merge = np.argmax(mergesoftmax, 0).astype(np.uint8)
#tiff.imsave('/home/lenovo/2Tdisk/Wkyao/_/2017/merge/fs1116.tif', merge)
softmax_merge = np.load('/home/lenovo/256Gdisk/tainchi/merge/1120.npy')
merge_list = []
im_2017_list = []
for i in range(25):
m = softmax_merge[:, :, i * 600:i * 600 + 600]
merge_list.append(m)
b = img17[:, i * 600:i * 600 + 600]
b = np.array([np.array([b for i in range(3)])])
b = b.transpose(0, 2, 3, 1)
im_2017_list.append(b)
merge_list.append(softmax_merge[:, :, 15000:15106])
im_2017_list.append(
np.array([np.array([img17[:, 15000:15106]
for i in range(3)])]).transpose(0, 2, 3, 1))
allImg_crf = []
allImg_soft = []
for n, im_2017_part in enumerate(im_2017_list):
# 使用crf:
soft = merge_list[n]
im_2017_mean = np.mean(im_2017_list[n], axis=0)
c = crf.crf(im_2017_mean, soft)
allImg_crf.append(c) # 保存整张crf图.
Crf = np.concatenate(tuple(allImg_crf), axis=1)
#tiff.imsave('/home/lenovo/2Tdisk/Wkyao/_/2017/merge/crf_merge_1108.tif', Crf)
img = open_and_close(Crf) #膨胀操作
tiff.imsave('/home/lenovo/256Gdisk/tainchi/merge/last.tif', img)
def main(argv=None):
# 定义一下regularization:
regularization = tf.Variable(0, dtype=tf.float32)
keep_probability = tf.placeholder(tf.float32,
name="keep_probabilty") # dropout
image = tf.placeholder(tf.float32,
shape=[None, None, None, 1],
name="input_image") # 输入
annotation = tf.placeholder(tf.int32,
shape=[None, None, None, 1],
name="annotation") # label
mean = tf.placeholder(tf.float32, name='mean')
pred_annotation, logits, softmax = inference(image, keep_probability,
mean) # logits=resize_F8
print('annotation', annotation.shape)
print('image', image.shape)
loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits,
# tf.nn.sparse_softmax_cross_entropy_with_logits自动对logits(resize_F8)做了softmax处理.
labels=tf.squeeze(annotation, squeeze_dims=[3]),
name="entropy"))
tf.summary.scalar("entropy", loss) # train val公用一个loss节点运算.
trainable_var = tf.trainable_variables(
) # Variable被收集在名为tf.GraphKeys.VARIABLES的colletion中
train_op = train(loss, trainable_var)
print("Setting up summary op...")
print("Setting up image reader...")
train_records, valid_records = scene_parsing.read_dataset(
FLAGS.data_dir) # 数据的转换输入.
print(len(train_records))
print(len(valid_records))
print("Setting up dataset reader")
image_options = {
'resize': False,
'resize_size': IMAGE_RESIZE
} # 将IMAGE_SIZE大小作为一个batch
if FLAGS.mode == 'train':
train_dataset_reader = dataset.BatchDatset(train_records,
image_options)
sess = tf.Session()
print("Setting up Saver...")
saver = tf.train.Saver() # 声明tf.train.Saver()类 用于存储模型.
summary_op = tf.summary.merge_all() # 汇总所有summary.
summary_writer_train = tf.summary.FileWriter(FLAGS.logs_dir + '/train',
sess.graph)
summary_writer_val = tf.summary.FileWriter(FLAGS.logs_dir +
'/val') # 这里不需要再加入graph.
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(
FLAGS.logs_dir) # 生成check_point,下次可以从check_point继续训练
if ckpt and ckpt.model_checkpoint_path: # 这两行的作用是:tf.train.get_checkpoint_state()函数通过checkpoint文件(它存储所有模型的名字)找到目录下最新的模型.
saver.restore(sess, ckpt.model_checkpoint_path)
print("Model restored...")
if FLAGS.mode == "train":
# mymean = train_dataset_reader._read_images() #强行运行这个函数,把mean传过来.
# train_dataset_reader 调用._read_images() 需要在里面修改mean是否运算和return.
# 生成本次数据集的mean值.
mymean = [73.9524, 73.9524, 73.9524]
for itr in xrange(MAX_ITERATION): # 修改itr数值,接着之前的模型数量后继续训练.
train_images, train_annotations = train_dataset_reader.next_batch(
FLAGS.batch_size)
feed_dict = {
image: train_images,
annotation: train_annotations,
keep_probability: 0.85,
mean: mymean
}
sess.run(train_op, feed_dict=feed_dict)
if itr % 10 == 0:
# 这里不要运算loss
train_loss, summary_str = sess.run([loss, summary_op],
feed_dict=feed_dict)
summary_writer_train.add_summary(summary_str, itr)
print("Step: %d, Train_loss:%g" % (itr, train_loss))
if itr % 100 == 0: # 每训100次测试一下验证集.
