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Wakchaure,Snehil
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.idea/*
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.vscode/*
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logs/*
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data/*
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.data/*
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_pycache_\*
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@ -1,4 +1,5 @@
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FROM nvidia/cuda:8.0-cudnn6-devel
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MAINTAINER snehil.wakchaure1@cerner.com
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ENV LANG=C.UTF-8 LC_ALL=C.UTF-8
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@ -36,6 +37,7 @@ RUN conda install -y python=${python_version} && \
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pip install opencv-contrib-python && \
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conda install -y Pillow scikit-learn scikit-image graphviz pydot notebook pandas matplotlib mkl nose pyyaml six h5py && \
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pip install keras && \
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pip install tensorflow && \
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conda clean -yt
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ENV PYTHONPATH='/src/:$PYTHONPATH'
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2
LICENSE
2
LICENSE
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@ -671,4 +671,4 @@ into proprietary programs. If your program is a subroutine library, you
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may consider it more useful to permit linking proprietary applications with
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the library. If this is what you want to do, use the GNU Lesser General
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Public License instead of this License. But first, please read
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<http://www.gnu.org/philosophy/why-not-lgpl.html>.
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<http://www.gnu.org/philosophy/why-not-lgpl.html>.
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51
README.md
51
README.md
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@ -1,8 +1,6 @@
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# Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder
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This repository hosts the codes for "Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder".
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Paper can be found at [Springer](https://link.springer.com/chapter/10.1007/978-3-319-59081-3_23) and [arXiv](https://arxiv.org/abs/1701.01546).
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# Anomaly detection in Smart Audio Videos using a Convolutional Spatio-temporal Auto-encoder and Convolutional Long-Short Term Memory (LSTM) Recurrent Neural Network based architecture.
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Prerequisites:
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## Project dependencies:
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- keras
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- tensorflow
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- h5py
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@ -12,32 +10,37 @@ Prerequisites:
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- tqdm (for progressbar)
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- coloredlogs (optional, for colored terminal logs only)
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You can use the `Dockerfile` provided to build the environment then enter the environment using `nvidia-docker run --rm -it -v HOST_FOLDER:/share DOCKER_IMAGE bash`.
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To build the docker image run the following docker command. This will take around 13GB space on your machine so makes sure you clean up any unused docker images and containers to avoid out of free space issues.
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```docker build -t snehil/video_anomaly_ai:v1 .```
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To train the model, just run `python start_train.py`. Default configuration can be found at `config.yml`. You need to prepare video dataset you plan to train/evaluate on. You may get the benchmark dataset videos from respective authors. For each dataset, put the training videos into `VIDEO_ROOT_PATH/DATASET_NAME/training_videos` and testing videos into `VIDEO_ROOT_PATH/DATASET_NAME/testing_videos`. Example structure of training videos for `avenue` dataset:
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- `VIDEO_ROOT_PATH/avenue/training_videos`
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You can then enter the environment using ```nvidia-docker run --rm -it -v HOST_FOLDER:/share DOCKER_IMAGE bash```.
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or run the following command:
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```docker run --rm -it -v ~/:/share snehil/<IMAGE_ID> bash```
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To login to a running container:
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```docker attach <CONTAINER_ID>```
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## To train the model
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- Just run `python start_train.py`.
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- Default configuration can be found at `config.yml`.
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- You need to prepare video dataset you plan to train/evaluate on (avi or mp4). For each dataset, put the training videos into ```
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./data/videos/training_videos``` and testing videos into ```./data/videos/testing_videos```. Example structure of training videos for `avenue` dataset:
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- `VIDEO_ROOT_PATH/avenue/training_videos`
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- `01.avi`
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- `02.avi`
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- ...
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- `16.avi`
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## To test the model
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Once you have trained the model, you may now run `python start_test.py` after setting the parameters at the beginning of the file.
