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wyzely-detect
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slashtechno:better-notifications
slashtechno:multi-camera-support
slashtechno:fix-dependency-issues
slashtechno:improve-argparse-organization
slashtechno:publish-to-pypi
slashtechno:object-detection
slashtechno:deepface
slashtechno:v0.2.1
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slashtechno:v0.1.1
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compare: slashtechno:v0.1.0
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slashtechno:master
slashtechno:rye
slashtechno:deepsource-transform-5fbd03e9
slashtechno:better-notifications
slashtechno:multi-camera-support
slashtechno:fix-dependency-issues
slashtechno:improve-argparse-organization
slashtechno:publish-to-pypi
slashtechno:object-detection
slashtechno:deepface
slashtechno:v0.2.1
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12
.deepsource.toml
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@ -1,12 +0,0 @@
version = 1
[[analyzers]]
name = "python"
[analyzers.meta]
runtime_version = "3.x.x"
max_line_length = 135
[[analyzers]]
name = "docker"

45
.devcontainer/.devcontainer.json
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@ -1,45 +0,0 @@
// For format details, see https://aka.ms/devcontainer.json. For config options, see the
// README at: https://github.com/devcontainers/templates/tree/main/src/python
{
"name": "Python 3",
// Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
"image": "mcr.microsoft.com/devcontainers/python:0-3.11",
"features": {
"ghcr.io/devcontainers-contrib/features/poetry:2": {
"version": "latest"
},
"ghcr.io/devcontainers-contrib/features/nox:2": {
"version": "latest"
},
"ghcr.io/devcontainers/features/github-cli:1": {
"version": "latest"
}
},
// Features to add to the dev container. More info: https://containers.dev/features.
// "features": {},
// Use 'forwardPorts' to make a list of ports inside the container available locally.
// "forwardPorts": [],
// Use 'postCreateCommand' to run commands after the container is created.
"postCreateCommand": "poetry install",
// Configure tool-specific properties.
"customizations": {
"vscode": {
"settings": {},
"extensions": [
"ms-python.python",
"ms-python.vscode-pylance"
]
}
},
"mounts": [
// Re-use local Git configuration
"source=${localEnv:HOME}/.gitconfig,target=/home/vscode/.gitconfig,type=bind,consistency=cached"
]
// Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
// "remoteUser": "vscode"
}

3
.dockerignore
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@ -1,6 +1,3 @@
Dockerfile
.venv
docker-compose.yml
*.ipnyb
dist/
*.pkl

1
.github/FUNDING.yml vendored
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@ -1 +0,0 @@
github: [slashtechno]

3
.github/workflows/python-publish.yml vendored
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@ -8,15 +8,12 @@
name: Upload Python Package
on:
release:
types: [published]
workflow_dispatch:
permissions:
contents: read
id-token: write
jobs:
deploy:

34
.vscode/launch.json vendored
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@ -4,44 +4,14 @@
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"name": "Quick Debug",
"type": "python",
"request": "launch",
"module": "wyzely_detect",
"args": [
"--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",
"name": "Debug Wyzely Detect",
"type": "python",
"request": "launch",
"module": "wyzely_detect",
// "justMyCode": true
"justMyCode": false
},
{
"name": "Debug --help",
"type": "python",
"request": "launch",
"module": "wyzely_detect",
"args": [
"--help"
],
"justMyCode": false
},
}
]
}

9
Dockerfile
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@ -1,19 +1,12 @@
FROM python:3.10.5-buster
LABEL org.opencontainers.image.description "Docker image for running wyzely-detect"
LABEL org.opencontainers.image.source "https://github.com/slashtechno/wyzely-detect"
RUN apt update && apt install libgl1 -y
RUN pip install poetry
WORKDIR /app
COPY . .
RUN poetry install
# RUN poetry run pip uninstall -y torchvision
# RUN poetry run pip install torchvision
ENTRYPOINT ["poetry", "run", "python", "-m", "--", "wyzely_detect", "--no-display"]
ENTRYPOINT ["poetry", "run", "python", "-m", "wyzely_detect"]

