PictureDataEnhance
目录
数据增强中的一些基本操作，例如裁剪图片大小，图片正则话标准化处理，图片数据转tensor向量，图片随机裁剪，旋转，过滤，图片锐化，以及图片模糊等。
1. Resize
class Resize(object):
def __init__(self, output_size):
self.output_size = output_size
def __call__(self, X, Y):
_X = cv2.resize(X, self.output_size)
w, h = self.output_size
c = Y.shape[-1]
_Y = np.zeros((h, w, c))
for i in range(Y.shape[-1]):
_Y[..., i] = cv2.resize(Y[..., i], self.output_size)
return _X, _Y
2. Clip
class Clip(object):
def __init__(self, mini, maxi=None):
if maxi is None:
self.mini, self.maxi = 0, mini
else:
self.mini, self.maxi = mini, maxi
def __call__(self, X, Y):
mini_mask = np.where(X < self.mini)
maxi_mask = np.where(X > self.maxi)
X[mini_mask] = self.mini
X[maxi_mask] = self.maxi
return X, Y
3. Normalize or Standardize
class Normalize(object):
def __init__(self, axis=None):
self.axis = axis
def __call__(self, X, Y):
mini = np.min(X, self.axis)
maxi = np.max(X, self.axis)
X = (X - mini) / (maxi - mini)
return X, Y
class Standardize(object):
def __init__(self, axis=None):
self.axis = axis
def __call__(self, X, Y):
mean = np.mean(X, self.axis)
std = np.std(X, self.axis)
X = (X - mean) / std
return X, Y
4. ToTensor
如果您使用的是Pytorch，则需要将图像转换为Torch.Tensor。 唯一需要注意的是，使用Pytorch，我们的图像维度中首先是通道，而不是最后是通道。 最后，我们还可以选择张量的输出类型。
class ToTensor(object):
def __init__(self, X_type=None, Y_type=None):
# must bu torch types
self.X_type = X_type
self.Y_type = Y_type
def __call__(self, X, Y):
# swap color axis because
# numpy img_shape: H x W x C
# torch img_shape: C X H X W
X = X.transpose((2, 0, 1))
Y = Y.transpose((2, 0, 1))
# convert to tensor
X = torch.from_numpy(X)
Y = torch.from_numpy(Y)
if self.X_type is not None:
X = X.type(self.X_type)
if self.Y_type is not None:
Y = Y.type(self.Y_type)
return X, Y
5. Flip
class Flip(object):
def __call__(self, X, Y):
for axis in [0, 1]:
if np.random.rand(1) < 0.5:
X = np.flip(X, axis)
Y = np.flip(Y, axis)
return X, Y
6. Random crop
class Crop(object):
def __init__(self, min_size_ratio, max_size_ratio=(1, 1)):
self.min_size_ratio = np.array(list(min_size_ratio))
self.max_size_ratio = np.array(list(max_size_ratio))
def __call__(self, X, Y):
size = np.array(X.shape[:2])
mini = self.min_size_ratio * size
maxi = self.max_size_ratio * size
# random size
h = np.random.randint(mini[0], maxi[0])
w = np.random.randint(mini[1], maxi[1])
# random place
shift_h = np.random.randint(0, size[0] - h)
shift_w = np.random.randint(0, size[1] - w)
X = X[shift_h:shift_h+h, shift_w:shift_w+w]
Y = Y[shift_h:shift_h+h, shift_w:shift_w+w]
return X, Y
7. Filter
class CustomFilter(object):
def __init__(self, kernel):
self.kernel = kernel
def __call__(self, X, Y):
X = cv2.filter2D(X, -1, self.kernel)
return X, Y
8. Sharpen
class Sharpen(object):
def __init__(self, max_center=4):
self.identity = np.array([[0, 0, 0],
[0, 1, 0],
[0, 0, 0]])
self.sharpen = np.array([[ 0, -1, 0],
[-1, 4, -1],
[ 0, -1, 0]]) / 4
def __call__(self, X, Y):
sharp = self.sharpen * np.random.random() * self.max_center
kernel = self.identity + sharp
X = cv2.filter2D(X, -1, kernel)
return X, Y
9. Blur
本质上，它是一种数据平滑技术（data smoothing）；所谓"模糊"，可以理解成每一个像素都取周边像素的平均值。图像都是连续的，越靠近的点关系越密切，越远离的点关系越疏远。因此，加权平均更合理，距离越近的点权重越大，距离越远的点权重越小。权重的分配使用高斯模型；
class GaussianBlur(object):
def __init__(self, max_kernel=(7, 7)):
self.max_kernel = max_kernel
def __call__(self, X, Y):
kernel_size = (
np.random.randint(1, self.max_kernel[0] + 1),
np.random.randint(1, self.max_kernel[1] + 1),
)
X = cv2.GaussianBlur(X, kernel_size, 0)
return X, Y
'''
高斯模糊处理
'''
def GaussianBlurHandle(self,imagepath):
kernel_size=(5,5)
sigma=1.5
img=cv2.imread(imagepath)
img=cv2.GaussianBlur(img,kernel_size,sigma)
cv2.imwrite("./temp/gaosi.png",img)
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