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data_processing.py
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data_processing.py
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import cv2, os, math as m, numpy as np, random as rand
from tensorflow.examples.tutorials.mnist import input_data
import psutil
def affine_transformation(img, theta, rho_x, rho_y, s_x, s_y, t_x, t_y):
out = img
rows, cols = out.shape
if rand.uniform(0,2) > 1:
a = m.cos(theta)
b = m.sin(theta)
M = np.float32([[a, b, ((1 - a) * 53) - (b * 53)],[-b, a, (b * 53) + ((1 - a) * 53)]])
out = cv2.warpAffine(img,M,(cols,rows),borderValue=1.0)
if rand.uniform(0,2) > 1:
M = np.float32([[1, rho_x, 0],[rho_y, 1, 0]])
out = cv2.warpAffine(img,M,(cols,rows),borderValue=1.0)
if rand.uniform(0,2) > 1:
out = cv2.resize(out,None,fx=s_x, fy=s_y, interpolation = cv2.INTER_CUBIC)
y_len, x_len = out.shape
if x_len > 105:
x_dif = x_len - 105
out = out[:,int(m.ceil(x_dif / 2.0)):int(m.ceil(x_len - x_dif / 2.0))]
else:
x_dif = 105 - x_len
temp = np.ones((y_len,105))
temp[:,int(m.ceil(x_dif / 2.0)):int(m.ceil(105 - x_dif / 2.0))] = out
out = temp
if y_len > 105:
y_dif = y_len - 105
out = out[int(m.ceil(y_dif / 2.0)):int(m.ceil(y_len - y_dif / 2.0)),:]
else:
y_dif = 105 - y_len
temp = np.ones((105,105))
temp[int(m.ceil(y_dif / 2.0)):int(m.ceil(105 - y_dif / 2.0)),:] = out
out = temp
if out.shape != (105, 105):
print(out.shape)
if rand.uniform(0,2) > 1:
M = np.float32([[1,0,t_x],[0,1,t_y]])
out = cv2.warpAffine(out,M,(cols,rows),borderValue=1.0)
return out
def get_data(num, trans_num, step):
pairs = []
labels = []
if step == 'train':
base = 'images_background'
alpha_lb = 0
alpha_ub = 30
img_lb = 0
img_ub = 12
#elif step == 'test':
# base = 'images_evaluation'
# alpha_lb = 0
# alpha_ub = 10
# img_lb = 12
# img_ub = 16
else:
base = 'images_evaluation'
alpha_lb = 10
alpha_ub = 20
img_lb = 16
img_ub = 20
alphabets = os.listdir(base)
for i in range(num):
alph1 = os.path.join(base, alphabets[rand.randint(alpha_lb,alpha_ub - 1)])
characters1 = os.listdir(alph1)
char1 = os.path.join(alph1, characters1[rand.randint(0,len(characters1) - 1)])
images1 = os.listdir(char1)
img1 = os.path.join(char1, images1[rand.randint(img_lb,img_ub - 1)])
x = cv2.imread(img1,0) / 255.0
if rand.uniform(0,2) > 1:
label = 1
img2 = os.path.join(char1, images1[rand.randint(img_lb,img_ub - 1)])
while img2 == img1:
img2 = os.path.join(char1, images1[rand.randint(img_lb,img_ub - 1)])
y = cv2.imread(img2,0) / 255.0
else:
label = 0
alph2 = os.path.join(base, alphabets[rand.randint(alpha_lb,alpha_ub - 1)])
characters2 = os.listdir(alph2)
char2 = os.path.join(alph2, characters2[rand.randint(0,len(characters2) - 1)])
while char2 == char1:
char2 = os.path.join(alph2, characters2[rand.randint(0,len(characters2) - 1)])
images2 = os.listdir(char2)
img2 = os.path.join(char2, images2[rand.randint(img_lb,img_ub - 1)])
y = cv2.imread(img2,0) / 255.0
pairs.append([x, y])
labels.append(label)
for j in range(trans_num):
a_x = affine_transformation(x, rand.uniform(-10,10), rand.uniform(-0.3,0.3), rand.uniform(-0.3,0.3), rand.uniform(0.8,1.2), rand.uniform(0.8,1.2), rand.uniform(-2,2), rand.uniform(-2,2))
