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wyzely-detect
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Merge pull request #11 from slashtechno/multi-camera-support 

Added support for multiple video sources
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slashtechno 2024-02-16 13:20:31 -06:00 committed by GitHub
parent 5c1a22fa72 f7f5db9f41
commit d56cee6751
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8 changed files with 1128 additions and 1034 deletions
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2
.python-version
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@ -1 +1 @@
3.10.5
3.10.12

12
.vscode/launch.json vendored
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@ -10,10 +10,20 @@
"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
},
// {
// "name": "Quick, Specific Debug",
// "type": "python",
// "request": "launch",
// "module": "wyzely_detect",
// "args": [
// "--run-scale", "0.25", "--view-scale", "0.5", "--no-remove-representations", "--detect-object", "person", "--detect-object", "cell phone"
// ],
// "justMyCode": true
// },
{
// "name": "Python: Module",
"name": "Full Debug",

4
README.md
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@ -16,6 +16,9 @@ Recognize faces/objects in a video stream (from a webcam or a security camera) a
- All RTSP feeds _should_ work, however.
- Python 3.10 or 3.11
- Poetry (optional)
- Windows or Linux
- I've tested this on MacOS - it works on my 2014 MacBook Air but not a 2011 MacBook Pro
- Both were upgraded with OpenCore, with the MacBook Air running Monterey and the MacBook Pro running a newer version of MacOS, which may have been the problem
### Docker
- A Wyze Cam
@ -46,6 +49,7 @@ This assumes you have Python 3.10 or 3.11 installed
#### Poetry
1. `poetry install`
a. For GPU support, use `poetry install -E cuda --with gpu`
2. `poetry run -- wyzely-detect`
### Configuration
The following are some basic CLI options. Most flags have environment variable equivalents which can be helpful when using Docker.

1638
poetry.lock generated
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32
pyproject.toml
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@ -21,11 +21,12 @@ ultralytics = "^8.0.190"
hjson = "^3.1.0"
numpy = "^1.23.2"
# https://github.com/python-poetry/poetry/issues/6409
torch = ">=2.0.0, !=2.0.1, !=2.1.0"
# https://github.com/python-poetry/poetry/issues/6409#issuecomment-1911735833
# To install with GPU, use poetry install -E cuda --with gpu
torch = {version = "2.1.*", source = "pytorch-cpu", markers = "extra!='cuda'" }
# https://stackoverflow.com/a/76477590/18270659
# https://discuss.tensorflow.org/t/tensorflow-io-gcs-filesystem-with-windows/18849/4
# https://discfuss.tensorflow.org/t/tensorflow-io-gcs-filesystem-with-windows/18849/4
# Might be able to remove this version constraint later
# Working versions:
# Python version 3.10.12 and 3.10.5 both work
@ -33,10 +34,33 @@ torch = ">=2.0.0, !=2.0.1, !=2.1.0"
# cuDNN version - 8.8.1
# Installed from Nvidia website - nvidia-cuda-toolkit is not installed, but default PopOS drivers are installed
tensorflow-io-gcs-filesystem = "0.31.0"
tensorflow = {version = "^2.14.0", extras = ["and-cuda"]}
tensorflow = {version = "^2.14.0", markers = "extra!='cuda'"}
deepface = "^0.0.79"
prettytable = "^3.9.0"
[tool.poetry.group.gpu]
optional = true
[tool.poetry.group.gpu.dependencies]
torch = {version = "2.1.*", source = "pytorch-cu121", markers = "extra=='cuda'"}
tensorflow = {version = "^2.14.0", extras = ["and-cuda"], markers = "extra=='cuda'"}
[tool.poetry.extras]
# Might be better to rename this to nocpu since it's more accurate
cuda = []
[[tool.poetry.source]]
name = "pytorch-cpu"
url = "https://download.pytorch.org/whl/cpu"
priority = "explicit"
[[tool.poetry.source]]
name = "pytorch-cu121"
url = "https://download.pytorch.org/whl/cu121"
priority = "explicit"
[tool.poetry.group.dev.dependencies]
black = "^23.9.1"

