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+# Future Frame Prediction for Anomaly Detection -- A New Baseline
+This repo is the official open source of [Future Frame Prediction for Anomaly Detection -- A New Baseline, CVPR 2018](https://arxiv.org/pdf/1712.09867.pdf) by Wen Liu, Weixinluo, Dongze Lian and Shenghua Gao. 
+A **demo** is shown in *https://www.youtube.com/watch?v=M--wv-Y_h0A*. It is implemented in tensorflow. Please follow the instructions to run the code.
+
+####1. Installation (Anaconda with python3.6 installation is recommended)
+* Install 3rd-package dependencies of python (listed in requirements.txt)
+```
+numpy==1.14.1
+scipy==1.0.0
+matplotlib==2.1.2
+tensorflow_gpu==1.4.1
+tensorflow==1.4.1
+Pillow==5.0.0
+pypng==0.0.18
+scikit_learn==0.19.1
+opencv-python==3.2.0.6
+```
+
+```shell
+pip install -r requirements.txt
+```
+* Other libraries
+```code
+CUDA 8.0
+Cudnn 6.0
+```
+####2. Download datasets
+cd into Data folder of project and run the shell scripts (**ped1.sh, ped2.sh, avenue.sh, shanghaitech.sh**) under the Data folder.
+```shell
+cd Data
+./ped2.sh
+./ped1.sh
+./avenue.sh
+./shanghaitech.sh
+```
+
+####3. Testing on saved models
+* Download the trained models
+```shell
+cd models
+./download_pretrains.sh
+```
+* Running the sript (as ped2 and avenue datasets for examples) and cd into **Codes** folder at first.
+```shell
+python inference.py  --dataset  ped2    \
+                    --test_folder  ../Data/ped2/testing/frames      \
+                    --gpu  1    \
+                    --snapshot_dir    models/pretrains/ped2
+```
+
+```shell
+python inference.py  --dataset  avenue    \
+                    --test_folder  ../Data/avenue/testing/frames      \
+                    --gpu  1    \
+                    --snapshot_dir    models/pretrains/avenue
+```
+
+
+####4. Training from scratch (here we use ped2 and avenue datasets for examples)
+* Set hyper-parameters
+The default hyper-parameters, such as $\lambda_{init}$, $\lambda_{gd}$, $\lambda_{op}$, $\lambda_{adv}$ and the learning rate of G, as well as D, are all initialized in **training_hyper_params/hyper_params.ini**. 
+* Running script (as ped2 or avenue for instances) and cd into **Codes** folder at first.
+```shell
+python train.py  --dataset  ped2    \
+                 --train_folder  ../Data/ped2/training/frames     \
+                 --test_folder  ../Data/ped2/testing/frames       \
+                 --gpu  0       \
+                 --iters    80000
+```
+* Model selection while training
+In order to do model selection, a popular way is to testing the saved models after a number of iterations or epochs (Since there are no validation set provided on above all datasets, and in order to compare the performance with other methods, we just choose the best model on testing set). Here, we can use another GPU to listen the **snapshot_dir** folder. When a new model.cpkt.xxx has arrived, then load the model and test. Finnaly, we choose the best model. Following is the script.
+```shell
+python inference.py  --dataset  ped2    \
+                     --test_folder  ../Data/ped2/testing/frames       \
+                     --gpu  1
+```
+Run **python train.py -h** to know more about the flag options or see the detials in **constant.py**.
+```shell
+Options to run the network.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -g GPU, --gpu GPU    the device id of gpu.
+  -i ITERS, --iters ITERS
+                        set the number of iterations, default is 1
+  -b BATCH, --batch BATCH
+                        set the batch size, default is 4.
+  --num_his NUM_HIS    set the time steps, default is 4.
+  -d DATASET, --dataset DATASET
+                        the name of dataset.
+  --train_folder TRAIN_FOLDER
+                        set the training folder path.
+  --test_folder TEST_FOLDER
+                        set the testing folder path.
+  --config CONFIG      the path of training_hyper_params, default is
+                        training_hyper_params/hyper_params.ini
+  --snapshot_dir SNAPSHOT_DIR
+                        if it is folder, then it is the directory to save
+                        models, if it is a specific model.ckpt-xxx, then the
+                        system will load it for testing.
+  --summary_dir SUMMARY_DIR
+                        the directory to save summaries.
+  --psnr_dir PSNR_DIR  the directory to save psnrs results in testing.
+  --evaluate EVALUATE  the evaluation metric, default is compute_auc
+```
+* (Option) Tensorboard visualization
+```shell
+tensorboard    --logdir=./summary    --port=10086
+```
+Open the browser and type **https://ip:10086**. Following is the screen shot of avenue on tensorboard.
+![scalars_tensorboard](assets/scalars.JPG)
+
+![images_tensorboard](assets/images.JPG)
+
+
+#### Notes
+The flow loss (temporal loss) module is based on [a TensorFlow implementation of FlowNet2](https://github.com/sampepose/flownet2-tf). Thanks for their nice work.
+#### Citation
+If you find this useful, please cite our work as follows:
+```code
+@article{liu2018ano_pred,
+Author = {Wen Liu and Weixin Luo and Dongze Lian and Shenghua Gao},
+Title = {Future Frame Prediction for Anomaly Detection -- A New Baseline},
+Journal = {ArXiv e-prints},
+Year = {2017},
+Eprint = {arXiv:1712.09867},
+}
+```
+While the open access of CVPR 2018 is available, welcome to cite the CVPR version. 
+Please contact with us if you have any questions.