CNN特征点训练和识别基于python3.7

环境：python3.7  

疫情期间在家无聊就学了下cnn对集装箱的箱门特征进行了训练，基本200张图片就可以达到95%准确率

 代码如下：

from keras.models import Sequential
from keras.layers import Conv2D, MaxPool2D, Activation, Dropout, Flatten, Dense
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
from keras.models import load_model
import numpy as np

# define model
model = Sequential()
model.add(Conv2D(input_shape=(150, 150, 3), filters=32, kernel_size=3, padding='same', activation='relu'))
model.add(Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'))
model.add(MaxPool2D(pool_size=2, strides=2))

model.add(Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))
model.add(Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))
model.add(MaxPool2D(pool_size=2, strides=2))

model.add(Conv2D(filters=128, kernel_size=3, padding='same', activation='relu'))
model.add(Conv2D(filters=128, kernel_size=3, padding='same', activation='relu'))
model.add(MaxPool2D(pool_size=2, strides=2))

model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))

# define optimizer
adam = Adam(lr=1e-4)

# define optimizer, value function, calculate accuracy
model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])

train_datagen = ImageDataGenerator(
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
rescale=1/255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest'
)

test_datagen = ImageDataGenerator(
rescale=1/255
)

batch_size = 32

# create train data
train_generator = train_datagen.flow_from_directory(
'test/train',
target_size=(150, 150),
batch_size=batch_size,
)

# create test data
test_generator = test_datagen.flow_from_directory(
'test/test',
target_size=(150, 150),
batch_size=batch_size,
)
print (train_generator.class_indices)
model.fit_generator(train_generator, epochs=30, validation_data=test_generator, steps_per_epoch=150/batch_size, validation_steps=1)
model.save('dor_cnn.h5')

运行结果：测试下来还是很准确的，优化下模型就可以达到可用级别