update docs

README.md

@@ -1,8 +1,45 @@
 # Human 2D images to 3D models
 
-Docker image inspired by http://files.is.tuebingen.mpg.de/classner/up/.
+Project to put the [Unite the People](http://files.is.tuebingen.mpg.de/classner/up/)
+project in a Docker image. The project contains 2 docker images because the
+*pose* and *segmentation* scripts require different environments.
 
-## Setup
+* **up_caffe**: run the pose prediction with the *p91* model
+* **up_deeplab**: run the segmentation script with the *s31* model
+
+## Usage
+
+When you `run` the containers you need to mount a local folder which contains
+the source images that you want to use for pose predictions and segmentations.
+
+### 1. Download
+
+```
+$ docker pull lukin0110/up_caffe
+$ docker pull lukin0110/up_deeplab
+```
+### 2. Execute pose prediction
+
+```
+$ docker run -it -v "$(pwd)"/input:/input lukin0110/up_caffe pose input/debruyne1.jpg
+```
+
+### 3. Execute bodyfit
+
+```
+$ docker run -it -v "$(pwd)"/input:/input lukin0110/up_caffe bodyfit input/debruyne1.jpg
+```
+
+### 4. Execute segmentation
+
+It's required to use [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker) to
+run the segmentation since CUDA is being used.
+
+```
+$ nvidia-docker run -it -v "$(pwd)"/input:/input lukin0110/up_deeplab segmentation input/debruyne1.jpg
+```
+
+## Setup from source
 
 Before you execute the script: download **SMPL_python_v.1.0.0.zip**
 from http://smpl.is.tue.mpg.de/ and put it in the ./models folder. You need an
@@ -15,41 +52,32 @@ account to download the package.
 Build image:
 ```
 $ docker-compose build
-or
-$ docker build . -t demo
 ```
 
 Generate pose prediction:
 ```
-$ docker-compose run demo pose input/debruyne1.jpg
-or
-$ docker run -it -v "$(pwd)"/input:/input demo bodyfit input/debruyne1.jpg
+$ docker-compose run caffe pose input/debruyne1.jpg
 ```
 
 This will generate `.npz` and `.png` files in the `input` folder.
 
 Generate 3D body:
 ```
-$ docker-compose run demo pose input/debruyne1.jpg
-or
-$ docker run -it -v "$(pwd)"/input:/input demo bodyfit input/debruyne1.jpg
+$ docker-compose run caffe bodyfit input/debruyne1.jpg
 ```
 
-## Using nvidia-docker-compose
+Generate segmentation:
 ```
-pip install nvidia-docker-compose
-# nvidia-docker-compose binary may not be in your path.  On ubuntu, for my installation, it installed to /home/edward/.local/bin/.  An Anaconda installation will have binaries in anaconda/bin.
-nvidia-docker-compose build
+$ nvidia-docker-compose run deeplab segmentation input/debruyne1.jpg
 ```
 
-Generate body fit:
-```
-# Download SMPL_python_v.1.0.0.zip and place in the models directory of this repo on the docker host
-nvidia-docker-compose build
-nvidia-docker-compose run demo bodyfit /input/zuck1.jpg
-```
+Install nvidia docker compose if it is not present yet: `pip install nvidia-docker-compose`.
+
+## Push to docker hub
 
-Generate segmentation:
 ```
-$ nvidia-docker run -it -v "$(pwd)"/input:/input dl segmentation input/debruyne1.jpg
+$ docker tag demo2d3d_caffe:latest lukin0110/up_caffe:latest
+$ docker tag demo2d3d_deeplab:latest lukin0110/up_deeplab:latest
+$ docker push lukin0110/up_caffe:latest
+$ docker push lukin0110/up_deeplab:latest
 ```

docker-compose.yml

@@ -1,12 +1,12 @@
 version: '3'
 
 services:
-  demo:
+  caffe:
     build: .
     volumes:
       - ./input:/input
 
-  dl:
+  deeplab:
     build:
       context: .
       dockerfile: Dockerfile.deeplabv2