Update README.md

Author: chenliu-wustl

README.md

@@ -25,28 +25,48 @@ We convert [ScanNet](http://www.scan-net.org/) data to *.tfrecords* files for tr
 ### Training
 To train the network from the pretrained DeepLab network, please first download the DeepLab model [here](https://github.com/DrSleep/tensorflow-deeplab-resnet) (under the Caffe to TensorFlow conversion), and then run the following command.
 ```bash
-python train_planenet.py --restore=0 --modelPathDeepLab="path to the deep lab model" --rootFolder="folder which contains tfrecords files"
+python train_planenet.py --restore=0 --modelPathDeepLab="path to the deep lab model" --dataFolder="folder which contains tfrecords files"
 ```
 
 ### Evaluation
 Please first download our trained network from [here](https://mega.nz/#!sjpT2DiQ!Uo-6hxyldmtnPoKk3TTdUHKZADRGy6nIPlmAeVzJs_8) and put the uncompressed folder under ./checkpoint folder.
 
 To evaluate the performance against existing methods, please run:
 ```bash
-python evaluate.py --rootFolder="folder which contains tfrecords files"
+python evaluate.py --dataFolder="folder which contains tfrecords files"
 ```
 
 ### Applications
-Please first download our trained network (see [Evaluation](### Evaluation) section for details). Script *predict.py* and *predict_custom.py* are for ScanNet testing images and custom images respectively. To predict and visualize ScanNet testing images, please run:
+Please first download our trained network (see [Evaluation](### Evaluation) section for details). Script *predict.py* predicts and visualizes custom images (if "customImageFolder" is specified) or ScanNet testing images (if "dataFolder" is specified).
 
 ```bash
-python predict.py --rootFolder="folder which contains tfrecords files" [--startIndex=0] [--numImages=30]
+python predict.py --customImageFolder="folder which contains custom images"
+python predict.py --dataFolder="folder which contains tfrecords files" [--startIndex=0] [--numImages=30]
 ```
 
-This will generate visualization images, a webpage containing all the visualization, as well as cache files under folder "predict". Similarly, the following command can be used to predict and visualize custom images:
+This will generate visualization images, a webpage containing all the visualization, as well as cache files under folder "predict/".
 
+Same commands can be used for various applications by providing optional arguments, *applicationType*, *imageIndex*, *textureImageFilename*, and some application-specific arguments. The following commands are used to generate visualizations in the submission. (The TV application needs more manual specification for better visualization.)
+
+```bash
+python predict.py --dataFolder=/mnt/vision/Data/PlaneNet/ --textureImageFilename=texture_images/CVPR.jpg --imageIndex=118 --applicationType=logo_texture --startIndex=118 --numImages=1
+python predict.py --dataFolder=/mnt/vision/Data/PlaneNet/ --textureImageFilename=texture_images/CVPR.jpg --imageIndex=118 --applicationType=logo_video --startIndex=118 --numImages=1
+python predict.py --dataFolder=/mnt/vision/Data/PlaneNet/ --textureImageFilename=texture_images/checkerboard.jpg --imageIndex=72 --applicationType=wall_texture --wallIndices=7,9 --startIndex=72 --numImages=1
+python predict.py --dataFolder=/mnt/vision/Data/PlaneNet/ --textureImageFilename=texture_images/checkerboard.jpg --imageIndex=72 --applicationType=wall_video --wallIndices=7,9 --startIndex=72 --numImages=1
+python predict.py --customImageFolder=my_images/TV/ --textureImageFilename=texture_images/TV.mp4 --imageIndex=0 --applicationType=TV --wallIndices=2,9
+python predict.py --customImageFolder=my_images/ruler --textureImageFilename=texture_images/ruler_36.png --imageIndex=0 --applicationType=ruler --startPixel=950,444 --endPixel=1120,2220
+```
+
+Note that, the above script generate image sequences for video applications. Please run the following command under the image sequence folder to generate a video:
+```bash
+ffmpeg -r 60 -f image2 -s 640x480 -i %04d.png -vcodec libx264 -crf 25 -pix_fmt yuv420p video.mp4
+```
+
+To check out the pool ball application, please run the following commands.
 ```bash
-python predict_custom.py --rootFolder="folder which contains custom images"
+python predict.py --customImageFolder=my_images/pool --imageIndex=0 --applicationType=pool --estimateFocalLength=False
+cd pool
+python pool.py
 ```
 
-Same commands can be used for various applications by providing arguments, *--imageIndex*, *--suffix*, and *--textureFilename*
+Use mouse to play:)