image——Data Augmentation的代码

def crop(image, random_crop, image_size):
    if image.shape[1]>image_size:
        sz1 = int(image.shape[1]//2)
        sz2 = int(image_size//2)
        if random_crop:
            diff = sz1-sz2
            (h, v) = (np.random.randint(-diff, diff+1), np.random.randint(-diff, diff+1))
        else:
            (h, v) = (0,0)
        image = image[(sz1-sz2+v):(sz1+sz2+v),(sz1-sz2+h):(sz1+sz2+h),:]
    return image

############################################################################
# 函数：crop
# 描述：随机裁剪图像
#
# 输入：图像image, crop_size
# 返回：图像image
############################################################################
def crop(image, crop_size, random_crop=True):
    if random_crop:  # 若随机裁剪
        if image.shape[1] > crop_size:
            sz1 = image.shape[1] // 2
            sz2 = crop_size // 2
            diff = sz1 - sz2
            (h, v) = (np.random.randint(0, diff + 1), np.random.randint(0, diff + 1))
            image = image[v:(v + crop_size), h:(h + crop_size), :]

    return image

def flip(image, random_flip=True):
    if random_flip and np.random.choice([True, False]):
        image = np.fliplr(image)
    if random_flip and np.random.choice([True, False]):
        image = np.flipud(image)
    return image

def random_rotate_image(image):
    angle = np.random.uniform(low=-10.0, high=10.0)
    return misc.imrotate(image, angle, 'bicubic')

############################################################################
# 函数：rotation
# 描述：随机旋转图片，增强数据，用图像边缘进行填充。
#
# 输入：图像image
# 返回：图像image
############################################################################
def rotation(image, random_flip=True):
    if random_flip and np.random.choice([True, False]):
        w,h = image.shape[1], image.shape[0]
        # 0-180随机产生旋转角度。
        angle = np.random.randint(0,180)
        RotateMatrix = cv2.getRotationMatrix2D(center=(image.shape[1]/2, image.shape[0]/2), angle=angle, scale=0.7)
        # image = cv2.warpAffine(image, RotateMatrix, (w,h), borderValue=(129,137,130))
        #image = cv2.warpAffine(image, RotateMatrix, (w,h),borderValue=(129,137,130))
        image = cv2.warpAffine(image, RotateMatrix, (w,h),borderMode=cv2.BORDER_REPLICATE)
    return image

def prewhiten(x):
    mean = np.mean(x)
    std = np.std(x)
    std_adj = np.maximum(std, 1.0/np.sqrt(x.size))
    y = np.multiply(np.subtract(x, mean), 1/std_adj)
    return y

############################################################################
# 函数：translation
# 描述：随机平移图片，增强数据，用图像边缘进行填充。
#
# 输入：图像image
# 返回：图像image
############################################################################
def translation(image, random_flip=True):
    if random_flip and np.random.choice([True, False]):
        w,h = 1920, 1080
        H1 = np.float32([[1,0],[0,1]])
        H2 = np.random.uniform(50,500, [2,1])
        H = np.hstack([H1, H2])
        # H = np.float32([[1,0,408],[0,1,431]])
        print (H)
        image = cv2.warpAffine(image, H, (w,h), borderMode=cv2.BORDER_REPLICATE)
    return image

from skimage import exposure
import numpy as np
def gen_exposure(image, random_xp=True):
    if random_xp and np.random.choice([True, False]):
        image = exposure.adjust_gamma(image, 1.2) # 调暗
    if random_xp and np.random.choice([True, False]):
        image = exposure.adjust_gamma(image, 1.5) # 调暗
    if random_xp and np.random.choice([True, False]):
        image = exposure.adjust_gamma(image, 0.9) # 调亮
    if random_xp and np.random.choice([True, False]):
        image = exposure.adjust_gamma(image, 0.8) # 调亮
    if random_xp and np.random.choice([True, False]):
        image = exposure.adjust_gamma(image, 0.7) # 调暗
    return image