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# Modified 2016-06-30 by Josiah Olson to add python3 support
# Context manager to generate batches in the background via a process pool
# Usage:
#
# def batch(seed):
#    .... # generate minibatch
#    return minibatch
#
# with BatchGenCM(batch) as bg:
#    minibatch = next(bg)

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from __future__ import absolute_import
from __future__ import print_function
import numpy as np

from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Lambda, merge, BatchNormalization, Activation, Input, Merge
from keras import backend as K


def euclidean_distance(vects):

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import errno
import os
import signal
import time

class TimeoutError(Exception):
    pass

class timeout:
    def __init__(self, seconds=1, error_message='Timeout'):

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# coding: utf-8

# # DressRank traning

# In[ ]:

import mxnet as mx
from mxnet.utils import *
import logging

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#include<opencv2/opencv.hpp>
#include<opencv2/imgproc.hpp>
#include<opencv2/imgcodecs/imgcodecs.hpp>
#include<opencv2/videoio.hpp>
#include<iostream>
#include<vector>

int main(int argc, char *argv[])
{
    cv::Mat frame;

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#include "caffeclassifier.h"

CaffeClassifier::CaffeClassifier(const string& model_file,
                       const string& trained_file,
                       const string& mean_file,
                       const string& label_file,
                       const bool use_GPU,
                       const int batch_size) {
   if (use_GPU)
       Caffe::set_mode(Caffe::GPU);