psycharo-zz

import threading

def _int64_feature(value):
  return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))

def _bytes_feature(value):
  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))

def _convert_example(rgb_path, label_path):
#   rgb_png = tf.gfile.GFile(rgb_path, 'rb').read()

psycharo-zz

def pdist(X):
  """
  Computes pairwise distance between each pair of points
  Args:
    X - [N,D] matrix representing N D-dimensional vectors 
  Returns:
    [N,N] matrix of (squared) Euclidean distances
  """
  x2 = tf.reduce_sum(X * X, 1, True)
  return x2 - 2 * tf.matmul(X, tf.transpose(X)) + tf.transpose(x2)

psycharo-zz

// register a callback when the IPython.notebook instance is created.
$([IPython.events]).on('app_initialized.NotebookApp', function(){
	function to(mode) {
		// this can be either 'vim' or 'emacs'
		var mode = mode || 'emacs';
		// first let's apply mode to all current cells
		function to_mode(c) { return c.code_mirror.setOption('keyMap', mode);};
		var cells = IPython.notebook.get_cells();
		if (cells != null) {

psycharo-zz

import numpy as np

def softmax(x, axis=None):
  e_x = np.exp(x - np.max(x, axis=axis, keepdims=True))
  return e_x / np.sum(e_x, axis=axis, keepdims=True)

psycharo-zz

vector<cv::Mat> gradient_histogram(const cv::Mat &M, const cv::Mat &O, int num_channels)
{
  vector<cv::Mat> channels;
  double step = 2 * M_PI / num_channels;
  for (int t = 0; t < num_channels; ++t)
  {
    cv::Mat C;
    cv::inRange(O, t*step, t*step+1, C);
    C.convertTo(C, CV_64F);
    C /= 255.0;

psycharo-zz

inline arma::mat integral_image(const arma::mat &a)
{
  using namespace arma;
  mat a_ii = zeros(a.n_rows+1, a.n_cols+1);
  for (size_t j = 0; j < a.n_rows; ++j)
  {
    double sum = 0.0;
    for (size_t i = 0; i < a.n_cols; ++i)
    {
      sum += a(j,i);

psycharo-zz

    map<double, size_t> top;
    size_t max_size = 100;

    auto top_add = [&top,max_size](double value, size_t id)
    {
        if (top.size() < max_size)
            top.insert(make_pair(value, id));
        else
        {
            if (top.begin()->first < value)

psycharo-zz

def loadmat(filename):
    '''
    this function should be called instead of direct spio.loadmat
    as it cures the problem of not properly recovering python dictionaries
    from mat files. It calls the function check keys to cure all entries
    which are still mat-objects
    '''
    data = sio.loadmat(filename, struct_as_record=False, squeeze_me=True)
    return _check_keys(data)

psycharo-zz

def read_seq(path):
    
    def read_header(ifile):
        feed = ifile.read(4)
        norpix = ifile.read(24)
        version = struct.unpack('@i', ifile.read(4))
        length = struct.unpack('@i', ifile.read(4))
        assert(length != 1024)
        descr = ifile.read(512)
        params = [struct.unpack('@i', ifile.read(4))[0] for i in range(0,9)]

psycharo-zz

string string_format(const string fmt, ...)
{
    int size = 512;
    std::string str;
    va_list ap;
    while (1) {
        str.resize(size);
        va_start(ap, fmt);
        int n = vsnprintf((char *)str.c_str(), size, fmt.c_str(), ap);