机器学习工具代码

(持续整理)

数组阈值处理

"""
img 为图像数组，同时也是numpy数组  
将img数据小于min的都设为min，同时将大于max的都设为max  
"""
img[np.where(img < min)] = min  
img[np.where(img > 250)] = max  

归一化和中心化

min = np.min(img)
max = np.max(img)
center = (min + max) / 2
img = (img - center) /(max - min) * 2

最大联通域

from skimage import measure


def max_connected_domain_3D(arr):
    # 取相同数字的最大连通域
    labels = measure.label(arr)  # <1.2s
    t = np.bincount(labels.flatten())[1:]  # <1.5s
    max_pixel = np.argmax(t) + 1  # 位置变了,去除了0
    labels[labels != max_pixel] = 0
    labels[labels == max_pixel] = 1
    return labels.astype(np.uint8)

# 测试  
arr = [[1, 1, 0, 3], [1, 0, 3, 3], [0, 1, 3, 3], [0, 0, 0, 0]]
arr = np.asarray(arr)
print(arr)
print(max_connected_domain_3D(arr))

[1 1 0 3\ 1 0 3 3\ 0 1 3 3\ 0 0 0 0\ ]

[Downarrow ]

[0 0 0 1\ 0 0 1 1\ 0 0 1 1\ 0 0 0 0 ]

arr = np.squeeze(arr) # 从数组的形状中删除单维度条目，即把shape中为1的维度去掉
y = np.transpose(y,(1,2,0))  # 将数组的轴交换 (0, 1, 2) => (1, 2, 0)
"""
出处为写nrrd文件的时候，可以考虑nrrd的数组存储形式与正常数组维度不一致
"""

绘制模型

from keras.utils import plot_model

plot_model(model, "RUnet.png", True)

demo

from keras import models
from keras import layers
from keras import regularizers
from keras.utils import plot_model


def get_model(x, y, z):
    model = models.Sequential()
    model.add(layers.Conv3D(16, (3, 3, 2), activation='relu', input_shape=(x, y, z, 1)))
    model.add(layers.BatchNormalization())
    model.add(layers.Conv3D(8, (3, 3, 2), activation='relu', kernel_regularizer=regularizers.l2(0.1)))
    model.add(layers.BatchNormalization())
    model.add(layers.Conv3D(8, (3, 3, 2), activation='relu', kernel_regularizer=regularizers.l2(0.1)))
    model.add(layers.BatchNormalization())
    model.add(layers.Conv3D(8, (3, 3, 1), activation='relu', kernel_regularizer=regularizers.l2(0.1)))
    model.add(layers.Dropout(rate=0.1))
    model.add(layers.BatchNormalization())
    model.add(layers.Flatten())
    model.add(layers.BatchNormalization())
    model.add(layers.Dense(13, activation='relu'))
    model.add(layers.BatchNormalization())
    model.add(layers.Dense(8, activation='relu'))
    model.add(layers.BatchNormalization())
    model.add(layers.Dense(8, activation='relu'))
    model.add(layers.Dense(2, activation='sigmoid'))
    model.summary()
    return model

if __name__ == '__main__':
    model = get_model(125, 125, 10)
    plot_model(model, r"C:UsersfanDesktopmodel.png", True)
    

效果图

注：需要安装graphviz

数据混淆

def data_confusion(data, label):
    # 进行数据混淆
    permutation = np.random.permutation(label.shape[0])
    shuffled_data = data[permutation, :, :]
    shuffled_label = label[permutation]
    return shuffled_data, shuffled_label