143
LICENSE
View File

@ -1,5 +1,5 @@
GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
@ -7,15 +7,17 @@
Preamble
The GNU Affero General Public License is a free, copyleft license for
software and other kinds of works, specifically designed to ensure
cooperation with the community in the case of network server software.
The GNU General Public License is a free, copyleft license for
software and other kinds of works.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
our General Public Licenses are intended to guarantee your freedom to
the GNU General Public License is intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users.
software for all its users. We, the Free Software Foundation, use the
GNU General Public License for most of our software; it applies also to
any other work released this way by its authors. You can apply it to
your programs, too.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
@ -24,34 +26,44 @@ them if you wish), that you receive source code or can get it if you
want it, that you can change the software or use pieces of it in new
free programs, and that you know you can do these things.
Developers that use our General Public Licenses protect your rights
with two steps: (1) assert copyright on the software, and (2) offer
you this License which gives you legal permission to copy, distribute
and/or modify the software.
To protect your rights, we need to prevent others from denying you
these rights or asking you to surrender the rights. Therefore, you have
certain responsibilities if you distribute copies of the software, or if
you modify it: responsibilities to respect the freedom of others.
A secondary benefit of defending all users' freedom is that
improvements made in alternate versions of the program, if they
receive widespread use, become available for other developers to
incorporate. Many developers of free software are heartened and
encouraged by the resulting cooperation. However, in the case of
software used on network servers, this result may fail to come about.
The GNU General Public License permits making a modified version and
letting the public access it on a server without ever releasing its
source code to the public.
For example, if you distribute copies of such a program, whether
gratis or for a fee, you must pass on to the recipients the same
freedoms that you received. You must make sure that they, too, receive
or can get the source code. And you must show them these terms so they
know their rights.
The GNU Affero General Public License is designed specifically to
ensure that, in such cases, the modified source code becomes available
to the community. It requires the operator of a network server to
provide the source code of the modified version running there to the
users of that server. Therefore, public use of a modified version, on
a publicly accessible server, gives the public access to the source
code of the modified version.
Developers that use the GNU GPL protect your rights with two steps:
(1) assert copyright on the software, and (2) offer you this License
giving you legal permission to copy, distribute and/or modify it.
An older license, called the Affero General Public License and
published by Affero, was designed to accomplish similar goals. This is
a different license, not a version of the Affero GPL, but Affero has
released a new version of the Affero GPL which permits relicensing under
this license.
For the developers' and authors' protection, the GPL clearly explains
that there is no warranty for this free software. For both users' and
authors' sake, the GPL requires that modified versions be marked as
changed, so that their problems will not be attributed erroneously to
authors of previous versions.
Some devices are designed to deny users access to install or run
modified versions of the software inside them, although the manufacturer
can do so. This is fundamentally incompatible with the aim of
protecting users' freedom to change the software. The systematic
pattern of such abuse occurs in the area of products for individuals to
use, which is precisely where it is most unacceptable. Therefore, we
have designed this version of the GPL to prohibit the practice for those
products. If such problems arise substantially in other domains, we
stand ready to extend this provision to those domains in future versions
of the GPL, as needed to protect the freedom of users.
Finally, every program is threatened constantly by software patents.
States should not allow patents to restrict development and use of
software on general-purpose computers, but in those that do, we wish to
avoid the special danger that patents applied to a free program could
make it effectively proprietary. To prevent this, the GPL assures that
patents cannot be used to render the program non-free.
The precise terms and conditions for copying, distribution and
modification follow.
@ -60,7 +72,7 @@ modification follow.
0. Definitions.
"This License" refers to version 3 of the GNU Affero General Public License.
"This License" refers to version 3 of the GNU General Public License.
"Copyright" also means copyright-like laws that apply to other kinds of
works, such as semiconductor masks.
@ -537,45 +549,35 @@ to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Remote Network Interaction; Use with the GNU General Public License.
Notwithstanding any other provision of this License, if you modify the
Program, your modified version must prominently offer all users
interacting with it remotely through a computer network (if your version
supports such interaction) an opportunity to receive the Corresponding
Source of your version by providing access to the Corresponding Source
from a network server at no charge, through some standard or customary
means of facilitating copying of software. This Corresponding Source
shall include the Corresponding Source for any work covered by version 3
of the GNU General Public License that is incorporated pursuant to the
following paragraph.
13. Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU General Public License into a single
under version 3 of the GNU Affero General Public License into a single
combined work, and to convey the resulting work. The terms of this
License will continue to apply to the part which is the covered work,
but the work with which it is combined will remain governed by version
3 of the GNU General Public License.
but the special requirements of the GNU Affero General Public License,
section 13, concerning interaction through a network will apply to the
combination as such.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
the GNU Affero General Public License from time to time. Such new versions
will be similar in spirit to the present version, but may differ in detail to
the GNU General Public License from time to time. Such new versions will
be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU Affero General
Program specifies that a certain numbered version of the GNU General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU Affero General Public License, you may choose any version ever published
GNU General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU Affero General Public License can be used, that proxy's
versions of the GNU General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
@ -633,29 +635,40 @@ the "copyright" line and a pointer to where the full notice is found.
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
GNU General Public License for more details.
You should have received a copy of the GNU Affero General Public License
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If your software can interact with users remotely through a computer
network, you should also make sure that it provides a way for users to
get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.

