update structure and image

README.md

@@ -4,17 +4,57 @@ This repository contains the code for two research projects:
 
 1. **MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization**   
 [README](https://github.com/vita-epfl/monstereo/tree/master/docs/MonStereo.md) & [Article](https://arxiv.org/abs/2008.10913)
-
+     
-    ![monstereo](docs/out_005523.png)
+     ![monstereo 1](docs/000840_multi.png)
- 
+    
-
 2.  **Perceiving Humans: from Monocular 3D Localization to Social Distancing (MonoLoco++)**         
  [README](https://github.com/vita-epfl/monstereo/tree/master/docs/MonoLoco_pp.md) & [Article](https://arxiv.org/abs/2009.00984)
  
     ![social distancing](docs/pull_sd.png)
  
-    ![monoloco](docs/truck.png)
+    ![monoloco_pp](docs/truck.png)
 
-Both projects has been built upon [Openpifpaf](https://github.com/vita-epfl/openpifpaf) 
+Both projects has been built upon the CVPR'19 project [Openpifpaf](https://github.com/vita-epfl/openpifpaf) 
-for 2D pose estimation and [MonoLoco](https://github.com/vita-epfl/monoloco) for monocular 3D localization. 
+for 2D pose estimation and the ICCV'19 project [MonoLoco](https://github.com/vita-epfl/monoloco) for monocular 3D localization. 
-All projects share the AGPL Licence.
+All projects share the AGPL Licence.
+
+
+# Setup
+Installation steps are the same for both projects.
+
+### Install
+The installation has been tested on OSX and Linux operating systems, with Python 3.6 or Python 3.7. 
+Packages have been installed with pip and virtual environments.
+For quick installation, do not clone this repository, 
+and make sure there is no folder named monstereo in your current directory.
+A GPU is not required, yet highly recommended for real-time performances. 
+MonStereo can be installed as a package, by:
+
+```
+pip3 install monstereo
+```
+
+For development of the monstereo source code itself, you need to clone this repository and then:
+```
+pip3 install sdist
+cd monstereo
+python3 setup.py sdist bdist_wheel
+pip3 install -e .
+```
+
+### Interfaces
+All the commands are run through a main file called `main.py` using subparsers.
+To check all the commands for the parser and the subparsers (including openpifpaf ones) run:
+
+* `python3 -m monstereo.run --help`
+* `python3 -m monstereo.run predict --help`
+* `python3 -m monstereo.run train --help`
+* `python3 -m monstereo.run eval --help`
+* `python3 -m monstereo.run prep --help`
+
+or check the file `monstereo/run.py`
+
+Further instructions for prediction, preprocessing, training and evaluation can be found here:
+
+* [MonStereo README](https://github.com/vita-epfl/monstereo/tree/master/docs/MonStereo.md)
+* [MonoLoco++ README](https://github.com/vita-epfl/monstereo/tree/master/docs/MonoLoco_pp.md)

docs/MonStereo.md

@@ -24,53 +24,15 @@ month = {August},
 year = {2020}
 }
 ```
+              
+# Prediction
+The predict script receives an image (or an entire folder using glob expressions), 
+calls PifPaf for 2d human pose detection over the image
+and runs MonStereo for 3d location of the detected poses.
 
-# Features
+Output options include json files and/or visualization of the predictions on the image in *frontal mode*, 
-The code has been built upon the ICCV'19 project [MonoLoco](https://github.com/vita-epfl/monoloco).
+*birds-eye-view mode* or *multi mode* and can be specified with `--output_types`
-This repository supports
 
-* the original MonoLoco
-* An improved Monocular version (MonoLoco++) for x,y,z coordinates, orientation, and dimensions
-* MonStereo
-
-# Setup
-
-### Install
-The installation has been tested on OSX and Linux operating systems, with Python 3.6 or Python 3.7. 
-Packages have been installed with pip and virtual environments.
-For quick installation, do not clone this repository, 
-and make sure there is no folder named monstereo in your current directory.
-A GPU is not required, yet highly recommended for real-time performances. 
-MonStereo can be installed as a package, by:
-
-```
-pip3 install monstereo
-```
-
-For development of the monstereo source code itself, you need to clone this repository and then:
-```
-pip3 install sdist
-cd monstereo
-python3 setup.py sdist bdist_wheel
-pip3 install -e .
-```
-
-### Data structure
-
-    Data         
-    ├── arrays                 
-    ├── models
-    ├── kitti
-    ├── logs
-    ├── output
-    
-
-Run the following to create the folders:
-```
-mkdir data
-cd data
-mkdir arrays models kitti logs output
-```
 
 ### Pre-trained Models
 * Download Monstereo pre-trained model from 
@@ -85,27 +47,6 @@ Alternatively, you can download a Pifpaf pre-trained model from [openpifpaf](htt
   If you'd like to use an updated version, we suggest to re-train the MonStereo model as well.
 * The model for the experiments is provided in *data/models/ms-200710-1511.pkl*
 
-# Interfaces
-All the commands are run through a main file called `main.py` using subparsers.
-To check all the commands for the parser and the subparsers (including openpifpaf ones) run:
-
-* `python3 -m monstereo.run --help`
-* `python3 -m monstereo.run predict --help`
-* `python3 -m monstereo.run train --help`
-* `python3 -m monstereo.run eval --help`
-* `python3 -m monstereo.run prep --help`
-
-or check the file `monstereo/run.py`
-              
-# Prediction
-The predict script receives an image (or an entire folder using glob expressions), 
-calls PifPaf for 2d human pose detection over the image
-and runs MonStereo for 3d location of the detected poses.
-
-
-Output options include json files and/or visualization of the predictions on the image in *frontal mode*, 
-*birds-eye-view mode* or *multi mode* and can be specified with `--output_types`
-
 
 ### Ground truth matching
 * In case you provide a ground-truth json file to compare the predictions of MonSter,
@@ -139,6 +80,22 @@ but require first to run a pose detector over
 all the training images and collect the annotations. 
 The code supports this option (by running the predict script and using `--mode pifpaf`).
 
+### Data structure
+
+    Data         
+    ├── arrays                 
+    ├── models
+    ├── kitti
+    ├── logs
+    ├── output
+    
+Run the following to create the folders:
+```
+mkdir data
+cd data
+mkdir arrays models kitti logs output
+```
+
 
 ### Datasets
 Download KITTI ground truth files and camera calibration matrices for training