The VMAF Python library offers full functionalities from running basic VMAF command line, running VMAF on a batch of video files, training and testing a VMAF model on video datasets, and visualization tools, etc. It is the playground to experiment with VMAF.
The VMAF Python library has its core feature extraction library written in C, and the rest scripting code written in Python. To build the C code, it requires gcc and g++ (>=4.8). To run scripts and tests, it requires Python 3.
It also requires a number of Python packages:
numpy(>=1.12.0)scipy(>=0.17.1)matplotlib(>=2.0.0)pandas(>=0.19.2)scikit-learn(>=0.18.1)scikit-image(>=0.13.1)h5py(>=2.6.0)sureal(>=0.1.1)
You will need to install gfortran for compiling scipy, freetype and pkg-config required by matplotlib, and hdf5 required by h5py (C header files needed). These can't be compiled from source here.
Install the dependencies:
sudo apt-get update -qq && \
sudo apt-get install -y \
pkg-config gfortran libhdf5-dev libfreetype6-dev liblapack-dev \
python3 \
python3-dev \
python3-pip \
python3-setuptools \
python3-tk
Upgrade pip to the newest version:
sudo -H pip3 install --upgrade pip
Then install the required Python packages:
pip3 install --user numpy scipy matplotlib pandas scikit-learn scikit-image h5py sureal
Make sure your user install executable directory is on your PATH. Add this to the end of ~/.bashrc and restart your shell:
export PATH="$PATH:$HOME/.local/bin"
First, install Homebrew, then install the dependencies:
brew install gcc freetype pkg-config homebrew/core/hdf5 python@2 ninja
This will install an up-to-date version of Python 2.7 and pip (see Homebrew's Python guide for more info).
Now install the required Python packages:
brew install numpy scipy
pip install matplotlib notebook pandas sympy nose scikit-learn scikit-image h5py sureal
pip3 install meson
You can verify if these packages are properly installed and its version/location by:
python3 -c 'import numpy as pkg; print(pkg.__version__); print(pkg.__file__)'
python3 -c 'import scipy as pkg; print(pkg.__version__); print(pkg.__file__)'
python3 -c 'import matplotlib as pkg; print(pkg.__version__); print(pkg.__file__)'
python3 -c 'import pandas as pkg; print(pkg.__version__); print(pkg.__file__)'
python3 -c 'import sklearn as pkg; print(pkg.__version__); print(pkg.__file__)'
python3 -c 'import skimage as pkg; print(pkg.__version__); print(pkg.__file__)'
python3 -c 'import h5py as pkg; print(pkg.__version__); print(pkg.__file__)'
python3 -c 'import sureal as pkg; print(pkg.__version__); print(pkg.__file__)'
If you see that the printed version number is older than the ones aforementioned, it could suggest that a previously installed package with the same name but older version at a different location may have overshadowed the new one. Make sure that the new one's path appears early in the path list, which can be printed by:
python3 -c 'import sys; print(sys.path)'
(Or simply delete the older one).
After cloning VMAF repository, cd to the repo directory and run:
make
to build the binaries.
Add the python/src subdirectories to the environment variable PYTHONPATH:
export PYTHONPATH="$(pwd)/python/src:$PYTHONPATH"
You can also add it to the environment permanently, by appending to ~/.bashrc:
echo export PYTHONPATH="$(pwd)/python/src:$PYTHONPATH" >> ~/.bashrc
source ~/.bashrc
Under macOS, use ~/.bash_profile instead.
The package has thus far been tested on Ubuntu 16.04 LTS and macOS 10.13.
After installation, run:
./unittest
and expect all tests pass.
One can run VMAF either in single mode by run_vmaf or in batch mode by run_vmaf_in_batch. Besides, ffmpeg2vmaf is a command line tool that offers the capability of taking compressed video bitstreams as input.
