高光谱的数据增强
数据增强
文章目录
- 数据增强
-
- 1. Data Augmentation for Hyperspectral Image Classification With Deep CNN
-
- 传统的方法对分类性能的提升有限
- pixel-block pair data augmentation
- 2. pytorch 中的数据增强方式
-
- 2.1 torchvision.transforms
- 2.2 Numpy 的数据增强
1. Data Augmentation for Hyperspectral Image Classification With Deep CNN
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 16, NO. 4, APRIL 2019
Labeled hyperspectral remote sensing data are always limited,
which restrict the development of deep CNN in classification
tasks
传统的方法对分类性能的提升有限
Some data augmentation strategies, such as traditional translation, clipping, commonly used flip, rotation, noise, and promising sample pairing, are utilized to increase both the amount and diversity of samples. However, there is no systematical research on how it affects the performance of deep CNN. Thus, in this letter, a series of work on HSI data augmentation is investigated, including the discussion of common data augmentation methods and a novel and efficient way to augment.
实验中对比了几种方法:
- rotating once
- rotating once and adding noise
- flip once
- flip once and adding noise
- adding noise
pixel-block pair data augmentation
In the proposed framework, each selected
pixel is built into a pixel block, and a pixel-block pair (PBP) is used for training. If any two-pixel blocks (i.e., one PBP) come from the same class, the label of the PBP remains unchanged, otherwise, a new label is designed (i.e., zero). During testing
process, the class label of the pixel is generated by voting from well-trained CNN classification results.
2. pytorch 中的数据增强方式
2.1 torchvision.transforms
2.2 Numpy 的数据增强
数据增强策略：
1 在线模式--训练中
随机裁剪(完全随机，四个角+中心) crop
def random_crop(img, scale=[0.8, 1.0], ratio=[3. / 4., 4. / 3.], resize_w=100, resize_h=100):
"""
随机裁剪
:param img:
:param scale: 缩放
:param ratio:
:param resize_w:
:param resize_h:
:return:
"""
aspect_ratio = math.sqrt(np.random.uniform(*ratio))
w = 1. * aspect_ratio
h = 1. / aspect_ratio
src_h, src_w = img.shape[:2]
bound = min((float(src_w) / src_h) / (w ** 2),
(float(src_h) / src_w) / (h ** 2))
scale_max = min(scale[1], bound)
scale_min = min(scale[0], bound)
target_area = src_h * src_w * np.random.uniform(scale_min,
scale_max)
target_size = math.sqrt(target_area)
w = int(target_size * w)
h = int(target_size * h)
i = np.random.randint(0, src_w - w + 1)
j = np.random.randint(0, src_h - h + 1)
img = img[j:j + h, i:i + w]
img = cv2.resize(img, (resize_w, resize_h))
return img
def rule_crop(img, box_ratio=(3. / 4, 3. / 4), location_type='LT', resize_w=100, resize_h=100):
"""
按照一定规则进行裁剪, 直接在原图尺寸上操作，不对原图进行
:param img:
:param box_ratio: 剪切的 比例： （宽度上的比例， 高度上的比例）
:param location_type: 具体在=哪个位置： 以下其中一个：
LR : 左上角
RT : 右上角
LB : 左下角
RB : 右下角
CC : 中心
:param resize_w: 输出图的width
:param resize_h: 输出图的height
:return:
"""
assert location_type in ('LT', 'RT', 'LB', 'RB', 'CC'), 'must have a location .'
is_gray = False
if len(img.shape) == 3:
h, w, c = img.shape
elif len(img.shape) == 2:
h, w = img.shape
is_gray = True
crop_w, crop_h = int(w * box_ratio[0]), int(h * box_ratio[1])
crop_img = np.zeros([10, 10])
if location_type == 'LT':
crop_img = img[:crop_h, :crop_w, :] if not is_gray else img[:crop_h, :crop_w]
elif location_type == 'RT':
crop_img = img[:crop_h:, w - crop_w:, :] if not is_gray else img[:crop_h:, w - crop_w:]
elif location_type == 'LB':
crop_img = img[h - crop_h:, :crop_w, :] if not is_gray else img[h - crop_h:, :crop_w]
elif location_type == 'RB':
crop_img = img[h - crop_h:, w - crop_w:, :] if not is_gray else img[h - crop_h:, w - crop_w:]
elif location_type == 'CC':
start_h = (h - crop_h) // 2
start_w = (w - crop_w) // 2