a_y = affine_transformation(y, rand.uniform(-10,10), rand.uniform(-0.3,0.3), rand.uniform(-0.3,0.3), rand.uniform(0.8,1.2), rand.uniform(0.8,1.2), rand.uniform(-2,2), rand.uniform(-2,2))
pairs.append([a_x, a_y])
labels.append(label)
# zipped = list(zip(pairs, labels))
# rand.shuffle(zipped)
# pairs, labels = zip(*zipped)
return (pairs, labels)
def get_test_data():
pairs = []
labels = []
base = 'images_evaluation'
alpha_lb = 0
alpha_ub = 10
img_lb = 12
img_ub = 16
alphabets = os.listdir(base)
for i in range(alpha_lb, alpha_ub):
alpha = os.path.join(base, alphabets[i])
characters = os.listdir(alpha)
drawers = list(range(img_lb, img_ub))
for j in range(2):
drawer1 = drawers.pop(rand.randint(0,len(drawers) - 1))
drawer2 = drawers.pop(rand.randint(0,len(drawers) - 1))
for k in range(20):
char1 = os.path.join(alpha, characters[k])
images1 = os.listdir(char1)
for l in range(20):
char2 = os.path.join(alpha, characters[l])
images2 = os.listdir(char2)
img1 = os.path.join(char1, images1[drawer1])
img2 = os.path.join(char2, images2[drawer2])
x = cv2.imread(img1,0) / 255.0
y = cv2.imread(img2,0) / 255.0
pairs.append([x, y])
# if k == l:
# labels.append(1)
# else:
# labels.append(0)
labels.append(k)
# zipped = list(zip(pairs, labels))
# rand.shuffle(zipped)
# pairs, labels = zip(*zipped)
return (pairs, labels)
def get_mnist_test_data():
pairs = []
labels = []
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
imgs = mnist.train.images
anss = mnist.train.labels
num_inds = [[], [], [], [], [], [], [], [], [], []]
for i in range(len(anss)):
num_inds[np.argmax(anss[i,:])].append(i)
for i in range(40):
for j in range(10):
x_ind = num_inds[j].pop(rand.randint(0,len(num_inds[j]) - 1))
for k in range(10):
y_ind = num_inds[k].pop(rand.randint(0,len(num_inds[k]) - 1))
x = cv2.resize(imgs[x_ind,:].reshape(28, 28), (35, 35))
y = cv2.resize(imgs[y_ind,:].reshape(28, 28), (35, 35))
pairs.append([x, y])
if j == k:
labels.append(1)
else:
labels.append(0)
zipped = list(zip(pairs, labels))
rand.shuffle(zipped)
pairs, labels = zip(*zipped)
return (pairs, labels)
def get_data_paths(num, trans_num, step):
pairs = []
labels = []
if step == 'train':
base = 'images_background'
alpha_lb = 0
alpha_ub = 30
img_lb = 0
img_ub = 12
else:
base = 'images_evaluation'
alpha_lb = 10
alpha_ub = 20
img_lb = 16
img_ub = 20
alphabets = os.listdir(base)
for i in range(num):
alph1 = os.path.join(base, alphabets[rand.randint(alpha_lb,alpha_ub - 1)])
characters1 = os.listdir(alph1)
char1 = os.path.join(alph1, characters1[rand.randint(0,len(characters1) - 1)])
images1 = os.listdir(char1)
img1 = os.path.join(char1, images1[rand.randint(img_lb,img_ub - 1)])
x = img1
if rand.uniform(0,2) > 1:
label = 1
img2 = os.path.join(char1, images1[rand.randint(img_lb,img_ub - 1)])
while img2 == img1:
img2 = os.path.join(char1, images1[rand.randint(img_lb,img_ub - 1)])
y = img2
else:
label = 0
alph2 = os.path.join(base, alphabets[rand.randint(alpha_lb,alpha_ub - 1)])
characters2 = os.listdir(alph2)
char2 = os.path.join(alph2, characters2[rand.randint(0,len(characters2) - 1)])
