def crop_candidate():
df_node = pd.read_csv(opt.candidate_center)
file_list = glob(opt.data_train + "*.mhd")
done = []
for fcount, img_file in enumerate(tqdm(file_list)):
file_name = img_file.split('/')[-1][:-4]
if is_exist(done, file_name):
continue
print "doing on ", file_name
mini_df = df_node[df_node["seriesuid"] ==
file_name] #get all nodules associate with file
if mini_df.shape[0] > 0:
img_arr, origin, spacing = load_ct(img_file)
patient_nodules = []
for node_idx, cur_row in mini_df.iterrows():
crop_img = np.zeros([64, 64, 64])
is_nodule = cur_row["isnodule"]
center = np.array(
[cur_row["coordZ"], cur_row["coordY"],
cur_row["coordX"]]) # nodule center
v_center = check_center(64, center, img_arr.shape)
v_center = v_center.astype(np.int32)
crop_img = img_arr[v_center[0] - 32:v_center[0] + 32,
v_center[1] - 32:v_center[1] + 32,
v_center[2] - 32:v_center[2] + 32]
#np.savez('/mnt/7/0701_train_nodule_candidate/'+file_name+\
# "_"+str(node_idx)+"_"+str(int(is_nodule))+".npz",data=crop_img,center=v_center)
np.save(
'/mnt/7/0705_train_48_64_candidate/' + file_name + "_" +
str(node_idx) + "_" + str(int(is_nodule)) + ".npy",
crop_img)
def crop_nodule():
'''
crop roi from the train dataset. for each train img,crop the nodule area in a rectangle
then reverse it in 3 ways to augment it as the positive samples.
random choose 10 point as the area center,crop the area as the negative samples
'''
df_node = pd.read_csv(opt.annotatiion_csv)
file_list = glob(opt.data_train + "*.mhd")
for fcount, img_file in enumerate(tqdm(file_list)):
file_name = img_file.split('/')[-1][:-4]
print "doing on ", file_name
mini_df = df_node[df_node["seriesuid"] ==
file_name] #get all nodules associate with file
if mini_df.shape[0] > 0:
img_arr, origin, spacing = load_ct(img_file)
patient_nodules = []
for node_idx, cur_row in mini_df.iterrows():
crop_img = np.zeros([64, 64, 64])
diam = cur_row["diameter_mm"]
center = np.array(
[cur_row["coordZ"], cur_row["coordY"],
cur_row["coordX"]]) # nodule center
v_center = np.rint(
(center - origin) / spacing
) # nodule center in voxel space (still x,y,z ordering)
v_center = check_center(64, v_center, img_arr.shape)
v_center = v_center.astype(np.int32)
crop_img = img_arr[v_center[0] - 32:v_center[0] + 32,
v_center[1] - 32:v_center[1] + 32,
v_center[2] - 32:v_center[2] + 32]
np.save(
opt.nodule_cubic + file_name + "_" + str(node_idx) +
".npy", crop_img)
def test_seg(save_dir, data_path, model_def, model_weight):
is_save = True
crop_size = [64, 64, 64]
prob_threshould = 0.4 #二值化阈值
data_list = indices = open(data_path, 'r').read().splitlines()
#data_list=["/home/x/data/datasets/tianchi/train/LKDS-00004.mhd"]
data_list.sort()
start = time.time()
net = caffe.Net(model_def, model_weight, caffe.TEST)
print "time:", time.time() - start
for file_name in tqdm(data_list):
mhd_name = file_name.split('/')[-1][:-4]
if os.path.exists(save_dir + mhd_name + "_nodule.npy"):
print mhd_name, " have finished"
continue
probs, img_arr = seg(net, file_name)
np.save(mhd_name + "_probs.npy", probs)
probs = probs > prob_threshould
probs = morphology.dilation(probs, np.ones([3, 3, 3]))
probs = morphology.dilation(probs, np.ones([3, 3, 3]))
probs = morphology.erosion(probs, np.ones([3, 3, 3]))
labels = measure.label(probs, connectivity=2)
