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seedpoints_patch_generater_postive.py
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# -*- coding: UTF-8 -*-
# @Time : 12/05/2020 20:06
# @Author : BubblyYi
# @FileName: patch_generater.py
# @Software: PyCharm
import SimpleITK as sitk
import matplotlib
matplotlib.use('AGG')
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
import numpy as np
import pandas as pd
import math
from scipy.ndimage.interpolation import zoom
import warnings
import os
np.random.seed(4)
from utils import resample, get_shell, get_proximity, get_closer_distence
def creat_data(path_name,spacing_path,gap_size,save_num):
spacing_info = np.loadtxt(spacing_path,
delimiter=",", dtype=np.float32)
proximity_list = []
patch_name = []
i = save_num
print("processing dataset %d" % i)
image_pre_fix = path_name + '0' + str(i) + '/' + 'image' + '0' + str(i)
file_name = image_pre_fix + '.nii.gz'
src_array = sitk.GetArrayFromImage(sitk.ReadImage(file_name, sitk.sitkFloat32))
spacing_x = spacing_info[i][0]
spacing_y = spacing_info[i][1]
spacing_z = spacing_info[i][2]
re_spacing_img, curr_spacing, resize_factor = resample(src_array, np.array([spacing_z, spacing_x, spacing_y]),
np.array([1, 1, 1]))
max_z, max_y, max_x = re_spacing_img.shape
vessels = []
for j in range(4):
reference_path = './train_data/dataset0'+str(i)+'/vessel' + str(j) + '/reference.txt'
txt_data = np.loadtxt(reference_path, dtype=np.float32)
temp_center = txt_data[..., 0:3]
vessels.append(temp_center)
record_set = set()
max_range = 4
max_points = 30
for v in range(4):
print("processing vessel %d" % v)
reference_path = path_name + '0' + str(i) + '/' + 'vessel' + str(v) + '/' + 'reference.txt'
txt_data = np.loadtxt(reference_path, dtype=np.float32)
center = txt_data[..., 0:3]
counter = 0
last_center_x_pixel = -1
last_center_y_pixel = -1
last_center_z_pixel = -1
for j in range(len(center)):
center_x = center[j][0]
center_y = center[j][1]
center_z = center[j][2]
record_set.add((center_x, center_y, center_z))
if j % gap_size == 0:
org_x_pixel = int(round(center_x))
org_y_pixel = int(round(center_y))
org_z_pixel = int(round(center_z))
record_set.add((org_x_pixel,org_y_pixel,org_z_pixel))
if org_x_pixel!=last_center_x_pixel or org_y_pixel!=last_center_y_pixel or org_z_pixel!=last_center_z_pixel:
last_center_x_pixel = org_x_pixel
last_center_y_pixel = org_y_pixel
last_center_z_pixel = org_z_pixel
for k in range(1,max_range+1):
x_list,y_list,z_list = get_shell(max_points,k)
for m in range(len(x_list)):
new_x = int(round(center_x + x_list[m]))
new_y = int(round(center_y + y_list[m]))
new_z = int(round(center_z + z_list[m]))
check_temp = (new_x,new_y,new_z)
if check_temp not in record_set:
record_set.add(check_temp)
center_x_pixel = new_x
center_y_pixel = new_y
center_z_pixel = new_z
target_point = np.array([center_x_pixel, center_y_pixel, center_z_pixel])
print("new center:", target_point)
min_dis = get_closer_distence(vessels, target_point)
curr_proximity = get_proximity(min_dis,cutoff_value=4)
print('proximity:', curr_proximity)
cut_size = 9
left_x = center_x_pixel - cut_size
right_x = center_x_pixel + cut_size
left_y = center_y_pixel - cut_size
right_y = center_y_pixel + cut_size
left_z = center_z_pixel - cut_size
right_z = center_z_pixel + cut_size
if (right_z + 1) < len(re_spacing_img) and left_z >= 0 and (right_y + 1) < max_y and left_y >= 0 and (right_x + 1) < max_x and left_x >= 0 and curr_proximity>0:
new_src_arr = np.zeros((cut_size * 2 + 1, cut_size * 2 + 1, cut_size * 2 + 1))
for ind in range(left_z, right_z + 1):
src_temp = re_spacing_img[ind].copy()
new_src_arr[ind - left_z] = src_temp[left_y:right_y + 1, left_x:right_x + 1]
folder_path = './patch_data/seeds_patch/positive/' + 'gp_' + str(gap_size) + '/d' + str(i)
if not os.path.exists(folder_path):
os.makedirs(folder_path)
record_name = 'seeds_patch/positive/' + 'gp_' + str(gap_size) + '/d' + str(i) + '/' + 'd_' + str(i) + '_v_' +str(v)+ '_x_' + str(center_x_pixel) + '_y_' + str(center_y_pixel) + '_z_' + str(center_z_pixel) + '.nii.gz'
print(record_name)
org_name = './patch_data/' + record_name
out = sitk.GetImageFromArray(new_src_arr)
sitk.WriteImage(out, org_name)
proximity_list.append(curr_proximity)
patch_name.append(record_name)
counter += 1
else:
print('out of bounder skip this block')
# break
return patch_name, proximity_list
def create_patch_images(path_name,spacing_path,gap_size):
for i in range(8):
patch_name,proximity_list = creat_data(path_name,spacing_path,gap_size,i)
dataframe = pd.DataFrame(
{'patch_name': patch_name, 'proximity': proximity_list})
print(dataframe.head())
csv_name = "./patch_data/seeds_patch/positive/"+ 'gp_' + str(gap_size)+'/'+'d'+str(i) + "_patch_info.csv"
dataframe.to_csv(csv_name, index=False, columns=['patch_name', 'proximity'], sep=',')
print("create patch info csv")
print("down")
path_name = 'train_data/dataset'
spacing_path = 'spacing_info.csv'
gap_size = 100
create_patch_images(path_name,spacing_path,gap_size)