n_bins = int(lbp.max() + 1)
hist, _ = np.histogram(lbp, density=True, bins=n_bins, range=(0, n_bins))
for name, ref in refs.items():
ref_hist, _ = np.histogram(ref,
density=True,
bins=n_bins,
range=(0, n_bins))
score = kullback_leibler_divergence(hist, ref_hist)
if score < best_score:
best_score = score
best_name = name
return best_name
brick = data.brick()
grass = data.grass()
gravel = data.gravel()
refs = {
'brick': local_binary_pattern(brick, n_points, radius, METHOD),
'grass': local_binary_pattern(grass, n_points, radius, METHOD),
'gravel': local_binary_pattern(gravel, n_points, radius, METHOD)
}
# classify rotated textures
print('Rotated images matched against references using LBP:')
print('original: brick, rotated: 30deg, match result: ',
match(refs, rotate(brick, angle=30, resize=False)))
print('original: brick, rotated: 70deg, match result: ',
match(refs, rotate(brick, angle=70, resize=False)))
print('original: grass, rotated: 145deg, match result: ',
# prepare filter bank kernels
kernels = []
for theta in range(4):
theta = theta / 4. * np.pi
for sigma in (1, 3):
for frequency in (0.05, 0.25):
kernel = np.real(gabor_kernel(frequency, theta=theta,
sigma_x=sigma, sigma_y=sigma))
kernels.append(kernel)
shrink = (slice(0, None, 3), slice(0, None, 3))
brick = img_as_float(data.brick())[shrink]
grass = img_as_float(data.grass())[shrink]
gravel = img_as_float(data.gravel())[shrink]
image_names = ('brick', 'grass', 'gravel')
images = (brick, grass, gravel)
# prepare reference features
ref_feats = np.zeros((3, len(kernels), 2), dtype=np.double)
ref_feats[0, :, :] = compute_feats(brick, kernels)
ref_feats[1, :, :] = compute_feats(grass, kernels)
ref_feats[2, :, :] = compute_feats(gravel, kernels)
print('Rotated images matched against references using Gabor filter banks:')
print('original: brick, rotated: 30deg, match result: ', end='')
feats = compute_feats(ndi.rotate(brick, angle=190, reshape=False), kernels)
print(image_names[match(feats, ref_feats)])
# ●
# Notice: beware of how the threshold is defined in the formulas of the previous slides. Your output
# should be compliant with our definition of threshold (first slide).
# https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_thresholding/py_thresholding.html#otsus-binarization
import numpy as np
import cv2 as cv2
from skimage import data, io
import matplotlib.pyplot as plt
from sklearn import preprocessing
from skimage.transform import resize
import random
# try page()
im = data.grass() # h 400 * w 600 * c 3
if len(im.shape) == 3: # converting to gray scale if image is RGB
im = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY) # h 400 * w 600
fig = plt.figure(figsize=(30, 30))
rows = 3 # grid 3x3
columns = 3
fig.add_subplot(rows, columns, 1)
plt.imshow(im, cmap="gray")
im = resize(
im,
(im.shape[0] // 2, im.shape[1] // 2),
mode="reflect",
preserve_range=True,