Fixed Docker support and updated README.md

This commit is contained in:
slashtechno 2023-10-14 22:31:48 -05:00
parent 1d17bb629b
commit 8cc9054e67
Signed by: slashtechno
GPG Key ID: 8EC1D9D9286C2B17
9 changed files with 451 additions and 174 deletions

42
.Dockerfile.old Normal file
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@ -0,0 +1,42 @@
FROM python:3.10-bullseye
# Install Dlib (for face_recognition)
RUN apt-get -y update && apt-get install -y --fix-missing \
build-essential \
cmake \
gfortran \
git \
wget \
curl \
graphicsmagick \
libgraphicsmagick1-dev \
libatlas-base-dev \
libavcodec-dev \
libavformat-dev \
libgtk2.0-dev \
libjpeg-dev \
liblapack-dev \
libswscale-dev \
pkg-config \
python3-dev \
python3-numpy \
software-properties-common \
zip
RUN apt-get clean
RUN rm -rf /tmp/* /var/tmp/*
ENV CFLAGS=-static
# Install dos2unix
# RUN apt-get install -y dos2unix
# Upgrade pip
RUN pip3 install --upgrade pip
# Copy directory to container
WORKDIR /app
COPY . ./
# Run dos2unix on all files in /app
# RUN dos2unix /app/*
# Install from requirements.txt
RUN pip3 install -r requirements.txt
# Install wait-for-it so this can easily be used with docker-compose
# Example: command: ["./wait-for-it.sh", "bridge:8554", "--", "python", "main.py"]
RUN wget https://raw.githubusercontent.com/vishnubob/wait-for-it/master/wait-for-it.sh && chmod +x wait-for-it.sh && mv wait-for-it.sh /bin
CMD ["python3", "main.py"]

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@ -1 +1,3 @@
.config/
Dockerfile
.venv
docker-compose.yml

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@ -25,7 +25,7 @@ jobs:
uses: actions/checkout@v3
- name: Log in to the Container registry
uses: docker/login-action@f4ef78c080cd8ba55a85445d5b36e214a81df20a
uses: docker/login-action@v3.0.0
with:
registry: ${{ env.REGISTRY }}
username: ${{ github.actor }}

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@ -1,42 +1,12 @@
FROM python:3.10-bullseye
FROM python:3.10.5-buster
RUN apt update && apt install libgl1 -y
RUN pip install poetry
# Install Dlib (for face_recognition)
RUN apt-get -y update && apt-get install -y --fix-missing \
build-essential \
cmake \
gfortran \
git \
wget \
curl \
graphicsmagick \
libgraphicsmagick1-dev \
libatlas-base-dev \
libavcodec-dev \
libavformat-dev \
libgtk2.0-dev \
libjpeg-dev \
liblapack-dev \
libswscale-dev \
pkg-config \
python3-dev \
python3-numpy \
software-properties-common \
zip
RUN apt-get clean
RUN rm -rf /tmp/* /var/tmp/*
ENV CFLAGS=-static
# Install dos2unix
# RUN apt-get install -y dos2unix
# Upgrade pip
RUN pip3 install --upgrade pip
# Copy directory to container
WORKDIR /app
COPY . ./
# Run dos2unix on all files in /app
# RUN dos2unix /app/*
# Install from requirements.txt
RUN pip3 install -r requirements.txt
# Install wait-for-it so this can easily be used with docker-compose
# Example: command: ["./wait-for-it.sh", "bridge:8554", "--", "python", "main.py"]
RUN wget https://raw.githubusercontent.com/vishnubob/wait-for-it/master/wait-for-it.sh && chmod +x wait-for-it.sh && mv wait-for-it.sh /bin
CMD ["python3", "main.py"]
COPY . .
RUN poetry install
ENTRYPOINT ["poetry", "run", "python", "-m", "set_detect_notify"]

