Category Archives: Python

FFmpeg and Code Coverage Tools

Code coverage tools likely occupy the same niche as profiling tools: Tools that you’re supposed to use somewhere during the software engineering process but probably never quite get around to it, usually because you’re too busy adding features or fixing bugs. But there may come a day when you wish to learn how much of your code is actually being exercised in normal production use. For example, the team charged with continuously testing the FFmpeg project, would be curious to know how much code is being exercised, especially since many of the FATE test specs explicitly claim to be “exercising XYZ subsystem”.

The primary GNU code coverage tool is called gcov and is probably already on your GNU-based development system. I set out to determine how much FFmpeg source code is exercised while running the full FATE suite. I ran into some problems when trying to use gcov on a project-wide scale. I spackled around those holes with some very ad-hoc solutions. I’m sure I was just overlooking some more obvious solutions about which you all will be happy to enlighten me.

Results
I’ve learned to cut to the chase earlier in blog posts (results first, methods second). With that, here are the results I produced from this experiment. This Google spreadsheet contains 3 sheets: The first contains code coverage stats for a bunch of FFmpeg C files sorted first by percent coverage (ascending), then by number of lines (descending), thus highlighting which files have the most uncovered code (ffserver.c currently tops that chart). The second sheet has files for which no stats were generated. The third sheet has “problems”. These files were rejected by my ad-hoc script.

Here’s a link to the data in CSV if you want to play with it yourself.

Using gcov with FFmpeg Continue reading →

Brute Force Dimensional Analysis

I was poking at the data files of a really bad (is there any other kind?) interactive movie video game known simply by one letter: D. The Sega Saturn version of the game is comprised primarily of Sega FILM/CPK files, about which I wrote the book. The second most prolific file type bears the extension ‘.dg2’. Cursory examination of sample files revealed an apparently headerless format. Many of the video files are 288×144 in resolution. Multiplying that width by that height and then doubling it (as in, 2 bytes/pixel) yields 82944, which happens to be the size of a number of these DG2 files. Now, if only I had a tool that could take a suspected raw RGB file and convert it to a more standard image format.

Here’s the FFmpeg conversion recipe I used:

 ffmpeg -f rawvideo -pix_fmt rgb555 -s 288x144 -i raw_file -y output.png

So that covers the files that are suspected to be 288×144 in dimension. But what about other file sizes? My brute force approach was to try all possible dimensions that would yield a particular file size. The Python code for performing this operation is listed at the end of this post.

It’s interesting to view the progression as the script compresses to different sizes:



That ‘D’ is supposed to be red. So right away, we see that rgb555(le) is not the correct input format. Annoyingly, FFmpeg cannot handle rgb5[5|6]5be as a raw input format. But this little project worked well enough as a proof of concept.

If you want to toy around with these files (and I know you do), I have uploaded a selection at: http://multimedia.cx/dg2/.

Here is my quick Python script for converting one of these files to every acceptable resolution.

work-out-resolution.py:
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Multiprocess FATE Revisited

I thought I had brainstormed a simple, elegant, multithreaded, deadlock-free refactoring for FATE in a previous post. However, I sort of glossed over the test ordering logic which I had not yet prototyped. The grim, possibly deadlock-afflicted reality is that the main thread needs to be notified as tests are completed. So, the main thread sends test specs through a queue to be executed by n tester threads and those threads send results to a results aggregator thread. Additionally, the results aggregator will need to send completed test IDs back to the main thread.



But when I step back and look at the graph, I can’t rationalize why there should be a separate results aggregator thread. That was added to cut down on deadlock possibilities since the main thread and the tester threads would not be waiting for data from each other. Now that I’ve come to terms with the fact that the main and the testers need to exchange data in realtime, I think I can safely eliminate the result thread. Adding more threads is not the best way to guard against race conditions and deadlocks. Ask xine.



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Monster Battery Power Revisited

So I have this new fat netbook battery and I performed an experiment to determine how long it really lasts. In my last post on the matter, it was suggested that I should rely on the information that gnome-power-manager is giving me. However, I have rarely seen GPM report more than about 2 hours of charge; even on a full battery, it only reports 3h25m when I profiled it as lasting over 5 hours in my typical use. So I started digging to understand how GPM gets its numbers and determine if, perhaps, it’s not getting accurate data from the system.

I started poking around /proc for the data I wanted. You can learn a lot in /proc as long as you know the right question to ask. I had to remember what the power subsystem is called — ACPI — and this led me to /proc/acpi/battery/BAT0/state which has data such as:

present:                 yes
capacity state:          ok
charging state:          charged
present rate:            unknown
remaining capacity:      100 mAh
present voltage:         8326 mV

“Remaining capacity” rated in mAh is a little odd; I would later determine that this should actually be expressed as a percentage (i.e., 100% charge at the time of this reading). Examining the GPM source code, it seems to determine as a function of the current CPU load (queried via /proc/stat) and the battery state queried via a facility called devicekit. I couldn’t immediately find any source code to the latter but I was able to install a utility called ‘devkit-power’. Mostly, it appears to rehash data already found in the above /proc file.

Curiously, the file /proc/acpi/battery/BAT0/info, which displays essential information about the battery, reports the design capacity of my battery as only 4400 mAh which is true for the original battery; the new monster battery is supposed to be 10400 mAh. I can imagine that all of these data points could be conspiring to under-report my remaining battery life.

Science project: Repeat the previous power-related science project but also parse and track the remaining capacity and present voltage fields from the battery state proc file.

Let’s skip straight to the results (which are consistent with my last set of results in terms of longevity):



So there is definitely something strange going on with the reporting– the 4400 mAh battery reports discharge at a linear rate while the 10400 mAh battery reports precipitous dropoff after 60%.

Another curious item is that my script broke at first when there was 20% power remaining which, as you can imagine, is a really annoying time to discover such a bug. At that point, the “time to empty” reported by devkit-power jumped from 0 seconds to 20 hours (the first state change observed for that field).

Here’s my script, this time elevated from Bash script to Python. It requires xdotool and devkit-power to be installed (both should be available in the package manager for a distro).
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