# valid_images, valid_annotations = validation_dataset_reader.next_batch(FLAGS.batch_size)
# valid_loss, summary_str = sess.run([loss, summary_op], feed_dict={image: valid_images,
# annotation: valid_annotations,
# keep_probability: 1.0,
# mean: mymean}) #计算新节点loss_valid的值.
# summary_writer_val.add_summary(summary_str, itr)
# print("%s ---> Validation_loss: %g" % (datetime.datetime.now(), valid_loss))
saver.save(sess, FLAGS.logs_dir + "model.ckpt", itr)
# summary_writer_val.close()
summary_writer_train.close()
elif FLAGS.mode == "visualize":
# mymean = [42.11049008, 65.75782253, 74.11216841]
mymean = [73.9524, 73.9524, 73.9524]
validation_dataset_reader = dataset.BatchDatset(
valid_records, image_options)
valid_images, valid_annotations = validation_dataset_reader.get_random_batch(
FLAGS.batch_size)
pred = sess.run(pred_annotation,
feed_dict={
image: valid_images,
annotation: valid_annotations,
keep_probability: 1.0,
mean: mymean
})
valid_annotations = np.squeeze(valid_annotations, axis=3)
pred = np.squeeze(pred, axis=3)
for itr in range(FLAGS.batch_size):
utils.save_image(valid_images[itr].astype(np.uint8),
FLAGS.logs_dir,
name="inp_" + str(5 + itr))
utils.save_image(valid_annotations[itr].astype(np.uint8),
FLAGS.logs_dir,
name="gt_" + str(5 + itr))
utils.save_image(pred[itr].astype(np.uint8),
FLAGS.logs_dir,
name="pred_" + str(5 + itr))
print("Saved image: %d" % itr)
elif FLAGS.mode == "test":
im_2017_list = []
im_2017_list2 = []
b = []
global im_2017 # [5106, 15106, 3]
global new_2017 # [8106, 15106, 3]
global new_2015
for i in range(25):
b = new_2017[0:4000, i * 600:i * 600 + 600,
3] # 训练第四通道的话,测试的时候也要换成第四通道.
b = np.array([np.array([b for i in range(3)])])
b = b.transpose(0, 2, 3, 1)
im_2017_list.append(b)
print(b.shape)
'''
# 多通道
b = im_2017[0:5106, i * 300 : i * 300 + 300, :]
b = np.array([b])
# print(b.shape)
# b = b.transpose(0, 2, 3, 1)
im_2017_list.append(b)
# im_2017_list.append(np.array([np.array([im_2017[0:5106, 15000:15106, 3] for i in range(3)])]).transpose(0, 2, 3, 1))
#im_2017_list.append(np.array([im_2017[0:5106, 15000:15106, :]])) # .transpose(0, 2, 3, 1))
im_2017_list.append(np.array([im_2017[0:5106, 15000:15106, :]]))
'''
im_2017_list.append(
np.array([
np.array([new_2017[0:4000, 15000:15106, 3] for i in range(3)])
]).transpose(0, 2, 3, 1))
'''
for i in range(50):
b = new_2017[5106:8106, i * 300:i * 300 + 300, 3]
b = np.array([np.array([b for i in range(3)])])
b = b.transpose(0, 2, 3, 1)
im_2017_list2.append(b)
im_2017_list2.append(
np.array([np.array([new_2017[5106:8106, 15000:15106, 3] for i in range(3)])]).transpose(0, 2, 3, 1))
'''
allImg = []
allImg2 = []
allImg_soft = []
allImg_crf = []
mymean = [73.9524, 73.9524, 73.9524]
for n, im_2017_part in enumerate(im_2017_list):
# print(im_2017_part.shape)
feed_dict_test = {
image: im_2017_part,
keep_probability: 1.0,
mean: mymean
}
a = sess.run(pred_annotation, feed_dict=feed_dict_test)
a = np.mean(a, axis=(0, 3))
allImg.append(a)
'''
# Shift + Tab
for n, im_2017_part2 in enumerate(im_2017_list2):
# print(im_2017_part.shape)
feed_dict_test = {image: im_2017_part2, keep_probability: 1.0, mean: mymean}
a = sess.run(pred_annotation, feed_dict=feed_dict_test)
a = np.mean(a, axis=(0, 3))
allImg2.append(a)
'''
# 使用crf:
soft = sess.run(softmax, feed_dict=feed_dict_test)
# 运行 sess.run(softmax, feed_dict=feed_dict_test) 得到softmax,即网络前向运算并softmax为概率分布后的结果.
soft = np.mean(soft, axis=0).transpose(2, 0, 1)
# soft = soft.transpose(2, 0, 1)
im_2017_mean = np.mean(im_2017_list[n], axis=0)
# print (im_2017_mean.shape) #(5106, 300, 3)
c = crf.crf(im_2017_mean, soft)
# print (c.shape) #(5106, 300)
allImg_crf.append(c) # 保存整张soft图.
allImg_soft.append(soft) # 保存整张soft图.
Crf = np.concatenate(tuple(allImg_crf),
axis=1) # axis = 1 在第二个维度上进行拼接.
softmax = np.concatenate(tuple(allImg_soft), axis=2)
# tiff.imsave('/home/lenovo/2Tdisk/Wkyao/_/2017/vgg/crf_vgg_1108.tif', Crf)
np.save('/home/lenovo/2Tdisk/Wkyao/_/2017/vgg/1116_3.npy',
softmax) # 保存拼接的Softmax为np
np.save(
'/home/lenovo/2Tdisk/Wkyao/FirstSetp/ChenJ/finall/1110/1116_3.npy',
softmax)
res1 = np.concatenate(tuple(allImg), axis=1).astype(np.uint8)
tiff.imsave('/home/lenovo/2Tdisk/Wkyao/_/2017/vgg/1116_3.tif', res1)
open_and_close(Crf) # 膨胀操作.