Catherine tamamolis

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

	import cv2
	import numpy as np


	src = 255 - cv2.imread('/home/stephen/Desktop/I7Ykpbs.jpg',0)
	scores = []

	h,w = src.shape
	small_dimention = min(h,w)
	src = src[:small_dimention, :small_dimention]

	#!/usr/bin/python
	# GoogleMapDownloader.py
	# Created by Hayden Eskriett [http://eskriett.com]
	#
	# A script which when given a longitude, latitude and zoom level downloads a
	# high resolution google map
	# Find the associated blog post at: http://blog.eskriett.com/2013/07/19/downloading-google-maps/

	import urllib.request
	from PIL import Image

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats

	import cv2
	import math
	import numpy as np
	import sys

	def apply_mask(matrix, mask, fill_value):
	masked = np.ma.array(matrix, mask=mask, fill_value=fill_value)
	return masked.filled()

	def apply_threshold(matrix, low_value, high_value):

	#!/usr/bin/python
	# GoogleMapDownloader.py
	# Created by Hayden Eskriett [http://eskriett.com]
	#
	# A script which when given a longitude, latitude and zoom level downloads a
	# high resolution google map
	# Find the associated blog post at: http://blog.eskriett.com/2013/07/19/downloading-google-maps/

	import urllib
	import Image