mono-depth

Experiments with learning depth from a single image for course project.

Details and history in presentation here: https://docs.google.com/presentation/d/1GZkUPq5xFVHfjKiVrPzIXjKb_lBb_r4KPjPVz_VMwK8/edit?usp=sharing

Example Results:

data

I used data from the Make3d Dataset found here: http://make3d.cs.cornell.edu/data.html

I organized the data as follows: The test and training data were downloaded to a directory called "data" and then into subdirectories:

../data/test/depthmaps

http://www.cs.cornell.edu/~asaxena/learningdepth/Data/Dataset2_Depths.tar.gz

../data/test/images

http://cs.stanford.edu/people/asaxena/learningdepth/Data/Dataset2_Images.tar.gz

../data/train/depthmaps

http://cs.stanford.edu/people/asaxena/learningdepth/Data/Dataset3_Depths.tar.gz

../data/train/images

http://www.cs.cornell.edu/~asaxena/learningdepth/Data/Dataset3_Images.tar.gz

After downloading and extracting the data to the appropriate paths, I rotated the images (they are originally flipped 90 degrees from the depthmaps and resized them to make the model train more quickly with the following commands:

sh resize_and_rotate.sh ../data/train/images/ ../data/train/small_images/

sh resize_and_rotate.sh ../data/test/images/ ../data/test/small_images/

install

conda env create -f mono_environment.yml

run

To run with debug statements on cpu:

THEANO_FLAGS="device=cpu,optimizer=None,compute_test_value=raise,floatX=float32" python predict_depth.py

To run on cpu:

THEANO_FLAGS="device=cpu,floatX=float32" python predict_depth.py

To run on gpu with no debug:

THEANO_FLAGS="device=gpu,floatX=float32" python predict_depth.py

bugs

Error in bottom few rows of pixels