local_binary_pattern | original LBP , circular LBP

I'm using scipy signaltonoise function below is the code but it returns an error. I searched regarding this in github too but couldn't find it. Can you please help.

I need to know if there's a faster way to get the LBP and the resulting histograms of the MNIST dataset. This will be used for handwritten text recognition, through a model that I haven't decided yet..

I've loaded the MNIST dataset and split it to its x, y training sets and x, y test sets based on tensorflow tutorials.

I've then used cv2 to invert the images.

From there I've defined a function using skimage to get the LBP and the corresponding histogram of an input image

I finally used a classic for loop to iterate through the images, get their histograms, store these in a separate list, and return the new list and the unaltered label list of both training and test sets.

Here is the function to load the MNIST dataset:

I am new to computer vision and trying to build an image classifier to classify the images from different datasets. I'm going to extract visual features of these images like LBP histogram, color histogram using OpenCV. Since these images are crawled from different websites, they have different image sizes (e.g 136x215 vs. 266x382). Should I adjust the images to the same size before extracting the features?

The code for extracting LBP histogram:

For example, the lbp code of the pixel with coordinate (1, 1) is possible to calculate it with the pixels (0, 0); (0, 1); (0, 2); (1, 2); (2, 2); (2, 1); (2, 0); (1, 0) but the pixels of the extremes do not have those 8 neighborhood pixels, that is, the pixel (0, 0) only has 3 neighbors.

This question comes to me because I have obtained the LBP image using sicikit image, the code is as follows:

lbp = feature.local_binary_pattern (gray, 8, 1, 'ror')

Then I printed the values ​​of the gray image and got these values:

I want to plot the mean local binary patterns histograms of a set of images. Here is what I did:

I'm running LBP algorithm to classify images by their texture features. Classifying method is LinearSVC in sklearn.svm package.

Getting histogram and fitting by SVM is done, but sometimes length of histogram varies depending on image.

Example is below:

I've been running into problems recently where the local binary pattern method in python skimage is producing unexpected results.

Have a look at the cartoon example below. It shows two flat color circles on a flat color background.

The local binary pattern (P=8 samples, Radius=1) output is:

(Image is color coded in jet colors). The gray color correctly represents 255. However, the blue color is 85 (binary 01010101).

So while the method correctly shows the background and the circle on the right as 255, it shows the left circle as 85. Apparently, the local binary pattern method in skimage thinks the region is completely noisy (hence the alternating binary pattern 01010101). This is not true, however, as I have double checked the individual pixels in the region shown in blue above and their values are identical (i.e. its flat color, just like the flat color background and other flat color circle).

Has anyone experienced a similar problem before?

Here is the code if you want to replicate this: