Keras 在fit_generator训练方式中加入图像random_crop操作

(x_train, y_train), (x_test, y_test) = cifar10.load_data()

datagen = ImageDataGenerator(
 featurewise_center=True,
 ...
 horizontal_flip=True)

# compute quantities required for featurewise normalization
datagen.fit(x_train)

# 使用fit_generator的【自动】训练方法: fits the model on batches with real-time data augmentation
model.fit_generator(datagen.flow(x_train, y_train, batch_size=32),
   steps_per_epoch=len(x_train), epochs=epochs)

# 自己写range循环的【手动】训练方法
for e in range(epochs):
 print 'Epoch', e
 batches = 0
 for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32):
 loss = model.train(x_batch, y_batch)
 batches += 1
 if batches >= len(x_train) / 32:
  # we need to break the loop by hand because
  # the generator loops indefinitely
  break

def my_random_crop(image):
 random_arr = numpy.random.randint(img_sz-crop_sz+1, size=2)
 y = int(random_arr[0])
 x = int(random_arr[1])
 h = img_crop
 w = img_crop
 image_crop = image[y:y+h, x:x+w, :]
 return image_crop

datagen = ImageDataGenerator(
 featurewise_center=False,
 ···
 preprocessing_function=my_random_crop)

datagen.fit(x_train)

def fit_generator(self, generator, samples_per_epoch, nb_epoch,
   verbose=1, callbacks=[],
   validation_data=None, nb_val_samples=None,
   class_weight=None, max_q_size=10, **kwargs):

def generate_batch_data_random(x, y, batch_size):
 """分批取batch数据加载到显存"""
 total_num = len(x)
 batches = total_num // batch_size
 while (True):
 i = randint(0, batches)
 x_batch = x[i*batch_size:(i+1)*batch_size]
 y_batch = y[i*batch_size:(i+1)*batch_size]
 random_arr = numpy.random.randint(img_sz-crop_sz+1, size=2)
 y_pos = int(random_arr[0])
 x_pos = int(random_arr[1])
 x_crop = x_batch[:, y_pos:y_pos+crop_sz, x_pos:x_pos+crop_sz, :]
 y_crop = y_batch[:, y_pos:y_pos+crop_sz, x_pos:x_pos+crop_sz, :]
 yield (x_crop, y_crop)

import tensorflow as tf
import matplotlib.image as img
import matplotlib.pyplot as plt
sess = tf.InteractiveSession()
image = img.imread('D:/Documents/Pictures/logo3.jpg')

reshaped_image = tf.cast(image,tf.float32)
size = tf.cast(tf.shape(reshaped_image).eval(),tf.int32)
height = sess.run(size[0]//2)
width = sess.run(size[1]//2)
distorted_image = tf.random_crop(reshaped_image,[height,width,3])
print(tf.shape(reshaped_image).eval())
print(tf.shape(distorted_image).eval())

fig = plt.figure()
fig1 = plt.figure()
ax = fig.add_subplot(111)
ax1 = fig1.add_subplot(111)
ax.imshow(sess.run(tf.cast(reshaped_image,tf.uint8)))
ax1.imshow(sess.run(tf.cast(distorted_image,tf.uint8)))
plt.show()