Docker is working again
Resorted to reinstalling `torchvision` via `pip`
This commit is contained in:
parent
4c398b9603
commit
771154cbef
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@ -1,15 +1,19 @@
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FROM python:3.10.5-buster
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LABEL org.opencontainers.image.description "Dokcer image for running wyzely-detect"
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LABEL org.opencontainers.image.description "Docker image for running wyzely-detect"
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LABEL org.opencontainers.image.source "https://github.com/slashtechno/wyzely-detect"
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RUN apt update && apt install libgl1 -y
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RUN pip install poetry
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WORKDIR /app
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COPY . .
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RUN poetry install
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ENTRYPOINT ["poetry", "run", "python", "-m", "wyzely_detect"]
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RUN poetry run pip uninstall -y torchvision
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RUN poetry run pip install torchvision
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ENTRYPOINT ["poetry", "run", "python", "-m", "--", "wyzely_detect", "--no-display"]
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15
README.md
15
README.md
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@ -14,6 +14,7 @@ Recognize faces/objects in a video stream (from a webcam or a security camera) a
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### Python
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- Camera, either a webcam or a Wyze Cam
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- All RTSP feeds _should_ work, however.
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- **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.
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- Python 3.10 or 3.11
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- Poetry (optional)
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- Windows or Linux
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@ -37,20 +38,24 @@ Cloning the repository is not required when installing from PyPi but is required
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2. `cd` into the cloned repository
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3. Then, either install with [Poetry](https://python-poetry.org/) or run with Docker
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#### Docker
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1. Modify to `docker-compose.yml` to achieve desired configuration
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2. Run in the background with `docker compose up -d
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#### Installing from PyPi with pip
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#### Installing from PyPi with pip (recommended)
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This assumes you have Python 3.10 or 3.11 installed
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1. `pip install wyzely-detect`
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a. You may need to use `pip3` instead of `pip`
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2. `wyzely-detect`
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#### Poetry
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#### Poetry (best for GPU support)
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1. `poetry install`
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a. For GPU support, use `poetry install -E cuda --with gpu`
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2. `poetry run -- wyzely-detect`
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#### Docker
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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.
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1. Modify to `docker-compose.yml` to achieve desired configuration
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2. Run in the background with `docker compose up -d`
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### Configuration
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The following are some basic CLI options. Most flags have environment variable equivalents which can be helpful when using Docker.
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@ -7,15 +7,18 @@ services:
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restart: unless-stopped
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image: mrlt8/wyze-bridge:latest
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# The ports can be removed since we're using the network
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# Just an unnecesary security risk to expose them but can be useful for debugging
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# Just an unnecesary security risk to expose them but can be useful for debugging
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# ports:
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# - 1935:1935 # RTMP
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# - 8554:8554 # RTSP (this is really the only one we need)
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# - 8888:8888 # HLS
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# - 5000:5000 # WEB-UI
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# - 5000:5000 # WEB-UI
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environment:
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- WYZE_EMAIL=${WYZE_EMAIL} # Replace with wyze email
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- WYZE_PASSWORD=${WYZE_PASSWORD} # Replace with wyze password
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# This is a simple configuration without 2FA.
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# For advanced configuration, including using an API key, see https://github.com/mrlt8/docker-wyze-bridge/wiki/Two-Factor-Authentication
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# Either replace the following with your Wyze username and password, or set the environment variables
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- WYZE_EMAIL=${WYZE_EMAIL}
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- WYZE_PASSWORD=${WYZE_PASSWORD}
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networks:
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all:
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ntfy:
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@ -35,12 +38,24 @@ services:
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all:
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wyzely-detect:
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container_name: wyzely-detect
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restart: unless-stopped
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image: ghcr.io/slashtechno/wyzely-detect:latest
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# restart: unless-stopped
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# image: ghcr.io/slashtechno/wyzely-detect:latest
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# Building from source is also an option
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# build:
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# context: .
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# dockerfile: Dockerfile
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build:
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context: .
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dockerfile: Dockerfile
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command:
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- "--ntfy-url"
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# Replace "wyzely-detect" with the desired notification stream
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- "http://ntfy:80/wyzely-detect"
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- "--rtsp-url"
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# Replace "cv" with the desired rtsp stream
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- "rtsp://bridge:8554/cv"
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# Example second rtsp stream
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# - "--rtsp-url"
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# - "rtsp://bridge:8554/camera"
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volumes:
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- ./faces:/app/faces
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networks:
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@ -1104,13 +1104,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
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[[package]]
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name = "ipython"
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version = "8.22.1"
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version = "8.22.2"
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description = "IPython: Productive Interactive Computing"
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optional = false
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python-versions = ">=3.10"
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files = [
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{file = "ipython-8.22.1-py3-none-any.whl", hash = "sha256:869335e8cded62ffb6fac8928e5287a05433d6462e3ebaac25f4216474dd6bc4"},
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{file = "ipython-8.22.1.tar.gz", hash = "sha256:39c6f9efc079fb19bfb0f17eee903978fe9a290b1b82d68196c641cecb76ea22"},
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{file = "ipython-8.22.2-py3-none-any.whl", hash = "sha256:3c86f284c8f3d8f2b6c662f885c4889a91df7cd52056fd02b7d8d6195d7f56e9"},
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{file = "ipython-8.22.2.tar.gz", hash = "sha256:2dcaad9049f9056f1fef63514f176c7d41f930daa78d05b82a176202818f2c14"},
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]
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[package.dependencies]
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[[package]]
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name = "nbconvert"
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version = "7.16.1"
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version = "7.16.2"
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description = "Converting Jupyter Notebooks (.ipynb files) to other formats. Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script. nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
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optional = false
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python-versions = ">=3.8"
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files = [
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{file = "nbconvert-7.16.1-py3-none-any.whl", hash = "sha256:3188727dffadfdc9c6a1c7250729063d7bc78b355ad7aa023138afa030d1cd07"},
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{file = "nbconvert-7.16.1.tar.gz", hash = "sha256:e79e6a074f49ba3ed29428ed86487bf51509d9aab613bd8522ac08f6d28fd7fd"},
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{file = "nbconvert-7.16.2-py3-none-any.whl", hash = "sha256:0c01c23981a8de0220255706822c40b751438e32467d6a686e26be08ba784382"},
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{file = "nbconvert-7.16.2.tar.gz", hash = "sha256:8310edd41e1c43947e4ecf16614c61469ebc024898eb808cce0999860fc9fb16"},
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]
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[package.dependencies]
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{file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
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{file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
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{file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
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{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
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{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
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{file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
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{file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
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[[package]]
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name = "ultralytics"
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version = "8.1.22"
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version = "8.1.23"
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description = "Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification."
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optional = false
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python-versions = ">=3.8"
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files = [
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{file = "ultralytics-8.1.22-py3-none-any.whl", hash = "sha256:219a0e292d5013718883e90b3a5252c08c0094e9d76123b4873a446ed93eb737"},
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{file = "ultralytics-8.1.22.tar.gz", hash = "sha256:d3a47863d24cc5c8ea648bb69e8066795d103a0bf1558d8077f5b25d5e000f70"},
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{file = "ultralytics-8.1.23-py3-none-any.whl", hash = "sha256:d0df385e5c6a90493ac25a3b18115d756b0e0aa4feb9fa6d196ad0b6c4a393e0"},
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{file = "ultralytics-8.1.23.tar.gz", hash = "sha256:2915e8c96a66344fef89f02de3cdf2a52f81d5591319f81d639011214674ab6d"},
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]
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[package.dependencies]
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