feature_extraction.py

from PIL import Image
import numpy as np
from scipy import ndimage
from skimage import filters
from skimage.filters import laplace, sobel_h, sobel_v
from skimage.feature import canny
from skimage.color import rgb2hsv

img = np.array(Image.open(img_path))
img_hsv = rgb2hsv(img)

episilon = 0.002

img_exg = (2 * img[:, :, 1]) - (img[:, :, 2]) - (img[:, :, 0])
img_exr = (1.4 * img[:, :, 2]) - (img[:, :, 1])
img_cive = (0.881 * img[:, :, 1]) - (0.441 * img[:, :, 2]) - (0.385 * img[:, :, 0]) - 18.78745
img_ndi = np.divide(((img[:, :, 1]) - (img[:, :, 2])), ((img[:, :, 1]) + (img[:, :, 2])) + episilon)

# Sobel X & Y
img_sobel_x = filters.sobel_h(img_exg)
img_sobel_y = filters.sobel_v(img_exg)

## Laplacian
gray = img.mean(axis=3)
noise_reduc = gaussian(gray, sigma=0, truncate=1/3)
laplacian = laplace(noise_reduc, ksize=3)

## Canny Edge Detection
canny = canny(gray)


channels = [img[:, :, 2], img[:, :, 1], img[:, :, 0],
            img_hsv[:, :, 0], img_hsv[:, :, 1], img_hsv[:, :, 2],
            img_exg, img_exr, img_cive, img_ndi, 
            img_sobel_x, img_sobel_y, laplacian, canny]

combined = np.stack(channels, axis=2)