commit 1dac1749bf022eb140dc05a1d5ed75490e856b5b Author: Mustafa KURU Date: Sun May 3 23:47:54 2026 +0300 Initial commit diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..fe12825 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,9 @@ +* text=auto + +*.py text eol=lf +*.yaml text eol=lf +*.yml text eol=lf +*.json text eol=lf +Dockerfile* text eol=lf +*.sh text eol=lf +addon/rootfs/etc/services.d/**/run text eol=lf diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..ca79ca5 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,6 @@ +version: 2 +updates: + - package-ecosystem: github-actions + directory: / + schedule: + interval: weekly diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml new file mode 100644 index 0000000..ceb9f6c --- /dev/null +++ b/.github/workflows/ci.yaml @@ -0,0 +1,66 @@ +name: CI + +on: + push: + tags-ignore: + - "v*" + pull_request: + workflow_dispatch: + +permissions: + contents: read + +jobs: + validate: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + + - uses: actions/setup-python@v6 + with: + python-version: "3.12" + + - name: Validate JSON + run: | + python -m json.tool hacs.json > /dev/null + python -m json.tool repository.json > /dev/null + python -m json.tool custom_components/elegoo_spaghetti_detection/manifest.json > /dev/null + python -m json.tool custom_components/elegoo_spaghetti_detection/translations/en.json > /dev/null + + - name: Validate Python syntax + run: python -m compileall custom_components/elegoo_spaghetti_detection addon/rootfs/app + + - name: Validate YAML + run: | + python -m pip install pyyaml + python - <<'PY' + from pathlib import Path + import yaml + + class Loader(yaml.SafeLoader): + pass + + def unknown_constructor(loader, tag_suffix, node): + if isinstance(node, yaml.MappingNode): + return loader.construct_mapping(node) + if isinstance(node, yaml.SequenceNode): + return loader.construct_sequence(node) + return loader.construct_scalar(node) + + Loader.add_multi_constructor("!", unknown_constructor) + + paths = [ + Path("docker-compose.yaml"), + Path("addon/config.yaml"), + Path("custom_components/elegoo_spaghetti_detection/services.yaml"), + Path(".github/workflows/ci.yaml"), + Path(".github/workflows/hassfest.yaml"), + Path(".github/workflows/validate.yaml"), + Path(".github/dependabot.yml"), + *Path("examples").glob("*.yaml"), + ] + + for path in sorted(paths): + with path.open("r", encoding="utf-8") as handle: + yaml.load(handle, Loader=Loader) + PY diff --git a/.github/workflows/hassfest.yaml b/.github/workflows/hassfest.yaml new file mode 100644 index 0000000..9b104ab --- /dev/null +++ b/.github/workflows/hassfest.yaml @@ -0,0 +1,20 @@ +name: Validate with hassfest + +on: + push: + tags-ignore: + - "v*" + pull_request: + schedule: + - cron: "0 0 * * *" + workflow_dispatch: + +permissions: + contents: read + +jobs: + validate: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + - uses: home-assistant/actions/hassfest@master diff --git a/.github/workflows/validate.yaml b/.github/workflows/validate.yaml new file mode 100644 index 0000000..0f2b33d --- /dev/null +++ b/.github/workflows/validate.yaml @@ -0,0 +1,23 @@ +name: Validate + +on: + push: + tags-ignore: + - "v*" + pull_request: + schedule: + - cron: "0 0 * * *" + workflow_dispatch: + +permissions: + contents: read + +jobs: + validate-hacs: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + - name: HACS validation + uses: hacs/action@main + with: + category: integration diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..327a8a4 --- /dev/null +++ b/.gitignore @@ -0,0 +1,162 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +.idea/ + + +.vscode/ \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..ef7bf2e --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,21 @@ +# Contributing + +Work locally, keep changes focused, and test in Home Assistant before opening a +pull request. + +- Use clear commit messages. +- Do not commit Home Assistant tokens, camera proxy tokens, SSH keys, or local + deployment notes. +- Keep the integration domain as `elegoo_spaghetti_detection`. +- Validate JSON, YAML, Python syntax, HACS, and Hassfest before opening a pull + request. + +## Local Validation + +```bash +python -m json.tool hacs.json > /dev/null +python -m json.tool custom_components/elegoo_spaghetti_detection/manifest.json > /dev/null +python -m compileall custom_components/elegoo_spaghetti_detection addon/rootfs/app +``` + +The GitHub workflows run the full repository checks. diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..f288702 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + 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. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + 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 GPL, see +. + + 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 +. diff --git a/README.md b/README.md new file mode 100644 index 0000000..013bdb0 --- /dev/null +++ b/README.md @@ -0,0 +1,156 @@ +# Elegoo Spaghetti Detection + +Home Assistant spaghetti/failure detection for Elegoo FDM printers. It is +tested with Elegoo Centauri Carbon 2 through +[`danielcherubini/elegoo-homeassistant`](https://github.com/danielcherubini/elegoo-homeassistant), +but the detector can use any Home Assistant camera entity. + +[![Open your Home Assistant instance and open a repository inside the Home Assistant Community Store.](https://my.home-assistant.io/badges/hacs_repository.svg)](https://my.home-assistant.io/redirect/hacs_repository/?owner=hepter&repository=ha-elegoo-spaghetti-detection&category=integration) + +This project started as an Elegoo-focused adaptation of +[`nberktumer/ha-bambu-lab-p1-spaghetti-detection`](https://github.com/nberktumer/ha-bambu-lab-p1-spaghetti-detection). +The original project provided the Obico ML workflow and Home Assistant +integration foundation. + +This repository is not affiliated with Elegoo, Home Assistant, HACS, Obico, or +the original upstream author. + +## Scope + +The integration detects possible print failures and exposes Home Assistant +entities/events. It does not directly control the printer. + +Detection flow: + +```text +camera snapshot -> ML server -> confidence/result sensors -> Home Assistant events +``` + +Printer-specific actions such as pause, resume, stop, and notifications belong +in user automations. Ready-to-edit examples are included. + +## Features + +- Works with Home Assistant camera entities, including Elegoo chamber cameras. +- Uses an Obico/TSD FDM failure model running in a local Docker/HA add-on server. +- Validates ML server health and camera image reachability during setup. +- Optional print status sensor gates scheduled detection to active print states. +- Elegoo `print_status` sensors are guarded by the companion `current_status` + sensor when it exists, avoiding scheduled checks during homing/idle states + where `print_status` can remain `printing`. +- Optional chamber light control can leave the light alone, turn it on and keep + it on, or temporarily turn it on and restore the previous state after each + snapshot. +- Manual `Test Spaghetti Detection` button. +- Confidence, raw score, detection count, status, last run, next run, and last + error sensors. +- `binary_sensor._spaghetti_detected`. +- Events for every result and for detected failures. +- ML server web dashboard, JSON status, recent request logs, and image debug + endpoint. +- CPU-first ML startup by default to avoid CUDA timeout failures on systems + without a working GPU runtime. + +## Documentation + +- [Installation](docs/installation.md) +- [Configuration](docs/configuration.md) +- [Automation examples](docs/automations.md) +- [Dashboard examples](docs/dashboard.md) +- [ML server and logs](docs/ml-server.md) +- [Troubleshooting](docs/troubleshooting.md) +- [HACS publishing notes](docs/HACS_PUBLISHING.md) + +## Screenshots + +Integration setup: + +![Elegoo Spaghetti Detection setup form](docs/images/config-flow-add-hub.png) + +Enhanced dashboard in idle state: + +![Enhanced dashboard idle state](docs/images/dashboard-hacs-waiting-for-print.png) + +Enhanced dashboard after a detected failure: + +![Enhanced dashboard detected failure](docs/images/dashboard-hacs-detected.png) + +Camera frame with an obvious spaghetti failure: + +![Camera frame with spaghetti failure](docs/images/camera-spaghetti-failure.png) + +## Quick Start + +1. Run the ML server. See [ML server and logs](docs/ml-server.md). +2. Install the custom integration through HACS or manually. See + [Installation](docs/installation.md). +3. Add `Elegoo Spaghetti Detection` from Home Assistant integrations. +4. Select the camera and optional print status sensor. See + [Configuration](docs/configuration.md). +5. Press `Test Spaghetti Detection`. +6. Add one of the [automation examples](docs/automations.md). +7. Add one of the [dashboard examples](docs/dashboard.md). + +## Typical Elegoo CC2 Entities + +Your entity IDs depend on the printer/device name in Home Assistant. With a +default-ish Elegoo Centauri Carbon 2 setup they often look like: + +```text +camera.elegoo_centauri_carbon2_chamber_camera +sensor.elegoo_centauri_carbon2_print_status +light.elegoo_centauri_carbon2_chamber_light +button.elegoo_centauri_carbon2_pause_print +button.elegoo_centauri_carbon2_resume_print +button.elegoo_centauri_carbon2_stop_print +``` + +The integration setup uses only camera, optional print status, and optional +light. Pause/stop/resume are shown only in automation examples. + +## Events + +Every detection result fires: + +```text +elegoo_spaghetti_detection_result +``` + +Detected failures fire: + +```text +elegoo_spaghetti_detection_detected +``` + +When a print status sensor is configured, scheduled detected events are emitted +once per active print window. If the printer leaves the configured active states +and later returns to an active state, a new detected failure can emit one new +event. + +Use this event for notification, pause, and stop automations. + +`elegoo_spaghetti_detection_result` still fires for every completed check. Use +that event for logging, dashboards, or advanced automations only; notification +automations based on the result event can repeat every detection interval. + +Event data includes: + +```text +config_entry +detector +name +camera +manual +printer_state +confidence +raw_score +detected +detections +image_url +last_error +last_run +next_run +status +``` + +Use these fields in notifications and advanced automations. diff --git a/addon/.dockerignore b/addon/.dockerignore new file mode 100644 index 0000000..c65d14b --- /dev/null +++ b/addon/.dockerignore @@ -0,0 +1,3 @@ +model/*.onnx +model/*.darknet + diff --git a/addon/.gitattributes b/addon/.gitattributes new file mode 100644 index 0000000..127a8a2 --- /dev/null +++ b/addon/.gitattributes @@ -0,0 +1 @@ +*.weights filter=lfs diff=lfs merge=lfs -text diff --git a/addon/.gitignore b/addon/.gitignore new file mode 100644 index 0000000..4f69c04 --- /dev/null +++ b/addon/.gitignore @@ -0,0 +1,2 @@ +model/*.onnx +model/*.darknet diff --git a/addon/Dockerfile b/addon/Dockerfile new file mode 100644 index 0000000..cce8515 --- /dev/null +++ b/addon/Dockerfile @@ -0,0 +1,37 @@ +FROM ghcr.io/home-assistant/amd64-base-debian:bookworm as darknet_builder +ENV DEBIAN_FRONTEND=noninteractive +RUN apt update && apt install -y ca-certificates build-essential gcc g++ cmake git +WORKDIR / + +# Lock darknet version for reproducibility. +RUN git clone https://github.com/AlexeyAB/darknet && cd darknet && git checkout 59c86222c5387bffd9108a21885f80e980ece234 +RUN cd darknet \ + && sed -i 's/GPU=1/GPU=0/' Makefile \ + && sed -i 's/CUDNN=1/CUDNN=0/' Makefile \ + && sed -i 's/CUDNN_HALF=1/CUDNN_HALF=0/' Makefile \ + && sed -i 's/LIBSO=0/LIBSO=1/' Makefile \ + && make -j 4 && mv libdarknet.so libdarknet_cpu.so + +FROM ghcr.io/home-assistant/amd64-base-debian:bookworm + +RUN apt update && apt install --no-install-recommends -y ca-certificates python3-pip wget python3 python3-venv + +COPY rootfs / +COPY --from=darknet_builder /darknet /darknet + +WORKDIR /app + +RUN python3 -m venv venv +ENV VIRTUAL_ENV=/app/venv +ENV PATH=/app/venv/bin:$PATH + +RUN pip3 install --upgrade pip && \ + pip3 install opencv_python_headless && \ + pip3 install -r requirements.txt + +RUN echo 'Downloading the latest failure detection AI model in Darknet format...' && \ + wget -O model/model-weights.darknet $(cat model/model-weights.darknet.url | tr -d '\r') && \ + echo 'Downloading the latest failure detection AI model in ONNX format...' && \ + wget -O model/model-weights.onnx $(cat model/model-weights.onnx.url | tr -d '\r') + +RUN chmod +x /etc/services.d/ha-elegoo-spaghetti-detection/run diff --git a/addon/Dockerfile.ha.base b/addon/Dockerfile.ha.base new file mode 100644 index 0000000..cf02d0c --- /dev/null +++ b/addon/Dockerfile.ha.base @@ -0,0 +1,44 @@ +FROM ghcr.io/home-assistant/amd64-base-debian:bookworm as darknet_builder +ENV DEBIAN_FRONTEND=noninteractive +RUN apt update && apt install -y ca-certificates build-essential gcc g++ cmake git +WORKDIR / +# Lock darknet version for reproducibility +RUN git clone https://github.com/AlexeyAB/darknet && cd darknet && git checkout 59c86222c5387bffd9108a21885f80e980ece234 +# compile CPU version +RUN cd darknet \ + && sed -i 's/GPU=1/GPU=0/' Makefile \ + && sed -i 's/CUDNN=1/CUDNN=0/' Makefile \ + && sed -i 's/CUDNN_HALF=1/CUDNN_HALF=0/' Makefile \ + && sed -i 's/LIBSO=0/LIBSO=1/' Makefile \ + && make -j 4 && mv libdarknet.so libdarknet_cpu.so + +# ----------------------------------------------------------------------------- + +FROM ghcr.io/home-assistant/amd64-base-debian:bookworm as ml_api_base_amd64 + +RUN apt update && apt install --no-install-recommends -y ca-certificates python3-pip wget python3 python3-venv + +COPY --from=darknet_builder /darknet /darknet + +WORKDIR /app +RUN mkdir -p model +COPY rootfs/app/requirements.txt /app/requirements.txt +COPY rootfs/app/model/model-weights.darknet.url /app/model/model-weights.darknet.url +COPY rootfs/app/model/model-weights.onnx.url /app/model/model-weights.onnx.url + +RUN python3 -m venv venv +ENV VIRTUAL_ENV /app/venv +ENV PATH /app/venv/bin:$PATH + +RUN pip3 install --upgrade pip && \ + pip3 install opencv_python_headless && \ + pip3 install -r requirements.txt + +RUN echo 'Downloading the latest failure detection AI model in Darknet format...' && \ + wget -O model/model-weights.darknet $(cat model/model-weights.darknet.url | tr -d '\r') && \ + echo 'Downloading the latest failure detection AI model in ONNX format...' && \ + wget -O model/model-weights.onnx $(cat model/model-weights.onnx.url | tr -d '\r') + +COPY rootfs / +RUN chmod +x /etc/services.d/ha-elegoo-spaghetti-detection/run + diff --git a/addon/Dockerfile.standalone.base b/addon/Dockerfile.standalone.base new file mode 100644 index 0000000..10de94f --- /dev/null +++ b/addon/Dockerfile.standalone.base @@ -0,0 +1,20 @@ +FROM thespaghettidetective/ml_api_base:1.3 +WORKDIR /app +EXPOSE 3333 + +RUN mkdir -p model +COPY rootfs/app/requirements.txt /app/requirements.txt +COPY rootfs/app/model/model-weights.darknet.url /app/model/model-weights.darknet.url +COPY rootfs/app/model/model-weights.onnx.url /app/model/model-weights.onnx.url +RUN pip install --upgrade pip +RUN pip install -r requirements.txt + +RUN echo 'Downloading the latest failure detection AI model in Darknet format...' +RUN wget -O model/model-weights.darknet $(cat model/model-weights.darknet.url | tr -d '\r') +RUN echo 'Downloading the latest failure detection AI model in ONNX format...' +RUN wget -O model/model-weights.onnx $(cat model/model-weights.onnx.url | tr -d '\r') + +ADD rootfs/app /app +ENV FLASK_APP server.py + +CMD gunicorn --bind "0.0.0.0:3333" --workers "${GUNICORN_WORKERS:-1}" --timeout "${GUNICORN_TIMEOUT:-120}" --error-logfile - --log-level info wsgi diff --git a/addon/__init__.py b/addon/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/addon/config.yaml b/addon/config.yaml new file mode 100644 index 0000000..3e8373a --- /dev/null +++ b/addon/config.yaml @@ -0,0 +1,18 @@ +name: "Elegoo Spaghetti Detection Server" +description: "Obico ML server for Elegoo spaghetti detection" +version: "1.0.0" +slug: "ha_elegoo_spaghetti_detection_addon" +init: false +arch: + - amd64 +startup: services +ports: + 3333/tcp: 3333 +options: + obico_api_secret: "obico_api_secret" + use_gpu: false + gunicorn_timeout: 120 +schema: + obico_api_secret: str + use_gpu: bool + gunicorn_timeout: int diff --git a/addon/detect.py b/addon/detect.py new file mode 100644 index 0000000..e600eda --- /dev/null +++ b/addon/detect.py @@ -0,0 +1,118 @@ +#!python3 +import cv2 +from dataclasses import asdict +import json +from addon import compare_detections, Detection +import os +import argparse +import time + +KNOWN_IMAGE_EXTENSIONS = ('.jpg', '.png') +KNOWN_VIDEO_EXTENSIONS = ('.mp4', '.avi') + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("image", type=str, help="Image file path") + parser.add_argument("--weights", type=str, help="Model weights file") + parser.add_argument("--det-threshold", type=float, default=0.25, help="Detection threshold") + parser.add_argument("--nms-threshold", type=float, default=0.4, help="NMS threshold") + parser.add_argument("--preheat", action='store_true', help="Make a dry run of NN for initlalization") + parser.add_argument("--cpu", action='store_true', help="Force use CPU") + parser.add_argument("--save-detections-to", type=str, help="Save detections into this file") + parser.add_argument("--compare-detections-with", type=str, help="Load detections from this file and compare with result") + parser.add_argument("--render-to", type=str, help="Save detections into this file or directory") + parser.add_argument("--print", action='store_true', help="Print detections") + opt = parser.parse_args() + + net_main_1 = load_net("rootfs/model/model.cfg", "rootfs/model/model.meta", weights_path=opt.weights) + + # force use CPU, only implemented for ONNX + if opt.cpu and onnx_ready and isinstance(net_main_1, OnnxNet): + net_main_1.force_cpu() + + filename = os.path.basename(opt.image) + filename, extension = os.path.splitext(filename) + + is_image = extension in KNOWN_IMAGE_EXTENSIONS + is_video = extension in KNOWN_VIDEO_EXTENSIONS + frame_number = 0 + vwr = None + if is_video: + cap = cv2.VideoCapture(opt.image) + fps = cap.get(cv2.CAP_PROP_FPS) + frame_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + frame_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + reading_success, custom_image_bgr = cap.read() + if opt.render_to: + fourcc = cv2.VideoWriter_fourcc("m", "p", "4", "v") + vwr = cv2.VideoWriter(opt.render_to, fourcc, fps, (frame_w, frame_h)) + else: + cap = None + fps = 0.0 + custom_image_bgr = cv2.imread(opt.image) + reading_success = True + + + # this will make library initialize all the required resources at the first run + # then the following runs will be much faster + if opt.preheat: + detections = detect(net_main_1, custom_image_bgr, thresh=opt.det_threshold, nms=opt.nms_threshold) + + while reading_success: + started_at = time.time() + detections = detect(net_main_1, custom_image_bgr, thresh=opt.det_threshold, nms=opt.nms_threshold) + finished_at = time.time() + execution_time = finished_at - started_at + print(f"Frame #{frame_number} execution time: {execution_time:.3} sec, detection count: {len(detections)}") + + detections = Detection.from_tuple_list(detections) + # dump detections into some file + if opt.save_detections_to: + output_filename, output_extension = os.path.splitext(opt.save_detections_to) + if is_video and not output_extension and not os.path.exists(opt.save_detections_to): + os.makedirs(opt.save_detections_to) + if os.path.isdir(opt.save_detections_to): + if is_video: + output_file_name = f"{filename}#{frame_number:04}.json" + else: + output_file_name = f"{filename}.json" + output_file_name = os.path.join(opt.save_detections_to, output_file_name) + else: + output_file_name = opt.save_detections_to + + with open(output_file_name, "w") as f: + json.dump([asdict(d) for d in detections], f) + + # load detections from some file and compare with detection result + if opt.compare_detections_with: + if is_video: + read_file_name = os.path.join(opt.compare_detections_with, f"{filename}#{frame_number:04}.json") + else: + read_file_name = opt.compare_detections_with + + with open(read_file_name) as f: + items = json.load(f) + loaded = [Detection.from_dict(d) for d in items] + compare_result = compare_detections(loaded, detections) + if not compare_result: + print(f"Frame #{frame_number} loaded detections and resulting are different") + if opt.render_to: + for d in detections: + cv2.rectangle(custom_image_bgr, + (int(d.box.left()), int(d.box.top())), (int(d.box.right()), int(d.box.bottom())), + (0, 255, 0), 2) + if vwr: + vwr.write(custom_image_bgr) + else: + cv2.imwrite(opt.render_to, custom_image_bgr) + + + if opt.print: + print(detections) + + if is_image: + reading_success = False + elif cap: + reading_success, custom_image_bgr = cap.read() + frame_number += 1 + diff --git a/addon/rootfs/app/__init__.py b/addon/rootfs/app/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/addon/rootfs/app/auth.py b/addon/rootfs/app/auth.py new file mode 100644 index 0000000..1cd8425 --- /dev/null +++ b/addon/rootfs/app/auth.py @@ -0,0 +1,25 @@ +import os +from functools import wraps + +from flask import Response, request + +ML_API_TOKEN = os.environ.get("ML_API_TOKEN") + + +def token_required(f): + @wraps(f) + def check_authorization(*args, **kwargs): + if ( + request.headers.get("Authorization") == f"Bearer {ML_API_TOKEN}" + or request.args.get("token") == ML_API_TOKEN + ): + return f(*args, **kwargs) + return Response(status=401) + + @wraps(f) + def passthru(*args, **kwargs): + return f(*args, **kwargs) + + if ML_API_TOKEN: + return check_authorization + return passthru diff --git a/addon/rootfs/app/lib/__init__.py b/addon/rootfs/app/lib/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/addon/rootfs/app/lib/darknet.py b/addon/rootfs/app/lib/darknet.py new file mode 100644 index 0000000..3d63a1e --- /dev/null +++ b/addon/rootfs/app/lib/darknet.py @@ -0,0 +1,254 @@ +# pylint: disable=R, W0401, W0614, W0703 +from ctypes import * +import random +import os +import cv2 +import platform +from typing import List, Tuple + +# C-structures from Darknet lib + +class BOX(Structure): + _fields_ = [("x", c_float), + ("y", c_float), + ("w", c_float), + ("h", c_float)] + + +class DETECTION(Structure): + _fields_ = [("bbox", BOX), + ("classes", c_int), + ("best_class_idx", c_int), + ("prob", POINTER(c_float)), + ("mask", POINTER(c_float)), + ("objectness", c_float), + ("sort_class", c_int), + ("uc", POINTER(c_float)), + ("points", c_int), + ("embeddings", POINTER(c_float)), + ("embedding_size", c_int), + ("sim", c_float), + ("track_id", c_int)] + +class IMAGE(Structure): + _fields_ = [("w", c_int), + ("h", c_int), + ("c", c_int), + ("data", POINTER(c_float))] + + +class METADATA(Structure): + _fields_ = [("classes", c_int), + ("names", POINTER(c_char_p))] + +class YoloNet: + """Darknet-based detector implementation""" + net: c_void_p + meta: METADATA + + def __init__(self, weight_path: str, meta_path: str, config_path: str, asked_to_use_gpu: bool): + if not os.path.exists(config_path): + raise ValueError("Invalid config path `"+os.path.abspath(config_path)+"`") + if not os.path.exists(weight_path): + raise ValueError("Invalid weight path `"+os.path.abspath(weight_path)+"`") + if not os.path.exists(meta_path): + raise ValueError("Invalid data file path `"+os.path.abspath(meta_path)+"`") + if not lib: + raise ImportError(f"Unable to load darknet module.") + + if asked_to_use_gpu and not using_gpu: + raise Exception('I respectfully decline to load the net as I am asked to use GPU but the loaded darknet module does NOT have GPU support') + + self.net = load_net_custom(config_path.encode("ascii"), weight_path.encode("ascii"), 0, 1) # batch size = 1 + self.meta = load_meta(meta_path.encode("ascii")) + + def detect(self, meta, image, alt_names, thresh=.5, hier_thresh=.5, nms=.45, debug=False) -> List[Tuple[str, float, Tuple[float, float, float, float]]]: + #pylint: disable= C0321 + custom_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + im, arr = array_to_image(custom_image) # you should comment line below: free_image(im) + if debug: + print("Loaded image") + num = c_int(0) + if debug: + print("Assigned num") + pnum = pointer(num) + if debug: + print("Assigned pnum") + predict_image(self.net, im) + if debug: + print("did prediction") + dets = get_network_boxes(self.net, custom_image.shape[1], custom_image.shape[0], thresh, hier_thresh, None, 0, pnum, 0) # OpenCV + if debug: + print("Got dets") + num = pnum[0] + if debug: + print("got zeroth index of pnum") + if nms: + do_nms_sort(dets, num, meta.classes, nms) + if debug: + print("did sort") + res = [] + if debug: + print("about to range") + for j in range(num): + if debug: + print("Ranging on "+str(j)+" of "+str(num)) + if debug: + print("Classes: "+str(meta), meta.classes, meta.names) + for i in range(meta.classes): + if debug: + print("Class-ranging on "+str(i)+" of "+str(meta.classes)+"= "+str(dets[j].prob[i])) + if dets[j].prob[i] > 0: + b = dets[j].bbox + if alt_names is None: + nameTag = meta.names[i] + else: + nameTag = alt_names[i] + if debug: + print("Got bbox", b) + print(nameTag) + print(dets[j].prob[i]) + print((b.x, b.y, b.w, b.h)) + res.append((nameTag, dets[j].prob[i], (b.x, b.y, b.w, b.h))) + if debug: + print("did range") + res = sorted(res, key=lambda x: -x[1]) + if debug: + print("did sort") + free_detections(dets, num) + if debug: + print("freed detections") + return res + +# Loads darknet shared library. May fail if some dependencies like OpenCV not installed +# libdarknet_gpu.so needs Cuda + Cudnn and other libraries in path, which may not exist +# For the such case, it will try to load libdarknet.so instead +lib = None +using_gpu = False + +print('\n') +so_path = os.path.join('/darknet', "libdarknet_cpu.so") +lib = CDLL(so_path, RTLD_GLOBAL) +print(f" Darknet is now running on CPU.") +print('\n') + +if lib: + lib.network_width.argtypes = [c_void_p] + lib.network_width.restype = c_int + lib.network_height.argtypes = [c_void_p] + lib.network_height.restype = c_int + + predict = lib.network_predict + predict.argtypes = [c_void_p, POINTER(c_float)] + predict.restype = POINTER(c_float) + + if using_gpu: + set_gpu = lib.cuda_set_device + set_gpu.argtypes = [c_int] + + make_image = lib.make_image + make_image.argtypes = [c_int, c_int, c_int] + make_image.restype = IMAGE + + get_network_boxes = lib.get_network_boxes + get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int), c_int] + get_network_boxes.restype = POINTER(DETECTION) + + make_network_boxes = lib.make_network_boxes + make_network_boxes.argtypes = [c_void_p] + make_network_boxes.restype = POINTER(DETECTION) + + free_detections = lib.free_detections + free_detections.argtypes = [POINTER(DETECTION), c_int] + + free_ptrs = lib.free_ptrs + free_ptrs.argtypes = [POINTER(c_void_p), c_int] + + network_predict = lib.network_predict + network_predict.argtypes = [c_void_p, POINTER(c_float)] + + reset_rnn = lib.reset_rnn + reset_rnn.argtypes = [c_void_p] + + load_net = lib.load_network + load_net.argtypes = [c_char_p, c_char_p, c_int] + load_net.restype = c_void_p + + load_net_custom = lib.load_network_custom + load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int] + load_net_custom.restype = c_void_p + + do_nms_obj = lib.do_nms_obj + do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] + + do_nms_sort = lib.do_nms_sort + do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] + + free_image = lib.free_image + free_image.argtypes = [IMAGE] + + letterbox_image = lib.letterbox_image + letterbox_image.argtypes = [IMAGE, c_int, c_int] + letterbox_image.restype = IMAGE + + load_meta = lib.get_metadata + lib.get_metadata.argtypes = [c_char_p] + lib.get_metadata.restype = METADATA + + load_image = lib.load_image_color + load_image.argtypes = [c_char_p, c_int, c_int] + load_image.restype = IMAGE + + rgbgr_image = lib.rgbgr_image + rgbgr_image.argtypes = [IMAGE] + + predict_image = lib.network_predict_image + predict_image.argtypes = [c_void_p, IMAGE] + predict_image.restype = POINTER(c_float) + +def sample(probs): + s = sum(probs) + probs = [a/s for a in probs] + r = random.uniform(0, 1) + for i in range(len(probs)): + r = r - probs[i] + if r <= 0: + return i + return len(probs)-1 + + +def c_array(ctype, values): + arr = (ctype*len(values))() + arr[:] = values + return arr + +def array_to_image(arr): + import numpy as np + # need to return old values to avoid python freeing memory + arr = arr.transpose(2, 0, 1) + c = arr.shape[0] + h = arr.shape[1] + w = arr.shape[2] + arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0 + data = arr.ctypes.data_as(POINTER(c_float)) + im = IMAGE(w, h, c, data) + return im, arr + + +def classify(net, meta, im): + global alt_names + + out = predict_image(net, im) + res = [] + for i in range(meta.classes): + if alt_names is None: + nameTag = meta.names[i] + else: + nameTag = alt_names[i] + res.append((nameTag, out[i])) + res = sorted(res, key=lambda x: -x[1]) + return res + + + + diff --git a/addon/rootfs/app/lib/detection_model.py b/addon/rootfs/app/lib/detection_model.py new file mode 100644 index 0000000..47fde27 --- /dev/null +++ b/addon/rootfs/app/lib/detection_model.py @@ -0,0 +1,95 @@ +#!python3 + +# pylint: disable=R, W0401, W0614, W0703 +from lib.meta import Meta +from os import environ, path + +alt_names = None + +darknet_ready = True +try: + from lib.darknet import YoloNet +except Exception as e: + print(f'Error during importing YoloNet! - {e}') + darknet_ready = False + +onnx_ready = True +try: + from lib.onnx import OnnxNet +except Exception as e: + print(f'Error during importing OnnxNet! - {e}') + onnx_ready = False + + +def load_net(config_path, meta_path, weights_path=None): + + def try_loading_net(net_config_priority): + for net_config in net_config_priority: + weights = net_config['weights_path'] + use_gpu = net_config['use_gpu'] + + net_main = None + try: + print(f'----- Trying to load weights: {weights} - use_gpu = {use_gpu} -----') + if weights.endswith(".onnx"): + if not onnx_ready: + raise Exception('Not loading ONNX net due to previous import failure. Check earlier log for errors.') + net_main = OnnxNet(weights, meta_path, use_gpu) + + elif weights.endswith(".darknet"): + if not darknet_ready: + raise Exception('Not loading darknet net due to previous import failure. Check earlier log for errors.') + net_main = YoloNet(weights, meta_path, config_path, use_gpu) + + else: + raise Exception(f'Can not recognize net from weights file surfix: {weights}') + + print('Succeeded!') + return net_main + except Exception as e: + print(f'Failed! - {e}') + + raise Exception(f'Failed to load any net after trying: {net_config_priority}') + + global alt_names # pylint: disable=W0603 + + model_dir = path.join(path.dirname(path.realpath(__file__)), '..', 'model') + use_gpu = environ.get('ML_USE_GPU', 'false').lower() in ('1', 'true', 'yes', 'on') + preferred_backend = environ.get('ML_MODEL_BACKEND', 'onnx').lower() + + cpu_priority = [ + dict(weights_path=path.join(model_dir, 'model-weights.onnx'), use_gpu=False), + dict(weights_path=path.join(model_dir, 'model-weights.darknet'), use_gpu=False), + ] + gpu_priority = [ + dict(weights_path=path.join(model_dir, 'model-weights.onnx'), use_gpu=True), + dict(weights_path=path.join(model_dir, 'model-weights.darknet'), use_gpu=True), + ] + + if preferred_backend == 'darknet': + cpu_priority.reverse() + gpu_priority.reverse() + + net_config_priority = gpu_priority + cpu_priority if use_gpu else cpu_priority + if weights_path is not None: + net_config_priority = ( + [dict(weights_path=weights_path, use_gpu=True), dict(weights_path=weights_path, use_gpu=False)] + if use_gpu + else [dict(weights_path=weights_path, use_gpu=False)] + ) + + net_main = try_loading_net(net_config_priority) + + if alt_names is None: + # In Python 3, the metafile default access craps out on Windows (but not Linux) + # Read the names file and create a list to feed to detect + try: + meta = Meta(meta_path) + alt_names = meta.names + except Exception: + pass + + return net_main + +def detect(net, image, thresh=.5, hier_thresh=.5, nms=.45, debug=False): + return net.detect(net.meta, image, alt_names, thresh, hier_thresh, nms, debug) diff --git a/addon/rootfs/app/lib/geometry.py b/addon/rootfs/app/lib/geometry.py new file mode 100644 index 0000000..963f4fd --- /dev/null +++ b/addon/rootfs/app/lib/geometry.py @@ -0,0 +1,111 @@ +from dataclasses import dataclass, asdict +from typing import Any, Dict, List, Tuple + +@dataclass +class Box: + """Detection rect""" + xc: float + yc: float + w: float + h: float + + @classmethod + def from_tuple(cls, box: Tuple[float, float, float, float]) -> 'Box': + return Box(xc=float(box[0]), yc=float(box[1]), w=float(box[2]), h=float(box[3])) + + def left(self) -> float: + return self.xc - self.w * 0.5 + + def right(self) -> float: + return self.xc + self.w * 0.5 + + def top(self) -> float: + return self.yc - self.h * 0.5 + + def bottom(self) -> float: + return self.yc + self.h * 0.5 + + def calc_iou(self, other: 'Box') -> float: + """Calculates intersection over union ration which can be used to compare boxes""" + al = self.left() + ar = self.right() + at = self.top() + ab = self.bottom() + + bl = other.left() + br = other.right() + bt = other.top() + bb = other.bottom() + + i_l = max(al, bl) + i_r = min(ar, br) + i_t = max(at, bt) + i_b = min(ab, bb) + + o_l = min(al, bl) + o_r = max(ar, br) + o_t = min(at, bt) + o_b = max(ab, bb) + + i_w = i_r - i_l + i_h = i_b - i_t + o_w = o_r - o_l + o_h = o_b - o_t + + o_a = o_w * o_h + if o_a <= 0.0: + return 0.0 + return i_w * i_h / o_a + + +@dataclass +class Detection: + """Detection result""" + name: str + confidence: float + box: Box + + @classmethod + def from_tuple_list(cls, detections: List[Tuple[str, float, Tuple[float, float, float, float]]]) -> List['Detection']: + return [Detection.from_tuple(d) for d in detections] + + @classmethod + def from_tuple(cls, detection: Tuple[str, float, Tuple[float, float, float, float]]) -> 'Detection': + box = Box.from_tuple(detection[2]) + return Detection(detection[0], float(detection[1]), box) + + @classmethod + def from_dict(cls, data: Dict[str, Any]) -> 'Detection': + return Detection(data['name'], data['confidence'], Box(**data['box'])) + + + +def compare_detections(l1: List[Detection], l2: List[Detection], threshold: float = 0.4) -> bool: + """Compares two lists of detections. Returns true if lists looks similar with some threshold""" + + # Are there all boxes from l1 matching any in l2 + for a in l1: + found = False + for b in l2: + iou = a.box.calc_iou(b.box) + if iou >= threshold: + found = True + break + if not found: + return False + + # are there all boxes in l2 matching any in l1 + # the list may differ and contain duplicates, + # that's why we need two checks + for b in l2: + found = False + for a in l1: + iou = a.box.calc_iou(b.box) + if iou >= threshold: + found = True + break + if not found: + return False + + return True + diff --git a/addon/rootfs/app/lib/meta.py b/addon/rootfs/app/lib/meta.py new file mode 100644 index 0000000..a9257a6 --- /dev/null +++ b/addon/rootfs/app/lib/meta.py @@ -0,0 +1,27 @@ +from typing import List, Tuple +from dataclasses import dataclass, field +import os +import re + +@dataclass +class Meta: + names: List[str] = field(default_factory=list) + + def __init__(self, meta_path: str): + names = None + with open(meta_path) as f: + meta_contents = f.read() + match = re.search("names *= *(.*)$", meta_contents, re.IGNORECASE | re.MULTILINE) + if match: + names_path = match.group(1) + try: + if os.path.exists(names_path): + with open(names_path) as namesFH: + names_list = namesFH.read().strip().split("\n") + names = [x.strip() for x in names_list] + except TypeError: + pass + if names is None: + names = ['failure'] + + self.names = names diff --git a/addon/rootfs/app/lib/onnx.py b/addon/rootfs/app/lib/onnx.py new file mode 100644 index 0000000..474b6a6 --- /dev/null +++ b/addon/rootfs/app/lib/onnx.py @@ -0,0 +1,132 @@ +from typing import List, Tuple +import onnxruntime +import numpy as np +import cv2 +import os + +from lib.meta import Meta + +class OnnxNet: + session: onnxruntime.InferenceSession + meta: Meta + + def __init__(self, onnx_path: str, meta_path: str, use_gpu: bool): + providers = ['CUDAExecutionProvider'] if use_gpu else ['CPUExecutionProvider'] + self.session = onnxruntime.InferenceSession(onnx_path, providers=providers) + self.meta = Meta(meta_path) + + def detect(self, meta, image, alt_names, thresh=.5, hier_thresh=.5, nms=.45, debug=False) -> List[Tuple[str, float, Tuple[float, float, float, float]]]: + input_h = self.session.get_inputs()[0].shape[2] + input_w = self.session.get_inputs()[0].shape[3] + width = image.shape[1] + height = image.shape[0] + + # Input + resized = cv2.resize(image, (input_w, input_h), interpolation=cv2.INTER_LINEAR) + img_in = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB) + img_in = np.transpose(img_in, (2, 0, 1)).astype(np.float32) + img_in = np.expand_dims(img_in, axis=0) + img_in /= 255.0 + + input_name = self.session.get_inputs()[0].name + outputs = self.session.run(None, {input_name: img_in}) + + detections = post_processing(outputs, width, height, thresh, nms, meta.names) + return detections[0] + + +def nms_cpu(boxes, confs, nms_thresh=0.5, min_mode=False): + # print(boxes.shape) + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + + areas = (x2 - x1) * (y2 - y1) + order = confs.argsort()[::-1] + + keep = [] + while order.size > 0: + idx_self = order[0] + idx_other = order[1:] + + keep.append(idx_self) + + xx1 = np.maximum(x1[idx_self], x1[idx_other]) + yy1 = np.maximum(y1[idx_self], y1[idx_other]) + xx2 = np.minimum(x2[idx_self], x2[idx_other]) + yy2 = np.minimum(y2[idx_self], y2[idx_other]) + + w = np.maximum(0.0, xx2 - xx1) + h = np.maximum(0.0, yy2 - yy1) + inter = w * h + + if min_mode: + over = inter / np.minimum(areas[order[0]], areas[order[1:]]) + else: + over = inter / (areas[order[0]] + areas[order[1:]] - inter) + + inds = np.where(over <= nms_thresh)[0] + order = order[inds + 1] + + return np.array(keep) + +def post_processing(output, width, height, conf_thresh, nms_thresh, names): + box_array = output[0] + confs = output[1] + + if type(box_array).__name__ != 'ndarray': + box_array = box_array.cpu().detach().numpy() + confs = confs.cpu().detach().numpy() + + num_classes = confs.shape[2] + + # [batch, num, 4] + box_array = box_array[:, :, 0] + + # [batch, num, num_classes] --> [batch, num] + max_conf = np.max(confs, axis=2) + max_id = np.argmax(confs, axis=2) + + box_x1x1x2y2_to_xcycwh_scaled = lambda b: \ + ( + float(0.5 * width * (b[0] + b[2])), + float(0.5 * height * (b[1] + b[3])), + float(width * (b[2] - b[0])), + float(width * (b[3] - b[1])) + ) + dets_batch = [] + for i in range(box_array.shape[0]): + + argwhere = max_conf[i] > conf_thresh + l_box_array = box_array[i, argwhere, :] + l_max_conf = max_conf[i, argwhere] + l_max_id = max_id[i, argwhere] + + bboxes = [] + # nms for each class + for j in range(num_classes): + + cls_argwhere = l_max_id == j + ll_box_array = l_box_array[cls_argwhere, :] + ll_max_conf = l_max_conf[cls_argwhere] + ll_max_id = l_max_id[cls_argwhere] + + keep = nms_cpu(ll_box_array, ll_max_conf, nms_thresh) + + if (keep.size > 0): + ll_box_array = ll_box_array[keep, :] + ll_max_conf = ll_max_conf[keep] + ll_max_id = ll_max_id[keep] + + for k in range(ll_box_array.shape[0]): + bboxes.append([ll_box_array[k, 0], ll_box_array[k, 1], ll_box_array[k, 2], ll_box_array[k, 3], ll_max_conf[k], ll_max_conf[k], ll_max_id[k]]) + + detections = [(names[b[6]], float(b[4]), box_x1x1x2y2_to_xcycwh_scaled((b[0], b[1], b[2], b[3]))) for b in bboxes] + dets_batch.append(detections) + + + return dets_batch + + + diff --git a/addon/rootfs/app/model/model-weights.darknet.url b/addon/rootfs/app/model/model-weights.darknet.url new file mode 100644 index 0000000..286c579 --- /dev/null +++ b/addon/rootfs/app/model/model-weights.darknet.url @@ -0,0 +1 @@ +https://tsd-pub-static.s3.amazonaws.com/ml-models/model-weights-8be06cde4e.darknet diff --git a/addon/rootfs/app/model/model-weights.onnx.url b/addon/rootfs/app/model/model-weights.onnx.url new file mode 100644 index 0000000..c170576 --- /dev/null +++ b/addon/rootfs/app/model/model-weights.onnx.url @@ -0,0 +1 @@ +https://tsd-pub-static.s3.amazonaws.com/ml-models/model-weights-5a6b1be1fa.onnx diff --git a/addon/rootfs/app/model/model.cfg b/addon/rootfs/app/model/model.cfg new file mode 100644 index 0000000..ca9f43e --- /dev/null +++ b/addon/rootfs/app/model/model.cfg @@ -0,0 +1,258 @@ +[net] +# Testing +batch=64 +subdivisions=8 +# Training +# batch=64 +# subdivisions=8 +height=416 +width=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 50000 +policy=steps +steps=40000,60000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + + +####### + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[route] +layers=-9 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=64 +activation=leaky + +[reorg3d] +stride=2 + +[route] +layers=-1,-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=30 +activation=linear + + +[region] +anchors = 1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071 +bias_match=1 +classes=1 +coords=4 +num=5 +softmax=1 +jitter=.3 +rescore=1 + +object_scale=5 +noobject_scale=1 +class_scale=1 +coord_scale=1 + +absolute=1 +thresh = .6 +random=1 diff --git a/addon/rootfs/app/model/model.meta b/addon/rootfs/app/model/model.meta new file mode 100644 index 0000000..fdd9701 --- /dev/null +++ b/addon/rootfs/app/model/model.meta @@ -0,0 +1,2 @@ +classes= 1 +names = /app/model/names diff --git a/addon/rootfs/app/model/names b/addon/rootfs/app/model/names new file mode 100644 index 0000000..7a4059e --- /dev/null +++ b/addon/rootfs/app/model/names @@ -0,0 +1 @@ +failure diff --git a/addon/rootfs/app/requirements.txt b/addon/rootfs/app/requirements.txt new file mode 100644 index 0000000..5987c03 --- /dev/null +++ b/addon/rootfs/app/requirements.txt @@ -0,0 +1,6 @@ +ipdb +flask>=1.0 +redis==3.0.1 +newrelic==4.12.0.113 +requests==2.21.0 +gunicorn==19.9.0 \ No newline at end of file diff --git a/addon/rootfs/app/server.py b/addon/rootfs/app/server.py new file mode 100644 index 0000000..9faa532 --- /dev/null +++ b/addon/rootfs/app/server.py @@ -0,0 +1,275 @@ +#!/usr/bin/env python +from __future__ import annotations + +from collections import deque +from datetime import datetime, timezone +import logging +from os import environ, path +from time import perf_counter +from urllib.parse import urlsplit, urlunsplit + +import cv2 +import flask +from flask import Response, jsonify, request +import numpy as np +import requests + +from auth import token_required +from lib.detection_model import detect, load_net + +THRESH = float(environ.get("ML_DETECTION_BOX_THRESHOLD", "0.08")) +REQUEST_TIMEOUT = ( + float(environ.get("ML_IMAGE_CONNECT_TIMEOUT", "2")), + float(environ.get("ML_IMAGE_READ_TIMEOUT", "10")), +) +MAX_RECENT_REQUESTS = int(environ.get("ML_RECENT_REQUESTS", "100")) + +app = flask.Flask(__name__) +app.config["DEBUG"] = environ.get("DEBUG") == "True" +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(name)s %(message)s", +) +app.logger.setLevel(logging.INFO) + +STARTED_AT = datetime.now(timezone.utc) +RECENT_REQUESTS: deque[dict] = deque(maxlen=MAX_RECENT_REQUESTS) + +model_dir = path.join(path.dirname(path.realpath(__file__)), "model") +net_main = load_net(path.join(model_dir, "model.cfg"), path.join(model_dir, "model.meta")) + + +def _now_iso() -> str: + return datetime.now(timezone.utc).isoformat() + + +def _redact_url(raw_url: str | None) -> str | None: + if not raw_url: + return None + parsed = urlsplit(raw_url) + return urlunsplit((parsed.scheme, parsed.netloc, parsed.path, "", "")) + + +def _record_request(entry: dict) -> None: + stored_entry = dict(entry) + if "image_url" in stored_entry: + stored_entry["image_url"] = _redact_url(stored_entry.get("image_url")) + RECENT_REQUESTS.appendleft({"time": _now_iso(), **stored_entry}) + app.logger.info( + "prediction status=%s detections=%s duration_ms=%s image_host=%s error=%s", + entry.get("status"), + entry.get("detections", 0), + entry.get("duration_ms"), + urlsplit(entry.get("image_url") or "").netloc, + entry.get("error"), + ) + + +def _fetch_image(image_url: str) -> np.ndarray: + response = requests.get(image_url, stream=True, timeout=REQUEST_TIMEOUT) + response.raise_for_status() + img_array = np.array(bytearray(response.content), dtype=np.uint8) + image = cv2.imdecode(img_array, -1) + if image is None: + raise ValueError("image_decode_failed") + return image + + +def _status_payload() -> dict: + return { + "ok": net_main is not None, + "started_at": STARTED_AT.isoformat(), + "model": { + "classes": ["failure"], + "box_threshold": THRESH, + "backend": type(net_main).__name__ if net_main is not None else None, + "use_gpu": environ.get("ML_USE_GPU", "false"), + "model_backend_preference": environ.get("ML_MODEL_BACKEND", "onnx"), + }, + "requests": { + "recent_count": len(RECENT_REQUESTS), + "max_recent": MAX_RECENT_REQUESTS, + }, + } + + +@app.route("/", methods=["GET"]) +def dashboard(): + """Render a small operational status page.""" + status = _status_payload() + rows = "\n".join( + "" + f"{entry['time']}" + f"{entry.get('status', '')}" + f"{entry.get('detections', 0)}" + f"{entry.get('duration_ms', '')}" + f"{entry.get('error') or ''}" + f"{_redact_url(entry.get('image_url')) or ''}" + "" + for entry in list(RECENT_REQUESTS)[:20] + ) + body = f""" + + + + + Elegoo Spaghetti Detection ML Server + + + +

Elegoo Spaghetti Detection ML Server

+

Status: {'ok' if status['ok'] else 'error'}

+

Backend: {status['model']['backend']} | GPU opt-in: {status['model']['use_gpu']} | Box threshold: {status['model']['box_threshold']}

+

Health: /hc/ | JSON status: /api/status | Token-protected logs: /api/logs?token=<token>

+

Recent Requests

+ + + {rows} +
TimeStatusDetectionsmsErrorImage URL without token
+ +""" + return Response(body, mimetype="text/html") + + +@app.route("/api/status", methods=["GET"]) +def api_status(): + """Return JSON server status.""" + return jsonify(_status_payload()) + + +@app.route("/api/logs", methods=["GET"]) +@token_required +def api_logs(): + """Return recent request logs.""" + return jsonify({"requests": list(RECENT_REQUESTS)}) + + +@app.route("/debug/image", methods=["GET"]) +@token_required +def debug_image(): + """Check whether the server can fetch and decode an image URL.""" + image_url = request.args.get("img") + if not image_url: + return jsonify({"ok": False, "error": "missing_image_url"}), 400 + started = perf_counter() + try: + image = _fetch_image(image_url) + return jsonify( + { + "ok": True, + "duration_ms": round((perf_counter() - started) * 1000), + "shape": list(image.shape), + "image_url": _redact_url(image_url), + } + ) + except requests.RequestException as err: + return jsonify({"ok": False, "error": "image_fetch_failed", "message": str(err)}), 502 + except ValueError as err: + return jsonify({"ok": False, "error": str(err)}), 422 + + +@app.route("/p/", methods=["GET"]) +@token_required +def get_p(): + """Run prediction for the image URL in the img query parameter.""" + image_url = request.args.get("img") + if not image_url: + _record_request({"status": 400, "error": "missing_image_url", "detections": 0}) + return jsonify( + { + "detections": [], + "error": "missing_image_url", + "message": "Missing img query parameter.", + } + ), 400 + + started = perf_counter() + try: + image = _fetch_image(image_url) + detections = detect(net_main, image, thresh=THRESH) + duration_ms = round((perf_counter() - started) * 1000) + _record_request( + { + "status": 200, + "detections": len(detections), + "duration_ms": duration_ms, + "image_url": image_url, + } + ) + return jsonify({"detections": detections, "duration_ms": duration_ms}) + except requests.RequestException as err: + duration_ms = round((perf_counter() - started) * 1000) + _record_request( + { + "status": 502, + "error": "image_fetch_failed", + "message": str(err), + "duration_ms": duration_ms, + "image_url": image_url, + "detections": 0, + } + ) + return jsonify( + { + "detections": [], + "error": "image_fetch_failed", + "message": str(err), + } + ), 502 + except ValueError as err: + duration_ms = round((perf_counter() - started) * 1000) + _record_request( + { + "status": 422, + "error": str(err), + "duration_ms": duration_ms, + "image_url": image_url, + "detections": 0, + } + ) + return jsonify( + { + "detections": [], + "error": str(err), + "message": "The image URL did not return a decodable image.", + } + ), 422 + except Exception as err: + duration_ms = round((perf_counter() - started) * 1000) + app.logger.exception("Unable to process image") + _record_request( + { + "status": 500, + "error": "prediction_failed", + "message": str(err), + "duration_ms": duration_ms, + "image_url": image_url, + "detections": 0, + } + ) + return jsonify( + { + "detections": [], + "error": "prediction_failed", + "message": str(err), + } + ), 500 + + +@app.route("/hc/", methods=["GET"]) +def health_check(): + """Health check for Home Assistant and Docker.""" + if net_main is not None: + return "ok", 200 + return "error", 503 + + +if __name__ == "__main__": + app.run(host="0.0.0.0", port=3333, threaded=False) diff --git a/addon/rootfs/app/wsgi.py b/addon/rootfs/app/wsgi.py new file mode 100644 index 0000000..556c6e9 --- /dev/null +++ b/addon/rootfs/app/wsgi.py @@ -0,0 +1,6 @@ +import server + +application = server.app + +if __name__ == "__main__": + application.run() diff --git a/addon/rootfs/etc/services.d/ha-elegoo-spaghetti-detection/run b/addon/rootfs/etc/services.d/ha-elegoo-spaghetti-detection/run new file mode 100644 index 0000000..2522727 --- /dev/null +++ b/addon/rootfs/etc/services.d/ha-elegoo-spaghetti-detection/run @@ -0,0 +1,17 @@ +#!