29
README.md
View File

@ -11,16 +11,11 @@ Recognize faces/objects in a video stream (from a webcam or a security camera) a
## Prerequisites
### Python
### Poetry/Python
- Camera, either a webcam or a Wyze Cam
- All RTSP feeds _should_ work, however.
- **WSL, by default, does not support USB devices.** It is recommended to natively run this, but it is possible to use it on WSL with streams or some workarounds.
- 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
- Poetry
### Docker
- A Wyze Cam
- Any other RTSP feed _should_ work, as mentioned above
@ -33,29 +28,17 @@ Recognize faces/objects in a video stream (from a webcam or a security camera) a
## Usage
### Installation
Cloning the repository is not required when installing from PyPi but is required when installing from source
1. Clone this repo with `git clone https://github.com/slashtechno/wyzely-detect`
2. `cd` into the cloned repository
3. Then, either install with [Poetry](https://python-poetry.org/) or run with Docker
#### Installing from PyPi with pip (recommended)
This assumes you have Python 3.10 or 3.11 installed
1. `pip install wyzely-detect`
a. You may need to use `pip3` instead of `pip`
2. `wyzely-detect`
#### Poetry (best for GPU support)
1. `poetry install`
a. For GPU support, use `poetry install -E cuda --with gpu`
2. `poetry run -- wyzely-detect`
#### Docker
Running with Docker has the benefit of having easier configuration, the ability to run headlessly, and easy setup of Ntfy and [mrlt8/docker-wyze-bridge](https://github.com/mrlt8/docker-wyze-bridge). However, for now, CPU-only is supported. Contributions are welcome to add GPU support. In addition, Docker is tested a less-tested method of running this program.
1. Modify to `docker-compose.yml` to achieve desired configuration
2. Run in the background with `docker compose up -d`
2. Run in the background with `docker compose up -d
#### Poetry
1. `poetry install`
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.

2
deepface-test.ipynb
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@ -36,7 +36,7 @@
"# cv2.imwrite(str(uuid_path), frame)\n",
"# dfs = DeepFace.find(img_path=str(uuid_path), db_path = \"faces\")\n",
"# Don't throw an error if no face is detected (enforce_detection=False)\n",
"dfs = DeepFace.find(frame, db_path = \"faces\", enforce_detection=True, silent=False, model_name=\"ArcFace\", detector_backend=\"opencv\")\n",
"dfs = DeepFace.find(frame, db_path = \"faces\", enforce_detection=False, silent=False, model_name=\"ArcFace\", detector_backend=\"opencv\")\n",
"# Get the identity of the person\n",
"for i, pd_dataframe in enumerate(dfs):\n",
" # Sort the dataframe by confidence\n",

