README.md

Update: Integrated into Kornia

pytorch-mutual-information

Batch computation of mutual information and histogram2d in Pytorch

This implementation uses kernel density estimation with a gaussian kernel to calculate histograms and joint histograms. We use a diagonal bandwidth matrix for the multivariate case, which allows us to decompose the multivariate kernel as the product of each univariate kernel. From wikipedia,

where the bandwith matrix

Example usage

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Setup

device = 'cuda:0'

img1 = Image.open('grad1.jpg').convert('L')
img2 = Image.open('grad.jpg').convert('L')

img1 = transforms.ToTensor() (img1).unsqueeze(dim=0).to(device)
img2 = transforms.ToTensor() (img2).unsqueeze(dim=0).to(device)

# Pair of different images, pair of same images
input1 = torch.cat([img2, img2])
input2 = torch.cat([img1, img2])

B, C, H, W = input1.shape   # shape: (2, 1, 300, 300)

Histogram usage:

hist = histogram(input1.view(B, H*W), torch.linspace(0,255,256), sigma)

Histogram 2D usage:

hist = histogram2d(input1.view(B, H*W), input2.view(B, H*W), torch.linspace(0,255,256), sigma)

Mutual Information (of images)

MI = MutualInformation(num_bins=256, sigma=0.4, normalize=True).to(device)
score = MI(input1, input2)

Results

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Histogram

Joint Histogram