src/LBP.py

from skimage import feature
import numpy as np
from PIL import Image


class LBPImageEncoder:
    def __init__(self, numPoints, radius):
        # store the number of points and radius
        self.numPoints = numPoints
        self.radius = radius

    def describe(self, image, eps=1e-7):
        # compute the Local Binary Pattern representation
        # of the image, and then use the LBP representation
        # to build the histogram of patterns
        print(image)
        if image.size == 0:
            print("Error: Input image is empty.")
        img_arr = np.array(image)
        print(img_arr.shape)
        gray_img = image.convert('L')
        if gray_img.size == 0:
            print("Error: Input image is empty.")
        gray_img_arr = np.array(gray_img)
        print(gray_img_arr.shape)
        print(gray_img)
        print(gray_img)
        lbp = feature.local_binary_pattern(gray_img, self.numPoints, self.radius, method="uniform")
        (hist, _) = np.histogram(lbp.ravel(), bins=np.arange(0, self.numPoints + 3), range=(0, self.numPoints + 2))
        # normalize the histogram
        hist = hist.astype("float")
        hist /= (hist.sum() + eps)
        print(hist)
        # return the histogram of Local Binary Patterns
        return hist