while char2 == char1:
char2 = os.path.join(alph2, characters2[rand.randint(0,len(characters2) - 1)])
images2 = os.listdir(char2)
img2 = os.path.join(char2, images2[rand.randint(img_lb,img_ub - 1)])
y = img2
pairs.append([x, y, False])
labels.append(label)
for j in range(trans_num):
pairs.append([x, y, True])
labels.append(label)
zipped = list(zip(pairs, labels))
rand.shuffle(zipped)
pairs, labels = zip(*zipped)
return (pairs, labels)
def get_image_pair(pair):
img1 = pair[0]
img2 = pair[1]
trans = pair[2]
x = cv2.imread(img1,0) / 255.0
y = cv2.imread(img2,0) / 255.0
if trans:
x = affine_transformation(x, rand.uniform(-10,10), rand.uniform(-0.3,0.3), rand.uniform(-0.3,0.3), rand.uniform(0.8,1.2), rand.uniform(0.8,1.2), rand.uniform(-2,2), rand.uniform(-2,2))
y = affine_transformation(y, rand.uniform(-10,10), rand.uniform(-0.3,0.3), rand.uniform(-0.3,0.3), rand.uniform(0.8,1.2), rand.uniform(0.8,1.2), rand.uniform(-2,2), rand.uniform(-2,2))
return (x, y)
def get_image_pair_mnist(pair):
img1 = pair[0]
img2 = pair[1]
trans = pair[2]
x = cv2.imread(img1,0) / 255.0
y = cv2.imread(img2,0) / 255.0
if trans:
x = affine_transformation(x, rand.uniform(-10,10), rand.uniform(-0.3,0.3), rand.uniform(-0.3,0.3), rand.uniform(0.8,1.2), rand.uniform(0.8,1.2), rand.uniform(-2,2), rand.uniform(-2,2))
x = cv2.resize(x, (35, 35))
y = affine_transformation(y, rand.uniform(-10,10), rand.uniform(-0.3,0.3), rand.uniform(-0.3,0.3), rand.uniform(0.8,1.2), rand.uniform(0.8,1.2), rand.uniform(-2,2), rand.uniform(-2,2))
y = cv2.resize(y, (35, 35))
return (x, y)
def get_image(img_path, trans):
x = cv2.imread(img_path,0) / 255.0
if trans:
x = affine_transformation(x, rand.uniform(-10,10), rand.uniform(-0.3,0.3), rand.uniform(-0.3,0.3), rand.uniform(0.8,1.2), rand.uniform(0.8,1.2), rand.uniform(-2,2), rand.uniform(-2,2))
return x
def get_data_paths2(num, trans_num, step, match_rate):
pairs = []
labels = []
if step == 'train':
base = 'images_background'
img_lb = 0
img_ub = 12
alphabets = os.listdir(base)
for a_ind in range(30):
alph1 = os.path.join(base, alphabets[a_ind])
for i in range(num // 30):
characters1 = os.listdir(alph1)
char1 = os.path.join(alph1, characters1[rand.randint(0,len(characters1) - 1)])
images1 = os.listdir(char1)
img1 = os.path.join(char1, images1[rand.randint(img_lb,img_ub - 1)])
x = img1
if rand.uniform(0,1) > (1 - match_rate):
label = 1
img2 = os.path.join(char1, images1[rand.randint(img_lb,img_ub - 1)])
while img2 == img1:
img2 = os.path.join(char1, images1[rand.randint(img_lb,img_ub - 1)])
y = img2
else:
label = 0
char2 = os.path.join(alph1, characters1[rand.randint(0,len(characters1) - 1)])
while char2 == char1:
char2 = os.path.join(alph1, characters1[rand.randint(0,len(characters1) - 1)])
images2 = os.listdir(char2)
img2 = os.path.join(char2, images2[rand.randint(img_lb,img_ub - 1)])
y = img2
pairs.append([x, y, False])
labels.append(label)
for j in range(trans_num):
pairs.append([x, y, True])
labels.append(label)
zipped = list(zip(pairs, labels))
rand.shuffle(zipped)
pairs, labels = zip(*zipped)
return (pairs, labels)