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wyzely-detect
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Merge pull request #6 from slashtechno/publish-to-pypi 

Prepare to publish to PyPi
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slashtechno 2023-10-22 16:57:45 -05:00 committed by GitHub
parent 4f8e26b2a2 259cc8f200
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15 changed files with 440 additions and 295 deletions
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39
.github/workflows/python-publish.yml vendored Normal file
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@ -0,0 +1,39 @@
# This workflow will upload a Python Package using Twine when a release is created
# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries
# This workflow uses actions that are not certified by GitHub.
# They are provided by a third-party and are governed by
# separate terms of service, privacy policy, and support
# documentation.
name: Upload Python Package
on:
release:
types: [published]
permissions:
contents: read
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v3
with:
python-version: '3.x'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install build
- name: Build package
run: python -m build
- name: Publish package
uses: pypa/gh-action-pypi-publish@v1.8.10
with:
user: __token__
password: ${{ secrets.PYPI_PROJECT_API_TOKEN }}

4
.gitignore vendored
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@ -2,7 +2,9 @@
config/
using_yolov8.ipynb
yolov8n.pt
.venv/
*venv/
__pycache__/
faces/*
!faces/.gitkeep
dist/
test.txt

8
.vscode/launch.json vendored
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@ -5,11 +5,13 @@
"version": "0.2.0",
"configurations": [
{
"name": "Python: Module",
// "name": "Python: Module",
"name": "Debug Wyzely Detect",
"type": "python",
"request": "launch",
"module": "set_detect_notify",
"justMyCode": true
"module": "wyzely_detect",
// "justMyCode": true
"justMyCode": false
}
]
}

3
.vscode/settings.json vendored Normal file
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@ -0,0 +1,3 @@
{
"files.eol": "\n"
}

2
Dockerfile
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@ -9,4 +9,4 @@ COPY . .
RUN poetry install
ENTRYPOINT ["poetry", "run", "python", "-m", "set_detect_notify"]
ENTRYPOINT ["poetry", "run", "python", "-m", "wyzely_detect"]

15
README.md
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@ -1,5 +1,5 @@
# Set, Detect, Notify
Recognize faces/objects in (Wyze Cam) footage and send notifications to your phone (or other devices)
# Wyzely Detect
Recognize faces/objects in a video stream (from a webcam or a security camera) and send notifications to your devices
### Features
- Recognize objects
@ -14,20 +14,21 @@ Recognize faces/objects in (Wyze Cam) footage and send notifications to your pho
### Poetry/Python
- Camera, either a webcam or a Wyze Cam
- All RTSP feeds _should_ work, however.
- Python
- Python 3.10 or 3.11
- Poetry
### Docker
- A Wyze Cam
- Any other RTSP feed _should_ work, as mentioned above
- Python
- Poetry
- Docker
- Docker Compose
## What's not required
- A Wyze subscription
## Usage
### Installation
1. Clone this repo with `git clone https://github.com/slashtechno/wyze-face-recognition.git`
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
@ -37,7 +38,7 @@ Recognize faces/objects in (Wyze Cam) footage and send notifications to your pho
#### Poetry
1. `poetry install`
2. `poetry run -- set-detect-notify`
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.

62
deepface-test.ipynb
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@ -36,12 +36,12 @@
"# 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=False)\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",
" # inplace=True means that the dataframe is modified so we don't need to assign it to a new variable\n",
" pd_dataframe.sort_values(by=['VGG-Face_cosine'], inplace=True, ascending=False)\n",
" # pd_dataframe.sort_values(by=['model_name=\"ArcFace\", detector_backend=\"opencv\")'], inplace=True, ascending=False)\n",
" print(f'On dataframe {i}')\n",
" print(pd_dataframe)\n",
" # Get the most likely identity\n",
@ -49,7 +49,7 @@
" # We could use Path to get the parent directory of the image to use as the identity\n",
" print(f'Most likely identity: {Path(pd_dataframe.iloc[0][\"identity\"]).parent.name}')\n",
" # Get the most likely identity's confidence\n",
" print(f'Confidence: {pd_dataframe.iloc[0][\"VGG-Face_cosine\"]}')\n",
" print(f'Confidence: {pd_dataframe.iloc[0][\"ArcFace_cosine\"]}')\n",
"\n",
"# uuid_path.unlink()"
]
@ -67,7 +67,61 @@
"metadata": {},
"outputs": [],
"source": [
"DeepFace.stream(db_path=\"faces\")"
"DeepFace.stream(db_path=\"faces\", model_name=\"ArcFace\", detector_backend=\"opencv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Stream normal frame by frame"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from deepface import DeepFace\n",
"import cv2\n",
"from pathlib import Path\n",
"import uuid\n",
"import pandas as pd\n",
"\n",
"def main():\n",
" cap = cv2.VideoCapture(0)\n",
" while True:\n",
" ret, frame = cap.read()\n",
" dfs = DeepFace.find(frame, db_path = \"faces\", enforce_detection=False, silent=False, model_name=\"ArcFace\", detector_backend=\"opencv\")\n",
" for i, pd_dataframe in enumerate(dfs):\n",
" print(f'On dataframe {i}')\n",
" print(pd_dataframe)\n",
" print(f'Most likely identity: {Path(pd_dataframe.iloc[0][\"identity\"]).parent.name}')\n",
" print(f'Confidence: {pd_dataframe.iloc[0][\"ArcFace_cosine\"]}')\n",
" cv2.imshow(\"frame\", frame)\n",
" if cv2.waitKey(1) & 0xFF == ord(\"q\"):\n",
" break\n",
" cap.release()\n",
" cv2.destroyAllWindows()\n",
" \n",
"\n",
"\n",
"main()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Other functions\n"
]
}
],

15
docker-compose.yml
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@ -3,7 +3,7 @@ networks:
all:
services:
bridge:
container_name: bridge-wyze
container_name: bridge-wyzely-detect
restart: unless-stopped
image: mrlt8/wyze-bridge:latest
# I think we can remove the ports, since we're using the network
@ -18,12 +18,9 @@ services:
- WYZE_PASSWORD=${WYZE_PASSWORD} # Replace with wyze password
networks:
all:
# aliases:
# - bridge
# - wyze-bridge
ntfy:
image: binwiederhier/ntfy
container_name: ntfy-wyze
container_name: ntfy-wyzely-detect
command:
- serve
environment:
@ -36,10 +33,10 @@ services:
restart: unless-stopped
networks:
all:
facial_recognition:
container_name: face-recognition-wyze
wyzely-detect:
container_name: wyzely-detect
restart: unless-stopped
# image: ghcr.io/slashtechno/wyze_face_recognition:latest
# image: ghcr.io/slashtechno/wyzely-detect:latest
build:
context: .
dockerfile: Dockerfile
@ -50,7 +47,7 @@ services:
environment:
- URL=rtsp://bridge:8554/cv
- NO_DISPLAY=true
- NTFY_URL=http://ntfy:80/set-detect-notify
- NTFY_URL=http://ntfy:80/wyzely-detect
depends_on:
- bridge

