Simple Theano Implementation of Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches (fast architecture) for creating Depth Maps.
Original Source Code (in Torch/Lua) and results can be found here.
$git clone https://github.com/epiception/theano-mc-cnn.git
$cd ~/path/to/theano-mc-cnn
Create Dataset:
$python ./Dataset_creation/Patches_Extractor.py
The maximum number of patches has been set to 4,00,000 patches.
Training:
$python ./Model/MC_CNN_Fast.py
Weights will be saved for each layer and epoch in the Weights folder in the following format:
weights_epoch_{epoch_number}_layer_{layer_no}.npy.
Training and Test Accuracy can be viewed in the Epoch_stats_training.txt and Epoch_stats_testing.txt files in the Model folder respectively.
To load or retrain from weights from a specific epoch, change the value of the start_epoch variable in MC_CNN_Fast.py.
It will load the weights from the previous epoch and continue training.
Finding Depth Map:
To save Depth Map after training:
$python ./Deploy/Deploy_Depth.py -path_to_left_image -path_to_right_image -disparity_range
For instructions
$python ./Deploy/Deploy_Depth.py -h/--help
This saves the Disparity values in Cost_grid_map.txt and displays the Depth Map
(Since the dot product layer is not multi-threaded and runs in single core, this will be slow)