Image capture

These benchmarks measure the FPS of image capture and observation data in TDW. Image capture is unavoidably slow.

See the Benchmark document for the test machine's system info.

1: Image capture

Performance benchmark for Images output data using different render settings:

100 objects	Pass masks	Render quality	Post-processing	Screen size	.png	FPS
False	`['_img']`	0	False	256	False	312
True	`['_id']`	0	False	256	False	400
False	`['_img']`	5	True	256	False	234
False	`['_img']`	5	True	1024	False	44
True	`['_id']`	0	False	1024	False	69
False	`['_img']`	5	True	1024	True	41
True	`['_img', '_id']`	5	True	1024	False	33
True	`['_img', '_id']`	5	True	1024	True	30

Performance benchmark to compare Images data to alternative observation data.

Render quality is 0
Post processing is disabled
Screen size is 256x256
Images are .jpg
There are 100 objects in the scene
Note that Occlusion is slow because it renders two images; it would normally require 2 frames to get the same data as Occlusion's 1 frame.

_img pass	_id pass	IdPassSegmentationColors	Occlusion	FPS
True	False	False	False	292
False	True	False	False	350
False	False	True	False	288
False	False	False	True	196

This is a simple benchmark of all image passes.

Result: 45 FPS

Follow instructions in the Benchmark document for cloning the repo, downloading the build, etc.
cd path/to/tdw/Python/benchmarking (replace path/to with the actual path)
python3 image_capture.py or python3 observation_data.py or python3 image_capture_all.py
Run the build
Wait for the performance benchmark to complete (this might take up to five minutes).
Compare your results to those listed above