zamba - a command line interface for species classification
Zamba means "forest" in the Lingala language.
Zamba is a command-line tool built in Python to automatically identify the species seen in camera trap videos from sites in central Africa. The tool makes predictions for 24 common species in these videos. For more information, see the documentation.
zamba command will be the entry point for users (see example usage below).
zamba (for more detail, see the documentation)
GPU or CPU
zamba is significantly faster when using a machine with a GPU instead of just a CPU. To use a GPU, you must be using an nvidia gpu, installed and configured CUDA, and installed and configured CuDNN per their specifications. Once this is done, you can select to install the version of zamaba that uses
tensorflow compiled for GPU.
When a user installs
zamba that user must specify to install the GPU or CPU version. If the user fails to make this specification, no version of tensorflow will be installed, thus everything will fail.
To install with tensorflow cpu (you do not have a GPU)
$ pip install zamba[cpu]
To install with tensorflow gpu
$ pip install zamba[gpu]
Once zamba is installed, you can see the commands with
Usage: zamba [OPTIONS] COMMAND [ARGS]... Options: --help Show this message and exit. Commands: predict Identify species in a video. train [NOT IMPLEMENTED] Retrain network from... tune [NOT IMPLEMENTED] Update network with new...
And you can see the options you can pass to the
predict command with:
zamba predict --help
Usage: zamba predict [OPTIONS] [DATA_PATH] [PRED_PATH] Identify species in a video. This is a command line interface for prediction on camera trap footage. Given a path to camera trap footage, the predict function use a deep learning model to predict the presence or absense of a variety of species of common interest to wildlife researchers working with camera trap data. Options: --tempdir PATH Path to temporary directory. If not specified, OS temporary directory is used. --proba_threshold FLOAT Probability threshold for classification. if specified binary predictions are returned with 1 being greater than the threshold, 0 being less than or equal to. If not specified, probabilities between 0 and 1 are returned. --output_class_names If True, we just return a video and the name of the most likely class. If False, we return a probability or indicator (depending on --proba_threshold) for every possible class. --model_profile TEXT Defaults to 'full' which is slow and accurate; can be 'fast' which is faster and less accurate. --weight_download_region TEXT Defaults to 'us', can also be 'eu' or 'asia'. Region for server to download weights. --verbose Displays additional logging information during processing. --help Show this message and exit.
zamba is installed, you can execute it on any directory of video files. The tool does not recursively search directories, so all of the files must be at the top level of the directory. The algorithm will work the best with 15 second videos since that is what it is trained on, though it will sample frames from longer videos, which may be less reliable.
zamba needs to download the "weights" files for the neural networks that it uses to make predictions. On first run it will download ~1GB of files with these weights. Once these are downloaded, the tool will use the local versions and will not need to perform this download again.
zamba predict path/to/videos
By default the output will be written to the file
output.csv in the current directory. If the file exists, it will be overwritten.
zamba test suite
Makefile contains code that uses pytest to run all tests in
The command is (from the project root),
$ make test
Testing End-To-End Prediction With
tests/test_cnnensemble.py runs an end-to-end prediction with
CnnEnsemble.predict(data_dir) using a video that automatically gets downloaded along with the
input directory (this and all required directories are downloaded upon instantiation of
CnnEnsemble if they are not already present in the project).
By default this test is skipped due to the
@pytest.mark.skip(reason="This test takes hours to run, makes network calls, and is really for local dev only.") def test_predict(): data_dir = Path(__file__).parent.parent / "models" / "cnnensemble" / "input" / "raw_test" manager = ModelManager('', model_class='cnnensemble', proba_threshold=0.5) manager.predict(data_dir, save=True)
It is reccomended that the decorator be commented out in order to test end-to-end prediction locally. However, this change should never be pushed, as the lightweight machines on codeship will not be happy, or able, to complete the end-to-end prediction.
To test end-to-end prediction using
make test on a different set of videos, simply edit