jonathanmoore

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bitsgalore

#! /usr/bin/env python
import time
import threading
import logging
try:
    import tkinter as tk # Python 3.x
    import tkinter.scrolledtext as ScrolledText
except ImportError:
    import Tkinter as tk # Python 2.x
    import ScrolledText

mikoim

Parameters

Container: MP4

Parameter	YouTube recommends setting
-movflags faststart	moov atom at the front of the file (Fast Start)

Video codec: H.264

bennylope

How to Add a Watermark to Video

FFMPEG filters provide a powerful way to programmatically enhance or alter videos, and it’s fairly simple to add a watermark to a video using the overlay filter. The easiest way to install ffmpeg is to download a pre-built binary for your specific platform. Then you don’t have to worry about including and installing all the right dependencies and codecs you will be using.

Once you have ffmpeg installed, adding a watermark is as easy as passing your existing source through an overlay filter like so:

ffmpeg -i test.mp4 -i watermark.png -filter_complex "overlay=10:10" test1.mp4

Basically, we’re passing in the original video, and an overlay image as inputs, then passing it through the filter, and saving the output as test1.mp4.

dannguyen

Using Python 3 + Google Cloud Vision API's OCR to extract text from photos and scanned documents

Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.

The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.

On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:

####### 1. A low-resolution photo of road signs

loderunner

Profiling an application in Mac OS X

Finding which process to profile

If your system is running slowly, perhaps a process is using too much CPU time and won't let other processes run smoothly. To find out which processes are taking up a lot of CPU time, you can use Apple's Activity Monitor.

The CPU pane shows how processes are affecting CPU (processor) activity:

moshekaplan

#!/usr/bin/env python

# Built-in modules
import logging
import Tkinter
import threading

class TextHandler(logging.Handler):
    """This class allows you to log to a Tkinter Text or ScrolledText widget"""
    def __init__(self, text):

jmandel

Proposal: Triggered Notifications

Use case: subscribing to specific lab observations

FHIR offers a REST API that lets clients search for resources on demand. Separately, there is a Messaging API that allows notifications to be "pushed" from one place to another. But neither API provides a clean solution to a common set of real-world "triggering" or notification-type requirements.

For example, let's say Mt. Auburn Hospital's Mother and Infant Unit wants to

acdha

#!/usr/bin/env python
# encoding: utf-8

from pprint import pformat, pprint
import logging


class PasswordMaskingFilter(logging.Filter):
    """Demonstrate how to filter sensitive data:"""

jbenet

a simple git branching model (written in 2013)

This is a very simple git workflow. It (and variants) is in use by many people. I settled on it after using it very effectively at Athena. GitHub does something similar; Zach Holman mentioned it in this talk.

Update: Woah, thanks for all the attention. Didn't expect this simple rant to get popular.