To run VMAF on a single reference/distorted video pair, run:
./run_vmaf format width height reference_path distorted_path [--out-fmt output_format]
The arguments are the following:
formatcan be one of:yuv420p,yuv422p,yuv444p(8-Bit YUV)yuv420p10le,yuv422p10le,yuv444p10le(10-Bit little-endian YUV)
widthandheightare the width and height of the videos, in pixelsreference_pathanddistorted_pathare the paths to the reference and distorted video filesoutput_formatcan be one of:textxmljson
For example:
./run_vmaf yuv420p 576 324 \
python/test/resource/yuv/src01_hrc00_576x324.yuv \
python/test/resource/yuv/src01_hrc01_576x324.yuv \
--out-fmt json
This will generate JSON output like:
"aggregate": {
"VMAF_feature_adm2_score": 0.93458780776205741,
"VMAF_feature_motion2_score": 3.8953518541666665,
"VMAF_feature_vif_scale0_score": 0.36342081156994926,
"VMAF_feature_vif_scale1_score": 0.76664738784617292,
"VMAF_feature_vif_scale2_score": 0.86285338927816291,
"VMAF_feature_vif_scale3_score": 0.91597186913930484,
"VMAF_score": 76.699271371151269,
"method": "mean"
}
where VMAF_score is the final score and the others are the scores for VMAF's elementary metrics.
adm2,vif_scalexscores range from 0 (worst) to 1 (best)motion2score typically ranges from 0 (static) to 20 (high-motion)
To run VMAF in batch mode, create an input text file, where each corresponds to the following format (check examples in example_batch_input):
format width height reference_path distorted_path
For example:
yuv420p 576 324 python/test/resource/yuv/src01_hrc00_576x324.yuv \
python/test/resource/yuv/src01_hrc01_576x324.yuv
yuv420p 576 324 python/test/resource/yuv/src01_hrc00_576x324.yuv \
python/test/resource/yuv/src01_hrc00_576x324.yuv
After that, run:
./run_vmaf_in_batch input_file [--out-fmt out_fmt] [--parallelize]
where enabling --parallelize allows execution on multiple reference-distorted video pairs in parallel.
For example:
./run_vmaf_in_batch resource/example/example_batch_input --parallelize
There is also an ffmpeg2vmaf command line tool which can compare any file format decodable by ffmpeg. ffmpeg2vmaf essentially pipes FFmpeg-decoded videos to VMAF. Note that you need a recent version of ffmpeg installed (for the first time, run the command line, follow the prompted instruction to specify the path of ffmpeg).
./ffmpeg2vmaf quality_width quality_height reference_path distorted_path \
[--model model_path] [--out-fmt out_fmt]
Here quality_width and quality_height are the width and height the reference and distorted videos are scaled to before VMAF calculation. This is different from run_vmaf's width and height, which specify the raw YUV's width and height instead. The input to ffmpeg2vmaf must already have such information specified in the header so that they are FFmpeg-decodable.
Note that with libvmaf as a filter in FFmpeg becoming available (see this section for details), ffmpeg2vmaf is no longer the preferred way to pass in compressed video streams to VMAF.
VMAF follows a machine-learning based approach to first extract a number of quality-relevant features (or elementary metrics) from a distorted video and its reference full-quality video, followed by fusing them into a final quality score using a non-linear regressor (e.g. an SVM regressor), hence the name “Video Multi-method Assessment Fusion”.
In addition to the basic commands, the VMAF package also provides a framework to allow any user to train his/her own perceptual quality assessment model. For example, directory model contains a number of pre-trained models, which can be loaded by the aforementioned commands:
./run_vmaf format width height reference_path distorted_path [--model model_path]
./run_vmaf_in_batch input_file [--model model_path] --parallelize
For example:
./run_vmaf yuv420p 576 324 \
python/test/resource/yuv/src01_hrc00_576x324.yuv \
python/test/resource/yuv/src01_hrc01_576x324.yuv \
--model model/nflxtrain_vmafv3.pkl
./run_vmaf_in_batch resource/example/example_batch_input \
--model model/nflxtrain_vmafv3.pkl --parallelize
A user can customize the model based on:
- The video dataset it is trained on
- The list of features used
- The regressor used (and its hyper-parameters)
Once a model is trained, the VMAF package also provides tools to cross validate it on a different dataset and visualization.
