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main_well.py
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main_well.py
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import time
from utils.well_process import *
from config.config0 import get_args
from train_well import train
from utils.utils import init_settings, setup_seed
from utils.create_model import get_model_by_name
if __name__ == '__main__':
args = get_args()
setup_seed(args.seed)
args.model_name = 'UNet'
log_dir = init_settings(args)
model = get_model_by_name(args.model_name)
run_time = []
# 将井像图分割成一定长度(256*n)的井段，分别进行补全,最后合并
for dir in os.listdir(args.data_dir):
print(dir)
args.data_dir_i = os.path.join(args.data_dir, dir)
args.res_dir_i = os.path.join(args.res_dir, dir)
if not os.path.exists(args.res_dir_i):
os.makedirs(args.res_dir_i)
start = time.time()
if dir != 'last':
args.size = 256 * args.n
args.l2 = 256
# 训练 生成补全空白带后的图像
train(args, model)
# 将生成的空白带部分 与 带空白带的原图 合并
merge(args)
else:
df = pd.read_csv(os.path.join(args.data_dir_i, 'last.csv'))
args.l1 = df['0'][0] # 最后一段完整的256行像素块的个数
args.l2 = df['0'][1] # 最后一段最后不完整的256行像素块需要补齐的像素行数
args.size = 256*(args.l1+1)
train(args, model)
merge(args)
end = time.time()
run_time.append(end - start)
# 记录补全图像所花费的时长
with open(os.path.join(log_dir, 'log.txt'), mode='w') as log_object:
log_object.write(time.strftime("%H:%M:%S", time.gmtime(int(sum(run_time)))))
# 将井段合并保存为las文件
save_las(args)