Y_train.append(seg_labels)
# 读完一个周期后重新开始
i = (i + 1) % n
yield (np.array(X_train), np.array(Y_train))
def loss(y_true, y_pred):
loss = K.categorical_crossentropy(y_true, y_pred)
return loss
if __name__ == "__main__":
log_dir = "logs/"
# 获取model
model = Deeplabv3(classes=NCLASSES, input_shape=(HEIGHT, WIDTH, 3))
# model.summary()
weights_path = get_file(
'deeplabv3_mobilenetv2_tf_dim_ordering_tf_kernels.h5',
WEIGHTS_PATH_MOBILE,
cache_subdir='models')
# model.load_weights(weights_path,by_name=True,skip_mismatch=True)
model.load_weights(weights_path, by_name=True, skip_mismatch=True)
# 打开数据集的txt
with open(r".\dataset2\train_data.txt", "r") as f:
lines = f.readlines()
# 打乱行，这个txt主要用于帮助读取数据来训练
# 打乱的数据更有利于训练
np.random.seed(10101)
from nets.deeplab import Deeplabv3
from PIL import Image
import numpy as np
import random
import copy
import os
class_colors = [[0, 0, 0], [0, 255, 0]]
NCLASSES = 2
HEIGHT = 416
WIDTH = 416
model = model = Deeplabv3(classes=2, input_shape=(HEIGHT, WIDTH, 3))
model.load_weights("logs/last1.h5")
imgs = os.listdir("./building_img")
for jpg in imgs:
img = Image.open("./building_img/" + jpg)
old_img = copy.deepcopy(img)
orininal_h = np.array(img).shape[0]
orininal_w = np.array(img).shape[1]
img = img.resize((WIDTH, HEIGHT))
img = np.array(img)
img = img / 255
img = img.reshape(-1, HEIGHT, WIDTH, 3)
pr = model.predict(img)[0]
pr = pr.reshape((int(HEIGHT), int(WIDTH), NCLASSES)).argmax(axis=-1)
from nets.deeplab import Deeplabv3 model = Deeplabv3(input_shape=(512 * 2, 512 * 2, 3), classes=2, OS=16) model.summary()
from nets.deeplab import Deeplabv3 from keras.utils import plot_model deeplab_model = Deeplabv3() deeplab_model.summary()
#---------------------------------------------#
# 该部分用于查看网络结构
#---------------------------------------------#
from nets.deeplab import Deeplabv3
if __name__ == "__main__":
model = Deeplabv3(classes=2, OS=16)
model.summary()
for i in range(len(model.layers)):
print(i, model.layers[i].name)