okiriza

import colorlover as cl
from   plotly import graph_objs as go
from   plotly import offline
from   sklearn.decomposition import PCA
import torch
from   torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from   torchvision import datasets, transforms

okiriza

import random

import torch
from   torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from   torchvision import datasets, transforms

okiriza

from keras.applications.resnet50 import ResNet50, preprocess_input
from keras.preprocessing import image
import numpy as np


resnet = ResNet50(include_top=False)

def extract_features(img_paths, batch_size=64):
    """ This function extracts image features for each image in img_paths using ResNet50 bottleneck layer.
        Returned features is a numpy array with shape (len(img_paths), 2048).

okiriza

from __future__ import print_function

import os
import time

import boto3
from boto3 import dynamodb
import cv2
import numpy as np
import scipy.misc

okiriza

from datetime import datetime, timedelta
import urllib2
from urllib2 import URLError

import boto3


START_BYTE = b'\xff\xd8'
END_BYTE = b'\xff\xd9'
ITER_LIMIT = 10000