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Please cite the following paper if you use our code / paper:
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```
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@inbook{Chong2017,
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author = {Chong, Yong Shean and
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Tay, Yong Haur},
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editor = {Cong, Fengyu and
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Leung, Andrew and
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Wei, Qinglai},
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title = {Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder},
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bookTitle = {Advances in Neural Networks - ISNN 2017: 14th International Symposium, ISNN 2017, Sapporo, Hakodate, and Muroran, Hokkaido, Japan, June 21--26, 2017, Proceedings, Part II},
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year = {2017},
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publisher = {Springer International Publishing},
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pages = {189--196},
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isbn = {978-3-319-59081-3},
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doi = {10.1007/978-3-319-59081-3_23},
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url = {https://doi.org/10.1007/978-3-319-59081-3_23}
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}
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# Troubleshooting
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In case you get a No free space left error (inspite of having free space) while running the docker build command, try cleaning up unused docker images and containers using the following commands
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```
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docker rm $(docker ps -q -f 'status=exited')
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docker rmi $(docker images -q -f "dangling=true")
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```
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152
classifier.py
152
classifier.py
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@ -1,36 +1,58 @@
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from dataset import preprocess_data
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import os
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from keras import backend as K
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import matplotlib
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from keras import backend as K
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from dataset import preprocess_data
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matplotlib.use('Agg')
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assert(K.image_data_format() == 'channels_last')
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def get_model(t):
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from keras.models import Model
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from keras.layers.convolutional import Conv2D, Conv2DTranspose
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from keras.models import Model
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from keras.layers.convolutional import Conv2D, Conv2DTranspose
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from keras.layers.convolutional_recurrent import ConvLSTM2D
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from keras.layers.normalization import BatchNormalization
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from keras.layers.wrappers import TimeDistributed
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from keras.layers.core import Activation
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from keras.layers import Input
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from keras.layers.normalization import BatchNormalization
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from keras.layers.wrappers import TimeDistributed
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from keras.layers.core import Activation
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from keras.layers import Input
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input_tensor = Input(shape=(t, 224, 224, 1))
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conv1 = TimeDistributed(Conv2D(128, kernel_size=(11, 11), padding='same', strides=(4, 4), name='conv1'),
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input_shape=(t, 224, 224, 1))(input_tensor)
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conv1 = TimeDistributed(BatchNormalization())(conv1)
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conv1 = TimeDistributed(Activation('relu'))(conv1)
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# First Conv layer
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conv1 = TimeDistributed(
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Conv2D(
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128,
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kernel_size = (11, 11),
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padding = 'same', # zero-padding
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strides = (4, 4),
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name = 'conv1'
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),
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input_shape=(t, 224, 224, 1)
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)(input_tensor)
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conv2 = TimeDistributed(Conv2D(64, kernel_size=(5, 5), padding='same', strides=(2, 2), name='conv2'))(conv1)
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conv2 = TimeDistributed(BatchNormalization())(conv2)
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conv2 = TimeDistributed(Activation('relu'))(conv2)
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conv1 = TimeDistributed(BatchNormalization())(conv1)
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conv1 = TimeDistributed(Activation('relu'))(conv1)
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# Second Conv layer
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conv2 = TimeDistributed(
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Conv2D(
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64,
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kernel_size = (5, 5),
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padding = 'same', # zero-padding
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strides = (2, 2),
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name = 'conv2'
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)
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)(conv1)
