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unet_camvid.py
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# -*- coding:utf-8 -*-
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint
import cv2
from data_camvid import *
class myUnet(object):
def __init__(self, img_rows=512, img_cols=512):
self.img_rows = img_rows
self.img_cols = img_cols
def load_data(self):
mydata = dataProcess(self.img_rows, self.img_cols)
imgs_train, imgs_mask_train = mydata.load_train_data()
imgs_test = mydata.load_test_data()
return imgs_train, imgs_mask_train, imgs_test
def get_unet(self):
inputs = Input((self.img_rows, self.img_cols, 3))
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
# print(conv1)
print "conv1 shape:", conv1.shape
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
print "conv1 shape:", conv1.shape
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
print "pool1 shape:", pool1.shape
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
print "conv2 shape:", conv2.shape
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
print "conv2 shape:", conv2.shape
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
print "pool2 shape:", pool2.shape
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
print "conv3 shape:", conv3.shape
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
print "conv3 shape:", conv3.shape
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
print "pool3 shape:", pool3.shape
conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(drop5))
merge6 = merge([drop4, up6], mode='concat', concat_axis=3)
print(up6)
print(merge6)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
print(conv6)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
print(conv6)
up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv6))
merge7 = merge([conv3, up7], mode='concat', concat_axis=3)
print(up7)
print(merge7)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
print(conv7)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
print(conv7)
up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv7))
merge8 = merge([conv2, up8], mode='concat', concat_axis=3)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv8))
merge9 = merge([conv1, up9], mode='concat', concat_axis=3)
print(up9)
print(merge9)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
print(conv9)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
print(conv9)
conv9 = Conv2D(12, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
print "conv9 shape:", conv9.shape
conv10 = Conv2D(12, 1, activation='softmax')(conv9)
print(conv10)
model = Model(input=inputs, output=conv10)
model.compile(optimizer=Adam(lr=1e-4), loss='categorical_crossentropy', metrics=['accuracy'])
return model
def train(self):
print("loading data")
imgs_train, imgs_mask_train, imgs_test = self.load_data()
print("loading data done")
model = self.get_unet()
print("got unet")
model_checkpoint = ModelCheckpoint('unet_camvid.hdf5', monitor='loss', verbose=1, save_best_only=True)
print('Fitting model...')
model.fit(imgs_train, imgs_mask_train, batch_size=1, epochs=50, verbose=1,
validation_split=0.1, shuffle=True, callbacks=[model_checkpoint])
print('predict test data')
imgs_mask_test = model.predict(imgs_test, batch_size=1, verbose=1)
np.save('./results/camvid_mask_test.npy', imgs_mask_test)
def save_img(self):
print("array to image")
imgs = np.load('./results/camvid_mask_test.npy')
piclist = []
for line in open("./results/camvid.txt"):
line = line.strip()
picname = line.split('/')[-1]
piclist.append(picname)
for i in range(imgs.shape[0]):
path = "./results/" + piclist[i]
img = np.zeros((imgs.shape[1], imgs.shape[2], 3), dtype=np.uint8)
for k in range(len(img)):
for j in range(len(img[k])): # cv2.imwrite也是BGR顺序
num = np.argmax(imgs[i][k][j])
if num == 0:
img[k][j] = [128, 128, 128]
elif num == 1:
img[k][j] = [128, 0, 0]
elif num == 2:
img[k][j] = [192, 192, 128]
elif num == 3:
img[k][j] = [255, 69, 0]
elif num == 4:
img[k][j] = [128, 64, 128]
elif num == 5:
img[k][j] = [60, 40, 222]
elif num == 6:
img[k][j] = [128, 128, 0]
elif num == 7:
img[k][j] = [192, 128, 128]
elif num == 8:
img[k][j] = [64, 64, 128]
elif num == 9:
img[k][j] = [64, 0, 128]
elif num == 10:
img[k][j] = [64, 64, 0]
elif num == 11:
img[k][j] = [0, 128, 192]
img = cv2.resize(img, (480, 360), interpolation=cv2.INTER_CUBIC)
cv2.imwrite(path, img)
if __name__ == '__main__':
myunet = myUnet()
model = myunet.get_unet()
# model.summary()
# plot_model(model, to_file='model.png')
myunet.train()
myunet.save_img()