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CNN2Head_input.py
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CNN2Head_input.py
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import numpy as np
import os
import cv2
import scipy
import random
SMILE_FOLDER = './data/smile_data/'
GENDER_FOLDER = './data/gender_data/'
AGE_FOLDER = './data/age_data/'
NUM_SMILE_IMAGE = 4000
SMILE_SIZE = 48
EMOTION_SIZE = 48
def getSmileImage():
print('Load smile image...................')
X1 = np.load(SMILE_FOLDER + 'train.npy')
X2 = np.load(SMILE_FOLDER + 'test.npy')
train_data = []
test_data = []
for i in range(X1.shape[0]):
train_data.append(X1[i])
for i in range(X2.shape[0]):
test_data.append(X2[i])
print('Done !')
print('Number of smile train data: ',str(len(train_data)))
print('---------------------------------------------------------------')
return train_data, test_data
def getGenderImage():
print('Load gender image...................')
X1 = np.load(GENDER_FOLDER + 'train.npy')
X2 = np.load(GENDER_FOLDER + 'test.npy')
train_data = []
test_data = []
for i in range(X1.shape[0]):
train_data.append(X1[i])
for i in range(X2.shape[0]):
test_data.append(X2[i])
print('Done !')
print('Number of gender train data: ', str(len(train_data)))
print('---------------------------------------------------------------')
return train_data, test_data
def getAgeImage():
print('Load age image...................')
X1 = np.load(AGE_FOLDER + 'train.npy')
X2 = np.load(AGE_FOLDER + 'test.npy')
train_data = []
test_data = []
for i in range(X1.shape[0]):
train_data.append(X1[i])
for i in range(X2.shape[0]):
test_data.append(X2[i])
print('Done !')
print('Number of age train data: ', str(len(train_data)))
print('---------------------------------------------------------------')
return train_data, test_data
def random_crop(batch, crop_shape, padding=None):
oshape = np.shape(batch[0])
if padding:
oshape = (oshape[0] + 2 * padding, oshape[1] + 2 * padding)
new_batch = []
npad = ((padding, padding), (padding, padding), (0, 0))
for i in range(len(batch)):
new_batch.append(batch[i])
if padding:
new_batch[i] = np.lib.pad(batch[i], pad_width=npad, mode='constant', constant_values=0)
nh = random.randint(0, oshape[0] - crop_shape[0])
nw = random.randint(0, oshape[1] - crop_shape[1])
new_batch[i] = new_batch[i][nh:nh + crop_shape[0], nw:nw + crop_shape[1]]
return new_batch
def random_flip_leftright(batch):
for i in range(len(batch)):
if bool(random.getrandbits(1)):
batch[i] = np.fliplr(batch[i])
return batch
def random_flip_updown(batch):
for i in range(len(batch)):
if bool(random.getrandbits(1)):
batch[i] = np.flipud(batch[i])
return batch
def random_90degrees_rotation(batch, rotations=[0, 1, 2, 3]):
for i in range(len(batch)):
num_rotations = random.choice(rotations)
batch[i] = np.rot90(batch[i], num_rotations)
return batch
def random_rotation(batch, max_angle):
for i in range(len(batch)):
if bool(random.getrandbits(1)):
angle = random.uniform(-max_angle, max_angle)
batch[i] = scipy.ndimage.interpolation.rotate(batch[i], angle, reshape=False)
return batch
def random_blur(batch, sigma_max=5.0):
for i in range(len(batch)):
if bool(random.getrandbits(1)):
sigma = random.uniform(0., sigma_max)
batch[i] = scipy.ndimage.filters.gaussian_filter(batch[i], sigma)
return batch
def augmentation(batch, img_size):
batch = random_crop(batch, (img_size, img_size), 10)
#batch = random_blur(batch)
batch = random_flip_leftright(batch)
batch = random_rotation(batch, 10)
return batch