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conv2 = TimeDistributed(BatchNormalization())(conv2)
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conv2 = TimeDistributed(Activation('relu'))(conv2)
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# LSTM layers
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convlstm1 = ConvLSTM2D(64, kernel_size=(3, 3), padding='same', return_sequences=True, name='convlstm1')(conv2)
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convlstm2 = ConvLSTM2D(32, kernel_size=(3, 3), padding='same', return_sequences=True, name='convlstm2')(convlstm1)
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convlstm3 = ConvLSTM2D(64, kernel_size=(3, 3), padding='same', return_sequences=True, name='convlstm3')(convlstm2)
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# De-convolution layers
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deconv1 = TimeDistributed(Conv2DTranspose(128, kernel_size=(5, 5), padding='same', strides=(2, 2), name='deconv1'))(convlstm3)
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deconv1 = TimeDistributed(BatchNormalization())(deconv1)
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deconv1 = TimeDistributed(Activation('relu'))(deconv1)
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@ -55,28 +77,28 @@ def compile_model(model, loss, optimizer):
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model.compile(loss=loss, optimizer=opt)
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def train(dataset, job_folder, logger, video_root_path='/share/data/videos'):
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def train(dataset, job_folder, logger, video_root_path='./data/videos'):
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"""Build and train the model
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"""
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import yaml
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import numpy as np
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from keras.callbacks import ModelCheckpoint, EarlyStopping
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from custom_callback import LossHistory
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import matplotlib.pyplot as plt
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from keras.utils.io_utils import HDF5Matrix
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import numpy as np
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from keras.callbacks import ModelCheckpoint, EarlyStopping
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from custom_callback import LossHistory
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import matplotlib.pyplot as plt
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from keras.utils.io_utils import HDF5Matrix
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logger.debug("Loading configs from {}".format(os.path.join(job_folder, 'config.yml')))
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with open(os.path.join(job_folder, 'config.yml'), 'r') as ymlfile:
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cfg = yaml.load(ymlfile)
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nb_epoch = cfg['epochs']
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batch_size = cfg['batch_size']
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loss = cfg['cost']
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optimizer = cfg['optimizer']
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nb_epoch = cfg['epochs']
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batch_size = cfg['batch_size']
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loss = cfg['cost']
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optimizer = cfg['optimizer']
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time_length = cfg['time_length']
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# shuffle = cfg['shuffle']
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logger.info("Building model of type {} and activation {}".format(model_type, activation))
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# logger.info("Building model of type {} and activation {}".format(model_type, activation))
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model = get_model(time_length)
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logger.info("Compiling model with {} and {} optimizer".format(loss, optimizer))
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compile_model(model, loss, optimizer)
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@ -91,20 +113,21 @@ def train(dataset, job_folder, logger, video_root_path='/share/data/videos'):
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data = HDF5Matrix(os.path.join(video_root_path, '{0}/{0}_train_t{1}.h5'.format(dataset, time_length)),
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'data')
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snapshot = ModelCheckpoint(os.path.join(job_folder,
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'model_snapshot_e{epoch:03d}_{val_loss:.6f}.h5'))
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earlystop = EarlyStopping(patience=10)
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snapshot = ModelCheckpoint(os.path.join(job_folder,
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'model_snapshot_e{epoch:03d}_{val_loss:.6f}.h5'))
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earlystop = EarlyStopping(patience=10)
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history_log = LossHistory(job_folder=job_folder, logger=logger)
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logger.info("Initializing training...")
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history = model.fit(
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data, data,
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batch_size=batch_size,
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epochs=nb_epoch,
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validation_split=0.15,
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shuffle='batch',
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callbacks=[snapshot, earlystop, history_log]
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data, # X
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data, # Y (Same as input for Auto-encoder)
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batch_size = batch_size,
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epochs = nb_epoch,
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validation_split = 0.15,
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shuffle = 'batch',
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callbacks = [snapshot, earlystop, history_log]
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)
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logger.info("Training completed!")
