Updated TODO comments

Resolved issues in wyzely_detect/utils/utils.py with DeepSource Autofix

.python-version

@@ -1 +1 @@
-3.11.5
+3.10.12

.vscode/launch.json

@@ -10,7 +10,7 @@
             "request": "launch",
             "module": "wyzely_detect",
             "args": [
-                "--run-scale", "0.25", "--view-scale", "0.5", "--no-remove-representations"
+                "--run-scale", "0.25", "--view-scale", "0.5", "--no-remove-representations", "--fake-second-source"
             ],
             "justMyCode": true
         },

wyzely_detect/__main__.py

@@ -1,8 +1,7 @@
 # import face_recognition
 from pathlib import Path
 import cv2
-import os
-
+import sys
 from prettytable import PrettyTable
 
 # import hjson as json
@@ -17,9 +16,7 @@ args = None
 
 
 def main():
-    global objects_and_peoples
-    global args 
-
+    global args
 
     args = argparser.parse_args()
 
@@ -27,7 +24,7 @@ def main():
     # https://github.com/ultralytics/ultralytics/issues/3084#issuecomment-1732433168
     # Currently, I have been unable to set up Poetry to use GPU for Torch
     for i in range(torch.cuda.device_count()):
-        print(f'Using {torch.cuda.get_device_properties(i).name} for pytorch')
+        print(f"Using {torch.cuda.get_device_properties(i).name} for pytorch")
     if torch.cuda.is_available():
         torch.cuda.set_device(0)
         print("Set CUDA device")
@@ -38,9 +35,10 @@ def main():
     if args.force_disable_tensorflow_gpu:
         print("Forcing tensorflow to use CPU")
         import tensorflow as tf
-        tf.config.set_visible_devices([], 'GPU')
-        if tf.config.experimental.list_logical_devices('GPU'):
-            print('GPU disabled unsuccessfully')
+
+        tf.config.set_visible_devices([], "GPU")
+        if tf.config.experimental.list_logical_devices("GPU"):
+            print("GPU disabled unsuccessfully")
         else:
             print("GPU disabled successfully")
 
@@ -50,15 +48,24 @@ def main():
     # Set the video capture to the appropriate source
     if not args.rtsp_url and not args.capture_device:
         print("No stream or capture device set, defaulting to capture device 0")
-        video_sources = {
-            "devices": [cv2.VideoCapture(0)]
-        }
+        video_sources = {"devices": [cv2.VideoCapture(0)]}
     else:
         video_sources = {
             "streams": [cv2.VideoCapture(url) for url in args.rtsp_url],
             "devices": [cv2.VideoCapture(device) for device in args.capture_device],
         }
 
+    if args.fake_second_source:
+        try:
+            video_sources["devices"].append(video_sources["devices"][0])
+        except KeyError:
+            print("No capture device to use as second source. Trying stream.")
+            try:
+                video_sources["devices"].append(video_sources["devices"][0])
+            except KeyError:
+                print("No stream to use as a second source")
+                # When the code tries to resize the nonexistent capture device 1, the program will fail
+
     # Eliminate lag by setting the buffer size to 1
     # This makes it so that the video capture will only grab the most recent frame
     # However, this means that the video may be choppy
@@ -74,40 +81,54 @@ def main():
     pretty_table = PrettyTable(field_names=["Source Type", "Resolution"])
     for source_type, sources in video_sources.items():
         for source in sources:
+            if (
+                source.get(cv2.CAP_PROP_FRAME_WIDTH) == 0
+                or source.get(cv2.CAP_PROP_FRAME_HEIGHT) == 0
+            ):
+                message = "Capture for a source failed as resolution is 0x0.\n"
+                if source_type == "streams":
+                    message += "Check if the stream URL is correct and if the stream is online."
+                else:
+                    message += "Check if the capture device is connected, working, and not in use by another program."
+                print(message)
+                sys.exit(1)
             pretty_table.add_row(
-                [source_type, f"{source.get(cv2.CAP_PROP_FRAME_WIDTH)}x{source.get(cv2.CAP_PROP_FRAME_HEIGHT)}"]
+                [
+                    source_type,
+                    f"{source.get(cv2.CAP_PROP_FRAME_WIDTH)}x{source.get(cv2.CAP_PROP_FRAME_HEIGHT)}",
+                ]
             )
     print(pretty_table)
-
-    print
     print("Beginning video capture...")
     while True:
         # Grab a single frame of video
-        ret, frame = video_capture.read()
-
-        frame_to_show = utils.process_footage(
-            frame = frame,
-            run_scale = args.run_scale,
-            view_scale = args.view_scale,
-
-            faces_directory=Path(args.faces_directory),
-            face_confidence_threshold=args.face_confidence_threshold,
-            no_remove_representations=args.no_remove_representations,
-
-            detection_window=args.detection_window,
-            detection_duration=args.detection_duration,
-            notification_window=args.notification_window,
-
-            ntfy_url=args.ntfy_url,
-
-            model=model,
-            detect_object=args.detect_object,
-            object_confidence_threshold=args.object_confidence_threshold,
-        )
+        frames = []
+        # frames = [source.read() for sources in video_sources.values() for source in sources]
+        for list_of_sources in video_sources.values():
+            frames.extend([source.read()[1] for source in list_of_sources])
+        frames_to_show = []
+        for frame in frames:
+            frames_to_show.append(
+                utils.process_footage(
+                    frame=frame,
+                    run_scale=args.run_scale,
+                    view_scale=args.view_scale,
+                    faces_directory=Path(args.faces_directory),
+                    face_confidence_threshold=args.face_confidence_threshold,
+                    no_remove_representations=args.no_remove_representations,
+                    detection_window=args.detection_window,
+                    detection_duration=args.detection_duration,
+                    notification_window=args.notification_window,
+                    ntfy_url=args.ntfy_url,
+                    model=model,
+                    detect_object=args.detect_object,
+                    object_confidence_threshold=args.object_confidence_threshold,
+                )
+            )
         # Display the resulting frame
-        # TODO: When multi-camera support is added, this needs to be changed to allow all feeds
         if not args.no_display:
-            cv2.imshow("Video", frame_to_show)
+            for i, frame_to_show in enumerate(frames_to_show):
+                cv2.imshow(f"Video {i}", frame_to_show)
 
