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hellow

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*.log
losp_filter
*.o
*~
.\#*
\#*
*.lsp

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import cv2
import time
import math
import numpy as np

capture = cv2.VideoCapture(0)
print capture.get(cv2.CAP_PROP_FPS)

t = 100
w = 640.0

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FROM ubuntu

RUN apt-get -y update
RUN apt-get -y upgrade
RUN apt-get -y dist-upgrade
RUN apt-get -y autoremove

# 2. INSTALL THE DEPENDENCIES

# Build tools:

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import logging

_FMT = '%(filename)s : %(asctime)s : %(levelname)s : %(message)s'

_log     = logging.getLogger()
_log.setLevel(logging.DEBUG)

debug    = _log.debug
dbug     = _log.debug
info     = _log.info

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'''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

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################ Lispy: Scheme Interpreter in Python

## (c) Peter Norvig, 2010; See http://norvig.com/lispy.html

################ Symbol, Procedure, Env classes

from __future__ import division

Symbol = str

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*.log
*~
*.pyc

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eel-assembler

=======

eel assembler

Install:

npm install eel-assembler