crop_img = img[start_h:start_h + crop_h, start_w:start_w + crop_w, :] if not is_gray else img[
start_h:start_h + crop_h,
start_w:start_w + crop_w]
resize = cv2.resize(crop_img, (resize_w, resize_h))
return resize
水平翻转 flip
def random_flip(img, mode=1):
"""
随机翻转
:param img:
:param model: 1=水平翻转 / 0=垂直 / -1=水平垂直
:return:
"""
assert mode in (0, 1, -1), "mode is not right"
flip = np.random.choice(2) * 2 - 1 # -1 / 1
if mode == 1:
img = img[:, ::flip, :]
elif mode == 0:
img = img[::flip, :, :]
elif mode == -1:
img = img[::flip, ::flip, :]
return img
def flip(img, mode=1):
"""
翻转
:param img:
:param mode: 1=水平翻转 / 0=垂直 / -1=水平垂直
:return:
"""
assert mode in (0, 1, -1), "mode is not right"
return cv2.flip(img, flipCode=mode)
随机锐化增强
def random_USM(img, gamma=0.):
"""
USM锐化增强算法可以去除一些细小的干扰细节和图像噪声，比一般直接使用卷积锐化算子得到的图像更可靠。
output = 原图像−w∗高斯滤波(原图像)/(1−w)
其中w为上面所述的系数，取值范围为0.1~0.9，一般取0.6。
:param img:
:param gamma:
:return:
"""
blur = cv2.GaussianBlur(img, (0, 0), 25)
img_sharp = cv2.addWeighted(img, 1.5, blur, -0.3, gamma)
return img_sharp
2 离线模式
2.1 随机扰动
噪声(高斯、自定义) noise
def random_noise(img, rand_range=(3, 20)):
"""
随机噪声
:param img:
:param rand_range: (min, max)
:return:
"""
img = np.asarray(img, np.float)
sigma = random.randint(*rand_range)
nosie = np.random.normal(0, sigma, size=img.shape)
img += nosie
img = np.uint8(np.clip(img, 0, 255))
return img
滤波(高斯、平滑、均值、中值、最大最小值、双边、引导、运动)
# 各种滤波原理介绍：https://blog.csdn.net/hellocsz/article/details/80727972
def gaussianBlue(img, ks=(7, 7), stdev=1.5):
"""
高斯模糊, 可以对图像进行平滑处理，去除尖锐噪声
:param img:
:param ks: 卷积核
:param stdev: 标准差
:return:
"""
return cv2.GaussianBlur(img, (7, 7), 1.5)
# 随机滤波
def ranndom_blur(img, ksize=(3, 3)):
"""
随机滤波
:param img:
:param ksize:
:return:
"""
blur_types = ['gaussian', 'median', 'bilateral', 'mean', 'box']
assert len(blur_types) > 0
blur_func = None
blur_index = random.choice(blur_types)
if blur_index == 0: # 高斯模糊, 比均值滤波更平滑，边界保留更加好
blur_func = cv2.GaussianBlur
elif blur_index == 1: # 中值滤波, 在边界保存方面好于均值滤波，但在模板变大的时候会存在一些边界的模糊。对于椒盐噪声有效
blur_func = cv2.medianBlur
elif blur_index == 2: # 双边滤波, 非线性滤波，保留较多的高频信息，不能干净的过滤高频噪声，对于低频滤波较好，不能去除脉冲噪声
blur_func = cv2.bilateralFilter
elif blur_index == 3: # 均值滤波, 在去噪的同时去除了很多细节部分，将图像变得模糊
blur_func = cv2.blur
elif blur_index == 4: # 盒滤波器
blur_func = cv2.boxFilter
img_blur = blur_func(src=img, ksize=ksize)
return img_blur
# 直方图均衡化
def equalize_hist(img):
"""
直方图均衡化
:param img:
:return:
"""
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
hist = cv2.equalizeHist(gray)
rgb = cv2.cvtColor(hist, cv2.COLOR_GRAY2RGB)
return rgb
2.2 转换
旋转 rorate
def rotate(img, angle, scale=1.0):
"""
旋转
:param img:
:param angle: 旋转角度， >0 表示逆时针，
:param scale:
:return:
"""
height, width = img.shape[:2] # 获取图像的高和宽
center = (width / 2, height / 2) # 取图像的中点
M = cv2.getRotationMatrix2D(center, angle, scale) # 获得图像绕着某一点的旋转矩阵
# cv2.warpAffine()的第二个参数是变换矩阵,第三个参数是输出图像的大小
rotated = cv2.warpAffine(img, M, (height, width))
return rotated
def random_rotate(img, angle_range=(-10, 10)):
"""
随机旋转
:param img:
:param angle_range: 旋转角度范围 (min,max) >0 表示逆时针，
:return:
"""
height, width = img.shape[:2] # 获取图像的高和宽
center = (width / 2, height / 2) # 取图像的中点
angle = random.randrange(*angle_range, 1)
M = cv2.getRotationMatrix2D(center, angle, 1.0) # 获得图像绕着某一点的旋转矩阵
# cv2.warpAffine()的第二个参数是变换矩阵,第三个参数是输出图像的大小
rotated = cv2.warpAffine(img, M, (height, width))
return rotated
偏移 shift
def shift(img, x_offset, y_offset):
"""
偏移，向右 向下
:param img:
:param x_offset: >0表示向右偏移px, <0表示向左
:param y_offset: >0表示向下偏移px, <0表示向上
:return:
"""
h, w, _ = img.shape
M = np.array([[1, 0, x_offset], [0, 1, y_offset]], dtype=np.float)
return cv2.warpAffine(img, M, (w, h))
倾斜 skew
...