         # Hit 'q' on the keyboard to quit!
         if cv2.waitKey(1) & 0xFF == ord("q"):
@@ -115,7 +136,7 @@ def main():
 
     # Release handle to the webcam
     print("Releasing video capture")
-    video_capture.release()
+    [source.release() for sources in video_sources.values() for source in sources]
     cv2.destroyAllWindows()
 
 

wyzely_detect/utils/cli_args.py

@@ -15,16 +15,14 @@ def set_argparse():
     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=":)",
+        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(
@@ -32,7 +30,9 @@ def set_argparse():
         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 [],
+        default=[os.environ["RTSP_URL"]]
+        if "RTSP_URL" in os.environ and os.environ["RTSP_URL"] != ""
+        else [],
         type=str,
         help="RTSP camera URL",
     )
@@ -41,7 +41,9 @@ def set_argparse():
         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 [],
+        default=[int(os.environ["CAPTURE_DEVICE"])]
+        if "CAPTURE_DEVICE" in os.environ and os.environ["CAPTURE_DEVICE"] != ""
+        else [],
         type=int,
         help="Capture device number",
     )
@@ -69,16 +71,20 @@ def set_argparse():
     video_options.add_argument(
         "--no-display",
         default=os.environ["NO_DISPLAY"]
-        if "NO_DISPLAY" in os.environ and 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',
+        "-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"] != ""
+        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",
@@ -122,7 +128,6 @@ def set_argparse():
         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",
@@ -146,13 +151,12 @@ def set_argparse():
         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_<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",
@@ -167,11 +171,25 @@ def set_argparse():
         default=os.environ["OBJECT_CONFIDENCE_THRESHOLD"]
         if "OBJECT_CONFIDENCE_THRESHOLD" in os.environ
         and os.environ["OBJECT_CONFIDENCE_THRESHOLD"] != ""
-        else 0.6,
+        # 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
 
 

wyzely_detect/utils/utils.py

@@ -2,16 +2,17 @@ import cv2
 import os
 import numpy as np
 from pathlib import Path
+
 # https://stackoverflow.com/a/42121886/18270659
-os.environ['TF_CPP_MIN_LOG_LEVEL']='3'
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 
 
