def get_iou(data_list, class_num, save_path=None):
from multiprocessing import Pool
from deeplab.metric import ConfusionMatrix
ConfM = ConfusionMatrix(class_num)
f = ConfM.generateM
pool = Pool()
m_list = pool.map(f, data_list)
pool.close()
pool.join()
for m in m_list:
ConfM.addM(m)
aveJ, j_list, M = ConfM.jaccard()
print('meanIOU: ' + str(aveJ) + '\n')
def get_iou(data_list, class_num, save_path=None):
from multiprocessing import Pool
from deeplab.metric import ConfusionMatrix #混淆矩阵/误差矩阵 检测分类的正确率
ConfM = ConfusionMatrix(class_num)
f = ConfM.generateM #f是生成的误差矩阵？
pool = Pool()
m_list = pool.map(f, data_list)
pool.close()
pool.join()
for m in m_list:
ConfM.addM(m)
aveJ, j_list, M = ConfM.jaccard() #M为jaccard相似系数 越大样本相似度越高
print('meanIOU: ' + str(aveJ) + '\n')
if save_path:
with open(save_path, 'w') as f:
f.write('meanIOU: ' + str(aveJ) + '\n')
f.write(str(j_list) + '\n')
f.write(str(M) + '\n')
def get_iou(data_list, class_num, hue_val, save_path=None):
from multiprocessing import Pool
from deeplab.metric import ConfusionMatrix
ConfM = ConfusionMatrix(class_num)
f = ConfM.generateM
pool = Pool()
m_list = pool.map(f, data_list)
pool.close()
pool.join()
for m in m_list:
ConfM.addM(m)
aveJ, j_list, M = ConfM.jaccard()
print('hue value is {}'.format(hue_val))
print('meanIOU: ' + str(aveJ) + '\n')
print('class scores: ' + str(j_list) + '\n')
if save_path:
with open(save_path, 'w') as f:
f.write('meanIOU: ' + str(aveJ) + '\n')
f.write(str(j_list) + '\n')
f.write(str(M) + '\n')
