Merge branch 'master' of github.com:phoenix104104/fast_blind_video_co…

…nsistency

README.md

@@ -1,4 +1,4 @@
-# Learning Blind Video Temporal Consistency (ECCV 2018) 
+# Learning Blind Video Temporal Consistency
 
 [Wei-Sheng Lai](http://graduatestudents.ucmerced.edu/wlai24/), 
 [Jia-Bin Huang](https://filebox.ece.vt.edu/~jbhuang/), 
@@ -21,6 +21,7 @@ and [Ming-Hsuan Yang](http://faculty.ucmerced.edu/mhyang/)
 1. [Dataset](#dataset)
 1. [Apply Pre-trained Models](#apply-pre-trained-models)
 1. [Training and Testing](#training-and-testing)
+1. [Evaluation](#evaluation)
 1. [Image Processing Algorithms](#image-processing-algorithms)
 
 ### Introduction
@@ -61,6 +62,7 @@ Download our training and testing datasets:
 
     cd data
     ./download_data.sh [train | test | all]
+    cd ..
 
 For example, download training data only:
 
@@ -74,14 +76,76 @@ You can also download the results of [Bonneel et al. 2015] and our approach:
 
     ./download_data.sh results
 
+
 ### Apply pre-trained models
 Download pretrained models (including FlowNet2 and our model):
 
     cd pretrained_models
     ./download_models.sh
+    cd ..
+
+Test pre-trained model:
 
+    python test_pretrained.py -dataset DAVIS -task WCT/wave
+
+The output frames are saved in `data/test/ECCV18/WCT/wave/DAVIS`.
 
 ### Training and testing
+Train a new model:
+
+    python train.py -datasets_tasks W3_D1_C1_I1
+
+We have specified all the default parameters in train.py. `lists/train_tasks_W3_D1_C1_I1.txt` specifies the dataset-task pairs for training.
+
+Test a model:
+
+    python test.py -method MODEL_NAME -epoch N -dataset DAVIS -task WCT/wave
+
+Check the checkpoint folder for the `MODEL_NAME`.
+The output frames are saved in `data/test/MODEL_NAME/epoch_N/WCT/wave/DAVIS`.
+
+
+You can also generate results for multiple tasks using the following script:
+
+    python batch_test.py -method output/MODEL_NAME/epoch_N
+
+which will test all the tasks in `lists/test_tasks.txt`.
+
+
+### Evaluation
+**Temporal Warping Error**
+
+To compute the temporal warping error, we first need to generate optical flow and occlusion masks:
+
+    python compute_flow_occlusion.py -dataset DAVIS -phase test
+
+The flow will be stored in `data/test/fw_flow/DAVIS`. The occlusion masks will be stored in `data/test/fw_occlusion/DAVIS`.
+
+Then, run the evaluation script:
+
+    python evaluate_WarpError.py -method output/MODEL_NAME/epoch_N -task WCT/wave
+
+**LPIPS**
+
+Download [LPIPS repository](https://github.com/richzhang/PerceptualSimilarity) and change `LPIPS_dir` in evalate_LPIPS.py if necesary (default path is `../LPIPS`).
+
+Run the evaluation script:
+
+    python evaluate_LPIPS.py -method output/MODEL_NAME/epoch_N -task WCT/wave
+
+**Batch evaluation**
+
+You can evaluate multiple tasks using the following script:
+
+    python batch_evaluate.py -method output/MODEL_NAME/epoch_N -metric LPIPS
+    python batch_evaluate.py -method output/MODEL_NAME/epoch_N -metric WarpError
+
+### Test on new videos
+To test our model on new videos or applications, please follow the folder structure in `./data`.
+
+Given a video, we extract frames named as `%05d.jpg` and save frames in `data/test/input/DATASET/VIDEO`.
+
+The per-frame processed video is stored in `data/test/processed/TASK/DATASET/VIDEO`, where `TASK` is the image processing algorithm applied on the original video.
 
 
 ### Image Processing Algorithms