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# This is all 'experimental code'; it loosely follows the instructions from 
# the 'ImageNet classification' and 'Filter visualization' ipynb tutorials from 
# http://caffe.berkeleyvision.org/

import numpy as np
import matplotlib.pyplot as plt


# Make sure that caffe is on the python path:
caffe_root = '../../caffe-master/'

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import os
import cv2
import sys

old_class = 0
truth = 0
w_file = open(sys.argv[1], 'w')
b_file = open(sys.argv[2], 'w')
m_file = open(sys.argv[3], 'w')
with open('test.txt') as f:

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#!/usr/bin/env bash
# Installation script for Deep Learning Libraries on Ubuntu 14.04, by Roelof Pieters (@graphific)
# BSD License

orig_executor="$(whoami)"
if [ "$(whoami)" == "root" ]; then
  echo "running as root, please run as user you want to have stuff installed as"
  exit 1
fi
###################################

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bool ReadCSVToDatum(const string& filename, Datum* datum) {
  // read in the CSV file into a vector
  fstream file(filename.c_str(), ios::in);
  vector<vector<int> > label;
  std::string line;
  while (std::getline(file, line)) {
    // replace commas with spaces
    for (int i = 0; i < line.length(); i++) {
      if (line[i] == ',')
        line[i] = ' ';

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import os
import numpy as np
import random
from PIL import Image
import numpy as np
import h5py
import matplotlib.pyplot as plt

import glob

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import os, sys
import cv2
import argparse
from multiprocessing import Process


def vid_to_images(video_file, output_dir, skip_frames = 0):
    
    print 'Processing video:', video_file
    

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from __future__ import division
import cv2
import cv2.cv as cv
import sys
import pdb

def detect(img, cascade):
        rects = cascade.detectMultiScale(img, scaleFactor=1.1, minNeighbors=3, minSize=(10, 10), flags = cv.CV_HAAR_SCALE_IMAGE)
        if len(rects) == 0:
                return []

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def generateHDF5FromText2(label_num):
	print '\nplease wait...'

	HDF5_FILE = ['hdf5_train.h5', 'hdf5_test1.h5']
        #store the training and testing data path and labels
 	LIST_FILE = ['train.txt','test.txt']
	for kk, list_file in enumerate(LIST_FILE):
	

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name: "MYNET"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224
layer {
  name: "conv1/7x7_s2"
  type: "Convolution"
  bottom: "data"