463
poetry.lock generated
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@ -124,33 +124,29 @@ lxml = ["lxml"]
[[package]]
name = "black"
version = "23.9.1"
version = "23.10.0"
description = "The uncompromising code formatter."
optional = false
python-versions = ">=3.8"
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[package.dependencies]
@ -528,13 +524,13 @@ test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
[[package]]
name = "cycler"
version = "0.12.0"
version = "0.12.1"
description = "Composable style cycles"
optional = false
python-versions = ">=3.8"
files = [
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[package.extras]
@ -723,53 +719,53 @@ files = [
[[package]]
name = "fonttools"
version = "4.43.0"
version = "4.43.1"
description = "Tools to manipulate font files"
optional = false
python-versions = ">=3.8"
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version = "1.11.3"
description = "Fundamental algorithms for scientific computing in Python"
optional = false
python-versions = ">=3.8"
python-versions = "<3.13,>=3.9"
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[package.dependencies]
numpy = ">=1.18.5,<1.26.0"
numpy = ">=1.21.6,<1.28.0"
[package.extras]
dev = ["flake8", "mypy", "pycodestyle", "typing_extensions"]
doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"]
test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
[[package]]
name = "seaborn"
@ -3432,13 +3441,13 @@ files = [
[[package]]
name = "ultralytics"
version = "8.0.193"
version = "8.0.200"
description = "Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification."
optional = false
python-versions = ">=3.8"
files = [
{file = "ultralytics-8.0.193-py3-none-any.whl", hash = "sha256:de604498fb234947f497c9d3db51e0246436bbcfb62da10f12f5a3f33224d409"},
{file = "ultralytics-8.0.193.tar.gz", hash = "sha256:c61ab70b228f060d37e5e9d833ce491a3d29c71ec31c0087dc26852133a0c2c9"},
{file = "ultralytics-8.0.200-py3-none-any.whl", hash = "sha256:68de5a0db71eb3c1eadc55d2303e559804630e3b07d30b66415520ca135716bc"},
{file = "ultralytics-8.0.200.tar.gz", hash = "sha256:d6f46048dcc0827e370e391c825c2aa26136e40da5464a8c667d2424c2bffd2f"},
]
[package.dependencies]
@ -3459,18 +3468,18 @@ torchvision = ">=0.9.0"
tqdm = ">=4.64.0"
[package.extras]
dev = ["check-manifest", "coverage", "ipython", "mkdocs-material", "mkdocs-redirects", "mkdocs-ultralytics-plugin (>=0.0.29)", "mkdocstrings[python]", "pytest", "pytest-cov"]
dev = ["check-manifest", "coverage", "ipython", "mkdocs-material", "mkdocs-redirects", "mkdocs-ultralytics-plugin (>=0.0.29)", "mkdocstrings[python]", "pre-commit", "pytest", "pytest-cov"]
export = ["coremltools (>=7.0)", "openvino-dev (>=2023.0)", "tensorflow (<=2.13.1)", "tensorflowjs"]
[[package]]
name = "urllib3"
version = "2.0.6"
version = "2.0.7"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.7"
files = [
{file = "urllib3-2.0.6-py3-none-any.whl", hash = "sha256:7a7c7003b000adf9e7ca2a377c9688bbc54ed41b985789ed576570342a375cd2"},
{file = "urllib3-2.0.6.tar.gz", hash = "sha256:b19e1a85d206b56d7df1d5e683df4a7725252a964e3993648dd0fb5a1c157564"},
{file = "urllib3-2.0.7-py3-none-any.whl", hash = "sha256:fdb6d215c776278489906c2f8916e6e7d4f5a9b602ccbcfdf7f016fc8da0596e"},
{file = "urllib3-2.0.7.tar.gz", hash = "sha256:c97dfde1f7bd43a71c8d2a58e369e9b2bf692d1334ea9f9cae55add7d0dd0f84"},
]
[package.extras]
@ -3618,4 +3627,4 @@ files = [
[metadata]
lock-version = "2.0"
python-versions = ">=3.10, <3.12"
content-hash = "952e41ab099f9d1c7055465d5a3c228df677c422d20dea8ff8d558d2e4d8f2be"
content-hash = "6712094a7e402c4587037939b7a33ad4c2463a545c222374bc471925c33242d9"

22
pyproject.toml
View File

@ -1,15 +1,19 @@
[tool.poetry]
name = "set_detect_notify"
name = "wyzely-detect"
version = "0.1.0"
description = "Detect all the things"
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"
keywords = ["object-detection", "face-detection", "wyze", "security", "yolov8", "unified-push"]
license = "MIT"
readme = "README.md"
packages = [{include = "set_detect_notify"}]
packages = [{include = "wyzely_detect"}]
[tool.poetry.dependencies]
# python = "^3.10"
# Works on 3.10 and 3.11, at least in my testing
python = ">=3.10, <3.12"
python-dotenv = "^1.0.0"
httpx = "^0.25.0"
opencv-python = "^4.8.1.78"
@ -20,10 +24,15 @@ numpy = "^1.23.2"
# 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://discuss.tensorflow.org/t/tensorflow-io-gcs-filesystem-with-windows/18849/4
# Might be able to remove this version constraint later
tensorflow-io-gcs-filesystem = "0.31.0"
deepface = "^0.0.79"
tensorflow = "^2.14.0"
deepface = "^0.0.79"
[tool.poetry.group.dev.dependencies]
black = "^23.9.1"
ruff = "^0.0.291"
@ -41,6 +50,7 @@ build-backend = "poetry.core.masonry.api"
# Where possible, `black` will attempt to format to 88 characters
# However, setting ruff to 135 will allow for longer lines that can't be auto-formatted
line-length = 135
extend-select= ["FIX002"]
[tool.poetry.scripts]
set-detect-notify = "set_detect_notify.__main__:main"
wyzely-detect = "wyzely_detect.__main__:main"

