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Preprocessing.py
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import cv2
import numpy as np
# from PIL import Image
from matplotlib import pyplot as plt
from sklearn.manifold import SpectralEmbedding
import math
from mlxtend.feature_selection import SequentialFeatureSelector
from sklearn.neighbors import KNeighborsClassifier
from mlxtend.feature_selection import SequentialFeatureSelector as SFS
class Preprocessing:
def __init__(self):
pass
# this method reads an image and preprocess it by rescaling it to 150 x 100 a applying CLAHE over the image
def read_image(self):
ear_pos = ['down_ear', 'front_ear', 'left_ear', 'up_ear']
person_num = ['000', '001', '002']
images = []
for i in person_num:
for j in ear_pos:
images.append(cv2.resize(cv2.imread("EarImages/"+i+'_'+j+".jpg", cv2.IMREAD_GRAYSCALE), dsize=(100, 150), interpolation=cv2.INTER_NEAREST))
# print(len(image))
# print(image[11].shape)
# image = Image.open("ear3.png").convert('RGB')
# open_cv_image = np.array(image)
# Convert RGB to BGR
# open_cv_image = open_cv_image[:, :, ::-1].copy()
# plt.imshow(image)
# plt.show()
# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# gray = cv2.resize(gray, dsize=(100, 150), interpolation=cv2.INTER_NEAREST)
# plt.imshow(gray)
# plt.show()
processed_images = []
for i in range(len(images)):
gray = images[i]
mean = cv2.mean(gray)[0]
variance = np.var(gray)
m_t = 100
v_t = 100
for i in range(150):
for j in range(100):
beta = math.sqrt(v_t * math.pow(gray[i][j] - mean, 2) / variance)
if gray[i][j] > mean:
gray[i][j] = m_t + beta
else:
gray[i][j] = m_t - beta
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
cl1 = clahe.apply(gray)
processed_images.append(cl1)
print(processed_images[0].shape)
return processed_images
# in this function we are creating sw of size 50 x 50 with step size 11 so total 50 SWs
def sub_window_creation(self, images, kernels):
gb_all_sw = []
label = []
for i in range(0, 100, 11):
for j in range(0, 50, 11):
for k in range(len(images)):
image = images[k]
sw_image = image[i:i+50, j:j+50]
sw_image = cv2.resize(sw_image, dsize=(12, 12), interpolation=cv2.INTER_NEAREST)
# print('sw size', sw_image.shape)
gabored_image = Preprocessing.process(self, sw_image, kernels)
# print('gab size', gabored_image.shape)
# model = SpectralEmbedding(n_components=100, n_neighbors=10)
# reduced_sw = model.fit_transform(gabored_image.reshape(-1, 1))
# print('gab size', gabored_image.reshape(1, -1).shape)
# gb_all_sw.append(gabored_image)
gb_all_sw.append(gabored_image)
label.append(int(k/4))
# print('red size', reduced_sw.reshape(-1, 1).shape)
# plt.imshow(image[i:i+50, j:j+50], cmap='gray')
# plt.show()
# plt.imshow(gabored_image, cmap='gray')
# plt.show()
print(len(gb_all_sw))
print(len(gb_all_sw[0]))
# LEM demension reduction
model = SpectralEmbedding(n_components=100, n_neighbors=10)
# reduced_sw = model.fit_transform(gb_all_sw)
reduced_sw = model.fit_transform(gb_all_sw)
knn = KNeighborsClassifier(n_neighbors=5)
sffs = SFS(knn,
k_features=5,
forward=True,
floating=True,
scoring='accuracy',
cv=4,
n_jobs=-1)
sffs = sffs.fit(reduced_sw, label)
print('\nSequential Forward Floating Selection (k=', i, '):')
print(sffs.k_feature_idx_)
print('CV Score:')
print(sffs.k_score_)
# print('final', len(reduced_sw))
# print('final', reduced_sw[0].shape)
# print(label)
# creating gabor kernel bank
def gabor_filter(self):
kernels = []
Lambda = 10
psi = 0
gamma = 0.5
for theta in [0, 45, 90, 180]:
for sigma in [5, 10, 15, 20]:
kernel = np.real(cv2.getGaborKernel((12, 12), sigma, theta, 10.0, 0.5, 0, ktype=cv2.CV_32F))
# print(kernel.shape)
# print(kernel)
# kernels = np.append(kernels, kernel, axis=0)
kernels.append(kernel)
# sigma_x = sigma
# sigma_y = float(sigma) / gamma
# nstds = 3
# xmax = max(abs(nstds * sigma_x * np.cos(theta)), abs(nstds * sigma_y * np.sin(theta)))
# xmax = np.ceil(max(1, xmax))
# ymax = max(abs(nstds * sigma_x * np.sin(theta)), abs(nstds * sigma_y * np.cos(theta)))
# ymax = np.ceil(max(1, ymax))
# xmin = -xmax
# ymin = -ymax
# y, x = np.meshgrid(np.arrange(ymin, ymax + 1), np.arrange(xmin, xmax + 1))
#
# x_theta = x * np.cos(theta) + y * np.sin(theta)
# y_theta = -x * np.sin(theta) + y * np.cos(theta)
#
# gb = np.exp( -.5 * (x_theta ** 2 / sigma_x ** 2 + y_theta ** 2 / sigma_y ** 2)) * np.cos(2 * np.pi / Lambda * x_theta + psi)
#
# kernels.append(gb)
return kernels
def process(self, img, filters):
gabored_images = np.array([])
# accum = np.zeros_like(img)
for kern in filters:
fimg = cv2.filter2D(img, cv2.CV_8UC3, kern)
gabored_images = np.append(gabored_images, fimg)
# np.maximum(accum, fimg, accum)
return gabored_images
def rank_calculation(predicted,actual,rank):
total_img = len(predicted)
true_prediction = 0;
for i in range(total_img):
# row_mat = predicted[i]
d1 = np.zeros((2,len(predicted[0])))
for j in range(len(d1[0])):
d1[0][j]=j
d1[1]= predicted[i]
##thinking its sorted
d = d1[:,d1[1].argsort()]
print(d)
for k in range(len(d[0])-1,len(d[0]) -rank -1, -1):
if(d[0][k] == actual[i]):
true_prediction = true_prediction+1
percentage = (true_prediction/total_img) *100
return percentage
def pca_without_gabor(self,processed_image1):
data =[]
for i in range(len(processed_image1)):
data.append(processed_image1[i].flatten())
pca=PCA()
print (data)
pca.fit(data)
c= data
# g = np.array(c)
# print("Raw data size")
# print(g.shape)
data = pca.transform(data)
t = np.array(data)
t.shape
print("changed data dimension")
print(t.shape)
return t
obj = Preprocessing()
processed_image = obj.read_image()
# print(len(processed_image))
# print(processed_image[0].shape)
kernel_bank = obj.gabor_filter()
obj.sub_window_creation(processed_image, kernel_bank)