pleasfenx.blogg.se - Syntra small python

SYNTRA SMALL PYTHON INSTALL

Fire up Blender and model and light a scene of your choosing (cycles rendering engine only).Otherwise, you can follow these steps to make your own. If you just want to see some synthetic examples I've already generated, look in model/syn_data/images. Run python3 src/score_model.py on the images you want to test your model with.I think that you can find info on this in the caffe-segnet tutorial. Run python3 src/compute_bn_statistics.py to generate your final.You may need to fiddle with batch size to get it to run more effectively - look at the caffe-segnet tutorial for help here. Wait for a long time depending on how good your gpu is.Run /SegNet/caffe-segnet-cudnn5/build/tools/caffe train -gpu -solver, where SOLVER_PROTOTXT is either /SegNet/models/nyu_segnet_solver.prototxt or /SegNet/models/combined_segnet_solver.prototxt based on whether or not you want to exclude the synthetic images I've generated already.Sorry, I know this isn't ideal, but it's how caffe-segnet wanted things to be installed. Alternatively, you can go through all files and replace instances of /SegNet with the path to the model folder. Create a symlink from /SegNet to the model/ folder.

SYNTRA SMALL PYTHON INSTALL

Install model/caffe-segnet-cudnn5 by following the instructions that are somewhere online :).This will populate model/nyu_data with the baseline data in the correct format. cd src and pip3 install -r requirements.txt.

Download the NYU V2 labelled dataset and save it somewhere.

It explored the generation of image semantic segmentation training data from 3D renders. This is the code from a small project I worked on during the summer of 2017.