Image Segmentation with Tensorflow using CNNs and Conditional Random Fields

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%matplotlib inline
from __future__ import print_function, division
import os
import sys
import tensorflow as tf
import skimage.io as io
import numpy as np

def get_kernel_size(factor):
    """
    Find the kernel size given the desired factor of upsampling.
    """
    return 2 * factor - factor % 2


def upsample_filt(size):
    """
    Make a 2D bilinear kernel suitable for upsampling of the given (h, w) size.
    """
    factor = (size + 1) // 2
    if size % 2 == 1:
        center = factor - 1
    else:
        center = factor - 0.5
    og = np.ogrid[:size, :size]
    return (1 - abs(og[0] - center) / factor) * \
           (1 - abs(og[1] - center) / factor)


def bilinear_upsample_weights(factor, number_of_classes):
    """
    Create weights matrix for transposed convolution with bilinear filter
    initialization.
    """
    
    filter_size = get_kernel_size(factor)
    
    weights = np.zeros((filter_size,
                        filter_size,
                        number_of_classes,
                        number_of_classes), dtype=np.float32)
    
    upsample_kernel = upsample_filt(filter_size)
    
    for i in xrange(number_of_classes):
        
        weights[:, :, i, i] = upsample_kernel
    
    return weights


    

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