Check if face directory exists; switch to ArcFace
Other fixes: * Improve error handling during face recognition * Change VS Code run configuration name * Update `deepface-test.ipynb` * Reset version to `0.1.0` (not pushed to PyPi yet)
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GPG Key ID:
8EC1D9D9286C2B17
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@ -5,7 +5,8 @@
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"version": "0.2.0",
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"configurations": [
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{
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"name": "Python: Module",
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// "name": "Python: Module",
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"name": "Debug Wyzely Detect",
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"type": "python",
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"request": "launch",
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"module": "wyzely_detect",
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@ -36,12 +36,12 @@
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"# cv2.imwrite(str(uuid_path), frame)\n",
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"# dfs = DeepFace.find(img_path=str(uuid_path), db_path = \"faces\")\n",
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"# Don't throw an error if no face is detected (enforce_detection=False)\n",
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"dfs = DeepFace.find(frame, db_path = \"faces\", enforce_detection=False)\n",
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"dfs = DeepFace.find(frame, db_path = \"faces\", enforce_detection=False, silent=False, model_name=\"ArcFace\", detector_backend=\"opencv\")\n",
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"# Get the identity of the person\n",
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"for i, pd_dataframe in enumerate(dfs):\n",
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" # Sort the dataframe by confidence\n",
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" # inplace=True means that the dataframe is modified so we don't need to assign it to a new variable\n",
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" pd_dataframe.sort_values(by=['VGG-Face_cosine'], inplace=True, ascending=False)\n",
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" # pd_dataframe.sort_values(by=['model_name=\"ArcFace\", detector_backend=\"opencv\")'], inplace=True, ascending=False)\n",
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" print(f'On dataframe {i}')\n",
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" print(pd_dataframe)\n",
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" # Get the most likely identity\n",
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@ -49,7 +49,7 @@
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" # We could use Path to get the parent directory of the image to use as the identity\n",
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" print(f'Most likely identity: {Path(pd_dataframe.iloc[0][\"identity\"]).parent.name}')\n",
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" # Get the most likely identity's confidence\n",
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" print(f'Confidence: {pd_dataframe.iloc[0][\"VGG-Face_cosine\"]}')\n",
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" print(f'Confidence: {pd_dataframe.iloc[0][\"model_name=\"ArcFace\", detector_backend=\"opencv\")\"]}')\n",
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"\n",
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"# uuid_path.unlink()"
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]
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@ -67,7 +67,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"DeepFace.stream(db_path=\"faces\")"
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"DeepFace.stream(db_path=\"faces\", model_name=\"ArcFace\", detector_backend=\"opencv\")"
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]
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}
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],
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@ -1,6 +1,6 @@
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[tool.poetry]
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name = "wyzely-detect"
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version = "0.1.8"
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version = "0.1.0"
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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"
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authors = ["slashtechno <77907286+slashtechno@users.noreply.github.com>"]
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license = "MIT"
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@ -33,6 +33,8 @@ def main():
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else:
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print("No .env file found")
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# TODO: If possible, move the argparse stuff to a separate file
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# It's taking up too many lines in this file
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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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@ -87,7 +89,7 @@ def main():
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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. Should contain 1 subdirectory of images per person",
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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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argparser.add_argument(
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"--detect-object",
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@ -118,7 +120,7 @@ def main():
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# Defaults for the stuff here and down are already set in notify.py.
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# Setting them here just means that argparse will display the default values as defualt
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# TODO: Perhaps just remove the default parameter and just add to the help message that the default is set is x
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# TODO: Make ntfy optional in ntfy.py
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# TODO: Make ntfy optional in ntfy.py. Currently, unless there is a local or LAN instance of ntfy, this can't run offline
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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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@ -198,6 +200,10 @@ def main():
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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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@ -205,20 +211,24 @@ def main():
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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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# TODO: Make it so this only runs if the faces directory is not empty
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if face_details := utils.recognize_face(
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path_to_directory=Path(args.faces_directory), run_frame=run_frame
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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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# 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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):
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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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@ -92,38 +92,53 @@ def recognize_face(
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"""
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global first_face_try
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# If it's the first time the function is being run, remove representations_vgg_face.pkl, if it exists
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# If it's the first time the function is being run, remove representations_arcface.pkl, if it exists
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if first_face_try:
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try:
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Path("representations_vgg_face.pkl").unlink()
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print("Removing representations_vgg_face.pkl")
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path_to_directory.joinpath("representations_arcface.pkl").unlink()
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print("Removing representations_arcface.pkl")
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except FileNotFoundError:
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pass
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print("representations_arcface.pkl does not exist")
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first_face_try = False
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# For debugging
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# if path_to_directory.joinpath("representations_arcface.pkl").exists():
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# print("representations_arcface.pkl exists")
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# else:
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# print("representations_arcface.pkl does not exist")
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# face_dataframes is a vanilla list of dataframes
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face_dataframes = []
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# It seems face_dataframes is empty if the face database (directory) doesn't exist. Seems to work if it's empty though
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# This line is here to prevent a crash if that happens. However, there is a check in __main__ so it shouldn't happen
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face_dataframes = []
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try:
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face_dataframes = DeepFace.find(
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run_frame,
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db_path=str(path_to_directory),
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enforce_detection=True,
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silent=True,
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)
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model_name="ArcFace", detector_backend="opencv"
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)
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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."
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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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):
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# print("No faces recognized") # For debugging
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return None
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else:
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raise e
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# Iteate over the dataframes
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for df in face_dataframes:
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# The last row is the highest confidence
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# So we can just grab the path from there
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# iloc = Integer LOCation
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path_to_image = Path(df.iloc[-1]["identity"])
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# Get the name of the parent directory
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label = path_to_image.parent.name
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# 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
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if path_to_image.parent == Path(path_to_directory):
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label = path_to_image.name
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else:
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label = path_to_image.parent.name
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# Return the coordinates of the box in xyxy format, rather than xywh
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# This is because YOLO uses xyxy, and that's how plot_label expects
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# Also, xyxy is just the top left and bottom right corners of the box
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@ -135,7 +150,7 @@ def recognize_face(
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}
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# After some brief testing, it seems positve matches are > 0.3
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# I have not seen any false positives, so there is no threashold yet
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distance = df.iloc[-1]["VGG-Face_cosine"]
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distance = df.iloc[-1]["ArcFace_cosine"]
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# if 0.5 < distance < 0.7:
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# label = "Unknown"
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to_return = dict(label=label, **coordinates)
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