缩放 scale
def resize_img(img, resize_w, resize_h):
height, width = img.shape[:2] # 获取图片的高和宽
return cv2.resize(img, (resize_w, resize_h), interpolation=cv2.INTER_CUBIC)
RGB/BGR->HSV
def rgb2hsv_py(r, g, b):
# from https://blog.csdn.net/weixin_43360384/article/details/84871521
r, g, b = r/255.0, g/255.0, b/255.0
mx = max(r, g, b)
mn = min(r, g, b)
m = mx-mn
if mx == mn:
h = 0
elif mx == r:
if g >= b:
h = ((g-b)/m)*60
else:
h = ((g-b)/m)*60 + 360
elif mx == g:
h = ((b-r)/m)*60 + 120
elif mx == b:
h = ((r-g)/m)*60 + 240
if mx == 0:
s = 0
else:
s = m/mx
v = mx
return h, s, v
def rgb2hsv_cv(img):
# from https://blog.csdn.net/qq_38332453/article/details/89258058
h = img.shape[0]
w = img.shape[1]
H = np.zeros((h,w),np.float32)
S = np.zeros((h, w), np.float32)
V = np.zeros((h, w), np.float32)
r,g,b = cv2.split(img)
r, g, b = r/255.0, g/255.0, b/255.0
for i in range(0, h):
for j in range(0, w):
mx = max((b[i, j], g[i, j], r[i, j]))
mn = min((b[i, j], g[i, j], r[i, j]))
V[i, j] = mx
if V[i, j] == 0:
S[i, j] = 0
else:
S[i, j] = (V[i, j] - mn) / V[i, j]
if mx == mn:
H[i, j] = 0
elif V[i, j] == r[i, j]:
if g[i, j] >= b[i, j]:
H[i, j] = (60 * ((g[i, j]) - b[i, j]) / (V[i, j] - mn))
else:
H[i, j] = (60 * ((g[i, j]) - b[i, j]) / (V[i, j] - mn))+360
elif V[i, j] == g[i, j]:
H[i, j] = 60 * ((b[i, j]) - r[i, j]) / (V[i, j] - mn) + 120
elif V[i, j] == b[i, j]:
H[i, j] = 60 * ((r[i, j]) - g[i, j]) / (V[i, j] - mn) + 240
H[i,j] = H[i,j] / 2
return H, S, V
图片叠加与融合
def addWeight(src1, alpha, src2, beta, gamma):
"""
g (x) = (1 − α)f0 (x) + αf1 (x) #a→（0，1）不同的a值可以实现不同的效果
dst = src1 * alpha + src2 * beta + gamma
:param src1: img1
:param alpha:
:param src2: img2
:param beta:
:param gamma:
:return:
"""
assert src1.shap == src2.shape
return cv2.addWeighted(src1, alpha, src2, beta, gamma)
颜色抖动(亮度\色度\饱和度\对比度) color jitter
def adjust_contrast_bright(img, contrast=1.2, brightness=100):
"""
调整亮度与对比度
dst = img * contrast + brightness
:param img:
:param contrast: 对比度 越大越亮
:param brightness: 亮度 0~100
:return:
"""
# 像素值会超过0-255， 因此需要截断
return np.uint8(np.clip((contrast * img + brightness), 0, 255))
def pytorch_color_jitter(img):
return torchvision.transforms.ColorJitter(brightness=0, contrast=0, saturation=0, hue=0)
# gamma 变换,
def gamma_transform(img, gamma=1.0):
"""
https://blog.csdn.net/zfjBIT/article/details/85113946
伽马变换就是用来图像增强，其提升了暗部细节，简单来说就是通过非线性变换，
让图像从暴光强度的线性响应变得更接近人眼感受的响应，即将漂白（相机曝光）或过暗（曝光不足）的图片，进行矫正
:param img:
:param gamma:
# gamma = random.random() * random.choice([0.5, 1, 3, 5])
>1, 变暗
<1, 漂白
:return:
"""
assert 0 < gamma < 25.
# 具体做法先归一化到1，然后gamma作为指数值求出新的像素值再还原
gamma_table = [np.power(x / 255.0, gamma) * 255.0 for x in range(256)]
gamma_table = np.round(np.array(gamma_table)).astype(np.uint8)
# 实现映射用的是Opencv的查表函数
return cv2.LUT(img, gamma_table)
# mix up 图片混合
def mixup(batch_x, batch_y, alpha):
"""
Returns mixed inputs, pairs of targets, and lambda
:param batch_x:
:param batch_y:
:param alpha:
:return:
"""
if alpha > 0:
lam = np.random.beta(alpha, alpha)
else:
lam = 1
batch_size = batch_x.shape[0]
index = [i for i in range(batch_size)]
random.shuffle(index)
mixed_x = lam * batch_x + (1 - lam) * batch_x[index, :]
y_a, y_b = batch_y, batch_y[index]
return mixed_x, y_a, y_b, lam
3D几何变换