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Libraries for a modern geospatial workflow

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• Publishing python libraries

Numpy

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import os
from optparse import OptionParser
import cPickle
import skimage
import numpy as np
from scipy import misc
from distutils.dir_util import mkpath

def loadImage(imgpath):
    try:

westamine

Freshly brewed !

With the availability of huge amount of data for research and powerfull machines to run your code on, Machine Learning and Neural Networks is gaining their foot again and impacting us more than ever in our everyday lives. With huge players like Google opensourcing part of their Machine Learning systems like the TensorFlow software library for numerical computation, there are many options for someone interested in starting off with Machine Learning/Neural Nets to choose from. Caffe, a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and its contributors, comes to the play with a fresh cup of coffee.

Installation Instructions (Ubuntu 14 Trusty)

The following section is divided in to two parts. Caffe's documentation suggest

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import numpy as np

def makeGaussian(size, fwhm = 3, center=None):
    """ Make a square gaussian kernel.

    size is the length of a side of the square
    fwhm is full-width-half-maximum, which
    can be thought of as an effective radius.
    """

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name	caffemodel	caffemodel_url	license	sha1	caffe_commit
Vanilla CNN Model	vanillaCNN.caffemodel	https://github.com/ishay2b/VanillaCNN/blob/master/ZOO/vanillaCNN.caffemodel?raw=true	unrestricted	b5e34ce75d078025e07452cb47e65d198fe27912	9c9f94e18a8909580a6b94c44dbb1e46f0ee8eb8

Implementation of the Vanilla CNN described in the paper: Yue Wu and Tal Hassner, "Facial Landmark Detection with Tweaked Convolutional Neural Networks", arXiv preprint arXiv:1511.04031, 12 Nov. 2015. See project page for more information about this project.

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#!/usr/bin/env python
import numpy
from numpy.distutils.system_info import get_info
import sys
import timeit

print("version: %s" % numpy.__version__)
print("maxint:  %i\n" % sys.maxsize)

info = get_info('blas_opt')

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import os
import numpy as np
import scipy.ndimage as ndimage
import matplotlib
import matplotlib.pyplot as plt

def frac_eq_to(image, value=0):
    return (image == value).sum() / float(np.prod(image.shape))

def extract_patches(image, patchshape, overlap_allowed=0.5, cropvalue=None,