0
set_detect_notify/__init__.py → wyzely_detect/__init__.py
View File

48
set_detect_notify/__main__.py → wyzely_detect/__main__.py
View File

@ -33,9 +33,11 @@ def main():
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="Detect It",
description="Detect it all!",
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=":)",
)
@ -87,7 +89,7 @@ def main():
if "FACES_DIRECTORY" in os.environ and os.environ["FACES_DIRECTORY"] != ""
else "faces",
type=str,
help="The directory to store the faces. Should contain 1 subdirectory of images per person",
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",
@ -118,13 +120,13 @@ def main():
# 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/set-detect-notify",
else "https://ntfy.sh/wyzely-detect",
type=str,
help="The URL to send notifications to",
)
@ -158,7 +160,7 @@ def main():
args = argparser.parse_args()
# Check if a CUDA GPU is available. If it is, set it via torch. Ff not, set it to cpu
# Check if a CUDA GPU is available. If it is, set it via torch. If not, set it to cpu
# 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()):
@ -198,6 +200,10 @@ def main():
# 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 = []
@ -205,19 +211,23 @@ def main():
# 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)
if face_details := utils.recognize_face(
path_to_directory=Path(args.faces_directory), 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,
)
# 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,
)
# The following is stuff for objects
# Setup dictionary of object names

0
set_detect_notify/utils/__init__.py → wyzely_detect/utils/__init__.py
View File

2
set_detect_notify/utils/notify.py → wyzely_detect/utils/notify.py
View File

@ -32,7 +32,7 @@ def thing_detected(
detection_window: int = 15,
detection_duration: int = 2,
notification_window: int = 15,
ntfy_url: str = "https://ntfy.sh/set-detect-notify",
ntfy_url: str = "https://ntfy.sh/wyzely-detect",
) -> dict:
"""
A function to make sure 2 seconds of detection is detected in 15 seconds, 15 seconds apart.

40
set_detect_notify/utils/utils.py → wyzely_detect/utils/utils.py
View File

@ -74,6 +74,7 @@ def recognize_face(
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/
@ -92,37 +93,52 @@ def recognize_face(
"""
global first_face_try
# If it's the first time the function is being run, remove representations_vgg_face.pkl, if it exists
# If it's the first time the function is being run, remove representations_arcface.pkl, if it exists
if first_face_try:
try:
Path("representations_vgg_face.pkl").unlink()
print("Removing representations_vgg_face.pkl")
path_to_directory.joinpath("representations_arcface.pkl").unlink()
print("Removing representations_arcface.pkl")
except FileNotFoundError:
pass
print("representations_arcface.pkl does not exist")
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
# This line is here to prevent a crash if that happens. However, there is a check in __main__ so it shouldn't happen
face_dataframes = []
try:
face_dataframes = DeepFace.find(
run_frame,
db_path=str(path_to_directory),
enforce_detection=True,
# Problem with enforce_detection=False is that it will always (?) return a face, no matter the confidence
# Thus, false-positives need to be filtered out
enforce_detection=False,
silent=True,
# Could use VGG-Face, but whilst fixing another issue, ArcFace seemed to be slightly faster
# I read somewhere that opencv is the fastest (but not as accurate). Could be changed later, but opencv seems to work well
model_name="ArcFace",
detector_backend="opencv",
)
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."
== "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
):
# print("No faces recognized") # For debugging
return None
else:
raise e
# Iteate over the dataframes
for df in face_dataframes:
# The last row is the highest confidence
# So we can just grab the path from there
# iloc = Integer LOCation
path_to_image = Path(df.iloc[-1]["identity"])
# Get the name of the parent directory
label = path_to_image.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:
label = path_to_image.parent.name
# Return the coordinates of the box in xyxy format, rather than xywh
# This is because YOLO uses xyxy, and that's how plot_label expects
# Also, xyxy is just the top left and bottom right corners of the box
@ -132,9 +148,11 @@ def recognize_face(
"x2": df.iloc[-1]["source_x"] + df.iloc[-1]["source_w"],
"y2": df.iloc[-1]["source_y"] + df.iloc[-1]["source_h"],
}
# After some brief testing, it seems positve matches are > 0.3
# I have not seen any false positives, so there is no threashold yet
distance = df.iloc[-1]["VGG-Face_cosine"]
# After some brief testing, it seems positive matches are > 0.3
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)