Fast-Shadow-Detection

This code is for the paper: S Hosseinzadeh, etc. "Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network", Proceedings of the IEEE/IROS 2018, https://arxiv.org/abs/1709.09283

Generating The Shadow Prior Map Images

These images are used as image-level prior that are defined in line 44 of main_fast_shadow_detection.py.

Install Paired_Region_Prob_Map using README in the folder. Run Paired_Region_Prob_Map/deshadow_driver.m by MATLAB

Reference paper http://dhoiem.cs.illinois.edu/publications/pami12_shadow.pdf

Dependencies:

1- nolearn

2- lasagne

3- theano

Python libraries:

4- scipy

5- sklearn

6- matplotlib

7- skimage

8- Python’s basic libraries (pickle, sys, os, urllib, gzip, cPickle, h5py, math, time, pdb)

How To Run The Code:

python2.7: run main_fast_shadow_detection.py

python3: run main_fast_shadow_detection_p3.py

Notes:

Build folders "data_cache" and "prediction_output_v1" for data training/testing output files, and output prediction result files.

TrainImgeFolder: Training Images

TrainMaskFolder: Training Masks (Ground Truth)

TrainFCNFolder: Shadow Prior Map Images

Likewise for testing images…

The Mask and Shadow Prior files should have 1 dimension, and Mask files also should be binary.

Using GPU:

Content in ~/.theanorc:

[global]

floatX = float32

[nvcc]

fastmath = True