Updated README fixed python2 issues

Dockerfile

@@ -23,13 +23,12 @@ RUN curl -o ~/miniconda.sh -O  https://repo.continuum.io/miniconda/Miniconda3-4.
      /opt/conda/bin/conda clean -ya 
 ENV PATH /opt/conda/envs/pytorch-py35/bin:$PATH
 RUN conda install --name pytorch-py35 -c soumith magma-cuda80
+RUN conda install pytorch torchvision cuda80 -c soumith
 
-# This must be done before pip so that requirements.txt is available
-WORKDIR /opt/pytorch
-COPY . .
-
-RUN TORCH_CUDA_ARCH_LIST="3.5 5.2 6.0 6.1+PTX" TORCH_NVCC_FLAGS="-Xfatbin -compress-all" \
-    CMAKE_PREFIX_PATH="$(dirname $(which conda))/../" \
-    pip install -v .
-
-# Install face-alignment package
+# Install face-alignment package
+WORKDIR /workspace
+RUN chmod -R a+w /workspace
+RUN git clone https://github.com/1adrianb/face-alignment
+WORKDIR /workspace/face-alignment
+RUN pip install -r requirements.txt
+RUN python setup.py install

README.md

@@ -8,18 +8,68 @@ Build using [FAN](https://www.adrianbulat.com)'s state-of-the-art deep learning
 
 Note: For the lua version check [here](https://github.com/1adrianb/2D-and-3D-face-alignment). For numerical evaluations it is recommended to use the lua version which uses indentical models with the ones evaluated in the paper.
 
-[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)
+[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) [![Build Status](https://travis-ci.com/1adrianb/face-alignment.svg)](https://travis-ci.com/1adrianb/face-alignment)
 
 ## Features
 
 #### Detect 2D facial landmarks in pictures
 
+<p align='center'>
+<img src='docs/images/2dlandmarks.png' title='3D-FAN-Full example' style='max-width:600px'></img>
+</p>
+
+```python
+import face_alignment
+import scipy.io as io
+
+fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, enable_cuda=True, flip_input=False)
+
+input = io.imread('../test/assets/aflw-test.jpg')
+preds = fa.get_landmarks(input)
+```
+
 #### Detect 3D facial landmarks in pictures
 
+<p align='center'>
+<img src='https://www.adrianbulat.com/images/image-z-examples.png' title='3D-FAN-Full example' style='max-width:600px'></img>
+</p>
+
+```python
+import face_alignment
+import scipy.io as io
+
+fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, enable_cuda=True, flip_input=False)
+
+input = io.imread('../test/assets/aflw-test.jpg')
+preds = fa.get_landmarks(input)
+```
+
 #### Find all faces present in a given image
 
+```python
+import face_alignment
+import scipy.io as io
+
+fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, enable_cuda=False, flip_input=False)
+
+input = io.imread('../test/assets/aflw-test.jpg')
+preds = fa.detect_faces(input)
+```
+
 #### Process an entire directory in one go
 
+```python
+import face_alignment
+import scipy.io as io
+
+fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, enable_cuda=False, flip_input=False)
+
+input = io.imread('../test/assets/aflw-test.jpg')
+preds = fa.process_folder('../test/assets/', all_faces=True)
+```
+
+Please also see the ``examples`` folder
+
 ## Instalation
 
 ### Requirements
@@ -92,6 +142,10 @@ docker build -t face-alignment .
 
 While here the work is presented as a black-box, if you want to know more about the intrisecs of the method please check the original paper either on arxiv or my [webpage](https://www.adrianbulat.com).
 
+## Contributions
+
+All contributions are welcomed. If you encounter any issue (including examples of images where it fails) feel free to open an issue.
+
 ## Citation
 
 ```

face_alignment/api.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 import os
 import glob
 import dlib

face_alignment/utils.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 import os
 import sys
 import time
@@ -69,7 +70,8 @@ def transform(point, center, scale, resolution, invert=False):
 
     if invert:
         t = torch.inverse(t)
-    new_point = (t @ _pt)[0:2]
+
+    new_point = (torch.matmul(t,_pt))[0:2]
 
     return new_point.int()