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added prediction env

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Michael Weig 2026-03-10 19:17:08 +01:00
parent 314c4433d3
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predict_pipeline/prediction_env.yaml Normal file
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name: 'prediction_env'
channels:
- defaults
- conda-forge
dependencies:
- _py-xgboost-mutex=2.0=cpu_2
- absl-py=2.3.1=py310haa95532_0
- aom=3.12.1=h00a0c3c_0
- arrow-cpp=21.0.0=hcdc3a1c_2
- asttokens=3.0.1=pyhd8ed1ab_0
- astunparse=1.6.3=py_0
- aws-c-auth=0.9.0=h02ab6af_2
- aws-c-cal=0.9.2=h02ab6af_1
- aws-c-common=0.12.4=h02ab6af_0
- aws-c-compression=0.3.1=h02ab6af_2
- aws-c-event-stream=0.5.6=h02ab6af_0
- aws-c-http=0.10.4=h02ab6af_0
- aws-c-io=0.21.4=h02ab6af_0
- aws-c-mqtt=0.13.3=h02ab6af_0
- aws-c-s3=0.8.7=h02ab6af_0
- aws-c-sdkutils=0.2.4=h02ab6af_1
- aws-checksums=0.2.7=h02ab6af_1
- aws-crt-cpp=0.34.0=h885b0b7_0
- aws-sdk-cpp=1.11.638=hf0af688_0
- blas=1.0=mkl
- brotlicffi=1.2.0.0=py310h885b0b7_0
- bzip2=1.0.8=h2bbff1b_6
- c-ares=1.34.6=h2c209ce_0
- ca-certificates=2026.1.4=h4c7d964_0
- cairo=1.18.4=he9e932c_0
- certifi=2026.01.04=py310haa95532_0
- cffi=2.0.0=py310h02ab6af_1
- charset-normalizer=3.4.4=py310haa95532_0
- colorama=0.4.6=pyhd8ed1ab_1
- comm=0.2.3=pyhe01879c_0
- dav1d=1.2.1=h2bbff1b_0
- debugpy=1.8.20=py310h699e580_0
- decorator=5.2.1=pyhd8ed1ab_0
- exceptiongroup=1.3.1=pyhd8ed1ab_0
- executing=2.2.1=pyhd8ed1ab_0
- expat=2.7.4=hd7fb8db_0
- flatbuffers=24.3.25=h21716d4_0
- fontconfig=2.15.0=hd211d86_0
- freeglut=3.8.0=hfcef157_0
- freetype=2.14.1=hfbffc0b_0
- fribidi=1.0.16=haf45083_0
- gast=0.7.0=pyhd3eb1b0_0
- gflags=2.2.2=hd77b12b_1
- giflib=5.2.2=h7edc060_0
- glog=0.5.0=hd77b12b_1
- google-pasta=0.2.0=pyhd3eb1b0_0
- graphite2=1.3.14=hd77b12b_1
- grpcio=1.74.1=py310h5c751cc_0
- h5py=3.15.1=py310he283ef2_1
- harfbuzz=12.3.0=h3ef6528_1
- hdf5=1.14.5=ha36df97_2
- icc_rt=2022.1.0=h6049295_2
- icu=73.1=h6c2663c_0
- idna=3.11=py310haa95532_0
- intel-openmp=2025.0.0=haa95532_1164
- ipykernel=7.2.0=pyh6dadd2b_1
- ipython=8.37.0=pyha7b4d00_0
- jedi=0.19.2=pyhd8ed1ab_1
- joblib=1.5.3=py310haa95532_0
- jpeg=9f=ha349fce_0
- jupyter_client=8.8.0=pyhcf101f3_0
- jupyter_core=5.9.1=pyh6dadd2b_0
- keras=3.11.2=py310h51baaa3_0
- krb5=1.21.3=hdf4eb48_0
- lcms2=2.17=h3732fa5_0
- lerc=4.0.0=h5da7b33_0
- libabseil=20250814.1=cxx17_hcd311fc_0
- libavif=1.3.0=h5bd13ec_0
- libbrotlicommon=1.2.0=h907acca_0
- libbrotlidec=1.2.0=h02c67a5_0
- libbrotlienc=1.2.0=h483e6b9_0
- libcurl=8.17.0=h6e672f4_1
- libdeflate=1.22=h5bf469e_0
- libexpat=2.7.4=hd7fb8db_0
- libffi=3.4.4=hd77b12b_1
- libglib=2.86.3=h9bccc14_0
- libgrpc=1.74.1=hde67744_0
- libhwloc=2.12.1=default_hfa10c62_1000
- libiconv=1.16=h2bbff1b_3
- libkrb5=1.22.1=hb237eb7_0
- libopenjpeg=2.5.4=h02ab6af_1
- libpng=1.6.54=ha15c746_0
- libprotobuf=6.33.0=h2a56892_1
- libre2-11=2025.11.05=ha6b10e7_0
- libsodium=1.0.20=hc70643c_0
- libssh2=1.11.1=h2addb87_0
- libthrift=0.22.0=ha2884a9_0
- libtiff=4.7.1=h3a18249_0
- libwebp-base=1.6.0=hbf3958f_0
- libxgboost=3.1.2=h585ebfc_0
- libxml2=2.13.9=h6201b9f_0
- libzlib=1.3.1=h02ab6af_0
- lz4-c=1.9.4=h2bbff1b_1
- m2w64-gcc-libgfortran=5.3.0=6
- m2w64-gcc-libs=5.3.0=7
- m2w64-gcc-libs-core=5.3.0=7
- m2w64-gmp=6.1.0=2
