for i in range(block_3d.shape[1]):
for j in range(block_3d.shape[2]):
list1 = block_3d[:, i, j]
r = np.float(np.max(list1))
mip[i, j, 1] = r
for i in range(block_3d.shape[0]):
for j in range(block_3d.shape[2]):
list1 = block_3d[i, :, j]
r = np.float(np.max(list1))
mip[i, j, 2] = r
return mip
if __name__ == '__main__':
margin = 32
imgvox = loadimg('/media/ttt/tyh1/shuju/m3.tif')
labelmap = loadimg('/media/ttt/tyh1/shuju/m3_b2.tif')
marker = loadmarker('/media/ttt/tyh1/shuju/m3_b.marker')
marker = marker.astype(int)
marker = marker - 1
marker[:, 1] = imgvox.shape[1] - marker[:, 1]
swc = loadswc('/media/ttt/tyh1/shuju/m3_b.marker')
swc = swc[:, 2:5]
swc = swc.astype(int)
swc = swc - 1
swc[:, 1] = imgvox.shape[1] - swc[:, 1]
labelmap = labelmap > 0
h5file = '/media/ttt/E81AA8261AA7EFAC/0000/train_dense9f1.hdf5'
pimg[margin:margin + image.shape[0],
margin:margin + image.shape[1],
margin:margin + image.shape[2]] = image
return pimg
def de_padding(pimg,margin):
imgvox = np.zeros((pimg.shape[0]-2*margin,
pimg.shape[1]-2*margin,
pimg.shape[2]-2*margin))
imgvox = pimg[margin:margin + imgvox.shape[0],
margin:margin + imgvox.shape[1],
margin:margin + imgvox.shape[2]]
return imgvox
if __name__=='__main__':
marker = loadmarker('/media/tyhhnu/tyh1/shuju/ms1_b.marker')
marker = marker.astype(int)
imgvox = loadimg('/media/tyhhnu/tyh1/shuju/ms1.tif')
margin = 28
R = 12
marker = marker - 1
marker[:,1] = imgvox.shape[1] - marker[:,1]
marker = marker + margin
pimg = padding(imgvox , margin)
bimg = np.zeros(pimg.shape)
for i in range(marker.shape[0]):
blocks = pimg[marker[i,0]-R:marker[i,0]+R+1,
marker[i,1]-R:marker[i,1]+R+1,
marker[i,2]-4:marker[i,2]+4+1]
# blocks = blocks>(pimg[marker[i,0],marker[i,1],marker[i,2]]-10)
blocks = blocks>0
bimg[marker[i,0]-R:marker[i,0]+R+1,
marker[i,1]-R:marker[i,1]+R+1,
np.random.shuffle(swc)
idx = np.vstack((ones, zeros[0:ones.shape[0], :], swc[0:z_s, :]))
np.random.shuffle(idx)
return idx + margin
def mip(block_3d):
mip = np.zeros([block_3d.shape[0], block_3d.shape[1], 3], dtype=float)
mip[:, :, 0] = np.max(block_3d, 0)
mip[:, :, 1] = np.max(block_3d, 1)
mip[:, :, 2] = np.max(block_3d, 2)
return mip
if __name__ == '__main__':
imgvox = loadimg(
'/media/ttt/E81AA8261AA7EFAC/xuexi/3Dyuantu/OP_5_stack.tif')
labelmap = np.zeros(imgvox.shape)
smarker = loadmarker(
'/media/ttt/E81AA8261AA7EFAC/xuexi/3Dyuantu/OP5_b.marker')
smarker = smarker - 1
smarker = smarker[:, 0:3]
# smarker[:,1]=imgvox.shape[1]-smarker[:,1]-1
marker = np.empty((0, 3))
for i in range(smarker.shape[0]):
x, y, z = smarker[i, :]
blocks = imgvox[math.floor(x) - 1:math.floor(x) + 2,
math.floor(y) - 1:math.floor(y) + 2,
math.floor(z) - 1:math.floor(z) + 2]
corrr = np.argwhere(blocks)
print(corrr.shape)
mip[i,j,0]=r
for i in range(block_3d.shape[1]):
for j in range(block_3d.shape[2]):
list1=block_3d[:,i,j]
r=np.float(np.max(list1))
mip[i,j,1]=r
for i in range(block_3d.shape[0]):
for j in range(block_3d.shape[2]):
list1=block_3d[i,:,j]
r=np.float(np.max(list1))
mip[i,j,2]=r
return mip
if __name__ == '__main__':
margin = 28
imgvox = loadimg('/media/tyhhnu/tyh1/shuju/ms1.tif')
labelmap = loadimg('/media/tyhhnu/tyh1/shuju/ms1_b2.tif')
marker = loadmarker('/media/tyhhnu/tyh1/shuju/ms1_b.marker')
marker = marker.astype(int)
marker = marker - 1
marker[:,1] = imgvox.shape[1] - marker[:,1]
labelmap = labelmap>0
h5file='/media/tyhhnu/tyh1/shuju/hdf5/train_dense11p2.hdf5'
img_name = 'ms1tif'
n_each = 1000
idx = getcand(imgvox,marker,margin,n_each)