To begin with, create a dataset file following the format in example_dataset.py. A dataset is a collection of distorted videos. Each has a unique asset ID and a corresponding reference video, identified by a unique content ID. Each distorted video is also associated with subjective quality score, typically a MOS (mean opinion score), obtained through subjective study. An example code snippet that defines a dataset is as follows:
dataset_name = 'example'
yuv_fmt = 'yuv420p'
width = 1920
height = 1080
ref_videos = [
{'content_id':0, 'path':'checkerboard.yuv'},
{'content_id':1, 'path':'flat.yuv'},
]
dis_videos = [
{'content_id':0, 'asset_id': 0, 'dmos':100, 'path':'checkerboard.yuv'},
{'content_id':0, 'asset_id': 1, 'dmos':50, 'path':'checkerboard_dis.yuv'},
{'content_id':1, 'asset_id': 2, 'dmos':100, 'path':'flat.yuv'},
{'content_id':1, 'asset_id': 3, 'dmos':80, 'path':'flat_dis.yuv'},
]
See the directory resource/dataset for more examples. Also refer to the Datasets document regarding publicly available datasets.
Once a dataset is created, first validate the dataset using existing VMAF or other (PSNR, SSIM or MS-SSIM) metrics. Run:
./run_testing quality_type test_dataset_file \
[--vmaf-model optional_VMAF_model_path] [--cache-result] [--parallelize]
where quality_type can be VMAF, PSNR, SSIM, MS_SSIM, etc.
Enabling --cache-result allows storing/retrieving extracted features (or elementary quality metrics) in a data store (since feature extraction is the most expensive operations here).
Enabling --parallelize allows execution on multiple reference-distorted video pairs in parallel. Sometimes it is desirable to disable parallelization for debugging purpose (e.g. some error messages can only be displayed when parallel execution is disabled).
For example:
./run_testing VMAF resource/example/example_dataset.py \
--cache-result --parallelize
Make sure matplotlib is installed to visualize the MOS-prediction scatter plot and inspect the statistics:
- PCC – Pearson correlation coefficient
- SRCC – Spearman rank order correlation coefficient
- RMSE – root mean squared error
When creating a dataset file, one may make errors (for example, having a typo in a file path) that could go unnoticed but make the execution of run_testing fail. For debugging purposes, it is recommended to disable --parallelize.
If the problem persists, one may need to run the script:
python3 python/script/run_cleaning_cache.py quality_type test_dataset_file
to clean up corrupted results in the store before retrying. For example:
python3 python/script/run_cleaning_cache.py VMAF \
resource/example/example_dataset.py
Now that we are confident that the dataset is created correctly and we have some benchmark result on existing metrics, we proceed to train a new quality assessment model. Run:
./run_vmaf_training train_dataset_filepath feature_param_file model_param_file \
output_model_file [--cache-result] [--parallelize]
For example:
./run_vmaf_training resource/example/example_dataset.py \
resource/feature_param/vmaf_feature_v2.py \
resource/model_param/libsvmnusvr_v2.py \
workspace/model/test_model.pkl \
--cache-result --parallelize
feature_param_file defines the set of features used. For example, both dictionaries below:
feature_dict = {'VMAF_feature':'all', }
and
feature_dict = {'VMAF_feature':['vif', 'adm'], }
are valid specifications of selected features. Here VMAF_feature is an 'aggregate' feature type, and vif, adm are the 'atomic' feature types within the aggregate type. In the first case, all specifies that all atomic features of VMAF_feature are selected. A feature_dict dictionary can also contain more than one aggregate feature types.
model_param_file defines the type and hyper-parameters of the regressor to be used. For details, refer to the self-explanatory examples in directory resource/model_param. One example is:
model_type = "LIBSVMNUSVR"
model_param_dict = {
# ==== preprocess: normalize each feature ==== #
'norm_type':'clip_0to1', # rescale to within [0, 1]
# ==== postprocess: clip final quality score ==== #
'score_clip':[0.0, 100.0], # clip to within [0, 100]
# ==== libsvmnusvr parameters ==== #
'gamma':0.85, # selected
'C':1.0, # default
'nu':0.5, # default
'cache_size':200 # default
}
The trained model is output to output_model_file. Once it is obtained, it can be used by the run_vmaf or run_vmaf_in_batch, or used by run_testing to validate another dataset.
