pyr-revs

Launch Chainer via AWS Lambda 


console.log('Launch-Chainer: Start');

var ec2Region = 'us-east-1';
var s3Region = 'ap-northeast-1';
var snsRegion = 'ap-northeast-1';

var s3Bucket = 'mybucket';

baraldilorenzo

##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

baraldilorenzo

A Deep Siamese Network for Scene Detection

This is a model from the paper:

A Deep Siamese Network for Scene Detection in Broadcast Videos
Lorenzo Baraldi, Costantino Grana, Rita Cucchiara
Proceedings of the 23rd ACM International Conference on Multimedia, 2015

Please cite the paper if you use the models.

natronics

#!/usr/bin/env sh
ffmpeg -i $1 -vf "transpose=1","crop=2" -an -f image2pipe -vcodec ppm - | montage ppm:- -tile x1 -mode concatenate - | convert - -flop scanimage.jpg

marcuslilja

Server setup for Ubuntu 16.04 on Digital Ocean

The setup installs the following software:

• Nginx
• MySQL
• PHP
• Node
• Composer

Zulko

# IMPORTS: THIS SCRIPT REQUIRES THE PACKAGES PYLAB AND MOVIEPY
# It produces this gif: http://i.imgur.com/LbU55oK.gif

from pylab import *
import moviepy.editor as mp
from moviepy.video.io.bindings import mplfig_to_npimage


# PARAMETERS OF THE CURVE AND THE GIF

Zulko

"""
This creates the following GIF, where the text appears to be "embedded"
in the video and "disappears" behind rapunzel.

http://i.imgur.com/gxEHfLX.gif
"""

from moviepy.editor import *
import numpy as np
import skimage.morphology as skm

dreness

#!/usr/bin/env python
# dre at mac dot com
# Graphical and statistical analysis of frame rate in a video
# Requires ffmpeg and gnuplot


from sys import platform
from subprocess import check_output, DEVNULL
from os import path
import json

Zulko

"""
This script checks the time accuracy of MoviePy.

First, a one-hour video is generated, where the frame
at time t displays t (in seconds, e.g. '1200.50') in white
on a black baground.

Then we ask MoviePy to open this video file, fetch
different times (1200.5, 850.2, 2000.3, 150.25, 150.25),
extract the corresponding frame as a JPEG image file, and

Zulko

import numpy as np

def supersample(clip, d, nframes):
    """ Replaces each frame at time t by the mean of `nframes` equally spaced frames
        taken in the interval [t-d, t+d]. This results in motion blur."""
    def fl(gf, t):
        tt = np.linspace(t-d, t+d, nframes) 
        avg = np.mean(1.0*np.array([gf(t_) for t_ in tt]),axis=0)
        return avg.astype("uint8")
    return clip.fl(fl)