| N=10000; | |
| d=1e-6; %Diameter of particle | |
| mu=1e-3; %viscosity of water | |
| T=293; %temprature | |
| KB=1.38e-23; %Boltzman const | |
| pi=3.1415; | |
| % drag Coeff -strength of random force exerting on particl | |
| g=3*pi*mu*d; %Drag coef | |
| disp (g); | |
| tt=1000; % Maximum time |
| # order list alphanumerically | |
| # test with "bespin" theme only for now | |
| # add others when things are working | |
| # see https://github.com/chriskempson/base16-schemes-source/blob/master/list.yaml | |
| # | |
| unclaimed: https://github.com/chriskempson/base16-unclaimed-schemes |
| import os | |
| import sys | |
| import json | |
| import zmq | |
| from IPython.zmq.session import Session | |
| from IPython.parallel.util import disambiguate_url | |
| from IPython.utils.path import get_ipython_dir | |
| def main(connection_file): |
| (testenv) root@ae3529a7c667:/# python pimstest.py | |
| (testenv) root@ae3529a7c667:/# |
| 2,1843c2,1882 | |
| < 0,512.0071477195371,4.273995915588836,278.235303351588,2.353972064243239,0.12516142050543072,10.796060102818595,6474.0,0.10214914058912275,0,0 | |
| < 250,24.98458510987209,279.64545752705806,288.7472566095956,2.3530612706411356,0.10643695966985917,11.743082918855315,6550.0,0.08385554779595847,0,1 | |
| < 249,4.898809523809524,280.1579131652661,270.4697162600869,2.3853251162816664,0.047452256482122056,10.322548694800236,6579.0,0.07849174640937663,0,2 | |
| < 248,289.0549597855228,278.4621313672922,282.59160830535694,2.186894305267831,0.15954627644392788,16.194090154227894,6438.0,0.11392144854080957,0,3 | |
| < 247,46.67976164202163,277.12271021849483,429.09603794623735,2.516202187736437,0.03472796038281141,14.868258211776487,6715.0,0.06037953231533099,0,4 | |
| < 246,190.05354227881162,275.36238981390795,290.073088552047,2.438535979063745,0.06413777002817747,10.796060102818595,6484.0,0.09929879452619292,0,5 | |
| < 245,335.90923781308913,273.65634258012386,351.62957159443374,2.2181704391134236,0.04635704635782087,19.035158602338 |
| In [1]: from skimage.io import imread as skimage_imread | |
| In [2]: from matplotlib.pyplot import imread as plt_imread | |
| In [3]: from scipy.ndimage import imread as scipy_imread | |
| In [4]: skimage_imread('bulk_water_000.png') | |
| Out[4]: | |
| array([[[146, 125, 54, 255], | |
| [146, 125, 54, 255], |
Start by running install_opencv.sh to install the correct versions of everything on the Mac.
Then run prepare_example.sh to download materials needed for the facial detection algorithm.
Finally, run python2 facial_detection.py to detect faces and output the bounding box for any faces detected in the image.
Related repositories:
| #!/bin/bash | |
| # | |
| # Download the Large-scale CelebFaces Attributes (CelebA) Dataset | |
| # from their Google Drive link. | |
| # | |
| # CelebA: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html | |
| # | |
| # Google Drive: https://drive.google.com/drive/folders/0B7EVK8r0v71pWEZsZE9oNnFzTm8 | |
| python3 get_drive_file.py 0B7EVK8r0v71pZjFTYXZWM3FlRnM celebA.zip |
| from fuel.datasets import IndexableDataset | |
| from collections import OrderedDict | |
| import numpy | |
| seed = 1234 | |
| rng = numpy.random.RandomState(seed) | |
| # Make some fake data | |
| features = rng.randint(256, size=(8, 2, 2)) | |
| targets = rng.randint(4, size=(8, 1)) |
| import keyboard | |
| import time | |
| # Written for Mac OS X. | |
| # 0x24 = Enter on Mac keyboards | |
| keyboard.write("i") | |
| time.sleep(3) | |
| keyboard.write("Wake up Neo...",delay=0.2) | |
| keyboard.press(0x24) |