labelmap = padding(labelmap,margin,)
pimg = padding(imgvox,margin)
tiqu(pimg,
labelmap,
'/media/ttt/Elements/TanYinghui/TP/5_4.tif',
'/media/ttt/Elements/TanYinghui/TP/5_7.tif'
]
filr = [
'/media/ttt/LANKEXIN/shuju/5_1.tif',
'/media/ttt/LANKEXIN/shuju/5_4.tif',
'/media/ttt/LANKEXIN/shuju/5_7.tif'
]
film = [
'/media/ttt/LANKEXIN/shuju/5_1.tif.marker',
'/media/ttt/LANKEXIN/shuju/5_4.tif.marker',
'/media/ttt/LANKEXIN/shuju/5_7.tif.marker'
]
for i in range(len(filr)):
imgvox = loadimg(filr[i])
imgvox1 = loadimg(fild[i])
imgvox[imgvox < 5] = 0
can_idx = getcand(imgvox, imgvox1, margin)
can_idx = can_idx.astype(int)
pimg = padding(imgvox, margin)
pp = test(pimg, ckptfile, batch_size, can_idx)
print('test over')
nm = np.argwhere(pp[:, 1] > 0.5)
zb = can_idx[nm, :]
zb = np.squeeze(zb)
zb = zb - 28
zb[:, 1] = imgvox.shape[1] - zb[:, 1] - 1
zb = zb + 1
# zb1 = np.hstack((zb,pp[nm,1]))
def train(trainpath, ckptfile):
immm = loadimg(trainpath)
print ('testing() called')
V = 64
vz = 32
margin = 32
re_filed = 0
# data_size = get_datasize(trainpath)
# print ('training size:', data_size)
with tf.Graph().as_default():
# placeholders for graph input
view_ = tf.placeholder('float32', shape=(None, V, V, vz,1), name='im0')
# y_ = tf.placeholder('int64', shape=(N`-one,V-16,V-16,V-16), name='y')
keep_prob_ = tf.placeholder('float32')
# graph outputs
fc8 = model.inference_multiview(view_, g_.NUM_CLASSES, keep_prob_)
# pr=tf.nn.softmax(fc8)
# loss = model.loss(fc8, y_)
# train_op = model.train(loss, global_step, data_size)
prediction = model.classify(fc8)
# must be after merge_all_summaries
# validation_loss = tf.placeholder('float32', shape=(), name='validation_loss')
# validation_summary = tf.summary.scalar('validation_loss', validation_loss)
# validation_acc = tf.placeholder('float32', shape=(), name='validation_accuracy')
# validation_acc_summary = tf.summary.scalar('validation_accuracy', validation_acc)
saver = tf.train.Saver(tf.all_variables(), max_to_keep=1000)
sess = tf.Session(config=tf.ConfigProto(log_device_placement=FLAGS.log_device_placement))
saver.restore(sess, ckptfile)
print ('restore variables done')
immm = padding(immm,margin)
# zmmm = np.zeros(immm.shape)
imum = get_cen(immm.shape,V,V,vz,margin,re_filed)
for num in range(len(imum)):
print(num)
center_point = imum[num]
# center_point[0] = center_point[0] +margin
# center_point[1] = center_point[1] +margin
#
# center_point[2] = center_point[2] +margi/home/ttt/model/model3.cpkt-41000n
#print('center point:', center_point)
image = immm[center_point[0]-V//2:center_point[0]+V//2,
center_point[1]-V//2:center_point[1]+V//2,
center_point[2]-vz//2:center_point[2]+vz//2]
if image.shape !=(V,V,vz):
break
image = np.expand_dims(image, axis = 0)
image = np.expand_dims(image, axis = 4)
# start_time = time.time()
feed_dict = {view_: image,
keep_prob_: 0.5 }
# feed_dict_1 = {view_: batch_x,
# keep_prob_: 0.5 }
# p_fc,p_softmax = sess.run(
# [fc8,pr],
# feed_dict=feed_dict_1)
# print(p_fc,p_softmax)
pred = sess.run(
prediction,
feed_dict=feed_dict)
# pred = np.argmax(pred,-1)
pred = pred[:,:,:,:,1]
pred = np.array(pred)
pred = np.squeeze(pred)
bnn = np.argwhere(pred)
pred = pred>0.5
print(bnn.shape[0])
immm[center_point[0]-V//2:center_point[0]+V//2,
center_point[1]-V//2:center_point[1]+V//2,
center_point[2]-vz//2:center_point[2]+vz//2] = pred
immm = depadding(immm,margin)
immm[immm>1] = 0
immm = immm*255
writetiff3d('/media/ttt/Elements/TanYinghui/TP/5_7.tif',immm.astype('uint8'))