So, I started messing around with this thing I called ava_k. Wasn’t really a grand plan, just had a bunch of video files piling up and thought maybe I could do something useful with them. Mostly just wanted to see if I could automatically find interesting parts in long, boring footage.
First step, I grabbed some code someone else wrote, something open source. Looked promising, you know? But getting it to actually run was the first hurdle. Took me a good while just sorting out the setup, fighting with libraries and versions. Always the same old story.
Once I got past the initial setup crashes, I fed it a small test video. It did… something. It picked out a few scene cuts, but it completely missed the parts I actually cared about. The default settings were clearly not cutting it for the kind of videos I had.
So, I rolled up my sleeves and started digging into the parameters. Had to figure out what each knob did. This was slow going. Change a number, run the process, check the output, see it fail, change another number. Felt like banging my head against the wall sometimes. Spent hours just tweaking and re-running.
After getting frustrated with that initial tool, I thought, maybe I need a simpler approach. What if I just looked for obvious things? Like sudden loud noises in the audio, or lots of movement in the picture? Seemed easier than trying to understand complex scene analysis.
I decided to combine these ideas. Had to cobble together a few small scripts myself. Nothing complicated, just stuff to handle the files and process the data.
My workflow kind of ended up like this:
- Get the video file ready.
- Use standard tools, like ffmpeg, to rip the audio out.
- Run a simple script I wrote to find the loud parts in the audio track. Basically just looking for spikes.
- Separately, run another basic script to detect general motion in the video frames. Again, nothing fancy, just checking pixel differences.
- Then, match them up. Look for timestamps where I had both a loud sound AND significant motion happening around the same time.
This actually started working better! It began flagging moments that were closer to what I considered “interesting”. Still got it wrong plenty of times, of course. A loud noise doesn’t always mean something important happened. Lots of false alarms.
But hey, it was progress. Made by me, just trying things out. That felt pretty good. The whole ava_k experiment became less about the final perfect outcome and more about the learning process. Figuring out how to handle video, how to connect different simple tools together to get something done.
It’s still pretty crude, honestly. Just a collection of scripts on my computer. Don’t know if I’ll ever polish it up properly. But that was the journey, just tinkering and seeing what worked. That was ava_k for me.