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@ -130,9 +153,9 @@ def get_gt_vid(dataset, vid_idx, pred_vid):
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import numpy as np
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if dataset in ("indoor", "plaza", "lawn"):
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gt_vid = np.load('/share/data/groundtruths/{0}_test_gt.npy'.format(dataset))
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gt_vid = np.load('./data/groundtruths/{0}_test_gt.npy'.format(dataset))
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else:
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gt_vid_raw = np.loadtxt('/share/data/groundtruths/gt_{0}_vid{1:02d}.txt'.format(dataset, vid_idx+1))
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gt_vid_raw = np.loadtxt('./data/groundtruths/gt_{0}_vid{1:02d}.txt'.format(dataset, vid_idx+1))
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gt_vid = np.zeros_like(pred_vid)
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try:
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@ -198,33 +221,32 @@ def calc_auc_overall(logger, dataset, n_vid, save_path):
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def test(logger, dataset, t, job_uuid, epoch, val_loss, visualize_score=True, visualize_frame=False,
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video_root_path='/share/data/videos'):
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import numpy as np
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from keras.models import load_model
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video_root_path='./data/videos'):
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import os
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import h5py
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from keras.utils.io_utils import HDF5Matrix
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import matplotlib.pyplot as plt
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from scipy.misc import imresize
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import numpy as np
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from keras.models import load_model
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from keras.utils.io_utils import HDF5Matrix
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import matplotlib.pyplot as plt
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from scipy.misc import imresize
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n_videos = {'avenue': 21, 'enter': 6, 'exit': 4, 'ped1': 36, 'ped2': 12}
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test_dir = os.path.join(video_root_path, '{0}/testing_h5_t{1}'.format(dataset, t))
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job_folder = os.path.join('/share/clean/{}/jobs'.format(dataset), job_uuid)
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n_videos = {'avenue': 1}
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test_dir = os.path.join(video_root_path, '{0}/testing_h5_t{1}'.format(dataset, t))
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job_folder = os.path.join('./data/clean/{}/jobs'.format(dataset), job_uuid)
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model_filename = 'model_snapshot_e{:03d}_{:.6f}.h5'.format(epoch, val_loss)
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temporal_model = load_model(os.path.join(job_folder, model_filename))
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save_path = os.path.join(job_folder, 'result')
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save_path = os.path.join(job_folder, 'result')
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os.makedirs(save_path, exist_ok=True)
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for videoid in range(n_videos[dataset]):
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videoname = '{0}_{1:02d}.h5'.format(dataset, videoid+1)
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filepath = os.path.join(test_dir, videoname)
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filepath = os.path.join(test_dir, videoname)
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logger.info("==> {}".format(filepath))
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f = h5py.File(filepath, 'r')
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filesize = f['data'].shape[0]
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f = h5py.File(filepath, 'r')
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filesize = f['data'].shape[0]
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f.close()
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gt_vid_raw = np.loadtxt('/share/data/groundtruths/gt_{0}_vid{1:02d}.txt'.format(dataset, videoid+1))
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# gt_vid_raw = np.loadtxt('./data/groundtruths/gt_{0}_vid{1:02d}.txt'.format(dataset, videoid+1))
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logger.debug("Predicting using {}".format(os.path.join(job_folder, model_filename)))
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X_test = HDF5Matrix(filepath, 'data')
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@ -269,17 +291,17 @@ def test(logger, dataset, t, job_uuid, epoch, val_loss, visualize_score=True, vi
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plt.ylim(0, 1)
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plt.xlim(1, score_vid.shape[0]+1)