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@ -1,21 +1,54 @@
# Wyze Face Recognition
Recognize faces in Wyze Cam footage and send notifications to your phone (or other devices)
# Set, Detect, Notify
Recognize faces/objects in (Wyze Cam) footage and send notifications to your phone (or other devices)
## Pre-requisites
* Docker
* Docker Compose
* A Wyze Cam
### Features
- Recognize objects
- Recognize faces
- Send notifications to your phone (or other devices) using [ntfy](https://ntfy.sh/)
- Optionally, run headless with Docker
- Either use a webcam or an RTSP feed
- Use [mrlt8/docker-wyze-bridge](https://github.com/mrlt8/docker-wyze-bridge) to get RTSP feeds from Wyze Cams
## What's not needed
* A Wyze Cam subscription
## How to use
1. Clone this repo
` git clone https://github.com/slackner/wyze-face-recognition.git`
2. Add images to the `config` directory
3. Copy `config/config.json.example` to `config/config.json` and edit the faces array to match the images you added, and the face names
4. Either set the `WYZE_EMAIL` and `WYZE_PASSWORD` environment variables, or edit `docker-compose.yml` and add your Wyze credentials
5. Run `docker-compose up -d`
## Prerequisites
### Poetry/Python
- Camera, either a webcam or a Wyze Cam
- All RTSP feeds _should_ work, however.
- Python
- Poetry
### Docker
- A Wyze Cam
- Any other RTSP feed _should_ work, as mentioned above
- Python
- Poetry
## What's not required
- A Wyze subscription
## Usage
### Installation
1. Clone this repo with `git clone https://github.com/slashtechno/wyze-face-recognition.git`
2. `cd` into the cloned repository
3. Then, either install with [Poetry](https://python-poetry.org/) or run with Docker
#### Docker
1. Modify to `docker-compose.yml` to achieve desired configuration
2. Run in the background with `docker compose up -d
#### Poetry
1. `poetry install`
2. `poetry run -- set-detect-notify`
### Configuration
The following are some basic CLI options. Most flags have environment variable equivalents which can be helpful when using Docker.
- For face recognition, put images of faces in subdirectories `./faces` (this can be changed with `--faces-directory`)
- Keep in mind, on the first run, face rec
- By default, notifications are sent for all objects. This can be changed with one or more occurrences of `--detect-object` to specify which objects to detect
- Currently, all classes in the [COCO](https://cocodataset.org/) dataset can be detected
- To specify where notifications are sent, specify a [ntfy](https://ntfy.sh/) URL with `--ntfy-url`
- To configure the program when using Docker, edit `docker-compose.yml` and/or set environment variables.
- **For further information, use `--help`**
### How to uninstall
1. Run `docker-compose down` in the `wyze-face-recognition` directory
- If you used Docker, run `docker-compose down --rmi all` in the cloned repository
- If you used Poetry, just delete the virtual environment and then the cloned repository

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@ -6,19 +6,21 @@ services:
container_name: bridge-wyze
restart: unless-stopped
image: mrlt8/wyze-bridge:latest
ports:
- 1935:1935 # RTMP
- 8554:8554 # RTSP
- 8888:8888 # HLS
- 5000:5000 # WEB-UI
# 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:
- WYZE_EMAIL=${WYZE_EMAIL} # Replace with wyze email
- WYZE_PASSWORD=${WYZE_PASSWORD} # Replace with wyze password
networks:
all:
aliases:
- bridge
- wyze-bridge
# aliases:
# - bridge
# - wyze-bridge
ntfy:
image: binwiederhier/ntfy
container_name: ntfy-wyze
@ -36,29 +38,34 @@ services:
all:
facial_recognition:
container_name: face-recognition-wyze
restart: unless-stopped
image: ghcr.io/slashtechno/wyze_face_recognition:latest
restart: unless-stopped
# image: ghcr.io/slashtechno/wyze_face_recognition:latest
build:
context: .
dockerfile: Dockerfile
volumes:
# ./config is mounted as /app/config
- ./config:/app/config
- ./faces:/app/faces
networks:
all:
environment:
- RUN_BY_COMPOSE=true
- URL=rtsp://bridge:8554/cv
- NO_DISPLAY=true
- NTFY_URL=http://ntfy:80/set-detect-notify
depends_on:
- bridge
# Use curl to check if the rtsp stream is up, then run the face recognition
command: >
/bin/sh -c "
while true; do
curl -s http://bridge:8888/cv/0.m3u8 > /dev/null
if [ $? -eq 0 ]; then
echo 'Stream is up, running face recognition'
python3 /app/main.py
else
echo 'Stream is down, waiting 5 seconds'
sleep 5
fi
done
"
# command: >
# /bin/sh -c "
# while true; do
# curl -s http://bridge:8888/cv/0.m3u8 > /dev/null
# if [ $? -eq 0 ]; then
# echo 'Stream is up, running face recognition'
# python3 /app/main.py
# else
# echo 'Stream is down, waiting 5 seconds'
# sleep 5
# fi
# done
# "
tty: true

