add basic requirements.txt and .gitignore files, and fix macOS compilation

.gitignore

@@ -0,0 +1,11 @@
+__pycache__
+*.so
+lib/coco/PythonAPI/build/
+lib/coco/PythonAPI/pycocotools/_mask.c
+lib/multicut_cython/build/
+lib/multicut_cython/multicut.cpp
+lib/nms_cython/build/
+lib/nms_cython/nms_grid.cpp
+models/mpii/mpii-single-resnet-101.*
+models/coco/coco-resnet-101.*
+models/coco/pairwise*

README.md

@@ -16,14 +16,15 @@ In _Conference on Computer Vision and Pattern Recognition (CVPR)_, 2017**
 For more information visit http://pose.mpi-inf.mpg.de
 
 Python 3 is required to run this code.
-First of all, you should install TensorFlow as described in the
-[official documentation](https://www.tensorflow.org/install/).
-We recommended to use `virtualenv`.
+On Linux, you may need to install the `python3-dev` and `python3-tk` packages.
 
-You will also need to install the following Python packages:
+We recommended using `virtualenv` to create an isolated Python environment.
+
+You will also need to install the Python packages listed in `requirements.txt`,
+including TensorFlow:
 
 ```
-$ pip3 install scipy scikit-image matplotlib pyyaml easydict cython munkres
+$ pip install -r requirements.txt
 ```
 
 When running training or prediction scripts, please make sure to set the environment variable
@@ -82,4 +83,3 @@ Please cite ArtTrack and DeeperCut in your publications if it helps your researc
         url = {http://arxiv.org/abs/1605.03170},
         author = {Eldar Insafutdinov and Leonid Pishchulin and Bjoern Andres and Mykhaylo Andriluka and Bernt Schiele}
     }
-

lib/multicut_cython/multicut.pyx

@@ -24,7 +24,7 @@ def solve_nl_lmp(np.ndarray[np.float64_t, ndim=2, mode="c"] unary_array,
                  is_sparse_graph, solver_type, do_suppression, logit_in_solver):
 
 
-  cdef np.ndarray[np.uint64_t, ndim=2, mode="c"] result = np.zeros([unary_array.shape[0], 2], dtype=np.uint64)
+  cdef np.ndarray[np.ulonglong_t, ndim=2, mode="c"] result = np.zeros([unary_array.shape[0], 2], dtype=np.ulonglong)
 
   solve_nl_lmp_cpp(&unary_array[0, 0], unary_array.shape[0], unary_array.shape[1],
                    &pwidx_array[0, 0], pwidx_array.shape[0], pwidx_array.shape[1],

lib/multicut_cython/solve_nl_lmp.hxx

@@ -5,19 +5,21 @@
 
 #include <algorithm>
 
+#include <array>
+
 #include <vector>
 
 #include <iostream>
 
 using namespace std;
 
-template <typename T> 
+template <typename T>
 andres::View<T> get_view2d(T *gridData, int grid_height, int grid_width)
 {
   std::array<size_t, 2> shape;
   shape[0] = grid_height;
   shape[1] = grid_width;
-  andres::View<T> view(shape.begin(), shape.end(), gridData, andres::FirstMajorOrder); 
+  andres::View<T> view(shape.begin(), shape.end(), gridData, andres::FirstMajorOrder);
   return view;
 }
 
@@ -26,7 +28,7 @@ void solve_nl_lmp_cpp(double *unValues, int un_H, int un_W,
                       uint16_t* pwIndices, int pwi_H, int pwi_W,
                       double* pwValues, int pwv_H, int pwv_W,
                       bool is_sparse_graph, bool solver_type, bool do_suppression, bool do_logit_transform,
-                      uint64_t *result)
+                      unsigned long long* result)
 {
   andres::View<uint16_t> pwind_view = get_view2d<uint16_t>(pwIndices, pwi_H, pwi_W);
   andres::View<double> un_view = get_view2d<double>(unValues, un_H, un_W);
@@ -58,12 +60,12 @@ void solve_nl_lmp_cpp(double *unValues, int un_H, int un_W,
     else {
       //unLab = solve_lmp_complete_graph(un_view, pwind_view, pw_view, do_suppression);
 
-      unLab = solve_nl_lmp_complete_graph(un_view, pwind_view, pw_view, method, do_logit_transform, do_suppression);  
+      unLab = solve_nl_lmp_complete_graph(un_view, pwind_view, pw_view, method, do_logit_transform, do_suppression);
     }
   }
   assert(unLab.size() > 0);
 
-  andres::View<uint64_t> result_view = get_view2d<uint64_t>(result, un_H, 2);
+  andres::View<unsigned long long> result_view = get_view2d<unsigned long long>(result, un_H, 2);
   for(int k = 0; k < un_H; ++k)
   {
     result_view(k, 0) = unLab(k, 0);

lib/nms_cython/include/nms_scoremap.hxx

@@ -4,6 +4,8 @@
 
 // #include <numpy/arrayobject.h>
 
+#include <array>
+
 #include <vector>
 
 #include <iostream>
@@ -25,11 +27,11 @@ std::vector<int> nms_grid_cpp(const float *unProbValues, size_t height, size_t w
   // cout << "width: " << width << endl;
     const size_t num_locs = height * width;
 
-    // MA: is this row or column major? 
+    // MA: is this row or column major?
     std::array<size_t, 2> shape;
     shape[0] = grid_height;
     shape[1] = grid_width;
-    andres::View<unsigned char> grid(shape.begin(), shape.end(), gridData); 
+    andres::View<unsigned char> grid(shape.begin(), shape.end(), gridData);
 
     std::vector<int> indices(height*width);
     for(int i = 0; i < num_locs; ++i)
@@ -100,12 +102,12 @@ std::vector<int> nms_grid_cpp_test(float *unProbValues, size_t height, size_t wi
   std::array<size_t, 2> shape;
   shape[0] = height;
   shape[1] = width;
-  andres::View<float> grid(shape.begin(), shape.end(), unProbValues); 
+  andres::View<float> grid(shape.begin(), shape.end(), unProbValues);
 
   std::cout << "Hello fuck! " << height << " " << width << std::endl;
   std::vector<int> result;
   result.push_back(5);
   result.push_back(3);
   result.push_back(1);
   return result;
-}
+}

requirements.txt

@@ -0,0 +1,9 @@
+cython
+easydict
+matplotlib
+munkres
+numpy
+pyyaml
+scikit-image
+scipy
+tensorflow