/usr/bin/with-contenv bashio + +declare ML_API_TOKEN + +ML_API_TOKEN=$(bashio::config 'obico_api_secret') +export ML_API_TOKEN +export ML_USE_GPU=$(bashio::config 'use_gpu') +export GUNICORN_TIMEOUT=$(bashio::config 'gunicorn_timeout') + +cd /app +FLASK_APP=server.py venv/bin/gunicorn \ + --bind "0.0.0.0:3333" \ + --workers "${GUNICORN_WORKERS:-1}" \ + --timeout "${GUNICORN_TIMEOUT:-120}" \ + --error-logfile - \ + --log-level info \ + wsgi:application diff --git a/addon/run.sh b/addon/run.sh new file mode 100644 index 0000000..b62a70f --- /dev/null +++ b/addon/run.sh @@ -0,0 +1,16 @@ +#!/usr/bin/env bashio +set -e + +ML_API_TOKEN=$(bashio::config 'obico_api_secret') +PORT=$(bashio::addon.port 3333) +export ML_API_TOKEN +export ML_USE_GPU=$(bashio::config 'use_gpu') +export GUNICORN_TIMEOUT=$(bashio::config 'gunicorn_timeout') + +venv/bin/gunicorn \ + --bind "0.0.0.0:$PORT" \ + --workers "${GUNICORN_WORKERS:-1}" \ + --timeout "${GUNICORN_TIMEOUT:-120}" \ + --error-logfile - \ + --log-level info \ + wsgi diff --git a/custom_components/elegoo_spaghetti_detection/__init__.py b/custom_components/elegoo_spaghetti_detection/__init__.py new file mode 100644 index 0000000..48f8371 --- /dev/null +++ b/custom_components/elegoo_spaghetti_detection/__init__.py @@ -0,0 +1,179 @@ +"""Home Assistant integration for Elegoo spaghetti detection.""" + +from __future__ import annotations + +import asyncio +import logging +from typing import Any + +import aiohttp +import voluptuous as vol +from homeassistant.config_entries import ConfigEntry +from homeassistant.core import HomeAssistant, ServiceCall, ServiceResponse, SupportsResponse +from homeassistant.exceptions import HomeAssistantError +import homeassistant.helpers.config_validation as cv +from homeassistant.helpers.aiohttp_client import async_get_clientsession + +from .const import ( + CONF_CONFIG_ENTRY, + CONF_DETECTOR, + CONF_FORCE, + CONF_IMAGE_URL, + CONF_OBICO_AUTH_TOKEN, + CONF_OBICO_HOST, + DOMAIN, + PLATFORMS, + REQUIRED_CONFIG_KEYS, + RUNTIME_ML_LOCK, + RUNTIME_BY_DETECTOR, + RUNTIME_DATA, + SERVICE_PREDICT, + SERVICE_RESET_STATE, + SERVICE_RUN_DETECTION, +) +from .runtime import SpaghettiDetectorRuntime + +LOGGER = logging.getLogger(__package__) + +CONFIG_SCHEMA = cv.empty_config_schema(DOMAIN) + +PREDICT_SCHEMA = vol.Schema( + { + vol.Required(CONF_OBICO_HOST): str, + vol.Required(CONF_OBICO_AUTH_TOKEN): str, + vol.Required(CONF_IMAGE_URL): str, + } +) + +DETECTOR_SERVICE_SCHEMA = vol.Schema( + { + vol.Optional(CONF_CONFIG_ENTRY): str, + vol.Optional(CONF_DETECTOR): str, + vol.Optional(CONF_FORCE, default=True): bool, + } +) + + +async def async_setup(hass: HomeAssistant, config: dict) -> bool: + """Set up global services for Elegoo spaghetti detection.""" + hass.data.setdefault(DOMAIN, {}) + hass.data[DOMAIN].setdefault(RUNTIME_DATA, {}) + hass.data[DOMAIN].setdefault(RUNTIME_BY_DETECTOR, {}) + hass.data[DOMAIN].setdefault(RUNTIME_ML_LOCK, asyncio.Lock()) + + async def predict_handler(call: ServiceCall) -> ServiceResponse: + """Run the Obico ML model for a raw image URL.""" + result = await _async_predict_raw( + hass, + call.data[CONF_OBICO_HOST], + call.data[CONF_OBICO_AUTH_TOKEN], + call.data[CONF_IMAGE_URL], + ) + return {"result": result} + + async def run_detection_handler(call: ServiceCall) -> ServiceResponse: + """Run one detection against the configured detector.""" + runtime = _runtime_from_call(hass, call) + return await runtime.async_run_detection(manual=bool(call.data[CONF_FORCE])) + + async def reset_handler(call: ServiceCall) -> None: + """Reset detector state.""" + runtime = _runtime_from_call(hass, call) + runtime.reset() + + hass.services.async_register( + DOMAIN, + SERVICE_PREDICT, + predict_handler, + schema=PREDICT_SCHEMA, + supports_response=SupportsResponse.ONLY, + ) + hass.services.async_register( + DOMAIN, + SERVICE_RUN_DETECTION, + run_detection_handler, + schema=DETECTOR_SERVICE_SCHEMA, + supports_response=SupportsResponse.ONLY, + ) + hass.services.async_register( + DOMAIN, + SERVICE_RESET_STATE, + reset_handler, + schema=DETECTOR_SERVICE_SCHEMA, + ) + + return True + + +async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool: + """Set up one spaghetti detector.""" + missing = sorted( + key + for key in REQUIRED_CONFIG_KEYS + if key not in entry.data and key not in entry.options + ) + if missing: + LOGGER.error( + "Config entry %s is incomplete and must be removed and recreated. Missing: %s", + entry.title, + ", ".join(missing), + ) + return False + + runtime = SpaghettiDetectorRuntime(hass, entry) + hass.data[DOMAIN][RUNTIME_DATA][entry.entry_id] = runtime + hass.data[DOMAIN][RUNTIME_BY_DETECTOR][runtime.detector_id] = runtime + await runtime.async_setup() + await hass.config_entries.async_forward_entry_setups(entry, PLATFORMS) + return True + + +async def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool: + """Unload one spaghetti detector.""" + unload_ok = await hass.config_entries.async_unload_platforms(entry, PLATFORMS) + runtime = hass.data[DOMAIN][RUNTIME_DATA].pop(entry.entry_id, None) + if runtime is not None: + hass.data[DOMAIN][RUNTIME_BY_DETECTOR].pop(runtime.detector_id, None) + await runtime.async_unload() + return unload_ok + + +async def _async_predict_raw( + hass: HomeAssistant, + obico_host: str, + obico_auth_token: str, + image_url: str, +) -> dict[str, Any]: + """Call Obico ML directly.""" + try: + session = async_get_clientsession(hass) + async with session.get( + f"{obico_host.rstrip('/')}/p/", + params={"img": image_url}, + headers={"Authorization": f"Bearer {obico_auth_token}"}, + timeout=aiohttp.ClientTimeout(total=60), + ) as response: + response.raise_for_status() + result = await response.json() + if not isinstance(result, dict): + return {"detections": []} + return result + except (aiohttp.ClientError, TimeoutError) as err: + LOGGER.warning("Obico ML request failed: %s", err) + return {"detections": []} + + +def _runtime_from_call( + hass: HomeAssistant, + call: ServiceCall, +) -> SpaghettiDetectorRuntime: + """Resolve a runtime from a service call.""" + runtime: SpaghettiDetectorRuntime | None = None + if config_entry_id := call.data.get(CONF_CONFIG_ENTRY): + runtime = hass.data[DOMAIN][RUNTIME_DATA].get(config_entry_id) + elif detector := call.data.get(CONF_DETECTOR): + runtime = hass.data[DOMAIN][RUNTIME_BY_DETECTOR].get(detector) + + if runtime is None: + raise HomeAssistantError("Unknown Elegoo spaghetti detector") + return runtime diff --git a/custom_components/elegoo_spaghetti_detection/binary_sensor.py b/custom_components/elegoo_spaghetti_detection/binary_sensor.py new file mode 100644 index 0000000..a9d0e50 --- /dev/null +++ b/custom_components/elegoo_spaghetti_detection/binary_sensor.py @@ -0,0 +1,47 @@ +"""Binary sensors for Elegoo spaghetti detection.""" + +from homeassistant.components.binary_sensor import BinarySensorEntity +from homeassistant.config_entries import ConfigEntry +from homeassistant.core import HomeAssistant + +from .const import CONF_INSTANCE_ID, DOMAIN, RUNTIME_DATA +from .entity import SpaghettiDetectorEntity + + +async def async_setup_entry( + hass: HomeAssistant, + entry: ConfigEntry, + async_add_entities, +) -> None: + """Set up binary sensors.""" + runtime = hass.data[DOMAIN][RUNTIME_DATA][entry.entry_id] + async_add_entities([SpaghettiDetectedBinarySensor(entry, runtime)]) + + +class SpaghettiDetectedBinarySensor(SpaghettiDetectorEntity, BinarySensorEntity): + """Spaghetti detected state.""" + + _attr_name = "Spaghetti Detected" + _attr_icon = "mdi:alert-octagram" + + def __init__(self, entry: ConfigEntry, runtime) -> None: + super().__init__(entry, runtime, "spaghetti_detected") + self.entity_id = ( + f"binary_sensor.{entry.data[CONF_INSTANCE_ID]}_spaghetti_detected" + ) + + @property + def is_on(self) -> bool: + """Return true if spaghetti was detected.""" + return self.runtime.detected + + @property + def extra_state_attributes(self) -> dict: + """Return debug attributes.""" + return { + "confidence": self.runtime.confidence, + "raw_score": self.runtime.raw_score, + "warning": self.runtime.warning, + "detections": self.runtime.detection_count, + "last_error": self.runtime.last_error, + } diff --git a/custom_components/elegoo_spaghetti_detection/brand/icon.png b/custom_components/elegoo_spaghetti_detection/brand/icon.png new file mode 100644 index 0000000..616735d Binary files /dev/null and b/custom_components/elegoo_spaghetti_detection/brand/icon.png differ diff --git a/custom_components/elegoo_spaghetti_detection/brand/logo.png b/custom_components/elegoo_spaghetti_detection/brand/logo.png new file mode 100644 index 0000000..616735d Binary files /dev/null and b/custom_components/elegoo_spaghetti_detection/brand/logo.png differ diff --git a/custom_components/elegoo_spaghetti_detection/button.py b/custom_components/elegoo_spaghetti_detection/button.py new file mode 100644 index 0000000..f04ee6d --- /dev/null +++ b/custom_components/elegoo_spaghetti_detection/button.py @@ -0,0 +1,77 @@ +"""Buttons for Elegoo spaghetti detection.""" + +from dataclasses import dataclass +from typing import Awaitable, Callable + +from homeassistant.components.button import ButtonEntity, ButtonEntityDescription +from homeassistant.config_entries import ConfigEntry +from homeassistant.core import HomeAssistant + +from .const import CONF_INSTANCE_ID, DOMAIN, RUNTIME_DATA +from .entity import SpaghettiDetectorEntity +from .runtime import SpaghettiDetectorRuntime + + +@dataclass(frozen=True, kw_only=True) +class DetectorButtonDescription(ButtonEntityDescription): + """Detector button description.""" + + press_fn: Callable[[SpaghettiDetectorRuntime], Awaitable[None]] + + +async def _run_detection(runtime: SpaghettiDetectorRuntime) -> None: + """Run one manual detection.""" + await runtime.async_run_detection(manual=True) + + +async def _reset_state(runtime: SpaghettiDetectorRuntime) -> None: + """Reset detection state.""" + runtime.reset() + + +BUTTONS: tuple[DetectorButtonDescription, ...] = ( + DetectorButtonDescription( + key="test_spaghetti_detection", + name="Test Spaghetti Detection", + icon="mdi:camera-iris", + press_fn=_run_detection, + ), + DetectorButtonDescription( + key="reset_detection_state", + name="Reset Detection State", + icon="mdi:restart", + press_fn=_reset_state, + ), +) + + +async def async_setup_entry( + hass: HomeAssistant, + entry: ConfigEntry, + async_add_entities, +) -> None: + """Set up buttons.""" + runtime = hass.data[DOMAIN][RUNTIME_DATA][entry.entry_id] + async_add_entities( + DetectorButton(entry, runtime, description) for description in BUTTONS + ) + + +class DetectorButton(SpaghettiDetectorEntity, ButtonEntity): + """Detector action button.""" + + entity_description: DetectorButtonDescription + + def __init__( + self, + entry: ConfigEntry, + runtime: SpaghettiDetectorRuntime, + description: DetectorButtonDescription, + ) -> None: + super().__init__(entry, runtime, description.key) + self.entity_description = description + self.entity_id = f"button.{entry.data[CONF_INSTANCE_ID]}_{description.key}" + + async def async_press(self) -> None: + """Handle button press.""" + await self.entity_description.press_fn(self.runtime) diff --git a/custom_components/elegoo_spaghetti_detection/config_flow.py b/custom_components/elegoo_spaghetti_detection/config_flow.py new file mode 100644 index 0000000..0abca88 --- /dev/null +++ b/custom_components/elegoo_spaghetti_detection/config_flow.py @@ -0,0 +1,499 @@ +"""Config flow for Elegoo spaghetti detection.""" + +from __future__ import annotations + +from typing import Any + +import aiohttp +from homeassistant import config_entries +from homeassistant.config_entries import ConfigEntry +from homeassistant.const import CONF_NAME +from homeassistant.core import callback +from homeassistant.data_entry_flow import FlowResult +from homeassistant.helpers import selector +from homeassistant.helpers.aiohttp_client import async_get_clientsession +from homeassistant.util import slugify +import voluptuous as vol + +from .const import ( + CONF_ACTIVE_PRINT_STATES, + CONF_CAMERA, + CONF_CHAMBER_LIGHT, + CONF_COOLDOWN_SECONDS, + CONF_DETECTION_INTERVAL, + CONF_FAILURE_THRESHOLD, + CONF_HOME_ASSISTANT_HOST, + CONF_INSTANCE_ID, + CONF_LIGHT_CONTROL_MODE, + CONF_LIGHT_SETTLE_SECONDS, + CONF_OBICO_AUTH_TOKEN, + CONF_OBICO_HOST, + CONF_PRINT_STATUS_SENSOR, + CONF_RUN_WITHOUT_PRINTING, + CONF_SENSITIVITY, + CONF_SNAPSHOT_URL, + CONF_WARNING_THRESHOLD, + DEFAULT_ACTIVE_PRINT_STATES, + DEFAULT_COOLDOWN_SECONDS, + DEFAULT_DETECTION_INTERVAL, + DEFAULT_FAILURE_THRESHOLD, + DEFAULT_HOME_ASSISTANT_HOST, + DEFAULT_INSTANCE_ID, + DEFAULT_LIGHT_CONTROL_MODE, + DEFAULT_LIGHT_SETTLE_SECONDS, + DEFAULT_NAME, + DEFAULT_OBICO_AUTH_TOKEN, + DEFAULT_OBICO_HOST, + DEFAULT_SENSITIVITY, + DEFAULT_WARNING_THRESHOLD, + DOMAIN, + LIGHT_CONTROL_LEAVE_ON, + LIGHT_CONTROL_OFF, + LIGHT_CONTROL_RESTORE, +) + + +OPTIONAL_ENTITY_FIELDS: tuple[tuple[str, str | list[str]], ...] = ( + (CONF_PRINT_STATUS_SENSOR, ["sensor", "binary_sensor"]), + (CONF_CHAMBER_LIGHT, "light"), +) + + +def _entry_values(entry: ConfigEntry) -> dict[str, Any]: + """Return config entry data with options overriding editable settings.""" + return {**entry.data, **entry.options} + + +def _default_value(defaults: dict[str, Any], key: str, fallback: Any) -> Any: + """Return a form default without leaking None into selectors.""" + value = defaults.get(key) + return fallback if value is None else value + + +def _optional_marker(key: str, defaults: dict[str, Any]) -> vol.Optional: + """Return an optional voluptuous marker with an existing default if present.""" + if defaults.get(key): + return vol.Optional(key, default=defaults[key]) + return vol.Optional(key) + + +def _light_control_mode(defaults: dict[str, Any]) -> str: + """Return the default light-control mode for setup/options forms.""" + mode = defaults.get(CONF_LIGHT_CONTROL_MODE) + if mode in {LIGHT_CONTROL_OFF, LIGHT_CONTROL_LEAVE_ON, LIGHT_CONTROL_RESTORE}: + return mode + return DEFAULT_LIGHT_CONTROL_MODE + + +def _schema( + defaults: dict[str, Any] | None = None, + *, + include_identity: bool, +) -> vol.Schema: + """Return detector setup/options schema.""" + defaults = defaults or {} + data_schema: dict[Any, Any] = {} + + if include_identity: + data_schema[ + vol.Required( + CONF_NAME, + default=_default_value(defaults, CONF_NAME, DEFAULT_NAME), + ) + ] = str + data_schema[ + vol.Required( + CONF_INSTANCE_ID, + default=_default_value( + defaults, + CONF_INSTANCE_ID, + DEFAULT_INSTANCE_ID, + ), + ) + ] = str + + data_schema[ + vol.Required( + CONF_HOME_ASSISTANT_HOST, + default=_default_value( + defaults, + CONF_HOME_ASSISTANT_HOST, + DEFAULT_HOME_ASSISTANT_HOST, + ), + ) + ] = str + data_schema[ + vol.Required( + CONF_OBICO_HOST, + default=_default_value(defaults, CONF_OBICO_HOST, DEFAULT_OBICO_HOST), + ) + ] = str + data_schema[ + vol.Required( + CONF_OBICO_AUTH_TOKEN, + default=_default_value( + defaults, + CONF_OBICO_AUTH_TOKEN, + DEFAULT_OBICO_AUTH_TOKEN, + ), + ) + ] = str + + camera_marker = ( + vol.Required(CONF_CAMERA, default=defaults[CONF_CAMERA]) + if defaults.get(CONF_CAMERA) + else vol.Required(CONF_CAMERA) + ) + data_schema[camera_marker] = selector.EntitySelector( + selector.EntitySelectorConfig(domain="camera") + ) + + data_schema[ + vol.Optional( + CONF_SNAPSHOT_URL, + default=_default_value(defaults, CONF_SNAPSHOT_URL, ""), + ) + ] = str + + for key, domain in OPTIONAL_ENTITY_FIELDS: + data_schema[_optional_marker(key, defaults)] = selector.EntitySelector( + selector.EntitySelectorConfig(domain=domain) + ) + + data_schema[ + vol.Required( + CONF_ACTIVE_PRINT_STATES, + default=_default_value( + defaults, + CONF_ACTIVE_PRINT_STATES, + DEFAULT_ACTIVE_PRINT_STATES, + ), + ) + ] = str + data_schema[ + vol.Required( + CONF_LIGHT_CONTROL_MODE, + default=_light_control_mode(defaults), + ) + ] = selector.SelectSelector( + selector.SelectSelectorConfig( + options=[ + {"label": "Do not control light", "value": LIGHT_CONTROL_OFF}, + { + "label": "Turn on before detection and leave on", + "value": LIGHT_CONTROL_LEAVE_ON, + }, + { + "label": "Restore previous state after detection", + "value": LIGHT_CONTROL_RESTORE, + }, + ], + mode="dropdown", + ) + ) + data_schema[ + vol.Required( + CONF_LIGHT_SETTLE_SECONDS, + default=_default_value( + defaults, + CONF_LIGHT_SETTLE_SECONDS, + DEFAULT_LIGHT_SETTLE_SECONDS, + ), + ) + ] = selector.NumberSelector( + selector.NumberSelectorConfig( + min=0, + max=30, + step=1, + mode="box", + unit_of_measurement="s", + ) + ) + data_schema[ + vol.Required( + CONF_RUN_WITHOUT_PRINTING, + default=_default_value(defaults, CONF_RUN_WITHOUT_PRINTING, False), + ) + ] = selector.BooleanSelector() + data_schema[ + vol.Required( + CONF_DETECTION_INTERVAL, + default=_default_value( + defaults, + CONF_DETECTION_INTERVAL, + DEFAULT_DETECTION_INTERVAL, + ), + ) + ] = selector.NumberSelector( + selector.NumberSelectorConfig( + min=5, + max=3600, + step=5, + mode="box", + unit_of_measurement="s", + ) + ) + data_schema[ + vol.Required( + CONF_SENSITIVITY, + default=_default_value(defaults, CONF_SENSITIVITY, DEFAULT_SENSITIVITY), + ) + ] = selector.SelectSelector( + selector.SelectSelectorConfig( + options=[ + {"label": "High sensitivity", "value": "high"}, + {"label": "Normal sensitivity", "value": "normal"}, + {"label": "Low sensitivity", "value": "low"}, + {"label": "Custom thresholds", "value": "custom"}, + ], + mode="dropdown", + ) + ) + data_schema[ + vol.Required( + CONF_WARNING_THRESHOLD, + default=_default_value( + defaults, + CONF_WARNING_THRESHOLD, + DEFAULT_WARNING_THRESHOLD, + ), + ) + ] = selector.NumberSelector( + selector.NumberSelectorConfig(min=0, max=1, step=0.01, mode="box") + ) + data_schema[ + vol.Required( + CONF_FAILURE_THRESHOLD, + default=_default_value( + defaults, + CONF_FAILURE_THRESHOLD, + DEFAULT_FAILURE_THRESHOLD, + ), + ) + ] = selector.NumberSelector( + selector.NumberSelectorConfig(min=0, max=1, step=0.01, mode="box") + ) + data_schema[ + vol.Required( + CONF_COOLDOWN_SECONDS, + default=_default_value( + defaults, + CONF_COOLDOWN_SECONDS, + DEFAULT_COOLDOWN_SECONDS, + ), + ) + ] = selector.NumberSelector( + selector.NumberSelectorConfig( + min=0, + max=3600, + step=5, + mode="box", + unit_of_measurement="s", + ) + ) + + return vol.Schema(data_schema) + + +def _build_image_url( + hass, + data: dict[str, Any], +) -> str | None: + """Build the image URL that the ML server will fetch during checks.""" + if snapshot_url := data.get(CONF_SNAPSHOT_URL): + return snapshot_url + + state = hass.states.get(data[CONF_CAMERA]) + if state is None: + return None + entity_picture = state.attributes.get("entity_picture") + if not entity_picture: + return None + return f"{data[CONF_HOME_ASSISTANT_HOST].rstrip('/')}{entity_picture}" + + +def _validate_thresholds(data: dict[str, Any]) -> dict[str, str]: + """Validate threshold fields.""" + if float(data[CONF_WARNING_THRESHOLD]) > float(data[CONF_FAILURE_THRESHOLD]): + return {CONF_WARNING_THRESHOLD: "warning_above_failure"} + return {} + + +def _camera_in_use( + entries: list[ConfigEntry], + camera: str, + *, + exclude_entry_id: str | None = None, +) -> bool: + """Return whether a camera is already used by a detector.""" + return any( + entry.entry_id != exclude_entry_id + and _entry_values(entry).get(CONF_CAMERA) == camera + for entry in entries + ) + + +class ConfigFlow(config_entries.ConfigFlow, domain=DOMAIN): + """Handle a config flow for Elegoo spaghetti detection.""" + + VERSION = 1 + + @staticmethod + @callback + def async_get_options_flow( + config_entry: ConfigEntry, + ) -> config_entries.OptionsFlow: + """Create the options flow.""" + return OptionsFlowHandler() + + async def async_step_user( + self, user_input: dict[str, Any] | None = None + ) -> FlowResult: + """Configure one detector target.""" + errors: dict[str, str] = {} + form_defaults = self._defaults_from_existing_entry() + + if user_input is not None: + data = dict(user_input) + data[CONF_INSTANCE_ID] = slugify(data[CONF_INSTANCE_ID]) + errors.update(_validate_thresholds(data)) + + if not data[CONF_INSTANCE_ID]: + errors[CONF_INSTANCE_ID] = "invalid_instance_id" + elif self._instance_id_exists(data[CONF_INSTANCE_ID]): + errors[CONF_INSTANCE_ID] = "instance_id_exists" + elif not errors: + await self.async_set_unique_id(data[CONF_CAMERA]) + self._abort_if_unique_id_configured() + + if not errors: + errors.update(await self._async_validate_backend(data)) + + if not errors: + name = data.pop(CONF_NAME) + return self.async_create_entry(title=name, data=data) + + form_defaults = {**form_defaults, **data} + + return self.async_show_form( + step_id="user", + data_schema=_schema(form_defaults, include_identity=True), + errors=errors, + ) + + def _defaults_from_existing_entry(self) -> dict[str, Any]: + """Use the first existing detector to reduce repeated server entry.""" + for entry in self._async_current_entries(): + values = _entry_values(entry) + defaults = { + CONF_HOME_ASSISTANT_HOST: values.get(CONF_HOME_ASSISTANT_HOST), + CONF_OBICO_HOST: values.get(CONF_OBICO_HOST), + CONF_OBICO_AUTH_TOKEN: values.get(CONF_OBICO_AUTH_TOKEN), + CONF_DETECTION_INTERVAL: values.get(CONF_DETECTION_INTERVAL), + CONF_LIGHT_CONTROL_MODE: values.get(CONF_LIGHT_CONTROL_MODE), + CONF_LIGHT_SETTLE_SECONDS: values.get(CONF_LIGHT_SETTLE_SECONDS), + CONF_SENSITIVITY: values.get(CONF_SENSITIVITY), + CONF_WARNING_THRESHOLD: values.get(CONF_WARNING_THRESHOLD), + CONF_FAILURE_THRESHOLD: values.get(CONF_FAILURE_THRESHOLD), + CONF_COOLDOWN_SECONDS: values.get(CONF_COOLDOWN_SECONDS), + } + return {key: value for key, value in defaults.items() if value is not None} + return {CONF_HOME_ASSISTANT_HOST: self._home_assistant_url_default()} + + def _home_assistant_url_default(self) -> str: + """Return the best available HA URL for the ML server to fetch images.""" + return ( + getattr(self.hass.config, "internal_url", None) + or getattr(self.hass.config, "external_url", None) + or DEFAULT_HOME_ASSISTANT_HOST + ) + + def _instance_id_exists(self, instance_id: str) -> bool: + """Return whether an entity prefix is already used.""" + return any( + entry.data.get(CONF_INSTANCE_ID) == instance_id + for entry in self._async_current_entries() + ) + + def _camera_exists(self, camera: str) -> bool: + """Return whether a camera is already used by another detector.""" + return _camera_in_use(self._async_current_entries(), camera) + + async def _async_validate_backend(self, data: dict[str, Any]) -> dict[str, str]: + """Validate ML health and whether it can fetch the configured image.""" + return await _async_validate_backend(self.hass, data) + + +class OptionsFlowHandler(config_entries.OptionsFlowWithReload): + """Handle detector options.""" + + async def async_step_init( + self, + user_input: dict[str, Any] | None = None, + ) -> FlowResult: + """Manage detector options.""" + errors: dict[str, str] = {} + defaults = _entry_values(self.config_entry) + + if user_input is not None: + data = dict(user_input) + errors.update(_validate_thresholds(data)) + + if _camera_in_use( + self.hass.config_entries.async_entries(DOMAIN), + data[CONF_CAMERA], + exclude_entry_id=self.config_entry.entry_id, + ): + errors[CONF_CAMERA] = "already_configured" + + if not errors: + errors.update(await _async_validate_backend(self.hass, data)) + + if not errors: + return self.async_create_entry(data=data) + + defaults = {**defaults, **data} + + return self.async_show_form( + step_id="init", + data_schema=_schema(defaults, include_identity=False), + errors=errors, + ) + + +async def _async_validate_backend(hass, data: dict[str, Any]) -> dict[str, str]: + """Return form errors for backend/camera connectivity problems.""" + image_url = _build_image_url(hass, data) + if not image_url: + return {CONF_CAMERA: "camera_image_unavailable"} + + session = async_get_clientsession(hass) + obico_host = data[CONF_OBICO_HOST].rstrip("/") + token = data[CONF_OBICO_AUTH_TOKEN] + headers = {"Authorization": f"Bearer {token}"} + + try: + async with session.get( + f"{obico_host}/hc/", + timeout=aiohttp.ClientTimeout(total=10), + ) as response: + if response.status >= 400: + return {CONF_OBICO_HOST: "ml_health_failed"} + except (aiohttp.ClientError, TimeoutError): + return {CONF_OBICO_HOST: "ml_health_failed"} + + try: + async with session.get( + f"{obico_host}/debug/image", + params={"img": image_url}, + headers=headers, + timeout=aiohttp.ClientTimeout(total=20), + ) as response: + if response.status == 401: + return {CONF_OBICO_AUTH_TOKEN: "ml_auth_failed"} + if response.status >= 400: + return {CONF_CAMERA: "ml_image_fetch_failed"} + except (aiohttp.ClientError, TimeoutError): + return {CONF_CAMERA: "ml_image_fetch_failed"} + + return {} diff --git a/custom_components/elegoo_spaghetti_detection/const.py b/custom_components/elegoo_spaghetti_detection/const.py new file mode 100644 index 0000000..e044a4f --- /dev/null +++ b/custom_components/elegoo_spaghetti_detection/const.py @@ -0,0 +1,86 @@ +"""Constants for Elegoo spaghetti detection.""" + +from