Bugzilla

:nalexander and I discussed this and came up with the following next steps forward:

1. We need a proper docker image for running `visualmetrics.py`. The POC that Nick used to demo this was a full image meant for driving a browser vehicle. We only really need Python, the Python libraries that `visualmetrics.py` requires, and `imagemagick`.

2. We need infrastructure to make `visualmetrics.py` available to the docker image. The POC did a full m-c checkout and ran `./mach browsertime -setup` to download it, but that is a lot of time and bandwidth to grab the script. Ideally we could grab it from https://github.com/browsertime at tagged releases, but for now hard-coding a known good commit SHA into the script will be sufficient.

3. `visualmetrics.py` needs to be parallelized to support processing multiple videos at once. The calculations on each frame are independent of each other and only need to be collated at the end. The [`multiprocessing`](https://docs.python.org/2/library/multiprocessing.html) module can be leveraged to implement this.

4. The actual taskcluster task to run this and report the results to perfherder.

Further work would include having a way to submit the captured video from e.g. a capture card and do the visual metrics processing of that video (e.g. a ./mach command to upload the video and create the task).

Did I miss anything, :nalexander?

:nalexander and I discussed this and came up with the following next steps forward:

1. We need a proper docker image for running `visualmetrics.py`. The POC that Nick used to demo this was a full image meant for driving a browser vehicle. We only really need Python, the Python libraries that `visualmetrics.py` requires, and `imagemagick`.

2. We need infrastructure to make `visualmetrics.py` available to the docker image. The POC did a full m-c checkout and ran `./mach browsertime -setup` to download it, but that is a lot of time and bandwidth to grab the script. Ideally we could grab it from https://github.com/mozilla/browsertime at tagged releases, but for now hard-coding a known good commit SHA into the script will be sufficient.

3. `visualmetrics.py` needs to be parallelized to support processing multiple videos at once. The calculations on each frame are independent of each other and only need to be collated at the end. The [`multiprocessing`](https://docs.python.org/2/library/multiprocessing.html) module can be leveraged to implement this.

4. The actual taskcluster task to run this and report the results to perfherder.

Further work would include having a way to submit the captured video from e.g. a capture card and do the visual metrics processing of that video (e.g. a ./mach command to upload the video and create the task).

Did I miss anything, :nalexander?