-from deepface import DeepFace # noqa: E402
-from . import notify # noqa: E402
+from deepface import DeepFace  # noqa: E402
+from . import notify  # noqa: E402
 
 first_face_try = True
 
-# TODO: When multi-camera support is added, this will need to be changed so that each camera has its own dict
+# TODO: When multi-camera support is ~~added~~ improved, this will need to be changed so that each camera has its own dict
 objects_and_peoples = {
     "objects": {},
     "peoples": {},
@@ -21,36 +22,27 @@ objects_and_peoples = {
 def process_footage(
     # Frame
     frame: np.ndarray = None,
-
     # scale
     run_scale: float = None,
     view_scale: float = None,
-
     # Face stuff
     faces_directory: str = None,
     face_confidence_threshold: float = None,
     no_remove_representations: bool = False,
-
     # Timer stuff
     detection_window: int = None,
     detection_duration: int = None,
     notification_window: int = None,
-
     ntfy_url: str = None,
-
     # Object stuff
     # YOLO object
-    model = None,
+    model=None,
     detect_object: list = None,
-    object_confidence_threshold = None
+    object_confidence_threshold=None,
 ) -> np.ndarray:
-    """
-    Takes in a frame and processes it
-    """ 
-
+    """Takes in a frame and processes it"""
     global objects_and_peoples
 
-    
     # Resize frame of video to a smaller size for faster recognition processing
     run_frame = cv2.resize(frame, (0, 0), fx=run_scale, fy=run_scale)
     # view_frame = cv2.resize(frame, (0, 0), fx=args.view_scale, fy=args.view_scale)
@@ -60,7 +52,7 @@ def process_footage(
     path_to_faces = Path(faces_directory)
     path_to_faces_exists = path_to_faces.is_dir()
 
-    for i, r in enumerate(results):
+    for r in results:
         # list of dicts with each dict containing a label, x1, y1, x2, y2
         plot_boxes = []
 
@@ -105,7 +97,8 @@ def process_footage(
             # Also, make sure that the objects to detect are in the list of objects_and_peoples
             # If it isn't, print a warning
             for obj in detect_object:
-                if obj not in objects_and_peoples["objects"].keys():
+                # .keys() shouldn't be needed
+                if obj not in objects_and_peoples["objects"]:
                     print(
                         f"Warning: {obj} is not in the list of objects the model can detect!"
                     )
@@ -153,7 +146,6 @@ def process_footage(
                 notification_window=notification_window,
                 ntfy_url=ntfy_url,
             )
-            
 
         # To debug plotting, use r.plot() to cross reference the bounding boxes drawn by the plot_label() and r.plot()
         frame_to_show = plot_label(
@@ -233,26 +225,27 @@ def recognize_face(
     no_remove_representations: bool = False,
 ) -> np.ndarray:
     """
-    Accepts a path to a directory of images of faces to be used as a refference
-    In addition, accepts an opencv image to be used as the frame to be searched
+        Accepts a path to a directory of images of faces to be used as a refference
+        In addition, accepts an opencv image to be used as the frame to be searched
 
-    Returns a single dictonary as currently only 1 face can be detected in each frame
-    Cosine threshold is 0.3, so if the confidence is less than that, it will return None
-    dict contains the following keys: label, x1, y1, x2, y2
-    The directory should be structured as follows:
-    faces/
-        name/
-            image1.jpg
-            image2.jpg
-            image3.jpg
-        name2/
-            image1.jpg
-            image2.jpg
-            image3.jpg
-    (not neccessarily jpgs, but you get the idea)
+        Returns a single dictonary as currently only 1 face can be detected in each frame
+        Cosine threshold is 0.3, so if the confidence is less than that, it will return None
+        dict conta                # Maybe use os.exit() instead?
+    ins the following keys: label, x1, y1, x2, y2
+        The directory should be structured as follows:
+        faces/
+            name/
+                image1.jpg
+                image2.jpg
+                image3.jpg
+            name2/
+                image1.jpg
+                image2.jpg
+                image3.jpg
+        (not neccessarily jpgs, but you get the idea)
 
-    Point is, `name` is the name of the person in the images in the directory `name`
-    That name will be used as the label for the face in the frame
+        Point is, `name` is the name of the person in the images in the directory `name`
+        That name will be used as the label for the face in the frame
     """
     global first_face_try
 
@@ -285,8 +278,11 @@ def recognize_face(
             model_name="ArcFace",
             detector_backend="opencv",
         )
-    
-    except (ValueError) as e:
+        '''
+        Example dataframe, for reference
+        identity  (path to image) | source_x | source_y | source_w | source_h | VGG-Face_cosine (pretty much the confidence \\_('_')_/) 
+        '''
+    except ValueError as e:
         if (
             str(e)
             == "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
@@ -295,11 +291,12 @@ def recognize_face(
             return None
         elif (
             # 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)
+            "Validate .jpg or .png files exist in this path."
+            in str(e)
         ):
             # If a verbose/silent flag is added, this should be changed to print only if verbose is true
             # print("No faces found in database")
-            return None 
+            return None
         else:
             raise e
     # Iteate over the dataframes
@@ -338,8 +335,3 @@ def recognize_face(
         )
         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 \_('_')_/) 
-    """