问题描述
当我将以下数据管道用于图像分割任务时，如何重现相同的图像增强结果？我想比较不同的模型，因此数据扩充必须是确定性的。
class data_loader(object):
def __init__(self,image_paths: List[str],mask_paths: List[str],image_size: int=IMAGE_SIZE,augment: bool=True):
self.image_paths = image_paths
self.mask_paths = mask_paths
self.image_size = image_size
self.augment = augment
def randomHueSaturationValue(self,image,hue_shift_limit=(-180,180),sat_shift_limit=(-255,255),val_shift_limit=(-255,u=0.5):
if np.random.random() < u:
image = cv2.cvtColor(image,cv2.COLOR_BGR2HSV)
h,s,v = cv2.split(image)
hue_shift = np.random.randint(hue_shift_limit[0],hue_shift_limit[1] + 1)
hue_shift = np.uint8(hue_shift)
h += hue_shift
sat_shift = np.random.uniform(sat_shift_limit[0],sat_shift_limit[1])
s = cv2.add(s,sat_shift)
val_shift = np.random.uniform(val_shift_limit[0],val_shift_limit[1])
v = cv2.add(v,val_shift)
image = cv2.merge((h,v))
image = cv2.cvtColor(image,cv2.COLOR_HSV2BGR)
return image
def randomShiftScaleRotate(self,mask,shift_limit=(-0.0,0.0),scale_limit=(-0.0,rotate_limit=(-0.0,aspect_limit=(-0.0,borderMode=cv2.BORDER_CONSTANT,u=0.5):
if np.random.random() < u:
height,width,channel = image.shape
angle = np.random.uniform(rotate_limit[0],rotate_limit[1])
scale = np.random.uniform(1 + scale_limit[0],1 + scale_limit[1])
aspect = np.random.uniform(1 + aspect_limit[0],1 + aspect_limit[1])
sx = scale * aspect / (aspect ** 0.5)
sy = scale / (aspect ** 0.5)
dx = round(np.random.uniform(shift_limit[0],shift_limit[1]) * width)
dy = round(np.random.uniform(shift_limit[0],shift_limit[1]) * height)
cc = np.math.cos(angle / 180 * np.math.pi) * sx
ss = np.math.sin(angle / 180 * np.math.pi) * sy
rotate_matrix = np.array([[cc,-ss],[ss,cc]])
Box0 = np.array([[0,0],[width,height],[0,])
Box1 = Box0 - np.array([width / 2,height / 2])
Box1 = np.dot(Box1,rotate_matrix.T) + np.array([width / 2 + dx,height / 2 + dy])
Box0 = Box0.astype(np.float32)
Box1 = Box1.astype(np.float32)
mat = cv2.getPerspectiveTransform(Box0,Box1)
image = cv2.warpPerspective(image,mat,(width,height),flags=cv2.INTER_LINEAR,borderMode=borderMode,borderValue=(
0,))
mask = cv2.warpPerspective(mask,borderValue=(
0,))
return image,mask
def randomHorizontalFlip(self,u=0.5):
if np.random.random() < u:
image = cv2.flip(image,1)
mask = cv2.flip(mask,1)
return image,mask
def randomVerticleFlip(self,0)
mask = cv2.flip(mask,0)
return image,mask
def randomrotate90(self,u=0.5):
if np.random.random() < u:
image = np.rot90(image)
mask = np.rot90(mask)
return image,mask
def read(self,image_path,mask_path):
image_path = image_path.decode()
image = cv2.imread(image_path,cv2.IMREAD_COLOR)
mask_path = mask_path.decode()
mask = cv2.imread(mask_path,cv2.IMREAD_GRAYSCALE)
return image,mask
def resize(self,mask):
image = cv2.resize(image,(self.image_size,self.image_size))
mask = cv2.resize(mask,self.image_size))
return image,mask
def normalize(self,mask):
image = image / 255.
image = image * 3.2 - 1.6
mask = mask / 255.
mask[mask >= 0.5] = 1
mask[mask <= 0.5] = 0
mask = np.expand_dims(mask,axis=-1)
return image,mask
def map_fn(self,mask_path):
def process(image_path,mask_path):
image,mask = self.read(image_path,mask_path)
image,mask = self.resize(image,mask)
if self.augment:
image = self.randomHueSaturationValue(image,hue_shift_limit=(-30,30),sat_shift_limit=(-5,5),val_shift_limit=(-15,15))
image,mask = self.randomShiftScaleRotate(image,shift_limit=(-0.1,0.1),scale_limit=(-0.1,aspect_limit=(-0.1,rotate_limit=(-0,0))
image,mask = self.randomHorizontalFlip(image,mask)
image,mask = self.randomVerticleFlip(image,mask = self.randomrotate90(image,mask)
image,mask = self.normalize(image,mask)
return image,mask
image,mask = tf.numpy_function(process,[image_path,mask_path],[tf.float64,tf.float64])
image.set_shape([self.image_size,self.image_size,3])
mask.set_shape([self.image_size,1])
return image,mask
def tf_data(self,batch_size):
dataset = tf.data.Dataset.from_tensor_slices((self.image_paths,self.mask_paths))
dataset = dataset.shuffle(len(self.image_paths),seed=101)
dataset = dataset.map(self.map_fn,num_parallel_calls=AUTOTUNE)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(buffer_size=AUTOTUNE)
return dataset
培训和验证数据集：
train_data = data_loader(image_paths=train_images,mask_paths=train_masks,image_size=IMAGE_SIZE,augment=True).tf_data(batch_size=BATCH_SIZE)
val_data = data_loader(image_paths=val_images,mask_paths=val_masks,augment=False).tf_data(batch_size=BATCH_SIZE)
在制作数据集之前，我已经设置了random.seed，np.random.seed和tf.random.set_seed，但是它不起作用。
解决方法
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