homeassistant.const import Platform + +DOMAIN = "elegoo_spaghetti_detection" +BRAND = "Elegoo Spaghetti Detection" + +PLATFORMS = [Platform.BINARY_SENSOR, Platform.SENSOR, Platform.BUTTON] + +CONF_INSTANCE_ID = "instance_id" +CONF_HOME_ASSISTANT_HOST = "home_assistant_host" +CONF_OBICO_HOST = "obico_host" +CONF_OBICO_AUTH_TOKEN = "obico_auth_token" +CONF_CAMERA = "camera" +CONF_SNAPSHOT_URL = "snapshot_url" +CONF_PRINT_STATUS_SENSOR = "print_status_sensor" +CONF_ACTIVE_PRINT_STATES = "active_print_states" +CONF_CHAMBER_LIGHT = "chamber_light" +CONF_LIGHT_CONTROL_MODE = "light_control_mode" +CONF_LIGHT_SETTLE_SECONDS = "light_settle_seconds" +CONF_DETECTION_INTERVAL = "detection_interval" +CONF_RUN_WITHOUT_PRINTING = "run_without_printing" +CONF_FAILURE_THRESHOLD = "failure_threshold" +CONF_WARNING_THRESHOLD = "warning_threshold" +CONF_SENSITIVITY = "sensitivity" +CONF_COOLDOWN_SECONDS = "cooldown_seconds" +CONF_IMAGE_URL = "image_url" +CONF_CONFIG_ENTRY = "config_entry" +CONF_DETECTOR = "detector" +CONF_FORCE = "force" + +DEFAULT_NAME = "Elegoo Spaghetti Detector" +DEFAULT_INSTANCE_ID = DOMAIN +DEFAULT_HOME_ASSISTANT_HOST = "http://homeassistant.local:8123" +DEFAULT_OBICO_HOST = "http://192.168.1.123:3333" +DEFAULT_OBICO_AUTH_TOKEN = "obico_api_secret" +DEFAULT_ACTIVE_PRINT_STATES = "printing" +DEFAULT_DETECTION_INTERVAL = 10 +DEFAULT_COOLDOWN_SECONDS = 900 +DEFAULT_FAILURE_THRESHOLD = 0.50 +DEFAULT_WARNING_THRESHOLD = 0.30 +DEFAULT_SENSITIVITY = "normal" +DEFAULT_LIGHT_CONTROL_MODE = "restore" +DEFAULT_LIGHT_SETTLE_SECONDS = 3 + +LIGHT_CONTROL_OFF = "off" +LIGHT_CONTROL_LEAVE_ON = "leave_on" +LIGHT_CONTROL_RESTORE = "restore" + +REQUIRED_CONFIG_KEYS = frozenset( + { + CONF_INSTANCE_ID, + CONF_HOME_ASSISTANT_HOST, + CONF_OBICO_HOST, + CONF_OBICO_AUTH_TOKEN, + CONF_CAMERA, + } +) + +SENSITIVITY_THRESHOLDS = { + "high": (0.20, 0.35), + "normal": (DEFAULT_WARNING_THRESHOLD, DEFAULT_FAILURE_THRESHOLD), + "low": (0.45, 0.70), + "custom": (DEFAULT_WARNING_THRESHOLD, DEFAULT_FAILURE_THRESHOLD), +} + +EVENT_DETECTION_RESULT = f"{DOMAIN}_result" +EVENT_SPAGHETTI_DETECTED = f"{DOMAIN}_detected" + +SERVICE_PREDICT = "predict" +SERVICE_RUN_DETECTION = "run_detection" +SERVICE_RESET_STATE = "reset_state" + +RUNTIME_DATA = "runtime" +RUNTIME_BY_DETECTOR = "runtime_by_detector" +RUNTIME_ML_LOCK = "ml_lock" + +ATTR_CONFIDENCE = "confidence" +ATTR_RAW_SCORE = "raw_score" +ATTR_DETECTED = "detected" +ATTR_DETECTIONS = "detections" +ATTR_IMAGE_URL = "image_url" +ATTR_LAST_ERROR = "last_error" +ATTR_LAST_RUN = "last_run" +ATTR_NEXT_RUN = "next_run" +ATTR_STATUS = "status" diff --git a/custom_components/elegoo_spaghetti_detection/entity.py b/custom_components/elegoo_spaghetti_detection/entity.py new file mode 100644 index 0000000..5be9338 --- /dev/null +++ b/custom_components/elegoo_spaghetti_detection/entity.py @@ -0,0 +1,36 @@ +"""Base entities for Elegoo spaghetti detection.""" + +from homeassistant.config_entries import ConfigEntry +from homeassistant.helpers.entity import DeviceInfo, Entity + +from .const import DOMAIN +from .runtime import SpaghettiDetectorRuntime + + +class SpaghettiDetectorEntity(Entity): + """Base entity for a detector runtime.""" + + _attr_has_entity_name = True + _attr_should_poll = False + + def __init__( + self, + entry: ConfigEntry, + runtime: SpaghettiDetectorRuntime, + key: str, + ) -> None: + self.entry = entry + self.runtime = runtime + self._attr_unique_id = f"{entry.entry_id}_{key}" + self._attr_device_info = DeviceInfo( + identifiers={(DOMAIN, entry.entry_id)}, + manufacturer="Elegoo", + model="Spaghetti detection", + name=entry.title, + ) + + async def async_added_to_hass(self) -> None: + """Subscribe to runtime updates.""" + self.async_on_remove( + self.runtime.async_add_listener(self.async_write_ha_state) + ) diff --git a/custom_components/elegoo_spaghetti_detection/manifest.json b/custom_components/elegoo_spaghetti_detection/manifest.json new file mode 100644 index 0000000..96b66e3 --- /dev/null +++ b/custom_components/elegoo_spaghetti_detection/manifest.json @@ -0,0 +1,16 @@ +{ + "domain": "elegoo_spaghetti_detection", + "name": "Elegoo Spaghetti Detection", + "codeowners": [ + "@hepter" + ], + "config_flow": true, + "dependencies": [], + "documentation": "https://github.com/hepter/ha-elegoo-spaghetti-detection", + "integration_type": "hub", + "iot_class": "calculated", + "issue_tracker": "https://github.com/hepter/ha-elegoo-spaghetti-detection/issues", + "requirements": [], + "version": "1.0.0" +} + diff --git a/custom_components/elegoo_spaghetti_detection/runtime.py b/custom_components/elegoo_spaghetti_detection/runtime.py new file mode 100644 index 0000000..4d4a74f --- /dev/null +++ b/custom_components/elegoo_spaghetti_detection/runtime.py @@ -0,0 +1,583 @@ +"""Runtime detection logic for Elegoo spaghetti detection.""" + +from __future__ import annotations + +import asyncio +from collections.abc import Callable +from datetime import datetime, timedelta +import logging +from typing import Any + +import aiohttp +from homeassistant.config_entries import ConfigEntry +from homeassistant.core import CALLBACK_TYPE, HomeAssistant, callback +from homeassistant.exceptions import HomeAssistantError +from homeassistant.helpers.aiohttp_client import async_get_clientsession +from homeassistant.helpers.event import ( + async_track_state_change_event, + async_track_time_interval, +) +from homeassistant.util import dt as dt_util + +from .const import ( + ATTR_CONFIDENCE, + ATTR_DETECTED, + ATTR_DETECTIONS, + ATTR_IMAGE_URL, + ATTR_LAST_ERROR, + ATTR_LAST_RUN, + ATTR_NEXT_RUN, + ATTR_RAW_SCORE, + ATTR_STATUS, + CONF_ACTIVE_PRINT_STATES, + CONF_CAMERA, + CONF_CHAMBER_LIGHT, + CONF_COOLDOWN_SECONDS, + CONF_DETECTION_INTERVAL, + CONF_FAILURE_THRESHOLD, + CONF_HOME_ASSISTANT_HOST, + CONF_INSTANCE_ID, + CONF_LIGHT_CONTROL_MODE, + CONF_LIGHT_SETTLE_SECONDS, + CONF_OBICO_AUTH_TOKEN, + CONF_OBICO_HOST, + CONF_PRINT_STATUS_SENSOR, + CONF_RUN_WITHOUT_PRINTING, + CONF_SENSITIVITY, + CONF_SNAPSHOT_URL, + CONF_WARNING_THRESHOLD, + DEFAULT_ACTIVE_PRINT_STATES, + DEFAULT_COOLDOWN_SECONDS, + DEFAULT_DETECTION_INTERVAL, + DEFAULT_FAILURE_THRESHOLD, + DEFAULT_LIGHT_CONTROL_MODE, + DEFAULT_LIGHT_SETTLE_SECONDS, + DEFAULT_SENSITIVITY, + DEFAULT_WARNING_THRESHOLD, + DOMAIN, + EVENT_DETECTION_RESULT, + EVENT_SPAGHETTI_DETECTED, + LIGHT_CONTROL_LEAVE_ON, + LIGHT_CONTROL_OFF, + LIGHT_CONTROL_RESTORE, + RUNTIME_ML_LOCK, + SENSITIVITY_THRESHOLDS, +) + +LOGGER = logging.getLogger(__name__) + + +def _parse_states(value: str | None) -> set[str]: + """Parse a comma-separated list of states.""" + if not value: + value = DEFAULT_ACTIVE_PRINT_STATES + return {item.strip().lower() for item in value.split(",") if item.strip()} + + +def _normalize_state(value: Any) -> str: + """Normalize a Home Assistant state string for comparisons.""" + return str(value).strip().lower() + + +def _score_detections(result: dict[str, Any]) -> tuple[float, int]: + """Return a simple confidence score from the Obico detection payload.""" + score = 0.0 + detections = result.get("detections") or [] + for detection in detections: + try: + score += float(detection[1]) + except (TypeError, ValueError, IndexError): + continue + return min(1.0, max(0.0, score)), len(detections) + + +class SpaghettiDetectorRuntime: + """Manage one camera/detector target.""" + + def __init__(self, hass: HomeAssistant, entry: ConfigEntry) -> None: + self.hass = hass + self.entry = entry + self.data = {**entry.data, **entry.options} + self.detector_id: str = self.data[CONF_INSTANCE_ID] + self.name = entry.title + self.listeners: list[Callable[[], None]] = [] + self.unsubscribers: list[CALLBACK_TYPE] = [] + + self.enabled = True + self.running = False + self.status = "idle" + self.printer_state: str | None = None + self.confidence = 0.0 + self.raw_score = 0.0 + self.detection_count = 0 + self.detected = False + self.warning = False + self.last_run: datetime | None = None + self.last_detected: datetime | None = None + self.next_run: datetime | None = None + self.last_error: str | None = None + self.last_image_url: str | None = None + self.last_result: dict[str, Any] = {"detections": []} + self.lifetime_frames = 0 + self.detected_event_sent_for_active_period = False + + @property + def active_states(self) -> set[str]: + """Return states that mean the printer is actively printing.""" + return _parse_states(self.data.get(CONF_ACTIVE_PRINT_STATES)) + + @property + def warning_threshold(self) -> float: + """Return warning threshold for this detector.""" + sensitivity = self.data.get(CONF_SENSITIVITY, DEFAULT_SENSITIVITY) + default_warning, _ = SENSITIVITY_THRESHOLDS.get( + sensitivity, + SENSITIVITY_THRESHOLDS[DEFAULT_SENSITIVITY], + ) + if sensitivity != "custom": + return float(default_warning) + return float(self.data.get(CONF_WARNING_THRESHOLD, default_warning)) + + @property + def failure_threshold(self) -> float: + """Return failure threshold for this detector.""" + sensitivity = self.data.get(CONF_SENSITIVITY, DEFAULT_SENSITIVITY) + _, default_failure = SENSITIVITY_THRESHOLDS.get( + sensitivity, + SENSITIVITY_THRESHOLDS[DEFAULT_SENSITIVITY], + ) + if sensitivity != "custom": + return float(default_failure) + return float(self.data.get(CONF_FAILURE_THRESHOLD, default_failure)) + + @property + def cooldown(self) -> timedelta: + """Return notification/action cooldown.""" + return timedelta( + seconds=int(self.data.get(CONF_COOLDOWN_SECONDS, DEFAULT_COOLDOWN_SECONDS)) + ) + + @property + def detection_interval(self) -> timedelta: + """Return scheduled detection interval.""" + return timedelta( + seconds=int( + self.data.get(CONF_DETECTION_INTERVAL, DEFAULT_DETECTION_INTERVAL) + ) + ) + + @property + def light_control_mode(self) -> str: + """Return how the detector should manage the configured light.""" + mode = self.data.get(CONF_LIGHT_CONTROL_MODE, DEFAULT_LIGHT_CONTROL_MODE) + if mode in {LIGHT_CONTROL_OFF, LIGHT_CONTROL_LEAVE_ON, LIGHT_CONTROL_RESTORE}: + return mode + return DEFAULT_LIGHT_CONTROL_MODE + + @property + def light_settle_seconds(self) -> int: + """Return seconds to wait after turning on a light before snapshot.""" + try: + seconds = int( + float( + self.data.get( + CONF_LIGHT_SETTLE_SECONDS, + DEFAULT_LIGHT_SETTLE_SECONDS, + ) + ) + ) + except (TypeError, ValueError): + seconds = DEFAULT_LIGHT_SETTLE_SECONDS + return max(0, seconds) + + async def async_setup(self) -> None: + """Start scheduled detection.""" + interval = self.detection_interval + self.next_run = dt_util.utcnow() + interval + self.unsubscribers.append( + async_track_time_interval( + self.hass, + self._async_interval_update, + interval, + ) + ) + + if status_entity := self.data.get(CONF_PRINT_STATUS_SENSOR): + status_entities = [status_entity] + if guard_entity := self._inferred_guard_entity(status_entity): + status_entities.append(guard_entity) + self.unsubscribers.append( + async_track_state_change_event( + self.hass, + status_entities, + self._async_status_changed, + ) + ) + + async def async_unload(self) -> None: + """Stop scheduled detection.""" + for unsubscribe in self.unsubscribers: + unsubscribe() + self.unsubscribers.clear() + self.listeners.clear() + + @callback + def async_add_listener(self, listener: Callable[[], None]) -> CALLBACK_TYPE: + """Add a listener for runtime state changes.""" + self.listeners.append(listener) + + @callback + def remove_listener() -> None: + self.listeners.remove(listener) + + return remove_listener + + @callback + def _notify_listeners(self) -> None: + """Notify entities that runtime state changed.""" + for listener in list(self.listeners): + listener() + + async def _async_interval_update(self, now: datetime) -> None: + """Run detection on interval if the target is active.""" + self.next_run = now + self.detection_interval + self._notify_listeners() + if self.enabled and self._should_run_scheduled(): + await self.async_run_detection(manual=False) + + @callback + def _async_status_changed(self, event) -> None: + """Reset state when a new print starts.""" + old_state = event.data.get("old_state") + new_state = event.data.get("new_state") + if new_state is None: + return + was_active = ( + old_state is not None + and _normalize_state(old_state.state) in self.active_states + ) + is_active = _normalize_state(new_state.state) in self.active_states + if was_active and not is_active: + self.detected_event_sent_for_active_period = False + if is_active and not was_active: + self.reset() + + def _should_run_scheduled(self) -> bool: + """Return if scheduled detection should run.""" + status_entity = self.data.get(CONF_PRINT_STATUS_SENSOR) + if not status_entity: + self.printer_state = None + if bool(self.data.get(CONF_RUN_WITHOUT_PRINTING, False)): + return True + self.status = "waiting_for_print" + self._notify_listeners() + return False + + state = self.hass.states.get(status_entity) + if state is None: + self.status = "status_unavailable" + self.printer_state = None + self._notify_listeners() + return False + + self.printer_state = str(state.state) + normalized_state = _normalize_state(state.state) + if normalized_state not in self.active_states: + self.status = ( + "status_unavailable" + if normalized_state in {"unknown", "unavailable"} + else "waiting_for_print" + ) + self._notify_listeners() + return False + + if not self._passes_inferred_guard_sensor(status_entity): + if self.status != "status_unavailable": + self.status = "waiting_for_print" + self._notify_listeners() + return False + + self._notify_listeners() + return True + + def _passes_inferred_guard_sensor(self, status_entity: str) -> bool: + """Return false when an inferred companion status says not active.""" + guard_entity = self._inferred_guard_entity(status_entity) + if guard_entity is None: + return True + + state = self.hass.states.get(guard_entity) + if state is None: + return True + + self.printer_state = f"{self.printer_state}; {guard_entity}={state.state}" + normalized_state = _normalize_state(state.state) + if normalized_state in {"unknown", "unavailable"}: + self.status = "status_unavailable" + return False + return normalized_state in self.active_states + + def _inferred_guard_entity(self, status_entity: str) -> str | None: + """Infer an Elegoo companion current-status sensor when available.""" + suffix = "_print_status" + if not status_entity.endswith(suffix): + return None + candidate = f"{status_entity[: -len(suffix)]}_current_status" + if candidate == status_entity: + return None + return candidate + + def reset(self) -> None: + """Reset detection state.""" + self.status = "idle" + self.confidence = 0.0 + self.raw_score = 0.0 + self.detection_count = 0 + self.detected = False + self.warning = False + self.last_detected = None + self.last_error = None + self.last_result = {"detections": []} + self.lifetime_frames = 0 + self.detected_event_sent_for_active_period = False + self._notify_listeners() + + async def async_run_detection(self, *, manual: bool) -> dict[str, Any]: + """Run one detection request.""" + if self.running: + self.status = "busy" + self._notify_listeners() + return self._service_result() + + if not manual and not self._should_run_scheduled(): + return self._service_result() + + self.running = True + self.status = "checking" + self.last_run = dt_util.utcnow() + self.last_error = None + self._notify_listeners() + + restore_light: str | None = None + try: + restore_light = await self._async_prepare_light() + if not manual and not self._should_run_scheduled(): + return self._service_result() + + image_url = self._build_image_url() + if not image_url: + self._set_error("camera_image_unavailable") + return self._service_result() + + self.last_image_url = image_url + + try: + ml_lock = self.hass.data[DOMAIN][RUNTIME_ML_LOCK] + async with ml_lock: + result = await self._async_predict(image_url) + except (aiohttp.ClientError, TimeoutError) as err: + self._set_error(str(err)) + LOGGER.warning( + "Obico ML request failed for %s: %s", + self.detector_id, + err, + ) + return self._service_result() + + self.last_result = result + self.raw_score, self.detection_count = _score_detections(result) + self.confidence = self.raw_score + self.warning = self.confidence >= self.warning_threshold + self.detected = self.confidence >= self.failure_threshold + self.status = ( + "detected" if self.detected else "warning" if self.warning else "clear" + ) + self.lifetime_frames += 1 + + self._fire_result_event(manual) + if self.detected and self._can_fire_detected_event(manual): + self.last_detected = dt_util.utcnow() + if not manual and self.data.get(CONF_PRINT_STATUS_SENSOR): + self.detected_event_sent_for_active_period = True + self._fire_detected_event(manual) + + self._notify_listeners() + return self._service_result() + finally: + if restore_light is not None: + await self._async_restore_light(restore_light) + self.running = False + + async def _async_predict(self, image_url: str) -> dict[str, Any]: + """Call the Obico ML API.""" + session = async_get_clientsession(self.hass) + async with session.get( + f"{self.data[CONF_OBICO_HOST].rstrip('/')}/p/", + params={"img": image_url}, + headers={"Authorization": f"Bearer {self.data[CONF_OBICO_AUTH_TOKEN]}"}, + timeout=aiohttp.ClientTimeout(total=60), + ) as response: + if response.status >= 400: + error_message = await _response_error_message(response) + raise aiohttp.ClientResponseError( + response.request_info, + response.history, + status=response.status, + message=error_message, + headers=response.headers, + ) + response.raise_for_status() + result = await response.json() + if not isinstance(result, dict): + return {"detections": []} + return result + + async def _async_prepare_light(self) -> str | None: + """Prepare the configured light before detection. + + Returns the entity ID to restore when the integration turned an off + light on and the selected mode wants the previous state restored. + """ + if self.light_control_mode == LIGHT_CONTROL_OFF: + return None + + light_entity = self.data.get(CONF_CHAMBER_LIGHT) + if not light_entity: + return None + state = self.hass.states.get(light_entity) + if state is None or _normalize_state(state.state) != "off": + return None + + try: + await self.hass.services.async_call( + "light", + "turn_on", + {"entity_id": light_entity}, + blocking=True, + ) + except HomeAssistantError as err: + LOGGER.warning("Could not turn on light %s: %s", light_entity, err) + return None + + restore_light = ( + light_entity if self.light_control_mode == LIGHT_CONTROL_RESTORE else None + ) + + try: + if self.light_settle_seconds: + await asyncio.sleep(self.light_settle_seconds) + except asyncio.CancelledError: + if restore_light is not None: + await self._async_restore_light(restore_light) + raise + + return restore_light + + async def _async_restore_light(self, light_entity: str) -> None: + """Restore a light that the detector temporarily turned on.""" + state = self.hass.states.get(light_entity) + if state is not None and _normalize_state(state.state) == "off": + return + + try: + await self.hass.services.async_call( + "light", + "turn_off", + {"entity_id": light_entity}, + blocking=True, + ) + except HomeAssistantError as err: + LOGGER.warning("Could not restore light %s: %s", light_entity, err) + + def _build_image_url(self) -> str | None: + """Build a snapshot URL for the configured camera.""" + if snapshot_url := self.data.get(CONF_SNAPSHOT_URL): + return snapshot_url + + camera_entity = self.data.get(CONF_CAMERA) + state = self.hass.states.get(camera_entity) + if state is None: + return None + entity_picture = state.attributes.get("entity_picture") + if not entity_picture: + return None + return f"{self.data[CONF_HOME_ASSISTANT_HOST].rstrip('/')}{entity_picture}" + + def _cooldown_elapsed(self) -> bool: + """Return whether a detected event can be fired.""" + if self.last_detected is None: + return True + return dt_util.utcnow() - self.last_detected >= self.cooldown + + def _can_fire_detected_event(self, manual: bool) -> bool: + """Return whether the detected event should be emitted.""" + if not manual and self.data.get(CONF_PRINT_STATUS_SENSOR): + return not self.detected_event_sent_for_active_period + return self._cooldown_elapsed() + + def _event_data(self, manual: bool) -> dict[str, Any]: + """Return event payload.""" + return { + "config_entry": self.entry.entry_id, + "detector": self.detector_id, + "name": self.name, + "camera": self.data.get(CONF_CAMERA), + "manual": manual, + "printer_state": self.printer_state, + ATTR_CONFIDENCE: self.confidence, + ATTR_RAW_SCORE: self.raw_score, + ATTR_DETECTED: self.detected, + ATTR_DETECTIONS: self.detection_count, + ATTR_IMAGE_URL: self.last_image_url, + ATTR_LAST_ERROR: self.last_error, + ATTR_LAST_RUN: self.last_run.isoformat() if self.last_run else None, + ATTR_NEXT_RUN: self.next_run.isoformat() if self.next_run else None, + ATTR_STATUS: self.status, + } + + def _fire_result_event(self, manual: bool) -> None: + """Fire an event for every detection result.""" + self.hass.bus.async_fire(EVENT_DETECTION_RESULT, self._event_data(manual)) + + def _fire_detected_event(self, manual: bool) -> None: + """Fire an event when spaghetti is detected.""" + self.hass.bus.async_fire(EVENT_SPAGHETTI_DETECTED, self._event_data(manual)) + + def _service_result(self) -> dict[str, Any]: + """Return service response payload.""" + return { + "result": self.last_result, + ATTR_CONFIDENCE: self.confidence, + ATTR_RAW_SCORE: self.raw_score, + ATTR_DETECTED: self.detected, + ATTR_DETECTIONS: self.detection_count, + ATTR_IMAGE_URL: self.last_image_url, + ATTR_LAST_ERROR: self.last_error, + ATTR_NEXT_RUN: self.next_run.isoformat() if self.next_run else None, + ATTR_STATUS: self.status, + } + + def _set_error(self, error: str) -> None: + """Set a runtime error and notify listeners.""" + self.status = "error" + self.last_error = error + self.detected = False + self.warning = False + self.confidence = 0.0 + self.raw_score = 0.0 + self.detection_count = 0 + self._notify_listeners() + + +async def _response_error_message(response: aiohttp.ClientResponse) -> str: + """Return a useful error message from an ML server error response.""" + try: + payload = await response.json() + except (aiohttp.ContentTypeError, ValueError): + return await response.text() + if not isinstance(payload, dict): + return str(payload) + if error := payload.get("error"): + message = payload.get("message") + return f"{error}: {message}" if message else str(error) + return str(payload) diff --git a/custom_components/elegoo_spaghetti_detection/sensor.py b/custom_components/elegoo_spaghetti_detection/sensor.py new file mode 100644 index 0000000..d213fa8 --- /dev/null +++ b/custom_components/elegoo_spaghetti_detection/sensor.py @@ -0,0 +1,106 @@ +"""Sensors for Elegoo spaghetti detection.""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any + +from homeassistant.components.sensor import ( + SensorDeviceClass, + SensorEntity, + SensorEntityDescription, + SensorStateClass, +) +from homeassistant.config_entries import ConfigEntry +from homeassistant.const import PERCENTAGE +from homeassistant.core import HomeAssistant +from homeassistant.helpers.entity import EntityCategory + +from .const import CONF_INSTANCE_ID, DOMAIN, RUNTIME_DATA +from .entity import SpaghettiDetectorEntity + + +@dataclass(frozen=True, kw_only=True) +class DetectorSensorDescription(SensorEntityDescription): + """Detector sensor description.""" + + value_fn: Any + + +SENSORS: tuple[DetectorSensorDescription, ...] = ( + DetectorSensorDescription( + key="confidence", + name="Confidence", + native_unit_of_measurement=PERCENTAGE, + state_class=SensorStateClass.MEASUREMENT, + value_fn=lambda runtime: round(runtime.confidence * 100, 1), + ), + DetectorSensorDescription( + key="raw_score", + name="Raw Score", + state_class=SensorStateClass.MEASUREMENT, + value_fn=lambda runtime: round(runtime.raw_score, 4), + ), + DetectorSensorDescription( + key="detections", + name="Detection Count", + state_class=SensorStateClass.MEASUREMENT, + value_fn=lambda runtime: runtime.detection_count, + ), + DetectorSensorDescription( + key="status", + name="Status", + value_fn=lambda runtime: runtime.status, + ), + DetectorSensorDescription( + key="last_error", + name="Last Error", + entity_category=EntityCategory.DIAGNOSTIC, + value_fn=lambda runtime: runtime.last_error or "none", + ), + DetectorSensorDescription( + key="last_run", + name="Last Run", + device_class=SensorDeviceClass.TIMESTAMP, + value_fn=lambda runtime: runtime.last_run, + ), + DetectorSensorDescription( + key="next_run", + name="Next Run", + device_class=SensorDeviceClass.TIMESTAMP, + value_fn=lambda runtime: runtime.next_run, + ), +) + + +async def async_setup_entry( + hass: HomeAssistant, + entry: ConfigEntry, + async_add_entities, +) -> None: + """Set up sensors.""" + runtime = hass.data[DOMAIN][RUNTIME_DATA][entry.entry_id] + async_add_entities( + DetectorSensor(entry, runtime, description) for description in SENSORS + ) + + +class DetectorSensor(SpaghettiDetectorEntity, SensorEntity): + """Detector sensor.""" + + entity_description: DetectorSensorDescription + + def __init__( + self, + entry: ConfigEntry, + runtime, + description: DetectorSensorDescription, + ) -> None: + super().