36
docker-compose.yml
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@ -6,19 +6,16 @@ services:
container_name: bridge-wyzely-detect
restart: unless-stopped
image: mrlt8/wyze-bridge:latest
# The ports can be removed since we're using the network
# Just an unnecesary security risk to expose them but can be useful for debugging
# I think we can remove the ports, since we're using the network
# Just an unnecesary security risk
# ports:
# - 1935:1935 # RTMP
# - 8554:8554 # RTSP (this is really the only one we need)
# - 8888:8888 # HLS
# - 5000:5000 # WEB-UI
environment:
# This is a simple configuration without 2FA.
# For advanced configuration, including using an API key, see https://github.com/mrlt8/docker-wyze-bridge/wiki/Two-Factor-Authentication
# Either replace the following with your Wyze username and password, or set the environment variables
- WYZE_EMAIL=${WYZE_EMAIL}
- WYZE_PASSWORD=${WYZE_PASSWORD}
- WYZE_EMAIL=${WYZE_EMAIL} # Replace with wyze email
- WYZE_PASSWORD=${WYZE_PASSWORD} # Replace with wyze password
networks:
all:
ntfy:
@ -39,27 +36,18 @@ services:
wyzely-detect:
container_name: wyzely-detect
restart: unless-stopped
image: ghcr.io/slashtechno/wyzely-detect:latest
# Building from source is also an option
# build:
# context: .
# dockerfile: Dockerfile
command:
- "--ntfy-url"
# Replace "wyzely-detect" with the desired notification stream
- "http://ntfy:80/wyzely-detect"
- "--rtsp-url"
# Replace "cv" with the desired rtsp stream
- "rtsp://bridge:8554/cv"
# Example second rtsp stream
# - "--rtsp-url"
# - "rtsp://bridge:8554/camera"
# image: ghcr.io/slashtechno/wyzely-detect:latest
build:
context: .
dockerfile: Dockerfile
volumes:
- ./faces:/app/faces
networks:
all:
environment:
- URL=rtsp://bridge:8554/cv
- NO_DISPLAY=true
- NTFY_URL=http://ntfy:80/wyzely-detect
depends_on:
- bridge

2856
poetry.lock generated
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53
pyproject.toml
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@ -1,6 +1,6 @@
[tool.poetry]
name = "wyzely-detect"
version = "0.2.1"
version = "0.1.0"
description = "Recognize faces/objects in a video stream (from a webcam or a security camera) and send notifications to your devices"
authors = ["slashtechno <77907286+slashtechno@users.noreply.github.com>"]
repository = "https://github.com/slashtechno/wyzely-detect"
@ -21,56 +21,17 @@ ultralytics = "^8.0.190"
hjson = "^3.1.0"
numpy = "^1.23.2"
# https://github.com/python-poetry/poetry/issues/6409#issuecomment-1911735833
# If GPU support doesn't work, `poetry install -E cuda --with gpu` will force it to be installed from the GPU PyTorch repo
# However, PyPi's `torch` has CUDA 12.1 support by default on Linux, so in that case it should not be needed.
torch = [
{version = "^2.2.1", source = "pypi", markers = "extra!='cuda' and (platform_system=='Linux' or platform_system=='Darwin')"},
{version = "^2.2.1", source = "pytorch-cpu", markers = "extra!='cuda' and platform_system=='Windows'"},
]
# https://github.com/python-poetry/poetry/issues/6409
torch = ">=2.0.0, !=2.0.1, !=2.1.0"
# https://stackoverflow.com/a/76477590/18270659
# https://discfuss.tensorflow.org/t/tensorflow-io-gcs-filesystem-with-windows/18849/4
# https://github.com/python-poetry/poetry/issues/8271#issuecomment-1712020965
# https://discuss.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
# CUDA version - 12.2
# cuDNN version - 8.8.1
# Installed from Nvidia website - nvidia-cuda-toolkit is not installed, but default PopOS drivers are installed
absl-py = "^2.1.0"
tensorflow = {version = "^2.13.0", markers = "extra!='cuda'"}
# TODO: Change platform to markers
tensorflow-macos = { version = "^2.13.0", platform = "darwin", markers = "platform_machine=='arm64'" }
tensorflow-intel = { version = "^2.13.0", platform = "win32" }
tensorflow-io-gcs-filesystem = [
{ version = "< 0.32.0", markers = "platform_system == 'Windows'" }
]
tensorflow-io-gcs-filesystem = "0.31.0"
tensorflow = "^2.14.0"
deepface = "^0.0.79"
prettytable = "^3.9.0"
[tool.poetry.group.gpu]
optional = true
[tool.poetry.group.gpu.dependencies]
torch = {version = "^2.2.1", source = "pytorch-cu121", markers = "extra=='cuda'"}
tensorflow = {version = "^2.14.0", extras = ["and-cuda"], markers = "extra=='cuda' and platform_system == 'Linux'"}
[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"

