theano-mc-cnn

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THEANO-MC-CNN

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.

Requirements

• Theano
• OpenCV
• Numpy
• Python Imaging Library (PIL)
• tqdm
• glob

Usage

$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)