def nua_data_create():
count = 0
X_nua = []
y_nua = []
X_gray = np.empty((1, 256, 256, 1))
X_dog = np.empty((1, 256, 256, 1))
X_lbp = np.empty((1, 256, 256, 1))
for root, dirs, filenames in os.walk('NUA'):
for file in filenames:
if file.endswith('.jpg'):
if count % 10 == 0:
print('file ' + str(file))
path = os.path.join(root, file)
img = cv2.imread(path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB)
# cb = ycrcb[0]
# print('converted to gray')
dog = calc_dog(gray)
# print('dog')
lbp = calc_lbp(gray)
# print('lbped')
# gray_img = cv2.resize(gray, (256, 256))
gray_img = cv2.resize(gray, (256, 256))
gray_img = np.expand_dims(gray_img, axis=-1)
dog = np.expand_dims(dog, axis=-1)
lbp = np.expand_dims(lbp, axis=-1)
# gray_img = np.concatenate((gray_img, gray_img, gray_img), axis=-1)
# dog = np.concatenate((dog, dog, dog), axis=-1)
# lbp = np.concatenate((lbp, lbp, lbp), axis=-1)
X_gray[0] = gray_img.astype('float32') / 255
X_dog[0] = dog.astype('float32') / 255
X_lbp[0] = lbp.astype('float32') / 255
intermediate_layer_model = Model(
inputs=model.input,
outputs=model.get_layer('conv2d_7').output)
intermediate_output = intermediate_layer_model.predict(
[X_gray, X_dog, X_lbp])
print('appending output')
X_nua.append(intermediate_output)
if path.strip('.jpg').split('_')[-1] == 'live':
print('live image')
y_nua.append(1)
else:
print('spoof image')
y_nua.append(0)
count += 1
print('saving numpy arrays')
np.save('X_nua_2.npy', np.array(X_nua))
np.save('y_nua.npy', np.array(y_nua))
def __data_generation(self, list_IDs_temp):
'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)
# Initialization
# X_gray = np.empty((self.batch_size, 256, 256, 1))
# X_dog = np.empty((self.batch_size, 256, 256, 1))
# X_lbp = np.empty((self.batch_size, 256, 256, 1))
# for resnet
X = np.empty((self.batch_size, 256, 256, 1))
y = np.empty((self.batch_size), dtype=int)
# Generate data
for i, ID in enumerate(list_IDs_temp):
# Store sample
img = cv2.imread(ID)
# img = cv2.resize(img, (256, 256))
# img = cv2.resize(img, (224, 224)) # resnet
# img = np.expand_dims(img, axis=-1)
idx = self.list_IDs.index(ID)
# print('id' + str(ID) +'label ' + str(self.labels[idx]))
try:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
lbp = calc_lbp(gray)
# print('lbped')
lbp = np.expand_dims(lbp, axis=-1)
# Store class
X[i] = lbp.astype('float32') / 255
y[i] = self.labels[idx]
# with open('log.txt', 'a') as lf:
# lf.write('ID ' + str(ID) + ' label ' + str(y[i])+'\n')
# print('y[' + str(i) + ']= ' + str(y[i]))
except cv2.error as e:
print(e)
# print('skipping id')
continue
# print('yyy = ' +str(keras.utils.to_categorical(y, num_classes=self.n_classes)))
# print('X_gray = ' + str(X_gray))
# print('X_dog = ' + str(X_dog))
# print('X_lbp = ' + str(X_lbp))
return X, y
def siw_data_create():
X_siw_paths = []
X_siw = []
y_siw = []
X_gray = np.empty((1, 256, 256, 1))
X_dog = np.empty((1, 256, 256, 1))
X_lbp = np.empty((1, 256, 256, 1))
count = 0
print('collecting validation and test data')
for root, dirnames, filenames in os.walk(TEST_DIR):
for filename in fnmatch.filter(filenames, "*.jpg"):
path = os.path.join(root, filename)
if path.split('/')[-3] == 'live':
X_siw_paths.append(path)
y_siw.append(1)
elif path.split('/')[-3] == 'spoof':
X_siw_paths.append(path)
y_siw.append(0)
count += 1
X_siw_paths, y_siw = shuffle(X_siw_paths, y_siw)
pairs = list(zip(X_siw_paths, y_siw)) # make pairs out of the two lists
pairs = random.sample(pairs, 1500) # pick 3 random pairs
filepaths, labels = zip(*pairs)
for image_path in filepaths:
img = cv2.imread(image_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB)
# cb = ycrcb[0]
# print('converted to gray')
dog = calc_dog(gray)
# print('dog')
lbp = calc_lbp(gray)
# print('lbped')
# gray_img = cv2.resize(gray, (256, 256))
gray_img = cv2.resize(gray, (256, 256))
gray_img = np.expand_dims(gray_img, axis=-1)
dog = np.expand_dims(dog, axis=-1)
lbp = np.expand_dims(lbp, axis=-1)
