GLib integration for the Python asyncio event loop

As an evening project, I’ve been working on a small library that integrates the GLib main loop with Python’s asyncio. I think I’ve gotten to the point where it might be useful to other people, so have pushed it up here:

https://github.com/jhenstridge/asyncio-glib

This isn’t the only attempt to integrate the two event loops, but the other I found (Gbulb) is unmaintained and seems to reimplement a fair bit of the asyncio (e.g. it has its own transport classes). So I thought I’d see if I could write something smaller and more maintainable, reusing as much code from the standard library as possible.

My first step was writing an implementation of the selectors.BaseSelector interface in terms of the GLib main loop. The select() method just runs a GMainLoop with a custom source that will quit the loop if any of the file descriptors are ready, or the timeout is reached.

For the asyncio event loop, I was able to reuse the standard library asyncio.SelectorEventLoop with my new selector. In action, it looks something like this:

  1. Let the GMainLoop spin until any asyncio events come in.
  2. Return control to the asyncio event loop to process those events.
  3. Repeat

As far as testing goes, the Python standard library comes with a suite of tests parameterised on an event loop implementation. So I’ve just reused that as the bulk of my test suite, and done the same with the selector tests. There are a handful of test failures I still need to diagnose, but for the most part things just work.

Making an asyncio application use this event loop is simple:

import asyncio
import asyncio_glib
asyncio.set_event_loop_policy(asyncio_glib.GLibEventLoopPolicy())

The main limitation of this code is that it relies on asyncio running the GLib main loop. If some other piece of code runs the main loop, asyncio callbacks will not be triggered and will probably lead to busy looping. This isn’t a problem my project (an asyncio server making use of GStreamer), but would be a problem for e.g. a graphical application calling gtk_dialog_run().

ThinkPad Infrared Camera

One of the options available when configuring the my ThinkPad was an Infrared camera. The main selling point being “Windows Hello” facial recognition based login. While I wasn’t planning on keeping Windows on the system, I was curious to see what I could do with it under Linux. Hopefully this is of use to anyone else trying to get it to work.

The camera is manufactured by Chicony Electronics (probably a CKFGE03 or similar), and shows up as two USB devices:

04f2:b5ce Integrated Camera
04f2:b5cf Integrated IR Camera

Both devices are bound by the uvcvideo driver, showing up as separate video4linux devices. Interestingly, the IR camera seems to be assigned /dev/video0, so generally gets picked by apps in preference to the colour camera. Unfortunately, the image it produces comes up garbled:

So it wasn’t going to be quite so easy to get things working. Looking at the advertised capture modes, the camera supports Motion-JPEG and YUYV raw mode. So I tried capturing a few JPEG frames with the following GStreamer pipeline:

gst-launch-1.0 v4l2src device=/dev/video0 num-buffers=10 ! image/jpeg ! multifilesink location="frame-%02d.jpg"

Unlike in raw mode, the red illumination LEDs started flashing when in JPEG mode, which resulted in frames having alternating exposures. Here’s one of the better exposures:

What is interesting is that the JPEG frames have a different aspect ratio to the raw version: a more normal 640×480 rather than 400×480. So to start, I captured a few raw frames:

gst-launch-1.0 v4l2src device=/dev/video0 num-buffers=10 ! "video/x-raw,format=(string)YUY2" ! multifilesink location="frame-%02d.raw"

The illumination LEDs stayed on constantly while recording in raw mode. The contents of the raw frames show something strange:

00000000  11 48 30 c1 04 13 44 20  81 04 13 4c 20 41 04 13  |.H0...D ...L A..|
00000010  40 10 41 04 11 40 10 81  04 11 44 00 81 04 12 40  |@.A..@....D....@|
00000020  00 c1 04 11 50 10 81 04  12 4c 10 81 03 11 44 00  |....P....L....D.|
00000030  41 04 10 48 30 01 04 11  40 10 01 04 11 40 10 81  |A..H0...@....@..|
...

The advertised YUYV format encodes two pixels in four bytes, so you would expect any repeating patterns to occur at a period of four bytes. But the data in these frames seems to repeat at a period of five bytes.

Looking closer it is actually repeating at a period of 10 bits, or four packed values for every five bytes. Furthermore, the 800 byte rows work out to 640 pixels when interpreted as packed 10 bit values (rather than the advertised 400 pixels), which matches the dimensions of the JPEG mode.

The following Python code can unpack the 10-bit pixel values:

def unpack(data):
    result = []
    for i in range(0, len(data), 5):
        block = (data[i] |
                 data[i+1] << 8 |
                 data[i+2] << 16 |
                 data[i+3] << 24 |
                 data[i+4] << 32)
        result.append((block >> 0) & 0x3ff)
        result.append((block >> 10) & 0x3ff)
        result.append((block >> 20) & 0x3ff)
        result.append((block >> 30) & 0x3ff)
    return result

After adjusting the brightness while converting to 8-bit greyscale, I get a usable image. Compare a fake YUYV frame with the decoded version:

I suppose this logic could be wrapped up in a GStreamer element to get usable infrared video capture.

I’m still not clear why the camera would lie about the pixel format it produces. My best guess is that they wanted to use the standard USB Video Class driver on Windows, and this let them get at the raw data to process in user space.