208
wyzely_detect/__main__.py
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@ -1,28 +1,22 @@
# import face_recognition
from pathlib import Path
import os
import cv2
import sys
from prettytable import PrettyTable
# import hjson as json
import torch
from ultralytics import YOLO
from .utils import notify, utils
from .utils import utils
from .utils.cli_args import argparser
DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
args = None
objects_and_peoples = {
"objects": {},
"peoples": {},
}
def main():
global objects_and_peoples
global args
# RUN_BY_COMPOSE = os.getenv("RUN_BY_COMPOSE") # Replace this with code to check for gpu
args = argparser.parse_args()
@ -30,7 +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")
@ -41,9 +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")
@ -51,140 +46,89 @@ 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.rtsp_url is not None:
video_capture = cv2.VideoCapture(args.rtsp_url)
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)]}
else:
video_capture = cv2.VideoCapture(args.capture_device)
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
video_capture.set(cv2.CAP_PROP_BUFFERSIZE, 1)
# Print the resolution of the video
print(
f"Video resolution: {video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)}x{video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)}" # noqa: E501
)
# Only do this for streams
try:
for stream in video_sources["streams"]:
stream.set(cv2.CAP_PROP_BUFFERSIZE, 1)
# If there are no streams, this will throw a KeyError
except KeyError:
pass
# Print out the resolution of the video sources. Ideally, change this so the device ID/url is also printed
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)}",
]
)
print(pretty_table)
print("Beginning video capture...")
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Resize frame of video to a smaller size for faster recognition processing
run_frame = cv2.resize(frame, (0, 0), fx=args.run_scale, fy=args.run_scale)
# view_frame = cv2.resize(frame, (0, 0), fx=args.view_scale, fy=args.view_scale)
results = model(run_frame, verbose=False)
path_to_faces = Path(args.faces_directory)
path_to_faces_exists = path_to_faces.is_dir()
for i, r in enumerate(results):
# list of dicts with each dict containing a label, x1, y1, x2, y2
plot_boxes = []
# The following is stuff for people
# This is still in the for loop as each result, no matter if anything is detected, will be present.
# Thus, there will always be one result (r)
# Only run if path_to_faces exists
# 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,
min_confidence=args.face_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,
):
plot_boxes.append(face_details)
objects_and_peoples = notify.thing_detected(
thing_name=face_details["label"],
objects_and_peoples=objects_and_peoples,
detection_type="peoples",
detection_window=args.detection_window,
detection_duration=args.detection_duration,
notification_window=args.notification_window,
ntfy_url=args.ntfy_url,
)
# The following is stuff for objects
# Setup dictionary of object names
if (
objects_and_peoples["objects"] == {}
or objects_and_peoples["objects"] is None
):
for name in r.names.values():
objects_and_peoples["objects"][name] = {
"last_detection_time": None,
"detection_duration": None,
# "first_detection_time": None,
"last_notification_time": None,
}
# 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 args.detect_object:
if obj not in objects_and_peoples:
print(
f"Warning: {obj} is not in the list of objects the model can detect!"
)
for box in r.boxes:
# Get the name of the object
class_id = r.names[box.cls[0].item()]
# Get the coordinates of the object
cords = box.xyxy[0].tolist()
cords = [round(x) for x in cords]
# Get the confidence
conf = round(box.conf[0].item(), 2)
# Print it out, adding a spacer between each object
# print("Object type:", class_id)
# print("Coordinates:", cords)
# print("Probability:", conf)
# print("---")
# Now do stuff (if conf > 0.5)
if conf < args.object_confidence_threshold or (
class_id not in args.detect_object and args.detect_object != []
):
# If the confidence is too low
# or if the object is not in the list of objects to detect and the list of objects to detect is not empty
# then skip this iteration
continue
# Add the object to the list of objects to plot
plot_boxes.append(
{
"label": class_id,
"x1": cords[0],
"y1": cords[1],
"x2": cords[2],
"y2": cords[3],
}
)
objects_and_peoples = notify.thing_detected(
thing_name=class_id,
objects_and_peoples=objects_and_peoples,
detection_type="objects",
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,
)
# To debug plotting, use r.plot() to cross reference the bounding boxes drawn by the plot_label() and r.plot()
frame_to_show = utils.plot_label(
boxes=plot_boxes,
full_frame=frame,
# full_frame=r.plot(),
run_scale=args.run_scale,
view_scale=args.view_scale,
)
# Display the resulting frame
# cv2.imshow("", r)
if not args.no_display:
cv2.imshow(f"Video{i}", frame_to_show)
# Display the resulting frame
if not args.no_display:
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"):
@ -192,7 +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()

54
wyzely_detect/utils/cli_args.py
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@ -15,31 +15,35 @@ 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(
"--rtsp-url",
default=os.environ["RTSP_URL"]
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 None, # noqa: E501
else [],
type=str,
help="RTSP camera URL",
)
stream_source.add_argument(
"--capture-device",
default=os.environ["CAPTURE_DEVICE"]
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 0, # noqa: E501
else [],
type=int,
help="Capture device number",
)
@ -67,16 +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",
@ -92,6 +100,7 @@ def set_argparse():
help="The URL to send notifications to",
)
# Various timers
timers = argparser.add_argument_group("Timers")
timers.add_argument(
"--detection-duration",
@ -119,7 +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",
@ -143,17 +151,17 @@ 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",
nargs="*",
action="append",
# Stuff is appended to default, as far as I can tell
default=[],
type=str,
help="The object(s) to detect. Must be something the model is trained to detect",
@ -163,11 +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

