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May 9, 2019 12:06
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# Based on https://www.mathworks.com/help/images/ref/imsegkmeans.html | |
import cv2 as cv | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from sklearn.cluster import KMeans | |
from sklearn import preprocessing | |
# Building some gabor kernels to filter image | |
orientations = np.arange(0, np.pi, np.pi / 4) | |
wavelengths = [] | |
lmbda = 1 | |
while lmbda <= 32: | |
wavelengths.append(lmbda) | |
lmbda *= 2 | |
def build_gabor_kernels(): | |
filters = [] | |
ksize = 45 | |
for rotation in orientations: | |
for wavelength in wavelengths: | |
kernel = cv.getGaborKernel( | |
(ksize, ksize), 1, rotation, wavelength, 0.5, 0, ktype=cv.CV_32F) | |
filters.append(kernel) | |
return filters | |
image = cv.imread('./mona.jpg') | |
rows, cols, channels = image.shape | |
# Resizing the image. | |
# Full image is taking to much time to process | |
image = cv.resize(image, (int(cols * 0.5), int(rows * 0.5))) | |
rows, cols, channels = image.shape | |
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY) | |
gaborKernels = build_gabor_kernels() | |
gaborFilters = [] | |
for (i, kernel) in enumerate(gaborKernels): | |
filteredImage = cv.filter2D(gray, cv.CV_8UC1, kernel) | |
# Blurring the image | |
sigma = int(3*0.5*wavelengths[i % len(wavelengths)]) | |
# Sigma needs to be odd | |
if sigma % 2 == 0: | |
sigma = sigma + 1 | |
blurredImage = cv.GaussianBlur(filteredImage, (int(sigma), int(sigma)), 0) | |
gaborFilters.append(blurredImage) | |
# numberOfFeatures = 1 (gray color) + number of gabor filters + 2 (x and y) | |
numberOfFeatures = 1 + len(gaborKernels) + 2 | |
# Empty array that will contain all feature vectors | |
featureVectors = [] | |
for i in range(0, rows, 1): | |
for j in range(0, cols, 1): | |
vector = [gray[i][j]] | |
for k in range(0, len(gaborKernels)): | |
vector.append(gaborFilters[k][i][j]) | |
vector.extend([i+1, j+1]) | |
featureVectors.append(vector) | |
# Some example results: | |
# featureVectors[0] = [164, 3, 10, 255, 249, 253, 249, 2, 43, 255, 249, 253, 249, 3, 10, 255, 249, 253, 249, 2, 43, 255, 249, 253, 249, 1, 1] | |
# featureVectors[1] = [163, 3, 17, 255, 249, 253, 249, 2, 43, 255, 249, 253, 249, 3, 17, 255, 249, 253, 249, 2, 43, 255, 249, 253, 249, 1, 2] | |
# Normalizing the feature vectors | |
scaler = preprocessing.StandardScaler() | |
scaler.fit(featureVectors) | |
featureVectors = scaler.transform(featureVectors) | |
kmeans = KMeans(n_clusters=2, random_state=170) | |
kmeans.fit(featureVectors) | |
centers = kmeans.cluster_centers_ | |
labels = kmeans.labels_ | |
result = centers[labels] | |
# Only keep first 3 columns to make it easy to plot as an RGB image | |
result = np.delete(result, range(3, numberOfFeatures), 1) | |
plt.figure(figsize=(15, 8)) | |
plt.imsave('test.jpg', result.reshape(rows, cols, 3) * 100) |
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