database, then set label to the image name instead of the parent name
         if path_to_image.parent == Path(path_to_directory):
             label = path_to_image.name
         else:
@@ -149,15 +154,13 @@ def recognize_face(
             "y2": df.iloc[-1]["source_y"] + df.iloc[-1]["source_h"],
         }
         # After some brief testing, it seems positive matches are > 0.3
-        distance = df.iloc[-1]["ArcFace_cosine"]
+        cosine_similarity = df.iloc[-1]["ArcFace_cosine"]
-        # TODO: Make this a CLI argument
+        if cosine_similarity < min_confidence:
-        if distance < 0.3:
             return None
-        # if 0.5 < distance < 0.7:
         # label = "Unknown"
         to_return = dict(label=label, **coordinates)
         print(
-            f"Confindence: {distance}, filname: {path_to_image.name}, to_return: {to_return}"
+            f"Cosine similarity: {cosine_similarity}, filname: {path_to_image.name}, to_return: {to_return}"
         )
         return to_return