374
poetry.lock generated
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@ -396,6 +396,35 @@ files = [
[package.dependencies]
colorama = {version = "*", markers = "platform_system == \"Windows\""}
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@ -757,41 +786,6 @@ ufo = ["fs (>=2.2.0,<3)"]
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adl = ["adlfs"]
arrow = ["pyarrow (>=1)"]
dask = ["dask", "distributed"]
devel = ["pytest", "pytest-cov"]
dropbox = ["dropbox", "dropboxdrivefs", "requests"]
full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
fuse = ["fusepy"]
gcs = ["gcsfs"]
git = ["pygit2"]
github = ["requests"]
gs = ["gcsfs"]
gui = ["panel"]
hdfs = ["pyarrow (>=1)"]
http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
libarchive = ["libarchive-c"]
oci = ["ocifs"]
s3 = ["s3fs"]
sftp = ["paramiko"]
smb = ["smbprotocol"]
ssh = ["paramiko"]
tqdm = ["tqdm"]
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@ -1428,6 +1422,16 @@ files = [
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@ -1807,6 +1811,164 @@ files = [
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[package.dependencies]
cmake = "*"
filelock = "*"
lit = "*"
torch = "*"
[package.extras]
tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
tutorials = ["matplotlib", "pandas", "tabulate"]
[[package]]
name = "typing-extensions"
version = "4.8.0"
@ -3404,4 +3618,4 @@ files = [
[metadata]
lock-version = "2.0"
python-versions = ">=3.10, <3.12"
content-hash = "2195b7b92ae6ef77fab180f74922d33d6912a77d9cd193e986db2d788336b2cb"
content-hash = "952e41ab099f9d1c7055465d5a3c228df677c422d20dea8ff8d558d2e4d8f2be"

View File

@ -18,7 +18,7 @@ hjson = "^3.1.0"
numpy = "^1.23.2"
# https://github.com/python-poetry/poetry/issues/6409
torch = "^2.1.0"
torch = ">=2.0.0, !=2.0.1, !=2.1.0"
tensorflow-io-gcs-filesystem = "0.31.0"
deepface = "^0.0.79"

View File

@ -62,6 +62,15 @@ def main():
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"]
@ -72,14 +81,6 @@ def main():
help="The confidence threshold to use",
)
argparser.add_argument(
"--detect-object",
nargs="*",
default=[],
type=str,
help="The object(s) to detect. Must be something the model is trained to detect",
)
argparser.add_argument(
"--faces-directory",
default=os.environ["FACES_DIRECTORY"]
@ -88,6 +89,13 @@ def main():
type=str,
help="The directory to store the faces. Should contain 1 subdirectory of images per person",
)
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(
@ -287,7 +295,8 @@ def main():
# Display the resulting frame
# cv2.imshow("", r)
cv2.imshow(f"Video{i}", frame_to_show)
if not args.no_display:
cv2.imshow(f"Video{i}", frame_to_show)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord("q"):