“We could do with more experimentation around how objects work, but that’s impossible in the languages most commonly thought of as object-oriented.
Here, then, is a (very) brief run through the inner workings of objects in four very dynamic languages. I don’t think I really appreciated objects until I’d spent some time with Python, and I hope this can help someone else whet their own appetite.”
Micropub is an open API standard to create posts on one's own domain using third-party clients and currently a W3C Candidate Recommendation. One of the (semi-) recent additions is the idea of a Micropub Media Endpoint. The Media Endpoint provides a way for Micropub clients to upload media files to a Micropub service, receiving a URL that is sent along in place of the file contents when the post is published.
Some of the things I like about Micropub media endpoints include:
Personally, I wanted a Micropub media endpoint server with a few extra properties:
My extra features above essentially describe a content-addressable storage storage system. CAS is a way of storing and accessing data based on some property of the actual content, rather than (potentially arbitrary) files and folders.
HashFS is a Python implementation of a content-addressable file management system. You give it files, it will put them in a directory structure based on a cryptographic hash function of the contents of that file. In other words - HashFS can take any file and give back a unique path to that file which will never change (if you later upload a new version of the file, it gets a different path).
Spano is a Micropub Media Endpoint server written in Python via the Flask framework which combines Flask-HashFS for file storage with Flask-IndieAuth (introduced earlier) to handle authentication and authorization.
Spano is a server-side web app that basically does one thing: it accepts HTTP POST requests with a valid IndieAuth token and a file named "file", stores that file, and returns a URL to that file. The task of serving uploaded files is left to a dedicated web server like nginx or Apache.
Once Spano has been set up and configured for your domain, uploading is a matter of getting a valid IndieAuth token. IndieAuth-enabled Micropub clients will do this automatically. For testing by hand I like to log in to Quill and copy the access token from the Quill settings page. With token in hand, uploads are as easy as:
curl -D - -F "email@example.com" \ -H"Authorization: Bearer xxxx..." \ https://media.example.com/micropub/
Which should output a response like:
HTTP/1.1 100 Continue HTTP/1.0 201 CREATED Content-Type: text/html; charset=utf-8 Content-Length: 108 Location: https://media.example.com/cc/a5/97/7c/2004..2cb.jpg Server: Werkzeug/0.11.4 Python/2.7.11 Date: Thu, 26 Jan 2017 02:40:05 GMT File created: https://media.example.com/cc/a5/97/7c/2004..2cb.jpg
If you want Micropub clients to use Spano as your Media Endpoint, you need to advertise it. This is handled by your "main" Micropub server using discovery. Essentially, a client will make a configuration request to your server like so:
And your server's response should be a JSON-formatted object specifying the "media-endpoint". A bare minimum example:
In addition to advertising the media-endpoint, your Micropub server must be able to handle lists of URLs in places where it would normally expect a file.
For example, when posting a photo from Quill without a media endpoint, your Micropub server will receive a multipart/form-data encoded file named "photo". When posting from Quill with a media endpoint, your Micropub server will instead receive a list of URLs represented as "photo=https://media.example.com/cc/...2cb.jpg". Presumably this pattern would hold for other media types such as video and audio, if you are using Micropub clients that support them.
This particular step has been an interesting challenge for my site, which is a static site generated by Jekyll. My previous Micropub file-handling implementation expected all uploaded assets to live on disk next to the post files, and updating my Jekyll theme and plugins to handle the change is a work in progress. I eventually plan to move all my uploads out of the source for my project in favor of storing them with Spano.
Spano is probably my second public Python project, so I'd love feedback! If you try it out and run into issues, please drop me a line on GitHub. Or you can find me in the #indieweb chat on freenode IRC.
I'd also like to thank Kyle Mahan for his Woodwind Flask server application, which inspired the structure of Spano.
One of the things I’ve been longing to do with my mobile photo-sharing site Camura is to offer image annotations, like objects and faces. Over the last couple of years I have been increasingly frustrated by the appearance of face tagging on services like Facebook, and the recent addition of face recognition to iPhoto has brought this frustration to the surface once again. I don’t even want to do something as complex as face recognition - I just want to find faces in an image.
Googling for things like “open source face detector” doesn’t come up with much. The landscape seems to be comprised of mostly expensive for-pay libraries written for Windows, abandoned research projects, and lots of research papers full of equations – but no code that I could get to run.
To make a long post short, it turns out that Intel’s OpenCV computer vision library comes with a face detector example that should work out of the box. Better yet, there are now some decent Python bindings for OpenCV that come pre-packaged with OpenCV for Ubuntu and Debian. You can install them with:
$ sudo apt-get install python-opencv
Now, it seems that most OpenCV face detector examples are meant to be run “live”, usually taking the image from a webcam and highlighting faces with a red box in real-time. However, I have a large database of static images that I want to consider individually, and I simply want to save the face coordinates for later use, rather than altering the picture.
So, with a bit more Googling, I found a Python script that I could chop up and use for this purpose, and here is what I came up with:
An example run of the script looks something like this:
$ python face_detect.py marty_mcguire.jpg [(50,36) -> (115,101)]
You can overlay that rectangle on an output image with ImageMagick’s “convert”:
$ convert marty_mcguire.jpg -stroke red -fill none -draw "rectangle 50,36 115,101" output.jpg
And the output might look something like this:
Pretty fun stuff!