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try:
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for event in range(gt_vid_raw.shape[1]):
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start = int(gt_vid_raw[0, event])
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end = int(gt_vid_raw[1, event]) + 1
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gt_vid[start:end] = 1
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plt.fill_between(np.arange(start, end), 0, 1, facecolor='red', alpha=0.4)
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except IndexError:
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start = int(gt_vid_raw[0])
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end = int(gt_vid_raw[1])
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gt_vid[start:end] = 1
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plt.fill_between(np.arange(start, end), 0, 1, facecolor='red', alpha=0.4)
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# try:
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# for event in range(gt_vid_raw.shape[1]):
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# start = int(gt_vid_raw[0, event])
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# end = int(gt_vid_raw[1, event]) + 1
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# gt_vid[start:end] = 1
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# plt.fill_between(np.arange(start, end), 0, 1, facecolor='red', alpha=0.4)
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# except IndexError:
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# start = int(gt_vid_raw[0])
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# end = int(gt_vid_raw[1])
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# gt_vid[start:end] = 1
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# plt.fill_between(np.arange(start, end), 0, 1, facecolor='red', alpha=0.4)
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plt.savefig(os.path.join(save_path, 'scores_{0}_video_{1:02d}.png'.format(dataset, videoid+1)), dpi=300)
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plt.close()
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@ -305,5 +327,5 @@ def test(logger, dataset, t, job_uuid, epoch, val_loss, visualize_score=True, vi
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plt.clf()
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logger.info("{}: Calculating overall metrics".format(dataset))
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auc_overall, eer_overall = calc_auc_overall(logger, dataset, n_videos[dataset], save_path)
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# auc_overall, eer_overall = calc_auc_overall(logger, dataset, n_videos[dataset], save_path)
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@ -1,5 +1,5 @@
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time_length: 8
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time_length: 4
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cost: mse
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epochs: 2000
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epochs: 2
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optimizer: sgd
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batch_size: 16
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@ -3,12 +3,14 @@ import skvideo.io
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from skimage.transform import resize
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from skimage.io import imsave
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video_root_path = '/share/data/videos'
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video_root_path = './data/videos'
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size = (224, 224)
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def video_to_frame(dataset, train_or_test):
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video_path = os.path.join(video_root_path, dataset, '{}_videos'.format(train_or_test))
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frame_path = os.path.join(video_root_path, dataset, '{}_frames'.format(train_or_test))
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print('video_path = ', video_path)
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print('frame_path = ', frame_path)
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os.makedirs(frame_path, exist_ok=True)
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for video_file in os.listdir(video_path):
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@ -23,22 +25,6 @@ def video_to_frame(dataset, train_or_test):
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imsave(os.path.join(vid_frame_path, '{:05d}.jpg'.format(count)), image) # save frame as JPEG file
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count += 1
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# avenue
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# convert
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video_to_frame('avenue', 'training')
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video_to_frame('avenue', 'testing')
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# ped1
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video_to_frame('ped1', 'training')
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video_to_frame('ped1', 'testing')
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# ped2
