def augmentImages_test():
# bug：包和包之间无法互相调用，只能注释另一个，或者放from xxx.preprocess import xxx放这里
from data_process.use_seg_tfrecord import create_inputs_seg_hand
images, masks = create_inputs_seg_hand(is_train=True)
session_config = tf.ConfigProto(
device_count={'GPU': 0},
gpu_options={
'allow_growth': 1,
# 'per_process_gpu_memory_fraction': 0.1,
'visible_device_list': '0'
},
allow_soft_placement=True) ##这个设置必须有，否则无论如何都会报cudnn不匹配的错误,BUG十分隐蔽，真是智障
with tf.Session(config=session_config) as sess:
tf.global_variables_initializer().run()
tf.local_variables_initializer().run()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# 输出100个batch观察
for i in range(100):
batch_size_n = 1 # 观察batch_size的第n张图片
pics, pics_masks = sess.run([images, masks]) # 取出一个batchsize的图片
########################## 主要调用的东西 ############################
pics = pics / 255
x_aug, y_aug = augmentImages(pics, pics_masks)
########################## end #######################################
pics = np.split(x_aug, cfg.batch_size, axis=0) # 按图片数量切分batchsize张
pic = pics[batch_size_n]
# pic = pic 255
pic = np.squeeze(pic, axis=0) # 去掉维度是1的维度，四维变成三维
pics_masks = np.split(y_aug, cfg.batch_size,
axis=0) # 按图片数量切分batchsize张
pics_mask = pics_masks[batch_size_n]
pics_mask = np.squeeze(pics_mask, axis=0) # 去掉维度是1的维度，四维变成三维
pics_mask = cv2.cvtColor(pics_mask, cv2.COLOR_GRAY2BGR) # 一通道转三通道
hstack = np.hstack((pic, pics_mask)) # 水平拼接
if cv2_show:
cv2.imshow('img and mask', hstack)
cv2.waitKey(2000)
cv2.destroyAllWindows()
else:
plt_imshow_data(hstack)
time.sleep(2)
coord.request_stop()
coord.join(threads)
mean_list = [
AUC_ROC_mean, AUC_prec_rec_mean, accuracy_mean, specificity_mean,
sensitivity_mean, precision_mean, jaccard_index_mean, F1_score_mean
]
data = {}
index = 1 # 只有一行 (为何不加这个index就会报错)
for name, mean in zip(name_list, mean_list):
data.update({name: mean})
mean_result = pd.DataFrame(data, index=[index])
mean_result.to_csv(save_mean_csv, encoding='gbk')
print('save to {}'.format(save_mean_csv))
if __name__ == '__main__':
images, labels = create_inputs_seg_hand(is_train=is_train)
session_config = tf.ConfigProto(
device_count={'GPU': 0},
gpu_options={
'allow_growth': 1,
# 'per_process_gpu_memory_fraction': 0.1,
'visible_device_list': '0'
},
allow_soft_placement=True) ##这个设置必须有，否则无论如何都会报cudnn不匹配的错误,BUG十分隐蔽，真是智障
with tf.Session(config=session_config) as sess:
# 1、先定义model才能执行第二步的初始化
model = SegCaps(sess, cfg, is_train=is_train)
# 2、初始化和启动线程
tf.global_variables_initializer().run()