- m2w64-libwinpthread-git=5.0.0.4634.697f757=2
- markdown=3.10=py310haa95532_0
- markdown-it-py=2.2.0=py310haa95532_1
- markupsafe=3.0.2=py310h827c3e9_0
- matplotlib-inline=0.2.1=pyhd8ed1ab_0
- mdurl=0.1.2=py310haa95532_0
- mkl=2025.0.0=h5da7b33_930
- mkl-service=2.5.2=py310h0b37514_0
- mkl_fft=2.1.1=py310h300f80d_0
- mkl_random=1.3.0=py310ha5e6156_0
- ml_dtypes=0.5.4=py310h42c1672_0
- mpi=1.0=msmpi
- mpi4py=4.0.3=py310h02ab6af_1
- msmpi=10.1.1=had4844c_0
- msys2-conda-epoch=20160418=1
- namex=0.1.0=py310haa95532_0
- nest-asyncio=1.6.0=pyhd8ed1ab_1
- numpy-base=2.1.3=py310he4e2855_3
- openssl=3.6.1=hf411b9b_1
- opt_einsum=3.3.0=pyhd3eb1b0_1
- optree=0.18.0=py310h03f52e7_0
- orc=2.2.0=h79e1e1e_1
- packaging=25.0=py310haa95532_1
- paho-mqtt=2.1.0=pyhe01879c_1
- parso=0.8.6=pyhcf101f3_0
- pcre2=10.46=h5740b90_0
- pickleshare=0.7.5=pyhd8ed1ab_1004
- pillow=12.1.0=py310h6b7a805_0
- pip=26.0.1=pyhc872135_0
- pixman=0.46.4=h4043f72_0
- platformdirs=4.9.2=pyhcf101f3_0
- prompt-toolkit=3.0.52=pyha770c72_0
- protobuf=6.33.0=py310ha4c6e68_0
- psutil=7.2.2=py310h1637853_0
- pure_eval=0.2.3=pyhd8ed1ab_1
- py-xgboost=3.1.2=py310haa95532_0
- pyarrow=21.0.0=py310h42c1672_1
- pycparser=2.23=py310haa95532_0
- pygments=2.19.2=pyhd8ed1ab_0
- pysocks=1.7.1=py310haa95532_1
- python=3.10.19=h981015d_0
- python-dateutil=2.9.0.post0=pyhe01879c_2
- python-flatbuffers=24.3.25=py310haa95532_0
- python_abi=3.10=2_cp310
- pywin32=311=py310h282bd7d_1
- pyyaml=6.0.3=py310hb9a58be_0
- pyzmq=27.1.0=py310h535538e_0
- re2=2025.11.05=hc24cdf5_0
- requests=2.32.5=py310haa95532_1
- rich=14.2.0=py310haa95532_0
- scipy=1.15.3=py310h1bbe36f_1
- setuptools=80.10.2=py310haa95532_0
- six=1.17.0=pyhe01879c_1
- snappy=1.2.2=hab6b7b3_1
- sqlite=3.51.1=hda9a48d_0
- stack_data=0.6.3=pyhd8ed1ab_1
- tbb=2022.3.0=h90c84d6_0
- tbb-devel=2022.3.0=h90c84d6_0
- tensorboard=2.20.0=py310haa95532_0
- tensorboard-data-server=0.7.0=py310haa95532_1
- tensorflow=2.20.0=cpu_py310h6605a60_0
- tensorflow-base=2.20.0=cpu_py310hce87ebc_0
- termcolor=3.2.0=py310haa95532_0
- threadpoolctl=3.5.0=py310h4442805_1
- tk=8.6.15=hf199647_0
- tornado=6.5.4=py310h29418f3_0
- traitlets=5.14.3=pyhd8ed1ab_1
- typing-extensions=4.15.0=py310haa95532_0
- typing_extensions=4.15.0=py310haa95532_0
- ucrt=10.0.22621.0=haa95532_0
- urllib3=2.6.3=py310haa95532_0
- utf8proc=2.6.1=h2bbff1b_1
- vc=14.42=haa95532_5
- vc14_runtime=14.44.35208=h4927774_10
- vs2015_runtime=14.44.35208=ha6b5a95_10
- wcwidth=0.6.0=pyhd8ed1ab_0
- werkzeug=3.1.3=py310haa95532_0
- wheel=0.46.3=py310haa95532_0
- win_inet_pton=1.1.0=py310haa95532_1
- wrapt=2.0.1=py310h02ab6af_0
- xgboost=3.1.2=py310haa95532_0
- xz=5.6.4=h4754444_1
- yaml=0.2.5=he774522_0
- zeromq=4.3.5=h5bddc39_9
- zlib=1.3.1=h02ab6af_0
- zstd=1.5.7=h56299aa_0
- pip:
- numpy==1.24.4
- pandas==2.3.0
- pyocclient==0.6
- pytz==2025.2
- scikit-learn==1.6.1
- tzdata==2025.3
prefix: C:\Users\micha\anaconda3\envs\310

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project_report.md
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@ -97,7 +97,7 @@ Included model families:
Supporting utilities in ```model_training/tools```: Supporting utilities in ```model_training/tools```:
- `scaler.py`: Functions to fit, transform, save and load either MinMaxScaler or StandardScaler, subject-wise and globally - for new subjects, a fallback scaler (using mean of all subjects scaling parameters) is used - `scaler.py`: Functions to fit, transform, save and load either MinMaxScaler or StandardScaler, subject-wise and globally - for new subjects, a fallback scaler (using mean of all subjects scaling parameters) is used
- `performance_split.py`: Provides a function to split a group of subjects based on their performance in the AdaBase experiments, based on the results created in `researchOnSubjectPerformance.ipynb` - `performance_split.py`: Provides a function to split a group of subjects based on their performance in the AdaBase experiments, based on the results created in `researchOnSubjectPerformance.ipynb`. To split into three groups for train, validation & test, call the function twice
- `mad_outlier_removal.py`: Functions to fit and transform data with MAD outlier removal - `mad_outlier_removal.py`: Functions to fit and transform data with MAD outlier removal
- `evaluation_tools.py`: Especially used for Isolation Forest, Functions for ROC curve as well as confusion matrix - `evaluation_tools.py`: Especially used for Isolation Forest, Functions for ROC curve as well as confusion matrix
@ -115,18 +115,18 @@ Main script:
Pipeline: Pipeline:
- Loads runtime config (`predict_pipeline/config.yaml`) - Loads runtime config (`predict_pipeline/config.yaml`)
- Pulls latest row from SQLite (`database.path/table/key`) - Pulls latest row from SQLite
- Replaces missing values using `fallback` map - Replaces missing values using `fallback` map from config file - if more than 50% of values need to be replaced, the sample is dropped and "valid=False"
- Optionally applies scaler (`.pkl`/`.joblib`) - Optionally applies scaler (`.pkl`/`.joblib`) - set via config file
- Loads model (`.keras`, `.pkl`, `.joblib`) and predicts - Loads model (`.keras`, `.pkl`, `.joblib`) and predicts
- Publishes JSON payload to MQTT topic - Publishes JSON payload to MQTT topic
Expected payload form: Expected payload form:
```json ```json
{ {
"valid": true, "valid": true, # false only if too many signals are invalid
"_id": 123, "_id": 123, # this is the sample ID from the database
"prediction": 0 "prediction": 0 # 0 for low load, 1 for high load
} }
``` ```
@ -135,11 +135,12 @@ Expected payload form:
Files: Files:
- `predict_pipeline/predict.service` - `predict_pipeline/predict.service`
- `predict_pipeline/predict.timer` - `predict_pipeline/predict.timer`
- `predict_pipeline/predict_service_timer_documentation.md`
Role: Role:
- Run inference repeatedly without manual execution - Run inference repeatedly without manual execution
- Timer/service configuration can be customized per target machine - Timer/service configuration can be customized
More information on how to use and interact with the system service and timer can be found in `predict_service_timer_documentation.md`
## 5.2 Runtime Configuration ## 5.2 Runtime Configuration
@ -155,23 +156,26 @@ Sections:
- `fallback`: default values for NaN replacement - `fallback`: default values for NaN replacement
Important: Important:
- The repository currently uses environment-specific absolute paths in some scripts/configs. - The repository currently uses environment-specific absolute paths in some scripts/configs to ensure functionality on Ohm-UX driving simulator.