210
wyzely_detect/utils/utils.py
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@ -1,10 +1,163 @@
import cv2
import os
import numpy as np
from pathlib import Path
from deepface import DeepFace
# https://stackoverflow.com/a/42121886/18270659
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
from deepface import DeepFace # noqa: E402
from . import notify # noqa: E402
first_face_try = True
# 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": {},
}
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,
detect_object: list = None,
object_confidence_threshold=None,
) -> np.ndarray:
"""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)
results = model(run_frame, verbose=False)
path_to_faces = Path(faces_directory)
path_to_faces_exists = path_to_faces.is_dir()
for r in results:
# list of dicts with each dict containing a label, x1, y1, x2, y2
plot_boxes = []
# The following is stuff for people
# This is still in the for loop as each result, no matter if anything is detected, will be present.
# Thus, there will always be one result (r)
# Only run if path_to_faces exists
# May be better to check every iteration, but this also works
if path_to_faces_exists:
if face_details := recognize_face(
path_to_directory=path_to_faces,
run_frame=run_frame,
# Perhaps make these names match?
min_confidence=face_confidence_threshold,
no_remove_representations=no_remove_representations,
):
plot_boxes.append(face_details)
objects_and_peoples = notify.thing_detected(
thing_name=face_details["label"],
objects_and_peoples=objects_and_peoples,
detection_type="peoples",
detection_window=detection_window,
detection_duration=detection_duration,
notification_window=notification_window,
ntfy_url=ntfy_url,
)
# The following is stuff for objects
# Setup dictionary of object names
if (
objects_and_peoples["objects"] == {}
or objects_and_peoples["objects"] is None
):
for name in r.names.values():
objects_and_peoples["objects"][name] = {
"last_detection_time": None,
"detection_duration": None,
# "first_detection_time": None,
"last_notification_time": None,
}
# 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:
# .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!"
)
for box in r.boxes:
# Get the name of the object
class_id = r.names[box.cls[0].item()]
# Get the coordinates of the object
cords = box.xyxy[0].tolist()
cords = [round(x) for x in cords]
# Get the confidence
conf = round(box.conf[0].item(), 2)
# Print it out, adding a spacer between each object
# print("Object type:", class_id)
# print("Coordinates:", cords)
# print("Probability:", conf)
# print("---")
# Now do stuff (if conf > 0.5)
if conf < object_confidence_threshold or (
class_id not in detect_object and detect_object != []
):
# If the confidence is too low
# or if the object is not in the list of objects to detect and the list of objects to detect is not empty
# then skip this iteration
continue
# Add the object to the list of objects to plot
plot_boxes.append(
{
"label": class_id,
"x1": cords[0],
"y1": cords[1],
"x2": cords[2],
"y2": cords[3],
}
)
objects_and_peoples = notify.thing_detected(
thing_name=class_id,
objects_and_peoples=objects_and_peoples,
detection_type="objects",
detection_window=detection_window,
detection_duration=detection_duration,
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(
boxes=plot_boxes,
full_frame=frame,
# full_frame=r.plot(),
run_scale=run_scale,
view_scale=view_scale,
)
# Unsure if this should also return the objects_and_peoples dict
return frame_to_show
def plot_label(
# list of dicts with each dict containing a label, x1, y1, x2, y2
@ -18,7 +171,7 @@ def plot_label(
# So the coordinates will be scaled appropriately when coming from run_frame
view_scale: float = None,
font: int = cv2.FONT_HERSHEY_SIMPLEX,
):
) -> np.ndarray:
# x1 and y1 are the top left corner of the box
# x2 and y2 are the bottom right corner of the box
# Example scaling: full_frame: 1 run_frame: 0.5 view_frame: 0.25
@ -72,26 +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
@ -124,8 +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
@ -134,7 +291,8 @@ 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")
@ -176,8 +334,4 @@ def recognize_face(
f"Cosine similarity: {cosine_similarity}, filname: {path_to_image.name}, to_return: {to_return}"
)
return to_return
"""
Example dataframe, for reference
identity (path to image) | source_x | source_y | source_w | source_h | VGG-Face_cosine (pretty much the confidence \_('_')_/)
"""
return None