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Implementation of Graph Based Visual Saliency algorithm by J. Harel, C. Koch, and P. Perona
When trying to run an image in BMP format, an error occurs, as follows:
Value error:Input contains NaN,infinity or a value too large for dtype(‘float64’)
Hi,
The code works perfectly for most of the images in my dataset. However, it yields this error with few samples, I doubled checked whether there's some problem with the input images, and apparently, this error is only yielded when computing the gbvs.compute_saliency while ittikochneibur.compute_saliency works just fine.
Dear Author:
I want to calculate GBVS map in my thesis. Since I don't have a Matlab license, is it possible to achieve the same functionality by using this code you provided.
Hi there,
I am trying to find the visual saliency of a set of images using the GBVS model (global saliency using np.average()
, minimum local saliency using np.min()
, and maximum local saliency using np.max()
. These 3 numbers are calculated for the saliency_map_gbvs
array of each image). Doing so, I realized that the range of saliency instead of being between 0 and 255, varies widely from image to image. For example, the minimum saliency can be -0.5 or -0.9 and the maximum can go up to 294 or 275 depending on the image. Why is this happening? This makes a comparison of saliency across images impossible. Is there a way to normalize the values using a 0-1 range (0 for the lowest saliency and 1 for the highest)?
Hi all,
I tried the example but got this exception.
Is there anything wrong with the build-up?
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