A Flask application to parse a screen's average color and send the value to connected Philips Hue Bulbs
ℹ️ More info at screenbloom.com
Basic explanations of the various editable settings within ScreenBloom.
Sets a hard limit for how bright or how dim ScreenBloom will be able to tune your lights. Each light has its own min/max brightness settings, but the global value will always take priority. Dynamic brightness can effectively be turned off by setting the min and max values equal to each other.
Contains two settings: Update Buffer and Transition Speed.
Sets a small delay in between update loops. This feature was introduced to address a problem with various CPUs running the ScreenBloom update loop inconsistently, potentially leading to large delays as the Hue bridge becomes overwhelmed with commands. This setting can provide a huge speedup on older/slower hardware.
maps to the Hue API value for the speed of the color transition animation. Lower values will seem more responsive while higher values will be smoother.
Is a Windows-only feature allowing you to set which display ScreenBloom will parse.
Sends a random RGB color to each of your selected bulbs using your chosen transition speed. Kind of outside the scope of ScreenBloom but I wanted the functionality and added it on a whim a few years ago.
Will divide up the screen into discrete ScreenBloom-parsable zones. A common use case is to split the screen in half and assign each to a light on either side of the room/TV/monitor.
Is where you select or de-select lights to be included in the ScreenBloom update loop.
Arbitrarily enhances the the color ScreenBloom parses to be more vibrant and saturated. Uses this method of PIL/Pillow
Determines if the ScreenBloom update loop starts automatically after the program is launched.
Saving a preset gathers up all your current settings, including selected bulbs and their individual settings, and saves them as a preset. Presets can be updated by expanding their options menu and clicking Update, which overrides the preset with the current ScreenBloom settings.
ScreenBloom can be extremely responsive but there are a number of factors that will contribute to how well it performs.
ScreenBloom will run on pretty much anything but you're going to have the best results on a relatively modern quad-core system. There's a pretty wide difference in performance between my beefy desktop gaming PC and my 2014 Macbook Pro, for instance.
You'll get the best results on a PC with a stable, wired connection. Router configurations and firewalls can also play a role, but I don't have much data about that to say definitively.
Each light that ScreenBloom addresses during its update loop adds another 2-4 commands that must be processed by the Hue bridge before continuing on to the next set of commands (i.e. the next light).
Philips recommends a budget of ~10 commands per second to prevent bridge congestion, meaning the more lights being addressed the higher potential for congestion and slowdown. I think the sweet spot is around 5 lights, with 1 light giving the best possible performance and anything under 10 giving pretty acceptable performance.
If you're on older hardware or are generally experiencing large delays between ScreenBloom light updates, consider experimenting with the Update Buffer setting (located in the Update Speed section).
On Windows, ScreenBloom can be launched with command line arguments. This functionality is limited to just silent mode at the moment, I hope to expand it in the future.
Use the -q or --silent args to launch ScreenBloom without opening a browser to the web interface. If you have autostart enabled the ScreenBloom update loop will begin.
Though it wasn't really designed for it from the outset, ScreenBloom is fully addressable and scriptable as a RESTful API.
Endpoints should be pretty easy to discern from the main screenbloom.py file, starting after the index() function.
Requests can be sent to the ScreenBloom web server:
https://<screenbloom_webserver_host>:<screenbloom_webserver_port>/endpoint
Example to start ScreenBloom update loop:
[GET] https://192.168.0.69:5000/start
POST endpoints accept their parameters in JSON format and will return a JSON response. Take a look at the individual endpoint functions to figure out the exact format it expects.
Forks and pull requests very welcome! Don't hesitate to contact me or raise an issue if you have any questions.
- Clone the repo
- Grab the static files
- Setup your virtualenv with Python 2
- Install the dependencies with
pip install -r requirements.txt