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#define OCTAVE 1 //   // Group buckets into octaves
#define OCT_NORM 0 // Don't normalise octave intensities by number of bins
#define FHT_N 256 // set to 256 point fht
#include <FHT.h> // include the library

int noise[] = {90, 125, 147, 143, 128, 123, 104, 89}; //just test output without in silence, and use values from ocatve bins
//int noise[] = {90, 85, 147, 143, 128, 123, 104, 89}; //just test output without in silence, and use values from ocatve bins

// leds

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http://www.codeproject.com/Articles/42067/2D-Circle-Packing-algorithm-ported-to-C
http://stackoverflow.com/questions/3851668/position-n-circles-of-different-radii-inside-a-larger-circle-without-overlapping

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package com.example.app;

import android.content.Intent;
import android.net.Uri;

import android.provider.Settings;
import android.content.pm.PackageManager;
import android.os.Build;
import android.os.Bundle;

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import logging
import math
import numpy as np
from scipy import spatial
import cv2

# ============================================================================

CAR_COLOURS = [ (0,0,255), (0,106,255), (0,216,255), (0,255,182), (0,255,76)
    , (144,255,0), (255,255,0), (255,148,0), (255,0,178), (220,0,255) ]

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import sys
sys.path.append("../libs")
import tfutils
import numpy as np
import tensorflow as tf
import tensorflow.contrib.graph_editor as ge

##########################################################################
VGG_MEAN = [103.939, 116.779, 123.68]

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    # old
    conv1 = _conv_layer(image, 32, 9, 1)
    conv2 = _conv_layer(conv1, 64, 3, 2)
    conv3 = _conv_layer(conv2, 128, 3, 2)
    resid1 = _residual_block(conv3, 3)
    resid2 = _residual_block(resid1, 3)
    resid3 = _residual_block(resid2, 3)
    resid4 = _residual_block(resid3, 3)
    resid5 = _residual_block(resid4, 3)
    conv_t1 = _conv_tranpose_layer(resid5, 64, 3, 2)

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from ctypes import *
import os
import random
import numpy as np
import skvideo.io

with open('names.txt') as fp:
    NAMES = {i: name.strip() for i, name in enumerate(fp)}

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import os
import logging
import logging.handlers
import random

import numpy as np
import skvideo.io
import cv2
import matplotlib.pyplot as plt

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def filter_mask(img):

    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2, 2))

    # Fill any small holes
    closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
    # Remove noise
    opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel)

    # Dilate to merge adjacent blobs

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def get_centroid(x, y, w, h):
    x1 = int(w / 2)
    y1 = int(h / 2)

    cx = x + x1
    cy = y + y1

    return (cx, cy)

def detect_vehicles(fg_mask, min_contour_width=35, min_contour_height=35):