#label_vals = np.unique(labels)
regions = measure.regionprops(labels)
centers = []
crops = []
bboxes = []
spans = []
for prop in regions:
B = prop.bbox
if B[3] - B[0] > 2 and B[4] - B[1] > 4 and B[5] - B[2] > 4:
z = int((B[3] + B[0]) / 2.0)
y = int((B[4] + B[1]) / 2.0)
x = int((B[5] + B[2]) / 2.0)
span = np.array(
[int(B[3] - B[0]),
int(B[4] - B[1]),
int(B[5] - B[2])])
spans.append(span)
centers.append(np.array([z, y, x]))
bboxes.append(B)
for idx, bbox in enumerate(bboxes):
crop = np.zeros(crop_size, dtype=np.float32)
crop_center = centers[idx]
half = np.array(crop_size) / 2
crop_center = check_center(crop_size, crop_center, img_arr.shape)
crop=img_arr[int(crop_center[0]-half[0]):int(crop_center[0]+half[0]),\
int(crop_center[1]-half[1]):int(crop_center[1]+half[1]),\
int(crop_center[2]-half[1]):int(crop_center[2]+half[1])]
crops.append(crop)
if is_save:
np.save(save_dir + mhd_name + "_nodule.npy", np.array(crops))
np.save(save_dir + mhd_name + "_center.npy", np.array(centers))
np.save(save_dir + mhd_name + "_size.npy", np.array(spans))
def crop_nodule(img_file):
'''
crop roi from the train dataset. for each train img,crop the nodule area in a rectangle
then reverse it in 3 ways to augment it as the positive samples.
random choose 10 point as the area center,crop the area as the negative samples
'''
file_name = img_file.split('/')[-1][:-4]
print file_name
mini_df = df_node[df_node["seriesuid"] == file_name]
print mini_df
if mini_df.shape[0] > 0:
size, origin, spacing = load_ct_info(img_file)
if size[0] < 108:
crop_size = [size[0], 108, 108]
else:
crop_size = [108] * 3
patient_nodules = []
#import ipdb;ipdb.set_trace()
for node_idx, cur_row in mini_df.iterrows():
#crop_img=np.zeros(crop_size)
crop_mask = np.zeros(crop_size)
diam = cur_row["diameter_mm"]
center = np.array(
[cur_row["coordZ"], cur_row["coordY"],
cur_row["coordX"]]) # nodule center
v_center = np.rint(
(center - origin) /
spacing) # nodule center in voxel space (still x,y,z ordering)
v_center = check_center(crop_size, v_center, size)
v_center = np.array(crop_size) / 2
#half=np.array(crop_size)/2
#crop_img=img_arr[v_center[0]-half[0]:v_center[0]+crop_size[0]-half[0],\
# v_center[1]-half[1]:v_center[1]+half[1],v_center[2]-half[2]:v_center[2]+half[2]]
radius = diam / 2
span = np.round(radius / spacing).astype(np.int32)
#print v_center
#print span
crop_mask[v_center[0] - span[0]:v_center[0] + span[0],
v_center[1] - span[1]:v_center[1] + span[1],
v_center[2] - span[2]:v_center[2] + span[2]] = 1.0
print crop_mask.sum()
np.save(
'/mnt/7/train_nodule_mask/' + file_name + "_" + str(node_idx) +
".npy", crop_mask)
def doTest(file_name, model):
probs, img_arr = seg(opt.img_dir + file_name, model)
probs = probs > opt.prob_threshould
probs = morphology.dilation(probs, np.ones([3, 3, 3]))
probs = morphology.dilation(probs, np.ones([3, 3, 3]))
probs = morphology.erosion(probs, np.ones([3, 3, 3]))
#np.save("probs.npy",probs)
labels = measure.label(probs, connectivity=2)
#label_vals = np.unique(labels)
regions = measure.regionprops(labels)
centers = []
crops = []
bboxes = []
spans = []
for prop in regions:
B = prop.bbox
if B[3] - B[0] > 2 and B[4] - B[1] > 4 and B[5] - B[2] > 4:
z = int((B[3] + B[0]) / 2.0)
y = int((B[4] + B[1]) / 2.0)
x = int((B[5] + B[2]) / 2.0)
span = np.array(
[int(B[3] - B[0]),