# gray_img = np.concatenate((gray_img, gray_img, gray_img), axis=-1)
# dog = np.concatenate((dog, dog, dog), axis=-1)
# lbp = np.concatenate((lbp, lbp, lbp), axis=-1)
X_gray[0] = gray_img.astype('float32') / 255
X_dog[0] = dog.astype('float32') / 255
X_lbp[0] = lbp.astype('float32') / 255
intermediate_layer_model = Model(
inputs=model.input, outputs=model.get_layer('conv2d_7').output)
intermediate_output = intermediate_layer_model.predict(
[X_gray, X_dog, X_lbp])
print('appending output')
X_siw.append(intermediate_output)
#
# if path.strip('.jpg').split('_')[-1] == 'live':
# print('live image')
# y_siw.append(1)
# else:
# print('spoof image')
# y_nua.append(0)
print('saving numpy arrays')
np.save('X_siw_2.npy', np.array(X_siw))
np.save('y_siw.npy', np.array(labels))
# roi = cv2.imread(file)
# roi = cv2.resize(roi, (224, 224))
# roi = roi.astype('float32')/255
# X[0] = roi.astype('float32')/255
# roi = cv2.resize(roi, (224, 224))
# cv2.imshow('frame', roi)
# cv2.imshow('roi', roi)
# cv2.imwrite('roi3.jpg', roi)
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
# ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB)
# cb = ycrcb[0]
# print('converted to gray')
dog = calc_dog(gray)
# print('dog')
lbp = calc_lbp(gray)
# print('lbped')
# gray_img = cv2.resize(gray, (256, 256))
height, width = roi.shape[:2]
# print('height ' + str(height))
# print('width' + str(width))
if height > 96 and width > 96:
for j in range(16):
# print('j = ' + str(j))
rH = random.uniform(0, height - 96)
rW = random.uniform(0, width - 96)
x, y = int(rH), int(rW)
gray_roi = gray[x:x + 96, y:y + 96]
with open('log.txt', 'a') as fw:
fw.write('gray roi shape ' + str(gray_roi.shape) +
'\n')
def __data_generation(self, list_IDs_temp):
'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)
# Initialization
X_gray = np.empty((self.batch_size, 96, 96, 16))
X_dog = np.empty((self.batch_size, 96, 96, 16))
X_lbp = np.empty((self.batch_size, 96, 96, 16))
# for resnet
# X = np.empty((self.batch_size, 256, 256, 3))
label = np.empty((self.batch_size), dtype=int)
# Generate data
for i, ID in enumerate(list_IDs_temp):
# Store sample
gray_patchs = []
lbp_patchs = []
dog_patchs = []
img = cv2.imread(ID)
# img = cv2.resize(img, (256, 256))
# img = cv2.resize(img, (256, 256)) # resnet
# img = np.expand_dims(img, axis=-1)`
idx = self.list_IDs.index(ID)
# print('id' + str(ID) +'label ' + str(self.labels[idx]))
try:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB)
# cb = ycrcb[0]
# print('converted to gray')
dog = calc_dog(gray)
# print('dog')
lbp = calc_lbp(gray)
# print('lbped')
# gray_img = cv2.resize(gray, (256, 256))
height, width = img.shape[:2]
# print('height ' + str(height))
# print('width' + str(width))
if height > 96 and width > 96:
for j in range(16):
# print('j = ' + str(j))
rH = random.uniform(0, height - 96)
rW = random.uniform(0, width - 96)
x, y = int(rH), int(rW)
gray_roi = gray[x:x + 96, y:y + 96]
with open('log.txt', 'a') as fw:
fw.write('gray roi shape ' + str(gray_roi.shape) +
'\n')
dog_roi = dog[x:x + 96, y:y + 96]
lbp_roi = lbp[x:x + 96, y:y + 96]
gray_patchs.append(gray_roi)
dog_patchs.append(dog_roi)
lbp_patchs.append(lbp_roi)
with open('log.txt', 'a') as fw:
fw.write('gray patches shape ' +
str(np.array(gray_patchs).shape))
# print('shape ' + str(np.moveaxis(np.array(gray_patchs), 0, -1).shape))
X_gray[i] = np.moveaxis(np.array(gray_patchs), 0,
-1).astype('float32') / 255
X_dog[i] = np.moveaxis(np.array(dog_patchs), 0,
-1).astype('float32') / 255
X_lbp[i] = np.moveaxis(np.array(lbp_patchs), 0,
-1).astype('float32') / 255
label[i] = self.labels[idx]
# with open('log.txt', 'a') as lf:
# lf.write('ID ' + str(ID) + ' label ' + str(y[i])+'\n')
# print('y[' + str(i) + ']= ' + str(y[i]))
except cv2.error as e:
print(e)
# print('skipping id')
continue
# print('yyy = ' +str(keras.utils.to_categorical(y, num_classes=self.n_classes)))
# print('X_gray = ' + str(X_gray))
# print('X_dog = ' + str(X_dog))
# print('X_lbp = ' + str(X_lbp))
return [X_gray, X_dog, X_lbp], label