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import numpy as np
import scipy.optimize

def fitPLaneLTSQ(XYZ):
    # Fits a plane to a point cloud, 
    # Where Z = aX + bY + c        ----Eqn #1
    # Rearanging Eqn1: aX + bY -Z +c =0
    # Gives normal (a,b,-1)
    # Normal = (a,b,-1)
    [rows,cols] = XYZ.shape

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import numpy as np
import scipy.optimize

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.gca(projection='3d')

def fitPlaneLTSQ(XYZ):

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import numpy as np
from sklearn.neighbors import NearestNeighbors


def chamfer_distance(x, y, metric='l2', direction='bi'):
    """Chamfer distance between two point clouds

    Parameters
    ----------
    x: numpy array [n_points_x, n_dims]

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    def rename(self, sess=None, ckpt_path=None, step=None):
        # if not sess:
        #     raise AttributeError('TensorFlow session not provided.')
        # print(self.vars)
        new_vars = {}
        for k,v in self.vars.items():
            new_vars[k.replace('meshnet','meshnetmvp2m')] = v
        
        saver = tf.train.Saver(new_vars, max_to_keep=0)

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#!/usr/bin/env python
import sys
from termcolor import colored, cprint

with open('.ssh/config', 'r') as f:
    for line in f:
        l = line.strip()

        #print('L', l)
        if l.startswith('Host '):

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import numpy as np
from skimage import measure
vox = np.load('prediction.npy')

vox2 = np.pad(vox, ((1,1),(1,1),(1,1)),'constant', constant_values=0)
verts, faces, normals, values = measure.marching_cubes_lewiner(vox2, 0.0)

with open('3d.obj','w') as d:
    for v in verts:
        d.write('v {} {} {}\n'.format(v[0],v[1],v[2]))

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import numpy as np
import OpenEXR as exr
import Imath

def readEXR(filename):
    """Read RGB + Depth data from EXR image file.

    Parameters
    ----------
    filename : str

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nvcc = /home/wc/lib/cuda-8.0/bin/nvcc
cudalib = /home/wc/lib/cuda-8.0/lib64
tensorflow = /home/wc/anaconda3/envs/mesh/lib/python2.7/site-packages/tensorflow/include

all: tf_approxmatch_so.so tf_approxmatch_g.cu.o tf_nndistance_so.so tf_nndistance_g.cu.o


tf_approxmatch_so.so: tf_approxmatch_g.cu.o tf_approxmatch.cpp
    g++ -std=c++11 tf_approxmatch.cpp tf_approxmatch_g.cu.o -o tf_approxmatch_so.so -shared -fPIC -I $(tensorflow) -lcudart -L $(cudalib) -O2 -D_GLIBCXX_USE_C

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name: "ResNet-50"

layer {
  name: "data"
  type: "Python"
  top: "data"
  python_param{
    module: "python_data_layer"
    layer: "AdaptAttributeLayer"
  }

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#!/usr/bin/env bash

cd ../lib/nndistance
CUDA_ROOT=/home/wc/lib/cuda
TF_INC=$(python -c 'import tensorflow as tf; print(tf.sysconfig.get_include())')
TF_LIB=$(python -c 'import tensorflow as tf; print(tf.sysconfig.get_lib())')

nvcc -std=c++11 -c -o tf_nndistance_g.cu.o tf_nndistance_g.cu \
-I $TF_INC -I$TF_INC/external/nsync/public -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC