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import os
import json
import numpy as np
import argparse
class_tag_1 = {1: '磨玻璃结节',
2: '实性结节',
3: '混合结节',
4: '钙化结节'}
class_tag_2 = {1: '良性',
2: '恶性'}
def followup_processing(args):
# 按病人id和时间整理匹配结果
followup_root = os.path.join(args.job_data_root, 'output/elastix')
files = os.listdir(followup_root)
working_dicts = {}
for name in files:
patientid = name.split('_')[0].split('-')[-1]
if patientid not in working_dicts.keys():
working_dicts[patientid] = []
working_dicts[patientid].append(os.path.join(followup_root, name))
else:
working_dicts[patientid].append(os.path.join(followup_root, name))
for key in list(working_dicts.keys()):
working_dicts[key] = sorted(working_dicts[key])
# 计算每一个病人的信息，（size、类别）
working_infos = {}
for key in list(working_dicts.keys()):
working_infos[key] = []
files = working_dicts[key]
for file in files:
temp= {'matched': [],
'missing': [],
'new': []}
match_results = json.load(open(os.path.join(file, 'match_results.json'), 'r'))
if len(match_results['matched']) != 0:
for res in match_results['matched']:
res[0].append(res[0][3] * res[0][4] * res[0][5])
res[1].append(res[1][3] * res[1][4] * res[1][5])
temp['matched'].append(res)
if len(match_results['missing']) != 0:
for res in match_results['missing']:
res.append(res[3] * res[4] * res[5])
temp['missing'].append(res)
if len(match_results['new']) != 0:
for res in match_results['new']:
res.append(res[3] * res[4] * res[5])
temp['new'].append(res)
working_infos[key].append(temp)
# 得出结论
class_change = {'1': [], '2': []}
for key in list(working_infos.keys()):
print('For Patient-{}'.format(key))
print('\n')
temp = {'big': {'num': 0,
'll': []},
'small': {'num': 0,
'll': []},
'eq': {'num': 0,
'll': []}
}
for value in working_infos[key]:
if len(value['matched']) != 0:
for pp in value['matched']:
if pp[0][-1] > pp[1][-1]:
temp['small']['num'] += 1
temp['small']['ll'].append(abs(pp[0][-1] - pp[1][-1]))
elif pp[0][-1] < pp[1][-1]:
temp['big']['num'] += 1
temp['big']['ll'].append(abs(pp[0][-1] - pp[1][-1]))
elif pp[0][-1] == pp[1][-1]:
temp['eq']['num'] += 1
temp['eq']['ll'].append(pp[0][-1])
class_change['1'].append(pp[0][-3])
class_change['1'].append(pp[0][-3])
class_change['2'].append(pp[0][-2])
class_change['2'].append(pp[0][-2])
choice_tag = None
choice_num = -100
choice_ll = []
for tag in list(temp.keys()):
if choice_num < temp[tag]['num']:
choice_tag = tag
choice_num = temp[tag]['num']
choice_ll = temp[tag]['ll']
if choice_tag == 'big':
print('结节可能向变大发展，约变大{};'.format(np.random.choice(choice_ll, size=1)[0]))
print('结节分类结果可能为：{};'.format(class_tag_1[np.random.choice(class_change['1'], size=1)[0]]))
print('结节良恶性分类结果可能为： {}.'.format(class_tag_2[np.random.choice(class_change['2'], size=1)[0]]))
elif choice_tag == 'small':
print('结节可能向变小发展，约变小{};'.format(np.random.choice(choice_ll, size=1)[0]))
print('结节分类结果可能为：{};'.format(class_tag_1[np.random.choice(class_change['1'], size=1)[0]]))
print('结节良恶性分类结果可能为： {}.'.format(class_tag_2[np.random.choice(class_change['2'], size=1)[0]]))
elif choice_tag == 'eq':
print('结节可能向不变发展，约保持{};'.format(np.random.choice(choice_ll, size=1)[0]))
print('结节分类结果可能为：{};'.format(class_tag_1[np.random.choice(class_change['1'], size=1)[0]]))
print('结节良恶性分类结果可能为： {}.'.format(class_tag_2[np.random.choice(class_change['2'], size=1)[0]]))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='followup processing')
parser.add_argument('--job_data_root', default='/data/job_715/job_data_preprocess', type=str, help='model')
args = parser.parse_args()
followup_processing(args)