340
wyzely_detect/__main__.py
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@ -1,22 +1,162 @@
# import face_recognition
from pathlib import Path
import cv2
import sys
from prettytable import PrettyTable
import dotenv
from pathlib import Path
import os
# import hjson as json
import torch
from ultralytics import YOLO
import argparse
from .utils import notify
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
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()
@ -24,110 +164,148 @@ 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(torch.cuda.get_device_properties(i).name)
if torch.cuda.is_available():
torch.cuda.set_device(0)
print("Set CUDA device")
else:
print("No CUDA device available, using CPU")
# Seems automatically, deepface (tensorflow) tried to use my GPU on Pop!_OS (I did not set up cudnn or anything)
# Not sure the best way, in Poetry, to manage GPU libraries so for now, just use CPU
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")
else:
print("GPU disabled successfully")
model = YOLO("yolov8n.pt")
# Depending on if the user wants to use a stream or a capture device,
# 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)]}
if args.url:
video_capture = cv2.VideoCapture(args.url)
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
video_capture = cv2.VideoCapture(args.capture_device)
# 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
# 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
video_capture.set(cv2.CAP_PROP_BUFFERSIZE, 1)
# 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 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
)
print(pretty_table)
print("Beginning video capture...")
while True:
# Grab a single frame of video
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,
ret, frame = video_capture.read()
# Only process every other frame of video to save time
# 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
):
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,
model=model,
detect_object=args.detect_object,
object_confidence_threshold=args.object_confidence_threshold,
)
# 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.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,
)
# 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:
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!
@ -136,7 +314,7 @@ def main():
# Release handle to the webcam
print("Releasing video capture")
[source.release() for sources in video_sources.values() for source in sources]
video_capture.release()
cv2.destroyAllWindows()

198
wyzely_detect/utils/cli_args.py
View File

@ -1,198 +0,0 @@
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="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",
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 [],
type=str,
help="RTSP camera URL",
)
stream_source.add_argument(
"--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 [],
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"] != ""
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",
default=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",
)
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"] != ""
# This is None but there is a default set in notify.py
else None,
type=str,
help="The URL to send notifications to",
)
# Various timers
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"] != ""
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",
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",
)
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"] != ""
# 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
# This will run when this file is imported
set_argparse()

5
wyzely_detect/utils/notify.py
View File

@ -104,11 +104,6 @@ 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",

200
wyzely_detect/utils/utils.py
View File

@ -1,163 +1,10 @@
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"
from deepface import DeepFace # noqa: E402
from . import notify # noqa: E402
from deepface import DeepFace
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
@ -171,7 +18,7 @@ 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
@ -221,8 +68,6 @@ 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
@ -230,8 +75,7 @@ def recognize_face(
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
dict contains the following keys: label, x1, y1, x2, y2
The directory should be structured as follows:
faces/
name/
@ -250,16 +94,13 @@ 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 and not no_remove_representations:
if first_face_try:
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
@ -278,10 +119,6 @@ def recognize_face(
model_name="ArcFace",
detector_backend="opencv",
)
'''
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)
@ -289,14 +126,6 @@ def recognize_face(
):
# print("No faces recognized") # For debugging
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)
):
# 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
else:
raise e
# Iteate over the dataframes
@ -304,13 +133,8 @@ def recognize_face(
# The last row is the highest confidence
# So we can just grab the path from there
# iloc = Integer LOCation
try:
path_to_image = Path(df.iloc[-1]["identity"])
# Seems this is caused when someone steps into frame and their face is detected but not recognized
except IndexError:
print("Face present but not recognized")
continue
# 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 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 path_to_image.parent == Path(path_to_directory):
label = path_to_image.name
else:
@ -325,13 +149,19 @@ 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
cosine_similarity = df.iloc[-1]["ArcFace_cosine"]
if cosine_similarity < min_confidence:
distance = df.iloc[-1]["ArcFace_cosine"]
# TODO: Make this a CLI argument
if distance < 0.3:
return None
# if 0.5 < distance < 0.7:
# label = "Unknown"
to_return = dict(label=label, **coordinates)
print(
f"Cosine similarity: {cosine_similarity}, filname: {path_to_image.name}, to_return: {to_return}"
f"Confindence: {distance}, filname: {path_to_image.name}, to_return: {to_return}"
)
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 \_('_')_/)
"""