int(B[4] - B[1]),
int(B[5] - B[2])])
spans.append(span)
centers.append(np.array([z, y, x]))
bboxes.append(B)
for idx, bbox in enumerate(bboxes):
crop = np.zeros([opt.crop_size, opt.crop_size, opt.crop_size],
dtype=np.float32)
crop_center = centers[idx]
half = opt.crop_size / 2
crop_center = check_center(opt.crop_size, crop_center, img_arr.shape)
crop=img_arr[int(crop_center[0]-half):int(crop_center[0]+half),\
int(crop_center[1]-half):int(crop_center[1]+half),\
int(crop_center[2]-half):int(crop_center[2]+half)]
crops.append(crop)
if opt.is_save:
np.save(opt.save_dir + file_name + "_nodule.npy", np.array(crops))
np.save(opt.save_dir + file_name + "_center.npy", np.array(centers))
np.save(opt.save_dir + file_name + "_size.npy", np.array(spans))
def test_seg(save_dir,data_path,model_def,model_weight):
is_save=True
prob_threshould=0.4#二值化阈值
save_dir=opt.save_dir#切块保存路径
data_list=indices = open(data_path, 'r').read().splitlines()
start=time.time()
net=caffe.Net(model_def,model_weight,caffe.TEST)
for file_name in tqdm(data_list[:2]):
img_arr,origin,spacing=load_ct(file_name)
img_new=normalize(img_arr)
seg_size = [64,64,64]
crop_z=64
dim_z=img_arr.shape[0]
if dim_z<seg_size[0]:
crop_z=img_arr.shape[0]
img_pad=np.zeros(img_arr.shape,dtype=np.float32)
img_pad[32-dim_z/2:32+dim_z-dim_z/2,:,:]=img_new
probs1=seg(net2,file_name,seg_size,img_pad)
probs1=probs1[32-dim_z/2:32+dim_z-dim_z/2]
del img_pad
else:
probs1=seg(net2,file_name,seg_size,img_new)
seg_size = [80,80,80]
dim_z=img_arr.shape[0]
if dim_z<seg_size[0]:
img_pad=np.zeros(img_arr.shape,dtype=np.float32)
img_pad[40-dim_z/2:40+dim_z-dim_z/2,:,:]=img_new
probs2=seg(net1,file_name,seg_size,img_pad)
probs2=probs2[40-dim_z/2:40+dim_z-dim_z/2]
del img_pad
else:
probs2=seg(net1,file_name,seg_size,img_new)
#seg_size=[img_arr.shape[0],80,80]
#net.blobs['data'].reshape(opt.batch_size,1,seg_size[0],seg_size[1],seg_size[2])
#import ipdb; ipdb.set_trace()
probs=(probs1+probs2)/2.0
np.save(mhd_name+"_probs.npy",probs)
crop_size=[crop_z,64,64]
mhd_name=file_name.split('/')[-1][:-4]
probs=probs>prob_threshould
probs=morphology.dilation(probs,np.ones([3,3,3]))
probs=morphology.dilation(probs,np.ones([3,3,3]))
probs=morphology.erosion(probs,np.ones([3,3,3]))
np.save(file_name+"_probs.npy",probs)
labels = measure.label(probs,connectivity=2)
#label_vals = np.unique(labels)
regions = measure.regionprops(labels)
centers = []
crops = []
bboxes = []
spans=[]
for prop in regions:
B = prop.bbox
if B[3]-B[0]>2 and B[4]-B[1]>4 and B[5]-B[2]>4:
z=int((B[3]+B[0])/2.0)
y=int((B[4]+B[1])/2.0)
x=int((B[5]+B[2])/2.0)
span=np.array([int(B[3]-B[0]),int(B[4]-B[1]),int(B[5]-B[2])])
spans.append(span)
centers.append(np.array([z,y,x]))
bboxes.append(B)
for idx,bbox in enumerate(bboxes):
crop=np.zeros(crop_size,dtype=np.float32)
crop_center=centers[idx]
half=np.array(crop_size)/2
crop_center=check_center(crop_size,crop_center,img_arr.shape)
crop=img_arr[int(crop_center[0]-half[0]):int(crop_center[0]+half[0]),\
int(crop_center[1]-half[1]):int(crop_center[1]+half[1]),\
int(crop_center[2]-half[1]):int(crop_center[2]+half[1])]
crops.append(crop)
if is_save:
np.save(save_dir+mhd_name+"_nodule.npy",np.array(crops))
np.save(save_dir+mhd_name+"_center.npy",np.array(centers))
np.save(save_dir+mhd_name+"_size.npy",np.array(spans))