# 安装图像处理 的两个包 simpleITK 和 ipyvolume
# 安装缓存相关的两个包 diskcache 和 cassandra-driver
import gzip
from diskcache import FanoutCache, Disk
from cassandra.cqltypes import BytesType
from diskcache import FanoutCache,Disk,core
from diskcache.core import io
from io import BytesIO
from diskcache.core import MODE_BINARY
from util.logconf import logging
log+ logging.getLogger(_name_)
log.setLevel(logging.INFO)
import matplotlib
matplotlib.use('nbagg')
import numpy as np
import matplotlib.pyplot as plt
from codel.dsets import Ct ,LunaDataset
clim = (-1000.0,300)
def findPositiveSamples(start_ndx=0,limit=100):
ds = LinaDataset()
positiveSample_list = []
for sample_tup in ds.candidateInfo_list:
if sample_tup.isNodule_bool:
#print(len(positiveSanple_list),sample_tup)
if len(positiveSample_list)>=linit:
break
return positiveSample_list
def showCandidate(series_uid,batch_ndx=None, **kwargs):
ds = LunaDataset(series_uid=series_uid,**kwargs)
pos_list = [i for i, x in enumerate(ds.candidateInfo_list) if x.isNodule_bool]
if batch_ndx is None:
if pos_list:
batch_ndx = pos_list[0]
else:
print("Warning: no positive samples found : using frist negative sample.")
batch_ndx = 0
cd = Ct(series_uid)
cd_t,pos_t,series_uid,center_irc = ds[batch_ndx]
ct_a = ct_t[0].numpy()
#图像的设置问题，设置图片的大小
fig = plt.figure(figsize=(30,50))
#设置切片位置
group_list = [[9,11,13],[15,16,17],[19,21,23]]
# add_subplot(3,4,,9) 写在三行四列第九个位置
subplot = fig.add_subplot(len(grooup_list)+2,3,1)
sub.set_title('index {}'.format(int(center_irc[0])),fontsize=30)
for label in (subplot.get_xticklabels()+ subplot.get_yticklabels()):
label.set_fontsize(20)
plt.imshow(ct.hu_a[int(center_irc[0])],clim=clim,cmap='gray')
for row index_list in enumerate(group_list):
for col,index in enumerate(index_list):
subplot=fig.add_subplot(len(group_list)+2,3,row*3+clo+7)
subplot.set_title('sicle {}'.format(index),fontsize=30)
for label in (subplot.get_xticklabels() + subplot.get_vticklabels()):
label.set_fontsize(20)
plt.imshow(ct_a[index],clim=clim,cmap='gary')
print(series_uid,batch_ndx,bool(pos_t[0]),pos_list)
总结：
（1）肺部肿瘤检测项目介绍
（2）了解CT数据，定制方案
（3）下载项目中使用的数据集
（4）对数据集进行处理
（5）如何可视化CT数据
问题挑战和思考调研
（1）缓存部分去掉加载数据集需要多长时间
（2）网站grand-challenge网站上还有木有以CT数据作为数据源的项目
（3）了解下除了CT图像数据，还有什么3D图像