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video_to_frame('ped2', 'training')
|
||||
video_to_frame('ped2', 'testing')
|
||||
|
||||
# enter
|
||||
video_to_frame('enter', 'training')
|
||||
video_to_frame('enter', 'testing')
|
||||
|
||||
# exit
|
||||
video_to_frame('exit', 'training')
|
||||
video_to_frame('exit', 'testing')
|
||||
|
|
|
|||
12
dataset.py
12
dataset.py
|
|
@ -1,5 +1,5 @@
|
|||
|
||||
def calc_mean(dataset, video_root_path='/share/data/videos'):
|
||||
def calc_mean(dataset, video_root_path='./data/videos'):
|
||||
import os
|
||||
from skimage.io import imread
|
||||
import numpy as np
|
||||
|
|
@ -22,7 +22,7 @@ def calc_mean(dataset, video_root_path='/share/data/videos'):
|
|||
np.save(os.path.join(video_root_path, dataset, 'mean_frame_224.npy'), frame_mean)
|
||||
|
||||
|
||||
def subtract_mean(dataset, video_root_path='/share/data/videos'):
|
||||
def subtract_mean(dataset, video_root_path='./data/videos'):
|
||||
import os
|
||||
from skimage.io import imread
|
||||
import numpy as np
|
||||
|
|
@ -56,7 +56,7 @@ def subtract_mean(dataset, video_root_path='/share/data/videos'):
|
|||
np.save(os.path.join(video_root_path, dataset, 'testing_frames_{}.npy'.format(frame_folder)), testing_frames_vid)
|
||||
|
||||
|
||||
def build_h5(dataset, train_or_test, t, video_root_path='/share/data/videos'):
|
||||
def build_h5(dataset, train_or_test, t, video_root_path='./data/videos'):
|
||||
import h5py
|
||||
from tqdm import tqdm
|
||||
import os
|
||||
|
|
@ -87,7 +87,7 @@ def build_h5(dataset, train_or_test, t, video_root_path='/share/data/videos'):
|
|||
build_volume(train_or_test, num_videos, time_length=t)
|
||||
|
||||
|
||||
def combine_dataset(dataset, t, video_root_path='/share/data/videos'):
|
||||
def combine_dataset(dataset, t, video_root_path='./data/videos'):
|
||||
import h5py
|
||||
import os
|
||||
from tqdm import tqdm
|
||||
|
|
@ -121,7 +121,7 @@ def combine_dataset(dataset, t, video_root_path='/share/data/videos'):
|
|||
output_file.close()
|
||||
|
||||
|
||||
def preprocess_data(logger, dataset, t, video_root_path='/share/data/videos'):
|
||||
def preprocess_data(logger, dataset, t, video_root_path='./data/videos'):
|
||||
import os
|
||||
|
||||
# Step 1: Calculate the mean frame of all training frames
|
||||
|
|
@ -167,6 +167,8 @@ def preprocess_data(logger, dataset, t, video_root_path='/share/data/videos'):
|
|||
num_videos = len(os.listdir(os.path.join(video_root_path, '{}/{}_frames'.format(dataset, train_or_test))))
|
||||
for i in range(num_videos):
|
||||
h5_file = os.path.join(video_root_path, '{0}/{1}_h5_t{2}/{0}_{3:02d}.h5'.format(dataset, train_or_test, t, i+1))
|
||||
print('-------------------')
|
||||
print(h5_file)
|
||||
assert(os.path.isfile(h5_file))
|
||||
except AssertionError:
|
||||
logger.info("Step 3/4: Generating volumes for {} {} set".format(dataset, train_or_test))
|
||||
|
|
|
|||
|
|
@ -6,19 +6,21 @@ import coloredlogs
|
|||
from classifier import test
|
||||
|
||||
|
||||
device = 'cpu'
|
||||
dataset = 'ped1'
|
||||
job_uuid = '86f47b9c-d0ca-49a8-beb6-84373ea9e880'
|
||||
epoch = 586
|
||||
val_loss = 0.001069
|
||||
time_length = 8
|
||||
device = 'cpu'
|
||||
dataset = 'avenue'
|
||||
job_uuid = '603213fe-3308-41d7-8ce2-d734ea4a547b'
|
||||
epoch = 2
|
||||
val_loss = 0.001326
|
||||
time_length = 4
|
||||
|
||||
job_folder = os.path.join('./data/clean/{}/jobs'.format(dataset), job_uuid)
|
||||
log_path = os.path.join(job_folder, 'logs')
|
||||
|
||||
job_folder = os.path.join('/share/clean/{}/jobs'.format(dataset), job_uuid)
|
||||
log_path = os.path.join(job_folder, 'logs')
|
||||
os.makedirs(log_path, exist_ok=True)
|
||||
logging.basicConfig(filename=os.path.join(log_path, "test-{}.log".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))),
|
||||
level=logging.DEBUG,
|
||||
format="%(asctime)s [%(levelname)s] %(message)s")
|
||||
|
||||
logging.basicConfig(filename = os.path.join(log_path, "test-{}.log".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))),
|
||||
level = logging.DEBUG,
|
||||
format = "%(asctime)s [%(levelname)s] %(message)s")
|
||||
coloredlogs.install()
|
||||
logger = logging.getLogger()
|
||||
|
||||
|
|
|
|||
|
|
@ -3,24 +3,25 @@ import datetime
|
|||
import os
|
||||
import sys
|
||||
import coloredlogs
|
||||
from classifier import train
|
||||
import uuid
|
||||
from shutil import copyfile
|
||||
from classifier import train
|
||||
from shutil import copyfile
|
||||
|
||||
dataset = 'ped1'
|
||||
device = 'gpu1'
|
||||
dataset = 'avenue'
|
||||
device = 'cpu'
|
||||
|
||||
job_uuid = str(uuid.uuid4())
|
||||
job_folder = os.path.join('/share/clean/{}/jobs'.format(dataset), job_uuid)
|
||||
job_folder = os.path.join('./data/clean/{}/jobs'.format(dataset), job_uuid)
|
||||
os.makedirs(job_folder)
|
||||
copyfile('config.yml', os.path.join(job_folder, 'config.yml'))
|
||||
|
||||
log_path = os.path.join(job_folder, 'logs')
|
||||
os.makedirs(log_path, exist_ok=True)
|
||||
logging.basicConfig(filename=os.path.join(log_path,
|
||||
"train-{}.log".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))),
|
||||
level=logging.DEBUG,
|
||||
format="%(asctime)s [%(levelname)s] %(message)s")
|
||||
logging.basicConfig(filename = os.path.join(log_path,
|
||||
"train-{}.log".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))),
|
||||
level = logging.DEBUG,
|
||||
format = "%(asctime)s [%(levelname)s] %(message)s")
|
||||
|
||||
coloredlogs.install(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue