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

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 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

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

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