如何将两个keras成像的脂肪烯量放置在图像集中

Question

我正在寻找以下任务的解决方案或示例：

我具有从不同角度拍摄的相同对象的一组图像。我想用keras构建一个深CNN，可以将两个图像的 sets 构建，分别对每个图像执行数据增强，然后将它们一起馈入连接的模型。

。

更详细的说明：

图像存储在HDF5文件中，并带有以下形状：

data['Xp'] # shape=(3000, 224, 224, 3) #RGB images
data['Xs'] # shape=(3000, 224, 224, 3) #RGB images
data['Y']  # shape=(3000, 9) #categorical data.

现在，我想要一个可以：

的生成器

1. 争夺数据集的索引。
2. 然后拍摄图像和
3. 数据中的类别增强了X1_train的图像，X2_train
4. 将其分别送入具有以下结构的NetWrok：

代码...

from keras.layers import Flatten, Dense, Input, Dropout, Convolution2D, MaxPooling2D, Merge

img_input = Input(shape=input_shape)
x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv1')(img_input)
x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# ... more network definition here ....
model1 = Model(img_input, x)
model2 = Model(img_input, x)
merged = Merge([model1, model2], mode='concat')
final_model = Sequential()
final_model.add(merged)
final_model.add(Dense(9, activation='softmax'))

我创建了以下生成器，该发电机产生了预期数据以馈送 模型的fit_generator ...

def aug_train_iterator(Xp, Xs, Y, database_file=database_file, is_binary=True):
    from itertools import izip
    from keras.preprocessing.image import ImageDataGenerator
    seed = 7 #make sure that two iterators give same tomato each time...
    ig = ImageDataGenerator(dim_ordering='tf', rotation_range=90,
                                               width_shift_range=0.05,
                                               height_shift_range=0.05,
                                               zoom_range=0.05,
                                               fill_mode='constant',
                                               cval=0.0,
                                               horizontal_flip=True,
                                               rescale=1./255)

    for batch in izip(ig.flow(Xp,Y,  seed=seed), ig.flow(Xs, seed=seed)):
        for i in range(len(batch[0][0])):
            x1 = batch[0][0][i].reshape(1,224, 224, 3)
            x2 = batch[1][i].reshape(1, 224, 224, 3)
            y = batch[0][1][i].reshape(1,2)
            yield ([x1, x2], y)

现在，当我尝试适合模型...

时

gen = aug_train_iterator(Xp, Xs, Y)
final_model.fit_generator(gen, 1000, 20)

它实际上是为了几个图像而运行...然后提出大约15张图像的错误：

Epoch 1/20
  15/1000 [..............................] - ETA: 606s - loss: 0.7001 - acc: 0.4000
Exception in thread Thread-44:
Traceback (most recent call last):
  File "/usr/lib/python2.7/threading.py", line 810, in __bootstrap_inner
    self.run()
  File "/usr/lib/python2.7/threading.py", line 763, in run
    self.__target(*self.__args, **self.__kwargs)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 404, in data_generator_task
    generator_output = next(generator)
  File "<ipython-input-134-f128a127c7ce>", line 35, in aug_train_iterator
    for batch in izip(ig.flow(Xp,Y,  seed=seed), ig.flow(Xs, seed=seed)):
  File "/usr/local/lib/python2.7/dist-packages/keras/preprocessing/image.py", line 495, in next
    x = self.X[j]
  File "/usr/lib/python2.7/dist-packages/h5py/_hl/dataset.py", line 367, in __getitem__
    if self._local.astype is not None:
AttributeError: 'thread._local' object has no attribute 'astype'

有什么问题？

Answer 1

使用pickle_safe = true解决了问题