One Paragraph of project description goes here
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
| from PIL import Image, ImageDraw | |
| import argparse | |
| import sys | |
| def get_colors(image_file, numcolors=10, resize=150): | |
| # Resize image to speed up processing | |
| img = Image.open(image_file) | |
| img = img.copy() | |
| img.thumbnail((resize, resize)) |
| """ | |
| Original Author Ernesto P. Adorio, Ph.D | |
| Original Source: http://my-other-life-as-programmer.blogspot.com/2012/02/python-finding-nearest-matching-color.html | |
| Modifed By: JDiscar | |
| This class maps an RGB value to the nearest color name it can find. Code is modified to include | |
| ImageMagick names and WebColor names. | |
| 1. Modify the minimization criterion to use least sum of squares of the differences. | |
| 2. Provide error checking for input R, G, B values to be within the interval [0, 255]. |
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
Picking the right architecture = Picking the right battles + Managing trade-offs
This is the Keras model of VGG-Face.
It has been obtained through the following method:
Details about the network architecture can be found in the following paper:
| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| from __future__ import print_function | |
| __author__ = 'maxim' | |
| import numpy as np | |
| import gensim | |
| import string |
def getPrimeFactors(n):
factors = []
while n%2==0:
factors.append(2)