* support for other yolov models and config checks
* apply code formatting
* Information about core mask and inference speed
* update rknn postprocess and remove params
* update model selection
* Apply suggestions from code review
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* support rknn on all socs
* apply changes from review and fix post process bug
* apply code formatting
* update tip in object_detectors docs
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* support for other yolov models and config checks
* apply code formatting
* Information about core mask and inference speed
* update rknn postprocess and remove params
* update model selection
* Apply suggestions from code review
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* Update to latest tensorrt (8.6.1) release
* Build trt libyolo_layer.so in container
* Update tensorrt_models script to convert models from the frigate container
* Fix typo in model script
* Fix paths to yolo lib and models folder
* Add S6 scripts to test and convert specified TensortRT models at startup.
Rearrange tensorrt files into a docker support folder.
* Update TensorRT documentation to reflect the new model conversion process and minimum HW support.
* Fix model_cache path to live in config directory
* Move tensorrt s6 files to the correct directory
* Fix issues in model generation script
* Disable global timeout for s6 services
* Add version folder to tensorrt model_cache path
* Include TensorRT version 8.5.3
* Add numpy requirement prior to removal of np.bool
* This TRT version uses a mixture of cuda dependencies
* Redirect stdout from noisy model conversion
* Add isort and ruff linter
Both linters are pretty common among modern python code bases.
The isort tool provides stable sorting and grouping, as well as pruning
of unused imports.
Ruff is a modern linter, that is very fast due to being written in rust.
It can detect many common issues in a python codebase.
Removes the pylint dev requirement, since ruff replaces it.
* treewide: fix issues detected by ruff
* treewide: fix bare except clauses
* .devcontainer: Set up isort
* treewide: optimize imports
* treewide: apply black
* treewide: make regex patterns raw strings
This is necessary for escape sequences to be properly recognized.
* Add Deepstack detector plugin with configurable API URL, timeout, and API key
* Update DeepStack plugin to recognize 'truck' as 'car' for label indexing
* Add debug logging to DeepStack plugin for better monitoring and troubleshooting
* Refactor DeepStack label loading from file to use merged labelmap
* Black format
* add documentation draft
* fix link to codeproject website
* Apply suggestions from code review
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
It supports the same entrypoints, given that tflite is a small cut-out
of the big tensorflow picture.
This patch was created for downstream usage in nixpkgs, where we don't
have the tflite python package, but do have the full tensorflow package.
* Initial commit that adds YOLOv5 and YOLOv8 support for OpenVINO detector
* Fixed double inference bug with YOLOv5 and YOLOv8
* Modified documentation to mention YOLOv5 and YOLOv8
* Changes to pass lint checks
* Change minimum threshold to improve model performance
* Fix link
* Clean up YOLO post-processing
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* Initial commit to enable Yolox models with OpenVINO in Frigate
* Fix ModelEnumtType import error in openvino.py
* Initial edit of the docs to include verbage about yolox
* Initial edit of the docs to include verbage about yolox
* Elaborate configuration and limitations in docs.
* Add capability to dynamically determine number of classes in yolox model
* Further refinements
* Removed unnecesarry comments, improved documentation, addressed PR items
* Fixed lint formatting issues
* Initial WIP dockerfile and scripts to add tensorrt support
* Add tensorRT detector
* WIP attempt to install TensorRT 8.5
* Updates to detector for cuda python library
* TensorRT Cuda library rework WIP
Does not run
* Fixes from rebase to detector factory
* Fix parsing output memory pointer
* Handle TensorRT logs with the python logger
* Use non-async interface and convert input data to float32. Detection runs without error.
* Make TensorRT a separate build from the base Frigate image.
* Add script and documentation for generating TRT Models
* Add support for TensorRT devcontainer
* Add labelmap to trt model script and docs. Cleanup of old scripts.
* Update detect to normalize input tensor using model input type
* Add config for selecting GPU. Fix Async inference. Update documentation.
* Update some CUDA libraries to clean up version warning
* Add CI stage to build TensorRT tag
* Add note in docs for image tag and model support
* Initial work for adding OpenVino detector. Not functional
* Load model and submit for inference.
Sucessfully load model and initialize OpenVino engine with either CPU or GPU as device.
Does not parse results for objects.
* Detection working with ssdlite_mobilenetv2 FP16 model
* Add OpenVIno support and model to docker image
* Add documentation for OpenVino detector configuration
* Adds support for ARM32/ARM64 and the Myriad X hardware
- Use custom-built openvino wheel for all platforms
- Add libusb build without udev for NCS2 support
* Add documentation around Intel CPU requirements and NCS2 setup
* Print all available output tensors
* Update documentation for config parameters
* Refactor EdgeTPU and CPU model handling to detector submodules.
* Fix selecting the correct detection device type from the config
* Remove detector type check when creating ObjectDetectProcess
* Fixes after rebasing to 0.11
* Add init file to detector folder
* Rename to detect_api
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* Add unit test for LocalObjectDetector class
* Add configuration for model inputs
Support transforming detection regions to RGB or BGR.
Support specifying the input tensor shape. The tensor shape has a standard format ["BHWC"] when handed to the detector, but can be transformed in the detector to match the model shape using the model input_tensor config.
* Add documentation for new model config parameters
* Add input tensor transpose to LocalObjectDetector
* Change the model input tensor config to use an enumeration
* Updates for model config documentation
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