- Paths should be normalized before deployment to a new machine.
## 5.3) Data and Feature Expectations
## 5.3 Data and Feature Expectations
Prediction expects SQLite rows containing: Prediction expects SQLite rows containing:
- `_Id` - `_Id`
- `start_time` - `start_time` - this is not yet used for either predictions or messages
- All configured model features (AUs + eye metrics) - All configured model features (AUs + eye metrics)
Common feature groups: Common feature groups (similar to own dataset):
- `FACE_AUxx_mean` columns - `FACE_AUxx_mean` columns
- Fixation counters and duration statistics - Fixation counters and duration statistics
- Saccade count/amplitude/duration statistics - Saccade count/amplitude/duration statistics
- Blink count/duration statistics - Blink count/duration statistics
- Pupil mean and IPA - Pupil mean and IPA
## 5.4 Create database from scratch
To (re-)create the custom database for deployment, use `fill_db.ipynb`. Enter the path to your dataset, drop unnecessary columns and insert a subset of data with tool functions from `tools/db_helpers`
## 6) Installation and Dependencies ## 6) Installation and Dependencies
Due to unsolvable dependency conflicts, several environemnts need to be used in the same time. Due to unsolvable dependency conflicts, several environemnts need to be used in the same time.
### 6.1 Environemnt for camera handling ### 6.1 Environemnt for camera handling
@ -179,16 +183,8 @@ TO DO
### 6.2 Environment for predictions ### 6.2 Environment for predictions
Install base requirements: If you want to use the existing deployment on Ohm-UX driving simulator's jetson board, activate conda environment `p310_FS_TF`, a python 3.10 environment including tensorflow and all other packages required to run `predict_sample.py`
```bash Otherwise, as described in `readme.md: Setup`, you can use `prediction_env.yaml`, to create a new environment that fulfills the requirements.
pip install -r requirements.txt
```
Typical key packages in this project:
- `numpy`, `pandas`, `scikit-learn`, `scipy`, `pyarrow`, `pyyaml`, `joblib`
- `opencv-python`, `mediapipe`, `torch`, `py-feat`, `pygazeanalyser`
- `paho-mqtt`
- optional data access stack (`pyocclient`, `h5py`, `tables`)
## 7) Repository File Inventory ## 7) Repository File Inventory
@ -201,10 +197,10 @@ Typical key packages in this project:
### Dataset Creation ### Dataset Creation
- `dataset_creation/parquet_file_creation.py` - local source to parquet conversion - `dataset_creation/parquet_file_creation.py` - local files to parquet conversion
- `dataset_creation/create_parquet_files_from_owncloud.py` - ownCloud download + parquet conversion - `dataset_creation/create_parquet_files_from_owncloud.py` - ownCloud download + parquet conversion
- `dataset_creation/combined_feature_creation.py` - sliding-window multimodal feature generation - `dataset_creation/combined_feature_creation.py` - sliding-window multimodal feature generation
- `dataset_creation/maxDist.py` - helper/statistical utility script - `dataset_creation/maxDist.py` - helper/statistical utility script for eye-tracking feature creation
#### AU Creation #### AU Creation
- `dataset_creation/AU_creation/AU_creation_service.py` - AU extraction service workflow - `dataset_creation/AU_creation/AU_creation_service.py` - AU extraction service workflow

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readme.md
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@ -11,11 +11,15 @@ Activate the conda-repository "".
```bash ```bash
conda activate conda activate
``` ```
Make sure, another environment that fulfills requirement.txt is available, matching with predict_pipeline/predict.service - See `predict_pipeline/predict_service_timer_documentation.md` Make sure, another environment that fulfills prediction_env.yaml is available, matching with predict_pipeline/predict.service - See `predict_pipeline/predict_service_timer_documentation.md`
To get an overview over all available conda environments on your device, use this command in anaconda prompt terminal: To get an overview over all available conda environments on your device, use this command in anaconda prompt terminal:
```bash ```bash
conda info --envs conda info --envs
``` ```
Create a new environment based on the yaml-file:
```bash
conda env create -f prediction_env.yaml
```
### 2) Camera AU + Eye Pipeline (`camera_stream_AU_and_ET_new.py`) ### 2) Camera AU + Eye Pipeline (`camera_stream_AU_and_ET_new.py`)