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n_masks = 100
n_channels = 100
# do not sample more masks than this
n_max_samples = 50

target_corr = 0.1
target_corr_eps = 1.0e-2

rate = target_corr

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def read_obj(fname, max_degree=6, dtype=np.float32):
    '''
    Reads a mesh from an obj file. 
    Faces are converted into triangles.
    Arguments:
        fname: path or file-like object
        max_degree: maximum degree for the adjacency
        dtype: type for the vertex array
    Returns:
        (verts, faces)

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# now, let's do tf implementation
def ph_splat(inputs, offsets, weights, nbs):
    N, C = inputs.shape
    F = weights.shape[1] - 1
    M = nbs.shape[0]
    weighted_inputs = tf.matmul(weights[:N,:,tf.newaxis],
                                inputs[:N,tf.newaxis,:])
    weighted_inputs = tf.reshape(weighted_inputs, [-1, C])
    idxs = tf.reshape(offsets[:N,:F+1], [-1,1])+1
    # TODO: the only thing is the unknown shape of M?

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def np_splat(inputs, offsets, weights, nbs):
    N, V = inputs.shape
    F = weights.shape[1] - 1
    M = nbs.shape[0]
    # splatting
    ## compute inputs multiplied by the weights
    weighted_inputs = np.matmul(weights[:N,:,np.newaxis], 
                                inputs[:N,np.newaxis,:])
    weighted_inputs = weighted_inputs.reshape([-1, V])
    ## sum up at corresponding indices (update with duplicatess)

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def mmul(*tensors):
    return tf.foldl(tf.matmul, tensors)

def msym(X):
    return (X + tf.matrix_transpose(X)) / 2

def mdiag(X):
    return tf.matrix_diag(tf.matrix_diag_part(X))

@tf.RegisterGradient('Svd')

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w = w.transpose([2, 3, 1, 0])
w = w[::-1,::-1,:,:]

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import threading
import numpy as np
import tensorflow as tf

class FeedingRunner(object):
  """Takes care of feeding/dequeueing data into the queue
  Based on tf.train.QueueRunner
  """

  def __init__(self, generator, dtypes, shapes, names, num_threads,

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def instance_to_regression_map(instances, cids):
  """Convert instance label map to the regression map
  Args:
    instances: instance label mask
    cids: ids of classes to load
  """
  # TODO: for all the classes that have instances, we can compute this
  image_size = instances.shape[:2]
  reg = np.zeros(image_size + (4,), dtype=np.uint16)
  # instead of this, we can simply ???

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def _next_instance(mask, iid, instances):
  """Process single instance and add it to the mask
  Args:
    mask: source mask
    iid: instance id
    instances: instance segmentation mask
  Returns:
    updated mask
  """
  yx = tf.to_int32(tf.where(tf.equal(instances, iid)))

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class ImageCoder(object):
  """Helper class for handling images in TensorFlow."""

  def __init__(self, channels=3, config=None):
    # Create a single TensorFlow Session for all image decoding calls.
    self._sess = tf.Session(config=config)

    # TensorFlow ops for JPEG decoding.
    self._src_png = tf.placeholder(dtype=tf.string)
    self._dst_raw = tf.image.decode_png(self._src_png, channels=channels)