__init__(entry, runtime, description.key) + self.entity_description = description + self.entity_id = f"sensor.{entry.data[CONF_INSTANCE_ID]}_{description.key}" + + @property + def native_value(self): + """Return the current sensor value.""" + return self.entity_description.value_fn(self.runtime) diff --git a/custom_components/elegoo_spaghetti_detection/services.yaml b/custom_components/elegoo_spaghetti_detection/services.yaml new file mode 100644 index 0000000..c0d21bd --- /dev/null +++ b/custom_components/elegoo_spaghetti_detection/services.yaml @@ -0,0 +1,60 @@ +predict: + name: "Predict spaghetti from URL" + description: "Runs the Obico ML model against a raw image URL. This is mainly for debugging." + fields: + obico_host: + description: "Obico ML Server URL." + example: "http://192.168.1.123:3333" + required: true + selector: + text: + obico_auth_token: + description: "Obico ML Server authentication token." + example: "obico_api_secret" + required: true + selector: + text: + image_url: + description: "Snapshot URL to check." + example: "https://home.example.com/api/camera_proxy/camera.example?token=..." + required: true + selector: + text: + +run_detection: + name: "Run detection" + description: "Runs one detection check for a configured detector. With force enabled this works even when the printer is not printing." + fields: + detector: + description: "Detector/entity prefix, for example elegoo_spaghetti_detection." + required: false + selector: + text: + config_entry: + description: "Detector config entry." + required: false + selector: + config_entry: + integration: elegoo_spaghetti_detection + force: + description: "Run as a manual test and bypass the print-status gate." + required: false + default: true + selector: + boolean: + +reset_state: + name: "Reset detection state" + description: "Clears the current detector confidence, result, and error state." + fields: + detector: + description: "Detector/entity prefix." + required: false + selector: + text: + config_entry: + description: "Detector config entry." + required: false + selector: + config_entry: + integration: elegoo_spaghetti_detection diff --git a/custom_components/elegoo_spaghetti_detection/translations/ar.json b/custom_components/elegoo_spaghetti_detection/translations/ar.json new file mode 100644 index 0000000..2c8e29d --- /dev/null +++ b/custom_components/elegoo_spaghetti_detection/translations/ar.json @@ -0,0 +1,150 @@ +{ + "config": { + "step": { + "user": { + "title": "Elegoo Spaghetti Detection", + "description": "ينشئ كاشفا لكاميرا طابعة Elegoo. هذا التكامل يكتشف الاخطاء فقط وينشئ entities/events؛ تبقى اجراءات pause و stop والتنبيهات داخل automations الخاصة بك.", + "data": { + "name": "اسم الكاشف", + "instance_id": "بادئة entity", + "home_assistant_host": "Home Assistant Host", + "obico_host": "Obico ML API Host", + "obico_auth_token": "Obico ML API Auth Token", + "camera": "الكاميرا", + "snapshot_url": "رابط snapshot مباشر", + "print_status_sensor": "حساس حالة الطباعة", + "active_print_states": "حالات الطباعة النشطة", + "chamber_light": "ضوء الحجرة", + "light_control_mode": "التحكم بالضوء", + "light_settle_seconds": "تأخير استقرار الضوء", + "run_without_printing": "تشغيل الكشف المجدول بدون حالة طباعة", + "detection_interval": "فاصل الكشف", + "sensitivity": "الحساسية", + "warning_threshold": "حد التحذير", + "failure_threshold": "حد الفشل", + "cooldown_seconds": "فترة تهدئة حدث detected" + }, + "data_description": { + "home_assistant_host": "URL يمكن لخادم ML الوصول اليه. عند تشغيل Docker على مضيف LAN اخر، استخدم HA LAN URL مثل http://192.168.1.90:8123.", + "obico_host": "Base URL لخادم ML الخاص بهذا المشروع، مثل http://192.168.1.100:3333. يقوم الاعداد بفحص /hc/ و /debug/image.", + "obico_auth_token": "يجب ان يطابق ML_API_TOKEN / obico_api_secret المكون على خادم ML.", + "instance_id": "Slug ثابت يستخدم في entity IDs. استخدم بادئة مختلفة لكل كاشف، مثل elegoo_cc2_left.", + "camera": "اي HA camera entity. مثال من elegoo-homeassistant: camera.elegoo_centauri_carbon2_chamber_camera. قد يختلف اسم جهازك.", + "snapshot_url": "اختياري. اتركه فارغا لاستخدام صورة Home Assistant camera proxy للكاميرا المحددة. استخدمه فقط للكاميرات غير المعتادة.", + "print_status_sensor": "اختياري لكن موصى به. مثال: sensor.elegoo_centauri_carbon2_print_status. للاسماء بأسلوب Elegoo، يتم استخدام حساس current_status المطابق تلقائيا كحارس اضافي.", + "active_print_states": "حالات مفصولة بفواصل تعني الطباعة، مثل printing,printing_recovery. تستخدم Elegoo CC2 عادة printing.", + "chamber_light": "اختياري. مثال: light.elegoo_centauri_carbon2_chamber_light. يستخدم فقط بواسطة اعداد التحكم بالضوء.", + "light_control_mode": "اختر ما اذا كان الكشف لا يتحكم بالضوء، او يشغله ويبقيه مشغلا، او يعيد حالة الضوء السابقة بعد كل snapshot.", + "light_settle_seconds": "عدد الثواني للانتظار بعد ان يشغل التكامل ضوءا كان مطفأ قبل اخذ snapshot. الافتراضي 3 ثوان للتعريض/التركيز.", + "run_without_printing": "اذا لم يتم تحديد حساس حالة طباعة، يعمل الكشف المجدول فقط عند تفعيل هذا الخيار. زر Test يشغل فحصا واحدا دائما.", + "detection_interval": "عدد الثواني بين الفحوصات المجدولة عندما تكون حالة الطباعة نشطة. امثلة: 600 لعشر دقائق، 900 لخمس عشرة دقيقة.", + "warning_threshold": "يستخدم عندما تكون الحساسية Custom thresholds.", + "failure_threshold": "يستخدم عندما تكون الحساسية Custom thresholds.", + "cooldown_seconds": "الافتراضي 900 ثانية. مع حساس حالة طباعة، ترسل الفحوصات المجدولة حدث detected واحدا لكل نافذة طباعة نشطة؛ وتستمر result events في الارسال مع كل فحص." + } + } + }, + "error": { + "invalid_instance_id": "يجب ان تحتوي بادئة entity على حرف slug صالح واحد على الاقل.", + "instance_id_exists": "بادئة entity هذه مستخدمة بالفعل بواسطة كاشف اخر.", + "warning_above_failure": "يجب ان يكون حد التحذير اقل من حد الفشل او مساويا له.", + "already_configured": "هذه الكاميرا مكونة بالفعل.", + "camera_image_unavailable": "الكاميرا المحددة لا تعرض entity_picture URL. جرب كاميرا اخرى او اضبط snapshot URL مباشر.", + "ml_health_failed": "فشل فحص صحة خادم ML. تأكد ان المضيف قابل للوصول ويشير الى base URL مثل http://192.168.1.100:3333.", + "ml_auth_failed": "رفض خادم ML الرمز.", + "ml_image_fetch_failed": "تعذر على خادم ML جلب صورة الكاميرا او فك ترميزها. تحقق من Home Assistant Host ووصول الكاميرا من خادم ML." + }, + "abort": { + "already_configured": "هذه الكاميرا مكونة بالفعل." + } + }, + "options": { + "step": { + "init": { + "title": "اعدادات الكاشف", + "description": "حدث اعدادات الكاميرا وخادم ML وحالة الطباعة والكشف لهذا الكاشف.", + "data": { + "home_assistant_host": "Home Assistant Host", + "obico_host": "Obico ML API Host", + "obico_auth_token": "Obico ML API Auth Token", + "camera": "الكاميرا", + "snapshot_url": "رابط snapshot مباشر", + "print_status_sensor": "حساس حالة الطباعة", + "active_print_states": "حالات الطباعة النشطة", + "chamber_light": "ضوء الحجرة", + "light_control_mode": "التحكم بالضوء", + "light_settle_seconds": "تأخير استقرار الضوء", + "run_without_printing": "تشغيل الكشف المجدول بدون حالة طباعة", + "detection_interval": "فاصل الكشف", + "sensitivity": "الحساسية", + "warning_threshold": "حد التحذير", + "failure_threshold": "حد الفشل", + "cooldown_seconds": "فترة تهدئة حدث detected" + }, + "data_description": { + "home_assistant_host": "URL يمكن لخادم ML الوصول اليه. عند تشغيل Docker على مضيف LAN اخر، استخدم HA LAN URL مثل http://192.168.1.90:8123.", + "obico_host": "Base URL لخادم ML الخاص بهذا المشروع، مثل http://192.168.1.100:3333. يقوم الاعداد بفحص /hc/ و /debug/image.", + "obico_auth_token": "يجب ان يطابق ML_API_TOKEN / obico_api_secret المكون على خادم ML.", + "camera": "اي HA camera entity. مثال من elegoo-homeassistant: camera.elegoo_centauri_carbon2_chamber_camera. قد يختلف اسم جهازك.", + "snapshot_url": "اختياري. اتركه فارغا لاستخدام صورة Home Assistant camera proxy للكاميرا المحددة.", + "print_status_sensor": "اختياري لكن موصى به. مثال: sensor.elegoo_centauri_carbon2_print_status. للاسماء بأسلوب Elegoo، يتم استخدام حساس current_status المطابق تلقائيا كحارس اضافي.", + "active_print_states": "حالات مفصولة بفواصل تعني الطباعة، مثل printing,printing_recovery.", + "chamber_light": "اختياري. مثال: light.elegoo_centauri_carbon2_chamber_light. يستخدم فقط بواسطة اعداد التحكم بالضوء.", + "light_control_mode": "اختر ما اذا كان الكشف لا يتحكم بالضوء، او يشغله ويبقيه مشغلا، او يعيد حالة الضوء السابقة بعد كل snapshot.", + "light_settle_seconds": "عدد الثواني للانتظار بعد ان يشغل التكامل ضوءا كان مطفأ قبل اخذ snapshot. الافتراضي 3 ثوان للتعريض/التركيز.", + "run_without_printing": "اذا لم يتم تحديد حساس حالة طباعة، يعمل الكشف المجدول فقط عند تفعيل هذا الخيار. زر Test يشغل فحصا واحدا دائما.", + "detection_interval": "عدد الثواني بين الفحوصات المجدولة عندما تكون حالة الطباعة نشطة. امثلة: 600 لعشر دقائق، 900 لخمس عشرة دقيقة.", + "warning_threshold": "يستخدم عندما تكون الحساسية Custom thresholds.", + "failure_threshold": "يستخدم عندما تكون الحساسية Custom thresholds.", + "cooldown_seconds": "الافتراضي 900 ثانية. مع حساس حالة طباعة، ترسل الفحوصات المجدولة حدث detected واحدا لكل نافذة طباعة نشطة؛ وتستمر result events في الارسال مع كل فحص." + } + } + }, + "error": { + "warning_above_failure": "يجب ان يكون حد التحذير اقل من حد الفشل او مساويا له.", + "camera_image_unavailable": "الكاميرا المحددة لا تعرض entity_picture URL. جرب كاميرا اخرى او اضبط snapshot URL مباشر.", + "ml_health_failed": "فشل فحص صحة خادم ML. تأكد ان المضيف قابل للوصول ويشير الى base URL مثل http://192.168.1.100:3333.", + "ml_auth_failed": "رفض خادم ML الرمز.", + "ml_image_fetch_failed": "تعذر على خادم ML جلب صورة الكاميرا او فك ترميزها. تحقق من Home Assistant Host ووصول الكاميرا من خادم ML." + } + }, + "services": { + "predict": { + "name": "توقع spaghetti من URL", + "description": "يشغل نموذج Obico ML على URL صورة خام", + "fields": { + "obico_host": { + "name": "Obico ML API Host", + "description": "Obico ML API host" + }, + "obico_auth_token": { + "name": "Obico ML API Auth Token", + "description": "رمز مصادقة Obico ML API" + }, + "image_url": { + "name": "Image URL", + "description": "Snapshot URL" + } + } + }, + "run_detection": { + "name": "تشغيل الكشف", + "description": "يشغل فحص كشف واحدا لكاشف مكون.", + "fields": { + "detector": { + "name": "الكاشف" + }, + "config_entry": { + "name": "Config entry" + }, + "force": { + "name": "اجبار" + } + } + }, + "reset_state": { + "name": "اعادة ضبط حالة الكشف", + "description": "يمسح confidence والنتيجة وحالة الخطأ للكاشف." + } + } +} diff --git a/custom_components/elegoo_spaghetti_detection/translations/en.json b/custom_components/elegoo_spaghetti_detection/translations/en.json new file mode 100644 index 0000000..f80ff64 --- /dev/null +++ b/custom_components/elegoo_spaghetti_detection/translations/en.json @@ -0,0 +1,150 @@ +{ + "config": { + "step": { + "user": { + "title": "Elegoo Spaghetti Detection", + "description": "Create one detector for an Elegoo printer camera. The integration only detects failures and fires entities/events; pause, stop, and notify actions stay in your own automations.", + "data": { + "name": "Detector name", + "instance_id": "Entity prefix", + "home_assistant_host": "Home Assistant Host", + "obico_host": "Obico ML API Host", + "obico_auth_token": "Obico ML API Auth Token", + "camera": "Camera", + "snapshot_url": "Direct snapshot URL", + "print_status_sensor": "Print status sensor", + "active_print_states": "Active print states", + "chamber_light": "Chamber light", + "light_control_mode": "Light control", + "light_settle_seconds": "Light settle delay", + "run_without_printing": "Run scheduled detection without print status", + "detection_interval": "Detection interval", + "sensitivity": "Sensitivity", + "warning_threshold": "Warning threshold", + "failure_threshold": "Failure threshold", + "cooldown_seconds": "Detected event cooldown" + }, + "data_description": { + "home_assistant_host": "URL reachable by the ML server. For Docker on another LAN host, use the HA LAN URL, for example http://192.168.1.90:8123.", + "obico_host": "Base URL of this project's ML server, for example http://192.168.1.100:3333. The setup checks /hc/ and /debug/image.", + "obico_auth_token": "Must match ML_API_TOKEN / obico_api_secret configured on the ML server.", + "instance_id": "Stable slug used in entity IDs. Use a different prefix for each detector, for example elegoo_cc2_left.", + "camera": "Any HA camera entity. Example from elegoo-homeassistant: camera.elegoo_centauri_carbon2_chamber_camera. Your device name may differ.", + "snapshot_url": "Optional. Leave empty to use the selected camera entity's Home Assistant camera proxy image. Use this only for unusual cameras.", + "print_status_sensor": "Optional but recommended. Example: sensor.elegoo_centauri_carbon2_print_status. For Elegoo-style names, the matching current_status sensor is used automatically as an extra guard.", + "active_print_states": "Comma-separated states that mean printing, for example printing,printing_recovery. Elegoo CC2 usually uses printing.", + "chamber_light": "Optional. Example: light.elegoo_centauri_carbon2_chamber_light. Used only by the light-control setting.", + "light_control_mode": "Choose whether detection should leave the light alone, turn it on and leave it on, or restore the previous light state after each snapshot.", + "light_settle_seconds": "Seconds to wait after this integration turns on an off light before taking the snapshot. Default is 3 seconds for camera exposure/focus.", + "run_without_printing": "If no print status sensor is selected, scheduled detection only runs when this is enabled. The Test button always runs one check.", + "detection_interval": "Seconds between scheduled checks while the print status is active. Examples: 600 for 10 minutes, 900 for 15 minutes.", + "warning_threshold": "Used when Sensitivity is set to Custom thresholds.", + "failure_threshold": "Used when Sensitivity is set to Custom thresholds.", + "cooldown_seconds": "Default is 900 seconds. Scheduled checks with a print status sensor emit one detected event per active print window; result events still fire for every check." + } + } + }, + "error": { + "invalid_instance_id": "Entity prefix must contain at least one valid slug character.", + "instance_id_exists": "This entity prefix is already used by another detector.", + "warning_above_failure": "Warning threshold must be lower than or equal to failure threshold.", + "already_configured": "This camera is already configured.", + "camera_image_unavailable": "The selected camera does not expose an entity_picture URL. Try another camera or set a direct snapshot URL.", + "ml_health_failed": "The ML server health check failed. Confirm the host is reachable and points to the base URL, for example http://192.168.1.100:3333.", + "ml_auth_failed": "The ML server rejected the token.", + "ml_image_fetch_failed": "The ML server could not fetch or decode the camera image. Check Home Assistant Host and camera access from the ML server." + }, + "abort": { + "already_configured": "This camera is already configured." + } + }, + "options": { + "step": { + "init": { + "title": "Detector settings", + "description": "Update camera, ML server, print-state, and detection settings for this detector.", + "data": { + "home_assistant_host": "Home Assistant Host", + "obico_host": "Obico ML API Host", + "obico_auth_token": "Obico ML API Auth Token", + "camera": "Camera", + "snapshot_url": "Direct snapshot URL", + "print_status_sensor": "Print status sensor", + "active_print_states": "Active print states", + "chamber_light": "Chamber light", + "light_control_mode": "Light control", + "light_settle_seconds": "Light settle delay", + "run_without_printing": "Run scheduled detection without print status", + "detection_interval": "Detection interval", + "sensitivity": "Sensitivity", + "warning_threshold": "Warning threshold", + "failure_threshold": "Failure threshold", + "cooldown_seconds": "Detected event cooldown" + }, + "data_description": { + "home_assistant_host": "URL reachable by the ML server. For Docker on another LAN host, use the HA LAN URL, for example http://192.168.1.90:8123.", + "obico_host": "Base URL of this project's ML server, for example http://192.168.1.100:3333. The setup checks /hc/ and /debug/image.", + "obico_auth_token": "Must match ML_API_TOKEN / obico_api_secret configured on the ML server.", + "camera": "Any HA camera entity. Example from elegoo-homeassistant: camera.elegoo_centauri_carbon2_chamber_camera. Your device name may differ.", + "snapshot_url": "Optional. Leave empty to use the selected camera entity's Home Assistant camera proxy image.", + "print_status_sensor": "Optional but recommended. Example: sensor.elegoo_centauri_carbon2_print_status. For Elegoo-style names, the matching current_status sensor is used automatically as an extra guard.", + "active_print_states": "Comma-separated states that mean printing, for example printing,printing_recovery.", + "chamber_light": "Optional. Example: light.elegoo_centauri_carbon2_chamber_light. Used only by the light-control setting.", + "light_control_mode": "Choose whether detection should leave the light alone, turn it on and leave it on, or restore the previous light state after each snapshot.", + "light_settle_seconds": "Seconds to wait after this integration turns on an off light before taking the snapshot. Default is 3 seconds for camera exposure/focus.", + "run_without_printing": "If no print status sensor is selected, scheduled detection only runs when this is enabled. The Test button always runs one check.", + "detection_interval": "Seconds between scheduled checks while the print status is active. Examples: 600 for 10 minutes, 900 for 15 minutes.", + "warning_threshold": "Used when Sensitivity is set to Custom thresholds.", + "failure_threshold": "Used when Sensitivity is set to Custom thresholds.", + "cooldown_seconds": "Default is 900 seconds. Scheduled checks with a print status sensor emit one detected event per active print window; result events still fire for every check." + } + } + }, + "error": { + "warning_above_failure": "Warning threshold must be lower than or equal to failure threshold.", + "camera_image_unavailable": "The selected camera does not expose an entity_picture URL. Try another camera or set a direct snapshot URL.", + "ml_health_failed": "The ML server health check failed. Confirm the host is reachable and points to the base URL, for example http://192.168.1.100:3333.", + "ml_auth_failed": "The ML server rejected the token.", + "ml_image_fetch_failed": "The ML server could not fetch or decode the camera image. Check Home Assistant Host and camera access from the ML server." + } + }, + "services": { + "predict": { + "name": "Predict spaghetti from URL", + "description": "Runs the Obico ML model against a raw image URL", + "fields": { + "obico_host": { + "name": "Obico ML API Host", + "description": "Obico ML API host" + }, + "obico_auth_token": { + "name": "Obico ML API Auth Token", + "description": "Obico ML API authentication token" + }, + "image_url": { + "name": "Image URL", + "description": "Snapshot URL" + } + } + }, + "run_detection": { + "name": "Run detection", + "description": "Runs one detection check for a configured detector.", + "fields": { + "detector": { + "name": "Detector" + }, + "config_entry": { + "name": "Config entry" + }, + "force": { + "name": "Force" + } + } + }, + "reset_state": { + "name": "Reset detection state", + "description": "Clears the detector confidence, result, and error state." + } + } +} diff --git a/custom_components/elegoo_spaghetti_detection/translations/es.json b/custom_components/elegoo_spaghetti_detection/translations/es.json new file mode 100644 index 0000000..c4837b0 --- /dev/null +++ b/custom_components/elegoo_spaghetti_detection/translations/es.json @@ -0,0 +1,150 @@ +{ + "config": { + "step": { + "user": { + "title": "Elegoo Spaghetti Detection", + "description": "Crea un detector para una camara de impresora Elegoo. La integracion solo detecta fallos y genera entidades/eventos; las acciones de pausar, detener y notificar quedan en tus automatizaciones.", + "data": { + "name": "Nombre del detector", + "instance_id": "Prefijo de entidad", + "home_assistant_host": "Host de Home Assistant", + "obico_host": "Host de la API ML de Obico", + "obico_auth_token": "Token de API ML de Obico", + "camera": "Camara", + "snapshot_url": "URL directa de captura", + "print_status_sensor": "Sensor de estado de impresion", + "active_print_states": "Estados activos de impresion", + "chamber_light": "Luz de camara", + "light_control_mode": "Control de luz", + "light_settle_seconds": "Espera de luz", + "run_without_printing": "Ejecutar deteccion programada sin estado de impresion", + "detection_interval": "Intervalo de deteccion", + "sensitivity": "Sensibilidad", + "warning_threshold": "Umbral de advertencia", + "failure_threshold": "Umbral de fallo", + "cooldown_seconds": "Enfriamiento del evento detectado" + }, + "data_description": { + "home_assistant_host": "URL accesible por el servidor ML. Para Docker en otro host LAN, usa la URL LAN de HA, por ejemplo http://192.168.1.90:8123.", + "obico_host": "URL base del servidor ML de este proyecto, por ejemplo http://192.168.1.100:3333. La configuracion comprueba /hc/ y /debug/image.", + "obico_auth_token": "Debe coincidir con ML_API_TOKEN / obico_api_secret configurado en el servidor ML.", + "instance_id": "Slug estable usado en los ID de entidad. Usa un prefijo distinto para cada detector, por ejemplo elegoo_cc2_left.", + "camera": "Cualquier entidad de camara de HA. Ejemplo de elegoo-homeassistant: camera.elegoo_centauri_carbon2_chamber_camera. El nombre de tu dispositivo puede variar.", + "snapshot_url": "Opcional. Dejalo vacio para usar la imagen proxy de la camara seleccionada en Home Assistant. Usalo solo para camaras poco comunes.", + "print_status_sensor": "Opcional pero recomendado. Ejemplo: sensor.elegoo_centauri_carbon2_print_status. Para nombres estilo Elegoo, el sensor current_status coincidente se usa automaticamente como proteccion extra.", + "active_print_states": "Estados separados por comas que significan impresion, por ejemplo printing,printing_recovery. Elegoo CC2 normalmente usa printing.", + "chamber_light": "Opcional. Ejemplo: light.elegoo_centauri_carbon2_chamber_light. Solo lo usa el ajuste de control de luz.", + "light_control_mode": "Elige si la deteccion no controla la luz, la enciende y la deja encendida, o restaura el estado anterior despues de cada captura.", + "light_settle_seconds": "Segundos que se esperan despues de encender una luz apagada antes de tomar la captura. El valor predeterminado es 3 segundos para exposicion/enfoque.", + "run_without_printing": "Si no se selecciona sensor de estado, la deteccion programada solo se ejecuta cuando esto esta activado. El boton Test siempre ejecuta una comprobacion.", + "detection_interval": "Segundos entre comprobaciones programadas mientras el estado de impresion esta activo. Ejemplos: 600 para 10 minutos, 900 para 15 minutos.", + "warning_threshold": "Se usa cuando Sensibilidad esta en Custom thresholds.", + "failure_threshold": "Se usa cuando Sensibilidad esta en Custom thresholds.", + "cooldown_seconds": "El valor predeterminado es 900 segundos. Con sensor de estado, las comprobaciones programadas emiten un evento detected por ventana activa de impresion; los eventos result siguen emitiendose en cada comprobacion." + } + } + }, + "error": { + "invalid_instance_id": "El prefijo de entidad debe contener al menos un caracter slug valido.", + "instance_id_exists": "Este prefijo de entidad ya lo usa otro detector.", + "warning_above_failure": "El umbral de advertencia debe ser menor o igual que el umbral de fallo.", + "already_configured": "Esta camara ya esta configurada.", + "camera_image_unavailable": "La camara seleccionada no expone una URL entity_picture. Prueba otra camara o define una URL directa de captura.", + "ml_health_failed": "La comprobacion de salud del servidor ML fallo. Confirma que el host sea accesible y apunte a la URL base, por ejemplo http://192.168.1.100:3333.", + "ml_auth_failed": "El servidor ML rechazo el token.", + "ml_image_fetch_failed": "El servidor ML no pudo obtener o decodificar la imagen de la camara. Revisa Home Assistant Host y el acceso a la camara desde el servidor ML." + }, + "abort": { + "already_configured": "Esta camara ya esta configurada." + } + }, + "options": { + "step": { + "init": { + "title": "Ajustes del detector", + "description": "Actualiza la camara, servidor ML, estado de impresion y ajustes de deteccion para este detector.", + "data": { + "home_assistant_host": "Host de Home Assistant", + "obico_host": "Host de la API ML de Obico", + "obico_auth_token": "Token de API ML de Obico", + "camera": "Camara", + "snapshot_url": "URL directa de captura", + "print_status_sensor": "Sensor de estado de impresion", + "active_print_states": "Estados activos de impresion", + "chamber_light": "Luz de camara", + "light_control_mode": "Control de luz", + "light_settle_seconds": "Espera de luz", + "run_without_printing": "Ejecutar deteccion programada sin estado de impresion", + "detection_interval": "Intervalo de deteccion", + "sensitivity": "Sensibilidad", + "warning_threshold": "Umbral de advertencia", + "failure_threshold": "Umbral de fallo", + "cooldown_seconds": "Enfriamiento del evento detectado" + }, + "data_description": { + "home_assistant_host": "URL accesible por el servidor ML. Para Docker en otro host LAN, usa la URL LAN de HA, por ejemplo http://192.168.1.90:8123.", + "obico_host": "URL base del servidor ML de este proyecto, por ejemplo http://192.168.1.100:3333. La configuracion comprueba /hc/ y /debug/image.", + "obico_auth_token": "Debe coincidir con ML_API_TOKEN / obico_api_secret configurado en el servidor ML.", + "camera": "Cualquier entidad de camara de HA. Ejemplo de elegoo-homeassistant: camera.elegoo_centauri_carbon2_chamber_camera. El nombre de tu dispositivo puede variar.", + "snapshot_url": "Opcional. Dejalo vacio para usar la imagen proxy de la camara seleccionada en Home Assistant.", + "print_status_sensor": "Opcional pero recomendado. Ejemplo: sensor.elegoo_centauri_carbon2_print_status. Para nombres estilo Elegoo, el sensor current_status coincidente se usa automaticamente como proteccion extra.", + "active_print_states": "Estados separados por comas que significan impresion, por ejemplo printing,printing_recovery.", + "chamber_light": "Opcional. Ejemplo: light.elegoo_centauri_carbon2_chamber_light. Solo lo usa el ajuste de control de luz.", + "light_control_mode": "Elige si la deteccion no controla la luz, la enciende y la deja encendida, o restaura el estado anterior despues de cada captura.", + "light_settle_seconds": "Segundos que se esperan despues de encender una luz apagada antes de tomar la captura. El valor predeterminado es 3 segundos para exposicion/enfoque.", + "run_without_printing": "Si no se selecciona sensor de estado, la deteccion programada solo se ejecuta cuando esto esta activado. El boton Test siempre ejecuta una comprobacion.", + "detection_interval": "Segundos entre comprobaciones programadas mientras el estado de impresion esta activo. Ejemplos: 600 para 10 minutos, 900 para 15 minutos.", + "warning_threshold": "Se usa cuando Sensibilidad esta en Custom thresholds.", + "failure_threshold": "Se usa cuando Sensibilidad esta en Custom thresholds.", + "cooldown_seconds": "El valor predeterminado es 900 segundos. Con sensor de estado, las comprobaciones programadas emiten un evento detected por ventana activa de impresion; los eventos result siguen emitiendose en cada comprobacion." + } + } + }, + "error": { + "warning_above_failure": "El umbral de advertencia debe ser menor o igual que el umbral de fallo.", + "camera_image_unavailable": "La camara seleccionada no expone una URL entity_picture. Prueba otra camara o define una URL directa de captura.", + "ml_health_failed": "La comprobacion de salud del servidor ML fallo. Confirma que el host sea accesible y apunte a la URL base, por ejemplo http://192.168.1.100:3333.", + "ml_auth_failed": "El servidor ML rechazo el token.", + "ml_image_fetch_failed": "El servidor ML no pudo obtener o decodificar la imagen de la camara. Revisa Home Assistant Host y el acceso a la camara desde el servidor ML." + } + }, + "services": { + "predict": { + "name": "Predecir spaghetti desde URL", + "description": "Ejecuta el modelo ML de Obico sobre una URL de imagen sin procesar", + "fields": { + "obico_host": { + "name": "Host de la API ML de Obico", + "description": "Host de la API ML de Obico" + }, + "obico_auth_token": { + "name": "Token de API ML de Obico", + "description": "Token de autenticacion de la API ML de Obico" + }, + "image_url": { + "name": "URL de imagen", + "description": "URL de captura" + } + } + }, + "run_detection": { + "name": "Ejecutar deteccion", + "description": "Ejecuta una comprobacion de deteccion para un detector configurado.", + "fields": { + "detector": { + "name": "Detector" + }, + "config_entry": { + "name": "Entrada de configuracion" + }, + "force": { + "name": "Forzar" + } + } + }, + "reset_state": { + "name": "Restablecer estado de deteccion", + "description": "Limpia la confianza, el resultado y el estado de error del detector." + } + } +} diff --git a/custom_components/elegoo_spaghetti_detection/translations/hi.json b/custom_components/elegoo_spaghetti_detection/translations/hi.json new file mode 100644 index 0000000..3c2af3f --- /dev/null +++ b/custom_components/elegoo_spaghetti_detection/translations/hi.json @@ -0,0 +1,150 @@ +{ + "config": { + "step": { + "user": { + "title": "Elegoo Spaghetti Detection", + "description": "Elegoo प्रिंटर कैमरा के लिए एक detector बनाता है। यह integration केवल failures detect करता है और entities/events बनाता है; pause, stop और notify actions आपकी अपनी automations में रहते हैं।", + "data": { + "name": "Detector name", + "instance_id": "Entity prefix", + "home_assistant_host": "Home Assistant Host", + "obico_host": "Obico ML API Host", + "obico_auth_token": "Obico ML API Auth Token", + "camera": "Camera", + "snapshot_url": "Direct snapshot URL", + "print_status_sensor": "Print status sensor", + "active_print_states": "Active print states", + "chamber_light": "Chamber light", + "light_control_mode": "Light control", + "light_settle_seconds": "Light settle delay", + "run_without_printing": "Print status के बिना scheduled detection चलाएं", + "detection_interval": "Detection interval", + "sensitivity": "Sensitivity", + "warning_threshold": "Warning threshold", + "failure_threshold": "Failure threshold", + "cooldown_seconds": "Detected event cooldown" + }, + "data_description": { + "home_assistant_host": "ML server द्वारा reachable URL। किसी दूसरे LAN host पर Docker के लिए HA LAN URL इस्तेमाल करें, जैसे http://192.168.1.90:8123.", + "obico_host": "इस project के ML server का base URL, जैसे http://192.168.1.100:3333। Setup /hc/ और /debug/image check करता है।", + "obico_auth_token": "ML server पर configured ML_API_TOKEN / obico_api_secret से match करना चाहिए।", + "instance_id": "Entity IDs में इस्तेमाल होने वाला stable slug। हर detector के लिए अलग prefix इस्तेमाल करें, जैसे elegoo_cc2_left.", + "camera": "कोई भी HA camera entity। elegoo-homeassistant example: camera.elegoo_centauri_carbon2_chamber_camera. आपका device name अलग हो सकता है।", + "snapshot_url": "Optional। Selected camera entity की Home Assistant camera proxy image इस्तेमाल करने के लिए खाली छोड़ें। इसे केवल unusual cameras के लिए इस्तेमाल करें।", + "print_status_sensor": "Optional लेकिन recommended। Example: sensor.elegoo_centauri_carbon2_print_status. Elegoo-style names में matching current_status sensor अपने आप extra guard के रूप में इस्तेमाल होता है।", + "active_print_states": "Printing बताने वाले comma-separated states, जैसे printing,printing_recovery. Elegoo CC2 आम तौर पर printing इस्तेमाल करता है।", + "chamber_light": "Optional। Example: light.elegoo_centauri_carbon2_chamber_light. केवल light-control setting द्वारा इस्तेमाल होता है।", + "light_control_mode": "चुनें कि detection light को न छुए, उसे on करके on रखे, या हर snapshot के बाद पिछली state restore करे।", + "light_settle_seconds": "Integration द्वारा off light को on करने के बाद snapshot से पहले wait करने के seconds। Camera exposure/focus के लिए default 3 seconds है।", + "run_without_printing": "अगर print status sensor selected नहीं है, scheduled detection केवल यह enabled होने पर चलता है। Test button हमेशा एक check चलाता है।", + "detection_interval": "Print status active होने पर scheduled checks के बीच seconds। Examples: 10 minutes के लिए 600, 15 minutes के लिए 900.", + "warning_threshold": "Sensitivity Custom thresholds होने पर इस्तेमाल होता है।", + "failure_threshold": "Sensitivity Custom thresholds होने पर इस्तेमाल होता है।", + "cooldown_seconds": "Default 900 seconds है। Print status sensor के साथ scheduled checks हर active print window में एक detected event emit करते हैं; result events हर check पर आते रहते हैं।" + } + } + }, + "error": { + "invalid_instance_id": "Entity prefix में कम से कम एक valid slug character होना चाहिए।", + "instance_id_exists": "यह entity prefix पहले से किसी दूसरे detector द्वारा इस्तेमाल हो रहा है।", + "warning_above_failure": "Warning threshold failure threshold से कम या उसके बराबर होना चाहिए।", + "already_configured": "यह camera पहले से configured है।", + "camera_image_unavailable": "Selected camera entity_picture URL expose नहीं करता। दूसरा camera try करें या direct snapshot URL set करें।", + "ml_health_failed": "ML server health check failed। Confirm करें कि host reachable है और base URL पर point करता है, जैसे http://192.168.1.100:3333.", + "ml_auth_failed": "ML server ने token reject किया।", + "ml_image_fetch_failed": "ML server camera image fetch या decode नहीं कर सका। Home Assistant Host और ML server से camera access check करें।" + }, + "abort": { + "already_configured": "यह camera पहले से configured है।" + } + }, + "options": { + "step": { + "init": { + "title": "Detector settings", + "description": "इस detector के लिए camera, ML server, print-state और detection settings update करें।", + "data": { + "home_assistant_host": "Home Assistant Host", + "obico_host": "Obico ML API Host", + "obico_auth_token": "Obico ML API Auth Token", + "camera": "Camera", + "snapshot_url": "Direct snapshot URL", + "print_status_sensor": "Print status sensor", + "active_print_states": "Active print states", + "chamber_light": "Chamber light", + "light_control_mode": "Light control", + "light_settle_seconds": "Light settle delay", + "run_without_printing": "Print status के बिना scheduled detection चलाएं", + "detection_interval": "Detection interval", + "sensitivity": "Sensitivity", + "warning_threshold": "Warning threshold", + "failure_threshold": "Failure threshold", + "cooldown_seconds": "Detected event cooldown" + }, + "data_description": { + "home_assistant_host": "ML server द्वारा reachable URL। किसी दूसरे LAN host पर Docker के लिए HA LAN URL इस्तेमाल करें, जैसे http://192.168.1.90:8123.", + "obico_host": "इस project के ML server का base URL, जैसे http://192.168.1.100:3333। Setup /hc/ और /debug/image check करता है।", + "obico_auth_token": "ML server पर configured ML_API_TOKEN / obico_api_secret से match करना चाहिए।", + "camera": "कोई भी HA camera entity। elegoo-homeassistant example: camera.elegoo_centauri_carbon2_chamber_camera. आपका device name अलग हो सकता है।", + "snapshot_url": "Optional। Selected camera entity की Home Assistant camera proxy image इस्तेमाल करने के लिए खाली छोड़ें।", + "print_status_sensor": "Optional लेकिन recommended। Example: sensor.elegoo_centauri_carbon2_print_status. Elegoo-style names में matching current_status sensor अपने आप extra guard के रूप में इस्तेमाल होता है।", + "active_print_states": "Printing बताने वाले comma-separated states, जैसे printing,printing_recovery.", + "chamber_light": "Optional। Example: light.elegoo_centauri_carbon2_chamber_light. केवल light-control setting द्वारा इस्तेमाल होता है।", + "light_control_mode": "चुनें कि detection light को न छुए, उसे on करके on रखे, या हर snapshot के बाद पिछली state restore करे।", + "light_settle_seconds": "Integration द्वारा off light को on करने के बाद snapshot से पहले wait करने के seconds। Camera exposure/focus के लिए default 3 seconds है।", + "run_without_printing": "अगर print status sensor selected नहीं है, scheduled detection केवल यह enabled होने पर चलता है। Test button हमेशा एक check चलाता है।", + "detection_interval": "Print status active होने पर scheduled checks के बीच seconds। Examples: 10 minutes के लिए 600, 15 minutes के लिए 900.", + "warning_threshold": "Sensitivity Custom thresholds होने पर इस्तेमाल होता है।", + "failure_threshold": "Sensitivity Custom thresholds होने पर इस्तेमाल होता है।", + "cooldown_seconds": "Default 900 seconds है। Print status sensor के साथ scheduled checks हर active print window में एक detected event emit करते हैं; result events हर check पर आते रहते हैं।" + } + } + }, + "error": { + "warning_above_failure": "Warning threshold failure threshold से कम या उसके बराबर होना चाहिए।", + "camera_image_unavailable": "Selected camera entity_picture URL expose नहीं करता। दूसरा camera try करें या direct snapshot URL set करें।", + "ml_health_failed": "ML server health check failed। Confirm करें कि host reachable है और base URL पर point करता है, जैसे http://192.168.1.100:3333.", + "ml_auth_failed": "ML server ने token reject किया।", + "ml_image_fetch_failed": "ML server camera image fetch या decode नहीं कर सका। Home Assistant Host और ML server से camera access check करें।" + } + }, + "services": { + "predict": { + "name": "URL से spaghetti predict करें", + "description": "Raw image URL पर Obico ML model चलाता है", + "fields": { + "obico_host": { + "name": "Obico ML API Host", + "description": "Obico ML API host" + }, + "obico_auth_token": { + "name": "Obico ML API Auth Token", + "description": "Obico ML API authentication token" + }, + "image_url": { + "name": "Image URL", + "description": "Snapshot URL" + } + } + }, + "run_detection": { + "name": "Detection चलाएं", + "description": "Configured detector के लिए एक detection check चलाता है।", + "fields": { + "detector": { + "name": "Detector" + }, + "config_entry": { + "name": "Config entry" + }, + "force": { + "name": "Force" + } + } + }, + "reset_state": { + "name": "Detection state reset करें", + "description": "Detector confidence, result और error state साफ करता है।" + } + } +} diff --git a/custom_components/elegoo_spaghetti_detection/translations/tr.json b/custom_components/elegoo_spaghetti_detection/translations/tr.json new file mode 100644 index 0000000..7f1d855 --- /dev/null +++ b/custom_components/elegoo_spaghetti_detection/translations/tr.json @@ -0,0 +1,150 @@ +{ + "config": { + "step": { + "user": { + "title": "Elegoo Spaghetti Detection", + "description": "Elegoo yazici kamerasi icin bir algilayici olusturur. Entegrasyon yalnizca hatalari algilar ve entity/event uretir; pause, stop ve bildirim aksiyonlari kendi otomasyonlarinizda kalir.", + "data": { + "name": "Algilayici adi", + "instance_id": "Entity on eki", + "home_assistant_host": "Home Assistant Host", + "obico_host": "Obico ML API Host", + "obico_auth_token": "Obico ML API Auth Token", + "camera": "Kamera", + "snapshot_url": "Dogrudan snapshot URL", + "print_status_sensor": "Baski durum sensoru", + "active_print_states": "Aktif baski durumlari", + "chamber_light": "Kabin isigi", + "light_control_mode": "Isik kontrolu", + "light_settle_seconds": "Isik bekleme suresi", + "run_without_printing": "Baski durumu olmadan zamanlanmis algilama calistir", + "detection_interval": "Algilama araligi", + "sensitivity": "Hassasiyet", + "warning_threshold": "Uyari esigi", + "failure_threshold": "Hata esigi", + "cooldown_seconds": "Algilandi eventi bekleme suresi" + }, + "data_description": { + "home_assistant_host": "ML sunucusunun erisebildigi URL. Baska bir LAN makinesindeki Docker icin HA LAN URL kullanin; ornegin http://192.168.1.90:8123.", + "obico_host": "Bu projenin ML sunucusu base URL adresi; ornegin http://192.168.1.100:3333. Kurulum /hc/ ve /debug/image kontrollerini yapar.", + "obico_auth_token": "ML sunucusunda ayarlanan ML_API_TOKEN / obico_api_secret ile ayni olmalidir.", + "instance_id": "Entity ID'lerinde kullanilan kalici slug. Her algilayici icin farkli bir on ek kullanin; ornegin elegoo_cc2_left.", + "camera": "Herhangi bir HA kamera entity'si. elegoo-homeassistant ornegi: camera.elegoo_centauri_carbon2_chamber_camera. Cihaz adiniz farkli olabilir.", + "snapshot_url": "Istege bagli. Secilen kamera entity'sinin Home Assistant camera proxy gorselini kullanmak icin bos birakin. Bunu yalnizca ozel kamera durumlarinda kullanin.", + "print_status_sensor": "Istege bagli ama onerilir. Ornek: sensor.elegoo_centauri_carbon2_print_status. Elegoo tarzi adlarda eslesen current_status sensoru otomatik ek koruma olarak kullanilir.", + "active_print_states": "Baski anlamina gelen virgulle ayrilmis durumlar; ornegin printing,printing_recovery. Elegoo CC2 genelde printing kullanir.", + "chamber_light": "Istege bagli. Ornek: light.elegoo_centauri_carbon2_chamber_light. Yalnizca isik kontrol ayari tarafindan kullanilir.", + "light_control_mode": "Algilama isigi hic kontrol etmesin mi, acip acik mi biraksin, yoksa her snapshot sonrasinda onceki duruma mi dondursun secin.", + "light_settle_seconds": "Bu entegrasyon kapali isigi actiktan sonra snapshot almadan once bekleyecegi saniye. Kamera pozlama/netleme icin varsayilan 3 saniyedir.", + "run_without_printing": "Baski durum sensoru secilmediyse zamanlanmis algilama yalnizca bu ayar acikken calisir. Test butonu her zaman tek kontrol calistirir.", + "detection_interval": "Baski durumu aktifken zamanlanmis kontroller arasindaki saniye. Ornek: 10 dakika icin 600, 15 dakika icin 900.", + "warning_threshold": "Hassasiyet Custom thresholds oldugunda kullanilir.", + "failure_threshold": "Hassasiyet Custom thresholds oldugunda kullanilir.", + "cooldown_seconds": "Varsayilan 900 saniyedir. Baski durum sensoru olan zamanlanmis kontroller aktif baski penceresi basina bir detected event uretir; result event'leri her kontrolde gelmeye devam eder." + } + } + }, + "error": { + "invalid_instance_id": "Entity on eki en az bir gecerli slug karakteri icermelidir.", + "instance_id_exists": "Bu entity on eki baska bir algilayici tarafindan kullaniliyor.", + "warning_above_failure": "Uyari esigi hata esiginden kucuk veya ona esit olmalidir.", + "already_configured": "Bu kamera zaten yapilandirilmis.", + "camera_image_unavailable": "Secilen kamera entity_picture URL sunmuyor. Baska kamera deneyin veya dogrudan snapshot URL ayarlayin.", + "ml_health_failed": "ML sunucusu saglik kontrolu basarisiz oldu. Host erisilebilir olmali ve base URL'ye isaret etmelidir; ornegin http://192.168.1.100:3333.", + "ml_auth_failed": "ML sunucusu token'i reddetti.", + "ml_image_fetch_failed": "ML sunucusu kamera gorselini alamadi veya decode edemedi. Home Assistant Host ve kamera erisimini ML sunucusundan kontrol edin." + }, + "abort": { + "already_configured": "Bu kamera zaten yapilandirilmis." + } + }, + "options": { + "step": { + "init": { + "title": "Algilayici ayarlari", + "description": "Bu algilayici icin kamera, ML sunucusu, baski durumu ve algilama ayarlarini guncelleyin.", + "data": { + "home_assistant_host": "Home Assistant Host", + "obico_host": "Obico ML API Host", + "obico_auth_token": "Obico ML API Auth Token", + "camera": "Kamera", + "snapshot_url": "Dogrudan snapshot URL", + "print_status_sensor": "Baski durum sensoru", + "active_print_states": "Aktif baski durumlari", + "chamber_light": "Kabin isigi", + "light_control_mode": "Isik kontrolu", + "light_settle_seconds": "Isik bekleme suresi", + "run_without_printing": "Baski durumu olmadan zamanlanmis algilama calistir", + "detection_interval": "Algilama araligi", + "sensitivity": "Hassasiyet", + "warning_threshold": "Uyari esigi", + "failure_threshold": "Hata esigi", + "cooldown_seconds": "Algilandi eventi bekleme suresi" + }, + "data_description": { + "home_assistant_host": "ML sunucusunun erisebildigi URL. Baska bir LAN makinesindeki Docker icin HA LAN URL kullanin; ornegin http://192.168.1.90:8123.", + "obico_host": "Bu projenin ML sunucusu base URL adresi; ornegin http://192.168.1.100:3333. Kurulum /hc/ ve /debug/image kontrollerini yapar.", + "obico_auth_token": "ML sunucusunda ayarlanan ML_API_TOKEN / obico_api_secret ile ayni olmalidir.", + "camera": "Herhangi bir HA kamera entity'si. elegoo-homeassistant ornegi: camera.elegoo_centauri_carbon2_chamber_camera. Cihaz adiniz farkli olabilir.", + "snapshot_url": "Istege bagli. Secilen kamera entity'sinin Home Assistant camera proxy gorselini kullanmak icin bos birakin.", + "print_status_sensor": "Istege bagli ama onerilir. Ornek: sensor.elegoo_centauri_carbon2_print_status. Elegoo tarzi adlarda eslesen current_status sensoru otomatik ek koruma olarak kullanilir.", + "active_print_states": "Baski anlamina gelen virgulle ayrilmis durumlar; ornegin printing,printing_recovery.", + "chamber_light": "Istege bagli. Ornek: light.elegoo_centauri_carbon2_chamber_light. Yalnizca isik kontrol ayari tarafindan kullanilir.", + "light_control_mode": "Algilama isigi hic kontrol etmesin mi, acip acik mi biraksin, yoksa her snapshot sonrasinda onceki duruma mi dondursun secin.", + "light_settle_seconds": "Bu entegrasyon kapali isigi actiktan sonra snapshot almadan once bekleyecegi saniye. Kamera pozlama/netleme icin varsayilan 3 saniyedir.", + "run_without_printing": "Baski durum sensoru secilmediyse zamanlanmis algilama yalnizca bu ayar acikken calisir. Test butonu her zaman tek kontrol calistirir.", + "detection_interval": "Baski durumu aktifken zamanlanmis kontroller arasindaki saniye. Ornek: 10 dakika icin 600, 15 dakika icin 900.", + "warning_threshold": "Hassasiyet Custom thresholds oldugunda kullanilir.", + "failure_threshold": "Hassasiyet Custom thresholds oldugunda kullanilir.", + "cooldown_seconds": "Varsayilan 900 saniyedir. Baski durum sensoru olan zamanlanmis kontroller aktif baski penceresi basina bir detected event uretir; result event'leri her kontrolde gelmeye devam eder." + } + } + }, + "error": { + "warning_above_failure": "Uyari esigi hata esiginden kucuk veya ona esit olmalidir.", + "camera_image_unavailable": "Secilen kamera entity_picture URL sunmuyor. Baska kamera deneyin veya dogrudan snapshot URL ayarlayin.", + "ml_health_failed": "ML sunucusu saglik kontrolu basarisiz oldu. Host erisilebilir olmali ve base URL'ye isaret etmelidir; ornegin http://192.168.1.100:3333.", + "ml_auth_failed": "ML sunucusu token'i reddetti.", + "ml_image_fetch_failed": "ML sunucusu kamera gorselini alamadi veya decode edemedi. Home Assistant Host ve kamera erisimini ML sunucusundan kontrol edin." + } + }, + "services": { + "predict": { + "name": "URL'den spaghetti tahmini", + "description": "Obico ML modelini ham bir gorsel URL'si uzerinde calistirir", + "fields": { + "obico_host": { + "name": "Obico ML API Host", + "description": "Obico ML API host" + }, + "obico_auth_token": { + "name": "Obico ML API Auth Token", + "description": "Obico ML API kimlik dogrulama token'i" + }, + "image_url": { + "name": "Gorsel URL", + "description": "Snapshot URL" + } + } + }, + "run_detection": { + "name": "Algilama calistir", + "description": "Yapilandirilmis bir algilayici icin tek algilama kontrolu calistirir.", + "fields": { + "detector": { + "name": "Algilayici" + }, + "config_entry": { + "name": "Config entry" + }, + "force": { + "name": "Zorla" + } + } + }, + "reset_state": { + "name": "Algilama durumunu sifirla", + "description": "Algilayici confidence, sonuc ve hata durumunu temizler." + } + } +} diff --git a/custom_components/elegoo_spaghetti_detection/translations/zh-Hans.json b/custom_components/elegoo_spaghetti_detection/translations/zh-Hans.json new file mode 100644 index 0000000..64f5694 --- /dev/null +++ b/custom_components/elegoo_spaghetti_detection/translations/zh-Hans.json @@ -0,0 +1,150 @@ +{ + "config": { + "step": { + "user": { + "title": "Elegoo Spaghetti Detection", + "description": "为 Elegoo 打印机摄像头创建一个检测器。该集成只检测失败并产生实体/事件；暂停、停止和通知动作仍由你的自动化处理。", + "data": { + "name": "检测器名称", + "instance_id": "实体前缀", + "home_assistant_host": "Home Assistant 主机", + "obico_host": "Obico ML API 主机", + "obico_auth_token": "Obico ML API 令牌", + "camera": "摄像头", + "snapshot_url": "直接快照 URL", + "print_status_sensor": "打印状态传感器", + "active_print_states": "活动打印状态", + "chamber_light": "腔体灯", + "light_control_mode": "灯光控制", + "light_settle_seconds": "灯光稳定延迟", + "run_without_printing": "无打印状态时运行计划检测", + "detection_interval": "检测间隔", + "sensitivity": "灵敏度", + "warning_threshold": "警告阈值", + "failure_threshold": "失败阈值", + "cooldown_seconds": "检测事件冷却时间" + }, + "data_description": { + "home_assistant_host": "ML 服务器可访问的 URL。Docker 在另一台局域网主机上运行时，请使用 HA 的局域网 URL，例如 http://192.168.1.90:8123。", + "obico_host": "本项目 ML 服务器的基础 URL，例如 http://192.168.1.100:3333。配置会检查 /hc/ 和 /debug/image。", + "obico_auth_token": "必须与 ML 服务器上配置的 ML_API_TOKEN / obico_api_secret 匹配。", + "instance_id": "用于实体 ID 的稳定 slug。每个检测器使用不同前缀，例如 elegoo_cc2_left。", + "camera": "任意 HA 摄像头实体。elegoo-homeassistant 示例：camera.elegoo_centauri_carbon2_chamber_camera。你的设备名称可能不同。", + "snapshot_url": "可选。留空则使用所选摄像头实体的 Home Assistant camera proxy 图像。仅在特殊摄像头场景中使用。", + "print_status_sensor": "可选但推荐。示例：sensor.elegoo_centauri_carbon2_print_status。对于 Elegoo 风格命名，匹配的 current_status 传感器会自动作为额外保护。", + "active_print_states": "表示正在打印的逗号分隔状态，例如 printing,printing_recovery。Elegoo CC2 通常使用 printing。", + "chamber_light": "可选。示例：light.elegoo_centauri_carbon2_chamber_light。仅由灯光控制设置使用。", + "light_control_mode": "选择检测时不控制灯光、打开并保持开启，或每次快照后恢复之前的灯光状态。", + "light_settle_seconds": "集成打开原本关闭的灯光后，拍摄快照前等待的秒数。默认 3 秒，用于曝光/对焦。", + "run_without_printing": "未选择打印状态传感器时，计划检测只会在启用此项后运行。测试按钮始终运行一次检查。", + "detection_interval": "打印状态活动时，两次计划检查之间的秒数。示例：600 表示 10 分钟，900 表示 15 分钟。", + "warning_threshold": "当灵敏度设置为 Custom thresholds 时使用。", + "failure_threshold": "当灵敏度设置为 Custom thresholds 时使用。", + "cooldown_seconds": "默认 900 秒。