CT重建过程中的各种工具类算法（包括产生稀疏视角图像、各种格式数据变换）

import astra import matplotlib.pyplot as plt # plt 用于显示图片 import phantominator from phantominator import shepp_logan import numpy as np import matplotlib.image as mpimg # mpimg 用于读取图片 import cv2 import skimage.io as io from skimage import data_dir,color from PIL import Image import os target_path='C:/Users/Administrator/Desktop/unet-denoising-dirty-documents-master/data/train_cleaned/' all_content=os.listdir(target_path) size=256 angle=20 for i in range(len(all_content)): picture_path = target_path+all_content[i] img = Image.open(picture_path) img_1 = img.convert('L') img_2 = img_1.resize((size, size), Image.ANTIALIAS) img_2=np.array(img_2) #cv2.imwrite('C:/Users/Administrator/Desktop/unet-denoising-dirty-documents-master/data/train_cleaned/' + all_content[i], img_2) # 生成仿真投影数据的框架，包括了投影的角度个数、投影图的大小、投影束的选择等 vol_geom = astra.create_vol_geom(size, size) proj_geom = astra.create_proj_geom('parallel', 1.0, 384, np.linspace(0, np.pi, angle, False)) # 开始投影，将投影出来的东西拼接成为正弦图 proj_id = astra.create_projector('line', proj_geom, vol_geom) sinogram_id, sinogram = astra.create_sino(img_2, proj_id) # 开始反投影 rec_id = astra.data2d.create('-vol', vol_geom) cfg = astra.astra_dict('FBP') cfg['ReconstructionDataId'] = rec_id cfg['ProjectionDataId'] = sinogram_id cfg['ProjectorId'] = proj_id alg_id = astra.algorithm.create(cfg) astra.algorithm.run(alg_id, 20) rec = astra.data2d.get(rec_id) cv2.imwrite('C:/Users/Administrator/Desktop/unet-denoising-dirty-documents-master/data/train/'+all_content[i], rec) print("第"+str(i+1)+"张图片原始数据创建完成")