Above are two example scatter plots obtained from running the run_vmaf_training and run_testing commands on a training and a testing dataset, respectively.
The commands ./run_vmaf_training and ./run_testing also support custom subjective models (e.g. DMOS (default), MLE and more), through the package sureal.
The subjective model option can be specified with option --subj-model subjective_model, for example:
./run_vmaf_training resource/example/example_raw_dataset.py \
resource/feature_param/vmaf_feature_v2.py \
resource/model_param/libsvmnusvr_v2.py \
workspace/model/test_model.pkl \
--subj-model MLE --cache-result --parallelize
./run_testing VMAF resource/example/example_raw_dataset.py \
--subj-model MLE --cache-result --parallelize
Note that for the --subj-model option to have effect, the input dataset file must follow a format similar to example_raw_dataset.py. Specifically, for each dictionary element in dis_videos, instead of having a key named 'dmos' or 'groundtruth' as in example_dataset.py, it must have a key named os (stands for opinion score), and the value must be a list of numbers. This is the "raw opinion score" collected from subjective experiments, which is used as the input to the custom subjective models.
run_vmaf_cross_validation.py provides tools for cross-validation of hyper-parameters and models. run_vmaf_cv runs training on a training dataset using hyper-parameters specified in a parameter file, output a trained model file, and then test the trained model on another test dataset and report testing correlation scores. run_vmaf_kfold_cv takes in a dataset file, a parameter file, and a data structure (list of lists) that specifies the folds based on video content's IDs, and run k-fold cross valiation on the video dataset. This can be useful for manually tuning the model parameters.
You can also customize VMAF by plugging in third-party features or inventing new features, and specify them in a feature_param_file. Essentially, the "aggregate" feature type (e.g. VMAF_feature) specified in the feature_dict corresponds to the TYPE field of a FeatureExtractor subclass (e.g. VmafFeatureExtractor). All you need to do is to create a new class extending the FeatureExtractor base class.
Similarly, you can plug in a third-party regressor or invent a new regressor and specify them in a model_param_file. The model_type (e.g. LIBSVMNUSVR) corresponds to the TYPE field of a TrainTestModel sublass (e.g. LibsvmnusvrTrainTestModel). All needed is to create a new class extending the TrainTestModel base class.
For instructions on how to extending the FeatureExtractor and TrainTestModel base classes, refer to CONTRIBUTING.md.
Overtime, a number of helper tools have been incorporated into the VDK, to facilitate training and validating VMAF models. An overview of the tools available can be found in this slide deck.
A Bjøntegaard-Delta (BD) rate implementation is added. Example usage can be found here. The implementation is validated against MPEG JCTVC-E137.
An implementation of LIME is also added as part of the repository. The main idea is to perform a local linear approximation to any regressor or classifier and then use the coefficients of the linearized model as indicators of feature importance. LIME can be used as part of the VMAF regression framework, for example:
./run_vmaf yuv420p 1920 1080 NFLX_dataset_public/ref/OldTownCross_25fps.yuv \
NFLX_dataset_public/dis/OldTownCross_90_1080_4300.yuv --local-explain
Naturally, LIME can also be applied to any other regression scheme as long as there exists a pre-trained model. For example, applying to BRISQUE:
./run_vmaf yuv420p 1920 1080 NFLX_dataset_public/ref/OldTownCross_25fps.yuv \
NFLX_dataset_public/dis/OldTownCross_90_1080_4300.yuv --local-explain \
--model model/vmaf_brisque_all_v0.0rc.pkl