带打印状态传感器的计划检查在每个活动打印窗口只发出一个 detected 事件；result 事件仍会在每次检查时发出。" + } + } + }, + "error": { + "invalid_instance_id": "实体前缀必须至少包含一个有效的 slug 字符。", + "instance_id_exists": "此实体前缀已被另一个检测器使用。", + "warning_above_failure": "警告阈值必须小于或等于失败阈值。", + "already_configured": "此摄像头已配置。", + "camera_image_unavailable": "所选摄像头没有提供 entity_picture URL。请尝试其他摄像头或设置直接快照 URL。", + "ml_health_failed": "ML 服务器健康检查失败。请确认主机可访问并指向基础 URL，例如 http://192.168.1.100:3333。", + "ml_auth_failed": "ML 服务器拒绝了令牌。", + "ml_image_fetch_failed": "ML 服务器无法获取或解码摄像头图像。请检查 Home Assistant Host 以及 ML 服务器对摄像头的访问。" + }, + "abort": { + "already_configured": "此摄像头已配置。" + } + }, + "options": { + "step": { + "init": { + "title": "检测器设置", + "description": "更新此检测器的摄像头、ML 服务器、打印状态和检测设置。", + "data": { + "home_assistant_host": "Home Assistant 主机", + "obico_host": "Obico ML API 主机", + "obico_auth_token": "Obico ML API 令牌", + "camera": "摄像头", + "snapshot_url": "直接快照 URL", + "print_status_sensor": "打印状态传感器", + "active_print_states": "活动打印状态", + "chamber_light": "腔体灯", + "light_control_mode": "灯光控制", + "light_settle_seconds": "灯光稳定延迟", + "run_without_printing": "无打印状态时运行计划检测", + "detection_interval": "检测间隔", + "sensitivity": "灵敏度", + "warning_threshold": "警告阈值", + "failure_threshold": "失败阈值", + "cooldown_seconds": "检测事件冷却时间" + }, + "data_description": { + "home_assistant_host": "ML 服务器可访问的 URL。Docker 在另一台局域网主机上运行时，请使用 HA 的局域网 URL，例如 http://192.168.1.90:8123。", + "obico_host": "本项目 ML 服务器的基础 URL，例如 http://192.168.1.100:3333。配置会检查 /hc/ 和 /debug/image。", + "obico_auth_token": "必须与 ML 服务器上配置的 ML_API_TOKEN / obico_api_secret 匹配。", + "camera": "任意 HA 摄像头实体。elegoo-homeassistant 示例：camera.elegoo_centauri_carbon2_chamber_camera。你的设备名称可能不同。", + "snapshot_url": "可选。留空则使用所选摄像头实体的 Home Assistant camera proxy 图像。", + "print_status_sensor": "可选但推荐。示例：sensor.elegoo_centauri_carbon2_print_status。对于 Elegoo 风格命名，匹配的 current_status 传感器会自动作为额外保护。", + "active_print_states": "表示正在打印的逗号分隔状态，例如 printing,printing_recovery。", + "chamber_light": "可选。示例：light.elegoo_centauri_carbon2_chamber_light。仅由灯光控制设置使用。", + "light_control_mode": "选择检测时不控制灯光、打开并保持开启，或每次快照后恢复之前的灯光状态。", + "light_settle_seconds": "集成打开原本关闭的灯光后，拍摄快照前等待的秒数。默认 3 秒，用于曝光/对焦。", + "run_without_printing": "未选择打印状态传感器时，计划检测只会在启用此项后运行。测试按钮始终运行一次检查。", + "detection_interval": "打印状态活动时，两次计划检查之间的秒数。示例：600 表示 10 分钟，900 表示 15 分钟。", + "warning_threshold": "当灵敏度设置为 Custom thresholds 时使用。", + "failure_threshold": "当灵敏度设置为 Custom thresholds 时使用。", + "cooldown_seconds": "默认 900 秒。带打印状态传感器的计划检查在每个活动打印窗口只发出一个 detected 事件；result 事件仍会在每次检查时发出。" + } + } + }, + "error": { + "warning_above_failure": "警告阈值必须小于或等于失败阈值。", + "camera_image_unavailable": "所选摄像头没有提供 entity_picture URL。请尝试其他摄像头或设置直接快照 URL。", + "ml_health_failed": "ML 服务器健康检查失败。请确认主机可访问并指向基础 URL，例如 http://192.168.1.100:3333。", + "ml_auth_failed": "ML 服务器拒绝了令牌。", + "ml_image_fetch_failed": "ML 服务器无法获取或解码摄像头图像。请检查 Home Assistant Host 以及 ML 服务器对摄像头的访问。" + } + }, + "services": { + "predict": { + "name": "从 URL 预测 spaghetti", + "description": "针对原始图像 URL 运行 Obico ML 模型", + "fields": { + "obico_host": { + "name": "Obico ML API 主机", + "description": "Obico ML API 主机" + }, + "obico_auth_token": { + "name": "Obico ML API 令牌", + "description": "Obico ML API 认证令牌" + }, + "image_url": { + "name": "图像 URL", + "description": "快照 URL" + } + } + }, + "run_detection": { + "name": "运行检测", + "description": "为已配置的检测器运行一次检测检查。", + "fields": { + "detector": { + "name": "检测器" + }, + "config_entry": { + "name": "配置项" + }, + "force": { + "name": "强制" + } + } + }, + "reset_state": { + "name": "重置检测状态", + "description": "清除检测器的置信度、结果和错误状态。" + } + } +} diff --git a/docker-compose.yaml b/docker-compose.yaml new file mode 100644 index 0000000..0fb419f --- /dev/null +++ b/docker-compose.yaml @@ -0,0 +1,18 @@ +--- +services: + ha_elegoo_spaghetti_detection: + build: + context: ./addon + dockerfile: Dockerfile.standalone.base + image: hepter/ha_elegoo_spaghetti_detection_standalone:latest + container_name: ha_elegoo_spaghetti_detection + restart: unless-stopped + ports: + - 3333:3333/tcp + environment: + - ML_API_TOKEN=obico_api_secret + - ML_USE_GPU=false + - ML_MODEL_BACKEND=onnx + - GUNICORN_TIMEOUT=120 + - GUNICORN_WORKERS=1 + - TZ=Europe/Istanbul diff --git a/docs/HACS_PUBLISHING.md b/docs/HACS_PUBLISHING.md new file mode 100644 index 0000000..c932639 --- /dev/null +++ b/docs/HACS_PUBLISHING.md @@ -0,0 +1,107 @@ +# HACS Publishing Checklist + +This repository is a HACS `integration` because it installs a Home Assistant +custom integration under `custom_components/elegoo_spaghetti_detection`. +It is not a HACS `plugin`/Dashboard item. Dashboard plugins are JavaScript +frontend assets, usually installed from `dist/`. + +## Current HACS Requirements + +For a custom integration repository: + +- The repository must be public and hosted on GitHub. +- The repository must have a clear GitHub description. +- The repository must have GitHub topics. +- GitHub issues must be enabled. +- The repository must have a README that explains how to use the integration. +- `hacs.json` must exist in the repository root and contain at least `name`. +- There must be only one integration directory under `custom_components/`. +- All files required for the integration itself must be inside + `custom_components/elegoo_spaghetti_detection/`. +- The integration `manifest.json` must define at least: + - `domain` + - `documentation` + - `issue_tracker` + - `codeowners` + - `name` + - `version` +- The integration must provide brand assets. This repo includes: + - `custom_components/elegoo_spaghetti_detection/brand/icon.png` + - `custom_components/elegoo_spaghetti_detection/brand/logo.png` +- If submitted as a default HACS repository, these GitHub Actions must pass: + - HACS Action with `category: integration` + - Hassfest +- A full GitHub release is required before submitting to `hacs/default`. A tag + alone is not enough. + +## Default Store Submission + +To request inclusion in the default HACS store: + +1. Confirm the repository can be added manually as a HACS custom repository. +2. Confirm HACS Action passes without errors or ignored checks. +3. Confirm Hassfest passes. +4. Create a full GitHub release, for example `v1.0.0`. +5. Fork `hacs/default`. +6. Add `hepter/ha-elegoo-spaghetti-detection` alphabetically to the + `integration` file. +7. Open a PR from a branch in the fork. Do not submit the PR from an + organization account, because the PR must be editable. + +HACS default repository reviews can take months. Until it is accepted, users can +install this repo through HACS as a custom repository. + +## Repository Metadata To Set On GitHub + +These were set on GitHub on 2026-05-01. Verify them before opening a HACS +default PR: + +- Description: + - `Elegoo FDM printer spaghetti detection for Home Assistant with a local Obico ML server` +- Topics: + - `home-assistant` + - `hacs` + - `hacs-integration` + - `custom-integration` + - `elegoo` + - `fdm` + - `3d-printer` + - `spaghetti-detection` + - `obico` +- Issues: + - Enabled + +## Workflows In This Repo + +- `.github/workflows/validate.yaml` + - Runs `hacs/action@main` with `category: integration`. +- `.github/workflows/hassfest.yaml` + - Runs `home-assistant/actions/hassfest@master`. +- `.github/workflows/ci.yaml` + - Runs basic JSON, Python syntax, and YAML validation. +- `.github/dependabot.yml` + - Keeps GitHub Actions versions current. + +## Release Notes + +For the first HACS-ready release: + +- Use a SemVer tag such as `v1.0.0`. +- Ensure `custom_components/elegoo_spaghetti_detection/manifest.json` + contains the matching version without the leading `v`, for example `1.0.0`. +- Publish a full GitHub release after workflows pass. + +## References + +- HACS publish general requirements: + - https://hacs.xyz/docs/publish/start/ +- HACS integration requirements: + - https://hacs.xyz/docs/publish/integration/ +- HACS default repository inclusion: + - https://hacs.xyz/docs/publish/include/ +- HACS validation action: + - https://hacs.xyz/docs/publish/action/ +- Home Assistant integration manifest: + - https://developers.home-assistant.io/docs/creating_integration_manifest/ +- Local custom integration brand assets: + - https://developers.home-assistant.io/blog/2026/02/24/brands-proxy-api diff --git a/docs/automations.md b/docs/automations.md new file mode 100644 index 0000000..2387351 --- /dev/null +++ b/docs/automations.md @@ -0,0 +1,67 @@ +# Automation Examples + +The integration only detects failures and emits entities/events. Printer actions +use your own Home Assistant entities directly. + +Example Elegoo CC2 entities used by the templates: + +```text +button.elegoo_centauri_carbon2_pause_print +button.elegoo_centauri_carbon2_resume_print +button.elegoo_centauri_carbon2_stop_print +camera.elegoo_centauri_carbon2_chamber_camera +sensor.elegoo_centauri_carbon2_print_status +``` + +Your entity IDs may differ if your printer/device name differs. + +## Included Examples + +- [Notify only](../examples/notify_only.yaml) +- [Actionable mobile notification with pause/stop/resume](../examples/actionable_notification.yaml) +- [Confidence-based pause/stop](../examples/smart_pause_stop_by_confidence.yaml) +- [Manual test notification](../examples/manual_test_notification.yaml) + +## Event Data + +Use `elegoo_spaghetti_detection_detected` for notifications and printer +actions. When a print status sensor is configured, scheduled detected events are +sent once per active print window to avoid repeated pause/notify loops. Use +`elegoo_spaghetti_detection_result` only when you intentionally want every +detection result, including clear and warning checks. + +These two events are intentionally different: + +| Event | When it fires | Use for notifications/actions? | +| --- | --- | --- | +| `elegoo_spaghetti_detection_detected` | Only when a failure is detected and the active print window has not already emitted one detected event. | Yes. Use this for Pushbullet, mobile notifications, pause, and stop automations. | +| `elegoo_spaghetti_detection_result` | Every completed detection check, including clear, warning, and repeated detected checks. | Usually no. Use it only for logging, dashboards, or advanced automations that implement their own throttling. | + +If an automation sends notifications from +`elegoo_spaghetti_detection_result`, it can still notify repeatedly every +detection interval. The included notification and pause/stop examples use +`elegoo_spaghetti_detection_detected` to avoid that. + +Use these fields in templates: + +```text +trigger.event.data.confidence +trigger.event.data.raw_score +trigger.event.data.detected +trigger.event.data.detections +trigger.event.data.image_url +trigger.event.data.printer_state +trigger.event.data.status +trigger.event.data.last_error +``` + +Confidence is a number between `0` and `1`. For notification text: + +```jinja +{{ (trigger.event.data.confidence | float(0) * 100) | round(1) }}% +``` + +The `image_url` field is the exact camera snapshot URL the ML server checked. +Mobile notifications can use it as an image attachment. If your phone is away +from the LAN, make sure the `Home Assistant Host` you configured is reachable +from that phone, or use a notification-only message without the image. diff --git a/docs/configuration.md b/docs/configuration.md new file mode 100644 index 0000000..5a346ad --- /dev/null +++ b/docs/configuration.md @@ -0,0 +1,124 @@ +# Configuration + +Open: + +```text +Settings -> Devices & services -> Add integration -> Elegoo Spaghetti Detection +``` + +![Elegoo Spaghetti Detection setup form](images/config-flow-add-hub.png) + +The setup form creates one detector. Add another detector for another camera. +Existing detector settings are reused as defaults to reduce repeated server +entry. + +## Fields + +| Field | Notes | +| --- | --- | +| `Detector name` | Display name for this camera/detector. | +| `Entity prefix` | Stable entity ID prefix, for example `elegoo_spaghetti_detection` or `elegoo_cc2_left`. | +| `Home Assistant Host` | URL reachable by the ML server. For Docker on another LAN host, prefer the HA LAN URL, for example `http://192.168.1.90:8123`. Do not use `homeassistant.local` unless the Docker host can resolve mDNS. | +| `Obico ML API Host` | Base URL of this project's ML server, for example `http://192.168.1.100:3333`. Do not enter `/hc/` or `/p/`. | +| `Obico ML API Auth Token` | Must match `ML_API_TOKEN` / `obico_api_secret` configured on the ML server. | +| `Camera` | Any HA camera entity. Example: `camera.elegoo_centauri_carbon2_chamber_camera`. Your device name may differ. | +| `Direct snapshot URL` | Optional. Leave empty to use the selected camera's HA camera proxy image. Use this only for unusual camera integrations. | +| `Print status sensor` | Optional but recommended. Example: `sensor.elegoo_centauri_carbon2_print_status`. Scheduled detection runs only when this entity is in an active print state. For Elegoo-style entity names, a matching `sensor._current_status` is used automatically as an extra guard; you do not select it separately. | +| `Active print states` | Comma-separated states that mean printing. For Elegoo CC2, `printing` is usually enough. You can use `printing,printing_recovery`. | +| `Chamber light` | Optional. Example: `light.elegoo_centauri_carbon2_chamber_light`. | +| `Light control` | `Do not control light`, `Turn on before detection and leave on`, or `Restore previous state after detection`. Restore mode only turns the light off again when it was off before this detection cycle. | +| `Light settle delay` | Seconds to wait after the integration turns on an off light before taking the snapshot. Default is `3`; useful for camera exposure/focus. | +| `Run scheduled detection without print status` | Keep off unless this detector is camera-only and has no print status entity. | +| `Detection interval` | Seconds between scheduled checks while the print status is active. Examples: `600` for 10 minutes, `900` for 15 minutes. | +| `Sensitivity` | High, normal, low, or custom thresholds. | +| `Warning/Failure threshold` | Used when sensitivity is `Custom thresholds`. | +| `Detected event cooldown` | Minimum seconds between detected events. Default is `900` seconds. Scheduled checks with a print status sensor use the active print window limit instead, so this is mainly a fallback for camera-only setups and manual testing. | + +## Print-State Guarding + +The setup form asks for one status entity: `Print status sensor`. + +For Elegoo printers, select the `print_status` entity: + +```text +sensor.elegoo_centauri_carbon2_print_status +``` + +If the selected entity ends with `_print_status`, the integration automatically +checks for a sibling `_current_status` entity: + +```text +sensor.elegoo_centauri_carbon2_current_status +``` + +There is no separate field for `current_status`. It is an automatic fallback +guard. Scheduled detection runs only when the selected `print_status` is active +and the inferred `current_status`, when present, is also active. + +Example: + +```text +print_status = printing +current_status = idle +result: scheduled detection does not run +``` + +This protects against printer/integration states where `print_status` remains +`printing` during homing, idle, or other non-print movement states. + +## Repeated Notification Guarding + +When a `Print status sensor` is selected, scheduled detection emits at most one +`elegoo_spaghetti_detection_detected` event while the printer remains in one of +the configured `Active print states`. If the selected status sensor or the +automatic `current_status` guard leaves the active states and later returns to +an active state, a new failure can emit one new detected event. + +This is separate from the fallback guard above. The fallback guard decides +whether scheduled detection should run. The repeated-notification guard decides +whether a detected result should fire another notification/action event. + +## Example Elegoo CC2 Values + +These are examples from one `elegoo-homeassistant` install. Your entity IDs may +change if the Home Assistant device name differs. + +```text +Entity prefix: elegoo_spaghetti_detection +Home Assistant Host: http://192.168.1.90:8123 +Obico ML API Host: http://192.168.1.100:3333 +Obico ML API Auth Token: obico_api_secret +Camera: camera.elegoo_centauri_carbon2_chamber_camera +Print status sensor: sensor.elegoo_centauri_carbon2_print_status +Active print states: printing +Chamber light: light.elegoo_centauri_carbon2_chamber_light +Light control: Restore previous state after detection +Light settle delay: 3 +``` + +With restore mode, a scheduled check does this: + +```text +light was off -> turn on -> wait -> snapshot/detect -> turn off +light was on -> snapshot/detect -> keep on +``` + +## Created Entities + +With prefix `elegoo_spaghetti_detection`, the integration creates: + +```text +binary_sensor.elegoo_spaghetti_detection_spaghetti_detected +sensor.elegoo_spaghetti_detection_confidence +sensor.elegoo_spaghetti_detection_raw_score +sensor.elegoo_spaghetti_detection_detections +sensor.elegoo_spaghetti_detection_status +sensor.elegoo_spaghetti_detection_last_error +sensor.elegoo_spaghetti_detection_last_run +sensor.elegoo_spaghetti_detection_next_run +button.elegoo_spaghetti_detection_test_spaghetti_detection +button.elegoo_spaghetti_detection_reset_detection_state +``` + +Press `Test Spaghetti Detection` to run one check immediately from the current +camera image, even if the printer is not printing. diff --git a/docs/dashboard.md b/docs/dashboard.md new file mode 100644 index 0000000..fac1d12 --- /dev/null +++ b/docs/dashboard.md @@ -0,0 +1,109 @@ +# Dashboard Examples + +These examples create a small Home Assistant dashboard section for one +spaghetti detector. + +Replace entity IDs if your detector prefix, camera, printer, or automation names +are different. The examples use: + +```text +camera.elegoo_centauri_carbon2_chamber_camera +sensor.elegoo_centauri_carbon2_print_status +sensor.elegoo_spaghetti_detection_status +sensor.elegoo_spaghetti_detection_next_run +sensor.elegoo_spaghetti_detection_last_run +sensor.elegoo_spaghetti_detection_last_error +sensor.elegoo_spaghetti_detection_confidence +sensor.elegoo_spaghetti_detection_raw_score +sensor.elegoo_spaghetti_detection_detections +binary_sensor.elegoo_spaghetti_detection_spaghetti_detected +button.elegoo_spaghetti_detection_test_spaghetti_detection +button.elegoo_spaghetti_detection_reset_detection_state +``` + +The enhanced example also references this placeholder automation: + +```text +automation.elegoo_cc2_spaghetti_pause_and_notify +``` + +Replace it with your own notification or pause automation entity. +The toggle card in `dashboard_hacs.yaml` is only a placeholder until you make +that replacement. + +## Status Labels + +The dashboard examples render raw detector states as user-facing English labels: + +| Raw state | Dashboard label | +| --- | --- | +| `clear` | Clear | +| `detected` | Failure detected | +| `warning` | Warning | +| `checking` | Checking | +| `waiting_for_print` | Waiting for print | +| `status_unavailable` | Print status unavailable | +| `busy` | Busy | +| `error` | Error | +| `idle` | Idle | + +This avoids a mixed display where Home Assistant shows an unknown printer state +while the detector correctly reports `waiting_for_print`. + +## Core Home Assistant Example + +Use this when you do not want extra Lovelace dependencies: + +- [examples/dashboard_core.yaml](../examples/dashboard_core.yaml) + +It uses only built-in cards: + +- `picture-entity` +- `markdown` +- `gauge` +- `conditional` +- `button` +- `entities` + +## Enhanced HACS Example + +Use this when custom Lovelace cards are allowed: + +- [examples/dashboard_hacs.yaml](../examples/dashboard_hacs.yaml) + +Idle state: + +![Enhanced dashboard idle state](images/dashboard-hacs-waiting-for-print.png) + +Detected failure state: + +![Enhanced dashboard detected failure](images/dashboard-hacs-detected.png) + +Recommended custom cards: + +- `custom:button-card` +- `custom:mushroom-template-card` +- `card_mod` + +Install those through HACS before pasting the enhanced YAML. The enhanced +version adds a compact status panel, better visual states, responsive metric +tiles, and an alert card when spaghetti is detected. + +## Next Scheduled Check + +The integration exposes: + +```text +sensor._next_run +``` + +For the default prefix this is: + +```text +sensor.elegoo_spaghetti_detection_next_run +``` + +The value is updated when the integration starts and every time the scheduled +interval fires. It represents the next scheduled interval tick. If the print +status is not active, the detector still waits at that tick and keeps the status +as `waiting_for_print`. diff --git a/docs/images/camera-spaghetti-failure.png b/docs/images/camera-spaghetti-failure.png new file mode 100644 index 0000000..be29f3a Binary files /dev/null and b/docs/images/camera-spaghetti-failure.png differ diff --git a/docs/images/config-flow-add-hub.png b/docs/images/config-flow-add-hub.png new file mode 100644 index 0000000..7671eb8 Binary files /dev/null and b/docs/images/config-flow-add-hub.png differ diff --git a/docs/images/dashboard-hacs-detected.png b/docs/images/dashboard-hacs-detected.png new file mode 100644 index 0000000..67dc146 Binary files /dev/null and b/docs/images/dashboard-hacs-detected.png differ diff --git a/docs/images/dashboard-hacs-waiting-for-print.png b/docs/images/dashboard-hacs-waiting-for-print.png new file mode 100644 index 0000000..dec5386 Binary files /dev/null and b/docs/images/dashboard-hacs-waiting-for-print.png differ diff --git a/docs/installation.md b/docs/installation.md new file mode 100644 index 0000000..16a2fd3 --- /dev/null +++ b/docs/installation.md @@ -0,0 +1,43 @@ +# Installation + +## HACS Custom Repository + +This repository is a HACS `integration`. It is not a dashboard card or Lovelace +plugin. + +Until it is accepted into the default HACS store, add it manually: + +1. Open Home Assistant. +2. Open `HACS`. +3. Open the three-dot menu. +4. Choose `Custom repositories`. +5. Repository: + + ```text + https://github.com/hepter/ha-elegoo-spaghetti-detection + ``` + +6. Category: `Integration`. +7. Install `Elegoo Spaghetti Detection`. +8. Restart Home Assistant. + +## Manual Install + +Copy: + +```text +custom_components/elegoo_spaghetti_detection +``` + +to: + +```text +/config/custom_components/elegoo_spaghetti_detection +``` + +Restart Home Assistant. + +## ML Server + +The integration needs the local ML server before setup can complete. See +[ML server and logs](ml-server.md). diff --git a/docs/ml-server.md b/docs/ml-server.md new file mode 100644 index 0000000..41c7174 --- /dev/null +++ b/docs/ml-server.md @@ -0,0 +1,96 @@ +# ML Server And Logs + +The ML server listens on port `3333` and exposes the Obico/TSD model used for +failure detection. + +## Standalone Docker Compose + +```bash +git clone https://github.com/hepter/ha-elegoo-spaghetti-detection.git +cd ha-elegoo-spaghetti-detection +docker compose up -d +``` + +Default URL: + +```text +http://:3333 +``` + +Default token: + +```text +obico_api_secret +``` + +Change `ML_API_TOKEN` before exposing this service outside a trusted local +network. + +## Runtime Endpoints + +| Endpoint | Auth | Purpose | +| --- | --- | --- | +| `/` | no | Small browser status page and recent redacted requests. | +| `/hc/` | no | Health check, returns `ok`. | +| `/api/status` | no | JSON status, model backend, threshold, request count. | +| `/api/logs?token=` | yes | Recent request logs. Image query tokens are not shown in the dashboard. | +| `/debug/image?img=&token=` | yes | Fetch and decode a camera image without running inference. Used by setup validation. | +| `/p/?img=` | yes | Prediction endpoint used by Home Assistant. | + +The token can be passed as either: + +```text +Authorization: Bearer +``` + +or, for browser debugging only: + +```text +?token= +``` + +## CPU/GPU Behavior + +The server is CPU-first by default: + +```text +ML_USE_GPU=false +ML_MODEL_BACKEND=onnx +GUNICORN_TIMEOUT=120 +GUNICORN_WORKERS=1 +``` + +This avoids slow CUDA probing and gunicorn worker timeouts on machines without a +working NVIDIA runtime. Enable GPU only when Docker has working NVIDIA support: + +```text +ML_USE_GPU=true +``` + +## Logs + +Docker: + +```bash +docker logs -f ha_elegoo_spaghetti_detection +``` + +Recent in-app request log: + +```bash +curl "http://:3333/api/logs?token=obico_api_secret" +``` + +Health and model backend: + +```bash +curl "http://:3333/api/status" +``` + +When testing a camera URL by hand, URL-encode the image URL: + +```bash +curl --get "http://:3333/debug/image" \ + --data-urlencode "img=http://homeassistant.local:8123/api/camera_proxy/camera.example?token=..." \ + --data-urlencode "token=obico_api_secret" +``` diff --git a/docs/troubleshooting.md b/docs/troubleshooting.md new file mode 100644 index 0000000..c3daf7a --- /dev/null +++ b/docs/troubleshooting.md @@ -0,0 +1,145 @@ +# Troubleshooting + +## Setup Fails On ML Health + +Use the ML server base URL, not a specific endpoint: + +```text +http://192.168.1.100:3333 +``` + +Do not enter: + +```text +http://192.168.1.100:3333/hc/ +http://192.168.1.100:3333/p/ +``` + +Check: + +```bash +curl "http://192.168.1.100:3333/hc/" +``` + +The browser dashboard is also useful: + +```text +http://192.168.1.100:3333/ +``` + +## Setup Fails On Camera Image Fetch + +The ML server must be able to fetch the Home Assistant camera image URL. + +Common cause: + +```text +http://homeassistant.local:8123 +``` + +works from a browser but not from a Docker container because mDNS is not +resolved there. + +Use a LAN IP URL reachable by the ML server: + +```text +http://192.168.1.90:8123 +``` + +You can test only image fetch/decode without running the model: + +```bash +curl --get "http://192.168.1.100:3333/debug/image" \ + --data-urlencode "img=http://192.168.1.90:8123/api/camera_proxy/camera.example?token=..." \ + --data-urlencode "token=obico_api_secret" +``` + +## Camera Entity Does Not Provide An Image + +Some camera integrations expose stream-only entities or changed entity IDs after +updates. Set `Direct snapshot URL` if the selected camera has no `entity_picture` +attribute, or update the detector options after the camera entity ID changes. + +## Multiple Detectors + +Each config entry has a separate detector runtime and entity prefix. The Home +Assistant side supports multiple cameras. + +The ML server is intentionally single-worker by default. The integration +serializes ML calls so multiple detectors do not hit the single worker at the +same instant. If you have a stronger host and many cameras, increase +`GUNICORN_WORKERS` carefully. + +## CUDA Or Worker Timeout + +The server defaults to CPU mode: + +```text +ML_USE_GPU=false +GUNICORN_TIMEOUT=120 +``` + +Only enable GPU when Docker has a working NVIDIA runtime. If you see CUDA driver +errors, keep GPU disabled. + +## Detection Looks Too Quiet + +Open the ML server dashboard: + +```text +http://:3333/ +``` + +Or check recent request logs: + +```bash +curl "http://:3333/api/logs?token=obico_api_secret" +``` + +You can also press the `Test Spaghetti Detection` button in Home Assistant. + +## Scheduled Detection Runs While Printer Is Idle + +Scheduled detection is gated by `Print status sensor` unless +`Run scheduled detection without print status` is enabled. + +Check these options first: + +```text +Print status sensor: sensor.elegoo_centauri_carbon2_print_status +Active print states: printing +Run scheduled detection without print status: off +``` + +If the status sensor is `idle`, `complete`, `paused`, `unknown`, or +`unavailable`, the scheduled interval should wait and the detector status should +show `waiting_for_print` or `status_unavailable`. + +For Elegoo-style entity names, a selected +`sensor._print_status` automatically uses +`sensor._current_status` as a second guard when that entity exists. If +`print_status` is `printing` but `current_status` is `idle`, `homing`, or +another non-active state, scheduled detection waits and `last_run` does not +advance. + +The `Test Spaghetti Detection` button and the `run_detection` service with +`force: true` always run one manual check, even when the printer is not +printing. + +## Notifications Repeat Every Interval + +Notification, pause, and stop automations should trigger on: + +```text +elegoo_spaghetti_detection_detected +``` + +Do not use this event for normal notifications: + +```text +elegoo_spaghetti_detection_result +``` + +The result event fires after every completed detection check, including repeated +detected checks. The detected event is limited to one scheduled failure event +per active print window when a print status sensor is configured. diff --git a/examples/actionable_notification.yaml b/examples/actionable_notification.yaml new file mode 100644 index 0000000..a2f7cb9 --- /dev/null +++ b/examples/actionable_notification.yaml @@ -0,0 +1,77 @@ +alias: Elegoo Spaghetti - Actionable Notification +mode: single + +triggers: + - trigger: event + event_type: elegoo_spaghetti_detection_detected + event_data: + detector: elegoo_spaghetti_detection + id: detected + - trigger: event + event_type: mobile_app_notification_action + event_data: + action: ELEGOO_SPAGHETTI_PAUSE_PRINT + id: pause_action + - trigger: event + event_type: mobile_app_notification_action + event_data: + action: ELEGOO_SPAGHETTI_STOP_PRINT + id: stop_action + - trigger: event + event_type: mobile_app_notification_action + event_data: + action: ELEGOO_SPAGHETTI_RESUME_PRINT + id: resume_action + +variables: + notify_service: notify.mobile_app_your_phone + pause_button: button.elegoo_centauri_carbon2_pause_print + resume_button: button.elegoo_centauri_carbon2_resume_print + stop_button: button.elegoo_centauri_carbon2_stop_print + +actions: + - choose: + - conditions: + - condition: trigger + id: detected + sequence: + - action: "{{ notify_service }}" + data: + title: "Possible print failure" + message: > + Confidence: + {{ (trigger.event.data.confidence | float(0) * 100) | round(1) }}%. + Detections: {{ trigger.event.data.detections }}. + data: + image: "{{ trigger.event.data.image_url }}" + actions: + - action: ELEGOO_SPAGHETTI_PAUSE_PRINT + title: Pause print + - action: ELEGOO_SPAGHETTI_STOP_PRINT + title: Stop print + - action: ELEGOO_SPAGHETTI_RESUME_PRINT + title: Resume print + + - conditions: + - condition: trigger + id: pause_action + sequence: + - action: button.press + target: + entity_id: "{{ pause_button }}" + + - conditions: + - condition: trigger + id: stop_action + sequence: + - action: button.press + target: + entity_id: "{{ stop_button }}" + + - conditions: + - condition: trigger + id: resume_action + sequence: + - action: button.press + target: + entity_id: "{{ resume_button }}" diff --git a/examples/dashboard_core.yaml b/examples/dashboard_core.yaml new file mode 100644 index 0000000..1d233d8 --- /dev/null +++ b/examples/dashboard_core.yaml @@ -0,0 +1,116 @@ +type: vertical-stack +cards: + - type: picture-entity + entity: camera.elegoo_centauri_carbon2_chamber_camera + name: Printer camera + camera_view: live + show_state: false + + - type: markdown + title: Spaghetti Detection + content: | + {% set status = states('sensor.elegoo_spaghetti_detection_status') %} + {% set confidence = states('sensor.elegoo_spaghetti_detection_confidence') | float(0) %} + {% set detections = states('sensor.elegoo_spaghetti_detection_detections') %} + {% set print_status = states('sensor.elegoo_centauri_carbon2_print_status') %} + {% set next_run = states('sensor.elegoo_spaghetti_detection_next_run') %} + {% set last_run = states('sensor.elegoo_spaghetti_detection_last_run') %} + {% set last_error = states('sensor.elegoo_spaghetti_detection_last_error') %} + + {% if status == 'detected' %} + ## Failure detected + The detector is above the failure threshold. + {% elif status == 'warning' %} + ## Warning + The detector is above the warning threshold. + {% elif status == 'checking' %} + ## Checking camera image + The current image is being analyzed by the ML server. + {% elif status == 'waiting_for_print' %} + ## Waiting for print + Scheduled checks are paused because the selected print status is not active. + {% elif status == 'status_unavailable' %} + ## Print status unavailable + The selected print status entity is missing, unknown, or unavailable. + {% elif status == 'clear' %} + ## Clear + No spaghetti was detected in the last check. + {% else %} + ## {{ status | replace('_', ' ') | title }} + The detector state is currently {{ status }}. + {% endif %} + + **Confidence:** {{ confidence | round(1) }}% + **Detections:** {{ detections }} + **Print status:** {{ print_status }} + **Next scheduled check:** {% if next_run in ['unknown', 'unavailable', 'none', ''] %}Not scheduled yet{% else %}{{ as_timestamp(next_run) | timestamp_custom('%Y-%m-%d %H:%M:%S', true) }}{% endif %} + **Last check:** {% if last_run in ['unknown', 'unavailable', 'none', ''] %}Never{% else %}{{ as_timestamp(last_run) | timestamp_custom('%Y-%m-%d %H:%M:%S', true) }}{% endif %} + **Last error:** {% if last_error in ['none', 'unknown', 'unavailable', ''] %}None{% else %}{{ last_error }}{% endif %} + + - type: gauge + entity: sensor.elegoo_spaghetti_detection_confidence + name: Confidence + min: 0 + max: 100 + needle: true + severity: + green: 0 + yellow: 30 + red: 50 + + - type: conditional + conditions: + - entity: binary_sensor.elegoo_spaghetti_detection_spaghetti_detected + state: "on" + card: + type: markdown + title: Action needed + content: | + Spaghetti was detected. + + Check the printer camera before resuming or stopping the print. + + - type: grid + columns: 2 + square: false + cards: + - type: button + entity: button.elegoo_spaghetti_detection_test_spaghetti_detection + name: Test detection + icon: mdi:camera-iris + tap_action: + action: perform-action + perform_action: button.press + target: + entity_id: button.elegoo_spaghetti_detection_test_spaghetti_detection + + - type: button + entity: button.elegoo_spaghetti_detection_reset_detection_state + name: Reset detector + icon: mdi:restart + tap_action: + action: perform-action + perform_action: button.press + target: + entity_id: button.elegoo_spaghetti_detection_reset_detection_state + + - type: entities + title: Detector details + show_header_toggle: false + entities: + - entity: sensor.elegoo_spaghetti_detection_status + name: Detector status + - entity: sensor.elegoo_spaghetti_detection_next_run + name: Next scheduled check + - entity: sensor.elegoo_spaghetti_detection_last_run + name: Last check + - entity: binary_sensor.elegoo_spaghetti_detection_spaghetti_detected + name: Spaghetti detected + - entity: sensor.elegoo_spaghetti_detection_confidence + name: Confidence + - entity: sensor.elegoo_spaghetti_detection_raw_score + name: Raw score + - entity: sensor.elegoo_spaghetti_detection_detections + name: Detection count + - entity: sensor.elegoo_spaghetti_detection_last_error + name: Last error diff --git a/examples/dashboard_hacs.yaml b/examples/dashboard_hacs.yaml new file mode 100644 index 0000000..9c2f3f9 --- /dev/null +++ b/examples/dashboard_hacs.yaml @@ -0,0 +1,492 @@ +type: vertical-stack +cards: + - type: custom:button-card + entity: sensor.elegoo_spaghetti_detection_status + show_name: false + show_icon: false + show_state: false + tap_action: + action: more-info + custom_fields: + content: | + [[[ + const state = (id) => states[id]?.state ?? 'unknown'; + const numberState = (id) => Number.parseFloat(state(id)) || 0; + const formatDate = (value, fallback = 'Not available') => { + if (!value || ['unknown', 'unavailable', 'none'].includes(value)) return fallback; + const date = new Date(value); + return Number.isNaN(date.getTime()) ? value : date.toLocaleString(); + }; + + const status = state('sensor.elegoo_spaghetti_detection_status').trim(); + const detected = state('binary_sensor.elegoo_spaghetti_detection_spaghetti_detected') === 'on'; + const confidence = numberState('sensor.elegoo_spaghetti_detection_confidence'); + const raw = numberState('sensor.elegoo_spaghetti_detection_raw_score'); + const detections = state('sensor.elegoo_spaghetti_detection_detections'); + const printStatus = state('sensor.elegoo_centauri_carbon2_print_status'); + const nextRun = state('sensor.elegoo_spaghetti_detection_next_run'); + const lastRun = state('sensor.elegoo_spaghetti_detection_last_run'); + const lastError = state('sensor.elegoo_spaghetti_detection_last_error'); + const automation = state('automation.elegoo_cc2_spaghetti_pause_and_notify'); + + const statusMap = { + clear: { + label: 'Clear', + color: '#34c759', + bg: 'rgba(52,199,89,.14)', + desc: 'No spaghetti was detected in the last check.' + }, + detected: { + label: 'Failure detected', + color: '#ff453a', + bg: 'rgba(255,69,58,.18)', + desc: 'Check the camera before continuing.' + }, + warning: { + label: 'Warning', + color: '#ff9f0a', + bg: 'rgba(255,159,10,.16)', + desc: 'The score is above the warning threshold but below failure.' + }, + checking: { + label: 'Checking', + color: '#0a84ff', + bg: 'rgba(10,132,255,.16)', + desc: 'The current camera image is being analyzed.' + }, + waiting_for_print: { + label: 'Waiting for print', + color: '#8e8e93', + bg: 'rgba(142,142,147,.14)', + desc: 'Waiting for an active print.' + }, + status_unavailable: { + label: 'Print status unavailable', + color: '#ff9f0a', + bg: 'rgba(255,159,10,.14)', + desc: 'Check the selected print status entity in integration options.' + }, + busy: { + label: 'Busy', + color: '#0a84ff', + bg: 'rgba(10,132,255,.14)', + desc: 'Another detection request is still running.' + }, + error: { + label: 'Error', + color: '#ff453a', + bg: 'rgba(255,69,58,.16)', + desc: 'The last run failed. Check the error field below.' + }, + idle: { + label: 'Idle', + color: '#8e8e93', + bg: 'rgba(142,142,147,.14)', + desc: 'The detector is idle.' + } + }; + + const view = statusMap[status] || { + label: status.replaceAll('_', ' '), + color: '#8e8e93', + bg: 'rgba(142,142,147,.14)', + desc: 'The detector state is currently unknown.' + }; + + const automationColor = automation === 'on' ? '#34c759' : automation === 'off' ? '#ff9f0a' : '#8e8e93'; + const automationText = automation === 'on' + ? 'Automation enabled' + : automation === 'off' + ? 'Automation disabled' + : 'Automation not found'; + + const errorText = + lastError && !['none', 'unknown', 'unavailable', ''].includes(lastError) + ? lastError + : 'None'; + + return ` +
+
+
+
+
Spaghetti Detection
+
${view.label}
+
+
+ ${automationText} +
+
+ +
+ ${view.desc} +
+ +
+
+ Confidence + ${confidence.toFixed(1)}% +
+
+
+
+
+ +
+
+
Print
+
${printStatus}
+
+
+
Next check
+
${formatDate(nextRun, 'Not scheduled')}
+
+
+
Detections
+
${detections}
+
+
+
Raw score
+
${raw.toFixed(3)}
+
+
+ +
+
Last check: ${formatDate(lastRun, 'Never')}
+
Last error: ${errorText}
+
+
+ `; + ]]] + styles: + card: + - border-radius: 22px + - padding: 14px + - background: rgba(255,255,255,.04) + - border: 1px solid rgba(255,255,255,.08) + - box-shadow: none + - overflow: hidden + grid: + - grid-template-areas: "\"content\"" + - grid-template-columns: 1fr + - grid-template-rows: auto + custom_fields: + content: + - width: 100% + - justify-self: stretch + - text-align: left + extra_styles: | + .wrap { + width: 100%; + box-sizing: border-box; + } + + .header { + display: grid; + grid-template-columns: auto 1fr auto; + gap: 12px; + align-items: center; + } + + .status-dot { + width: 18px; + height: 18px; + border-radius: 999px; + } + + .title { + font-size: 21px; + font-weight: 800; + color: rgba(255,255,255,.96); + line-height: 1.15; + } + + .subtitle { + margin-top: 3px; + font-size: 14px; + opacity: .74; + font-weight: 700; + text-transform: capitalize; + } + + .automation-pill { + border: 1px solid; + border-radius: 999px; + padding: 7px 10px; + font-size: 12px; + font-weight: 800; + white-space: nowrap; + } + + .desc { + margin-top: 14px; + padding: 11px 12px; + border: 1px solid; + border-radius: 14px; + color: rgba(255,255,255,.82); + font-size: 13px; + line-height: 1.35; + white-space: normal; + overflow-wrap: anywhere; + } + + .confidence { + margin-top: 14px; + } + + .confidence-top { + display: flex; + justify-content: space-between; + margin-bottom: 7px; + font-size: 13px; + opacity: .85; + } + + .bar { + height: 12px; + border-radius: 999px; + background: rgba(255,255,255,.11); + overflow: hidden; + } + + .bar-fill { + height: 100%; + border-radius: 999px; + transition: width .3s ease; + } + + .metrics { + display: grid; + grid-template-columns: repeat(2, minmax(0, 1fr)); + gap: 10px; + margin-top: 14px; + } + + .metric { + border-radius: 14px; + padding: 10px; + background: rgba(255,255,255,.06); + text-align: center; + min-width: 0; + } + + .metric-label { + opacity: .62; + font-size: 12px; + font-weight: 700; + } + + .metric-value { + margin-top: 4px; + font-size: 14px; + font-weight: 800; + color: rgba(255,255,255,.96); + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; + } + + .footer { + margin-top: 13px; + font-size: 12px; + opacity: .70; + line-height: 1.45; + } + + @media (max-width: 520px) { + .header { + grid-template-columns: auto 1fr; + } + + .automation-pill { + grid-column: 1 / -1; + justify-self: start; + } + + .metrics { + grid-template-columns: 1fr 1fr; + } + } + + - type: conditional + conditions: + - entity: binary_sensor.elegoo_spaghetti_detection_spaghetti_detected + state: "on" + card: + type: custom:mushroom-template-card + entity: binary_sensor.elegoo_spaghetti_detection_spaghetti_detected + primary: Spaghetti detected + secondary: >- + Confidence: {{ states('sensor.elegoo_spaghetti_detection_confidence') }}% + • Detections: {{ states('sensor.elegoo_spaghetti_detection_detections') }} + icon: mdi:alert-octagon + icon_color: red + badge_icon: mdi:pause + badge_color: red + layout: horizontal + tap_action: + action: more-info + card_mod: + style: | + ha-card { + border-radius: 18px; + background: rgba(255,69,58,.16); + border: 1px solid rgba(255,69,58,.34); + animation: spaghettiPulse 1.6s ease-in-out infinite; + } + + @keyframes spaghettiPulse { + 0% { box-shadow: 0 0 0 rgba(255,69,58,0); } + 50% { box-shadow: 0 0 28px rgba(255,69,58,.30); } + 100% { box-shadow: 0 0 0 rgba(255,69,58,0); } + } + + - type: grid + columns: 2 + square: false + cards: + # Replace this placeholder automation with your own notification/pause + # automation entity before using the toggle card below. + - type: custom:mushroom-template-card + entity: automation.elegoo_cc2_spaghetti_pause_and_notify + primary: Auto pause + secondary: >- + {% if is_state('automation.elegoo_cc2_spaghetti_pause_and_notify', 'on') %} + Enabled: pause and notify on failure + {% elif is_state('automation.elegoo_cc2_spaghetti_pause_and_notify', 'off') %} + Disabled: notification-only or manual review mode + {% else %} + Replace this entity with your own automation + {% endif %} + icon: >- + {% if is_state('automation.elegoo_cc2_spaghetti_pause_and_notify', 'on') %} + mdi:shield-check + {% else %} + mdi:shield-off + {% endif %} + icon_color: >- + {% if is_state('automation.elegoo_cc2_spaghetti_pause_and_notify', 'on') %} + green + {% else %} + amber + {% endif %} + layout: vertical + tap_action: + action: toggle + hold_action: + action: more-info + card_mod: + style: | + ha-card { + border-radius: 18px; + background: rgba(255,255,255,.045); + border: 1px solid rgba(255,255,255,.08); + } + + - type: custom:mushroom-template-card + entity: button.elegoo_spaghetti_detection_test_spaghetti_detection + primary: Manual test + secondary: Take one snapshot and run detection + icon: mdi:camera-iris + icon_color: blue + layout: vertical + tap_action: + action: perform-action + perform_action: button.press + target: + entity_id: button.elegoo_spaghetti_detection_test_spaghetti_detection + card_mod: + style: | + ha-card { + border-radius: 18px; + background: rgba(10,132,255,.10); + border: 1px solid rgba(10,132,255,.24); + } + + - type: custom:mushroom-template-card + entity: button.elegoo_spaghetti_detection_reset_detection_state + primary: Reset detector + secondary: Clear confidence and detected state + icon: mdi:restart + icon_color: purple + layout: vertical + tap_action: + action: perform-action + perform_action: button.press + target: + entity_id: button.elegoo_spaghetti_detection_reset_detection_state + card_mod: + style: | + ha-card { + border-radius: 18px; + background: rgba(175,82,222,.10); + border: 1px solid rgba(175,82,222,.24); + } + + - type: custom:mushroom-template-card + entity: sensor.elegoo_spaghetti_detection_last_error + primary: Last error + secondary: >- + {% set e = states('sensor.elegoo_spaghetti_detection_last_error') %} + {% if e in ['none', 'unknown', 'unavailable', ''] %} + None + {% else %} + {{ e }} + {% endif %} + icon: >- + {% set e = states('sensor.elegoo_spaghetti_detection_last_error') %} + {% if e in ['none', 'unknown', 'unavailable', ''] %} + mdi:check-circle + {% else %} + mdi:alert-circle + {% endif %} + icon_color: >- + {% set e = states('sensor.elegoo_spaghetti_detection_last_error') %} + {% if e in ['none', 'unknown', 'unavailable', ''] %} + green + {% else %} + red + {% endif %} + layout: vertical + multiline_secondary: true + tap_action: + action: more-info + card_mod: + style: | + ha-card { + border-radius: 18px; + background: rgba(255,255,255,.045); + border: 1px solid rgba(255,255,255,.08); + } + + - type: entities + title: Spaghetti detector details + show_header_toggle: false + entities: + - entity: sensor.elegoo_spaghetti_detection_status + name: Status + - entity: sensor.elegoo_spaghetti_detection_next_run + name: Next scheduled check + - entity: binary_sensor.elegoo_spaghetti_detection_spaghetti_detected + name: Detected + - entity: sensor.elegoo_spaghetti_detection_confidence + name: Confidence + - entity: sensor.elegoo_spaghetti_detection_raw_score + name: Raw score + - entity: sensor.elegoo_spaghetti_detection_detections + name: Detection count + - entity: sensor.elegoo_spaghetti_detection_last_run + name: Last check + - entity: automation.elegoo_cc2_spaghetti_pause_and_notify + name: Auto pause automation + card_mod: + style: | + ha-card { + border-radius: 18px; + background: rgba(255,255,255,.04); + border: 1px solid rgba(255,255,255,.08); + } diff --git a/examples/manual_test_notification.yaml b/examples/manual_test_notification.yaml new file mode 100644 index 0000000..ab0fd38 --- /dev/null +++ b/examples/manual_test_notification.yaml @@ -0,0 +1,25 @@ +alias: Elegoo Spaghetti - Manual Test Result Notification +mode: single + +triggers: + - trigger: event + event_type: elegoo_spaghetti_detection_result + event_data: + detector: elegoo_spaghetti_detection + manual: true + +variables: + notify_service: notify.mobile_app_your_phone + +actions: + - action: "{{ notify_service }}" + data: + title: "Spaghetti detection test result" + message: > + Status: {{ trigger.event.data.status }}. + Confidence: + {{ (trigger.event.data.confidence | float(0) * 100) | round(1) }}%. + Detections: {{ trigger.event.data.detections }}. + Error: {{ trigger.event.data.last_error or 'none' }}. + data: + image: "{{ trigger.event.data.image_url }}" diff --git a/examples/notify_only.yaml b/examples/notify_only.yaml new file mode 100644 index 0000000..a867fff --- /dev/null +++ b/examples/notify_only.yaml @@ -0,0 +1,23 @@ +alias: Elegoo Spaghetti - Notify Only +mode: single + +triggers: + - trigger: event + event_type: elegoo_spaghetti_detection_detected + event_data: + detector: elegoo_spaghetti_detection + +variables: + notify_service: notify.mobile_app_your_phone + +actions: + - action: "{{ notify_service }}" + data: + title: "Possible print failure" + message: > + {{ trigger.event.data.name }} detected a possible spaghetti failure. + Confidence: + {{ (trigger.event.data.confidence | float(0) * 100) | round(1) }}%. + Detections: {{ trigger.event.data.detections }}. + data: + image: "{{ trigger.event.data.image_url }}" diff --git a/examples/smart_pause_stop_by_confidence.yaml b/examples/smart_pause_stop_by_confidence.yaml new file mode 100644 index 0000000..0d027eb --- /dev/null +++ b/examples/smart_pause_stop_by_confidence.yaml @@ -0,0 +1,50 @@ +alias: Elegoo Spaghetti - Smart Pause Stop +mode: single + +triggers: + - trigger: event + event_type: elegoo_spaghetti_detection_detected + event_data: + detector: elegoo_spaghetti_detection + +variables: + notify_service: notify.mobile_app_your_phone + pause_button: button.elegoo_centauri_carbon2_pause_print + stop_button: button.elegoo_centauri_carbon2_stop_print + confidence: "{{ trigger.event.data.confidence | float(0) }}" + confidence_percent: "{{ (confidence | float(0) * 100) | round(1) }}" + detections: "{{ trigger.event.data.detections | int(0) }}" + +actions: + - choose: + - conditions: + - condition: template + value_template: "{{ confidence >= 0.85 }}" + sequence: + - action: "{{ notify_service }}" + data: + title: "High confidence print failure" + message: > + Confidence {{ confidence_percent }}%, detections {{ detections }}. + Stopping the print. + data: + image: "{{ trigger.event.data.image_url }}" + - action: button.press + target: + entity_id: "{{ stop_button }}" + + - conditions: + - condition: template + value_template: "{{ confidence < 0.85 }}" + sequence: + - action: "{{ notify_service }}" + data: + title: "Print paused for review" + message: > + Confidence {{ confidence_percent }}%, detections {{ detections }}. + Pausing the print for manual review. + data: + image: "{{ trigger.event.data.image_url }}" + - action: button.press + target: + entity_id: "{{ pause_button }}" diff --git a/hacs.json b/hacs.json new file mode 100644 index 0000000..f10c579 --- /dev/null +++ b/hacs.json @@ -0,0 +1,4 @@ +{ + "name": "Elegoo Spaghetti Detection", + "homeassistant": "2026.4.0" +} diff --git a/repository.json b/repository.json new file mode 100644 index 0000000..17a5517 --- /dev/null +++ b/repository.json @@ -0,0 +1,5 @@ +{ + "name": "Elegoo Spaghetti Detection", + "url": "https://github.com/hepter/ha-elegoo-spaghetti-detection", + "maintainer": "hepter" +}