caffee学习——图像切割

参考使用https://github.com/dajunC/DSB3Tutorial

最近在使用caffee进行Deep learning的图像处理学习。学习过程中，需要对图片进行切割，用以消除不需要的训练杂质。通过查找大牛的方法，找到一个很好的方案。但大牛的方法是基于Tensorflow的，所以不能使用Keras库，但不影响图像切割的使用，只是需要一定的修改。

（1）对图像进行mask

from __future__ import print_function, division import SimpleITK as sitk import numpy as np import os import csv from glob import glob import pandas as pd try: from tqdm import tqdm # 进度条插件 except: print('TQDM does make much nicer wait bars...') tqdm = lambda x: x def make_mask(center,diam,z,width,height,spacing,origin): ''' Center : centers of circles px -- list of coordinates x,y,z diam : diameters of circles px -- diameter widthXheight : pixel dim of image spacing = mm/px conversion rate np array x,y,z origin = x,y,z mm np.array z = z position of slice in world coordinates mm ''' mask = np.zeros([height,width]) # 0's everywhere except nodule swapping x,y to match img #convert to nodule space from world coordinates # Defining the voxel range in which the nodule falls v_center = (center-origin)/spacing v_diam = int(diam/spacing[0]+5) v_xmin = np.max([0,int(v_center[0]-v_diam)-5]) v_xmax = np.min([width-1,int(v_center[0]+v_diam)+5]) v_ymin = np.max([0,int(v_center[1]-v_diam)-5]) v_ymax = np.min([height-1,int(v_center[1]+v_diam)+5]) v_xrange = range(v_xmin,v_xmax+1) v_yrange = range(v_ymin,v_ymax+1) # Convert back to world coordinates for distance calculation x_data = [x*spacing[0]+origin[0] for x in range(width)] y_data = [x*spacing[1]+origin[1] for x in range(height)] # Fill in 1 within sphere around nodule for v_x in v_xrange: for v_y in v_yrange: p_x = spacing[0]*v_x + origin[0] p_y = spacing[1]*v_y + origin[1] if np.linalg.norm(center-np.array([p_x,p_y,z]))<=diam: mask[int((p_y-origin[1])/spacing[1]),int((p_x-origin[0])/spacing[0])] = 1.0 return(mask) def matrix2int16(matrix): ''' matrix must be a numpy array NXN Returns uint16 version ''' m_min= np.min(matrix) m_max= np.max(matrix) matrix = matrix-m_min return(np.array(np.rint( (matrix-m_min)/float(m_max-m_min) * 65535.0),dtype=np.uint16))  # # Getting list of image files luna_path = "F:/test/" luna_subset_path = luna_path+"dataset/" output_path = "F:/test/output/" file_list=glob(luna_subset_path+"*.mhd")  # # Helper function to get rows in data frame associated  # with each file def get_filename(file_list, case): for f in file_list: if case in f: return(f) # # The locations of the nodes df_node = pd.read_csv(luna_path+"test.csv") df_node["file"] = df_node["seriesuid"].map(lambda file_name: get_filename(file_list, file_name)) df_node = df_node.dropna()  # # Looping over the image files # for fcount, img_file in enumerate(tqdm(file_list)): mini_df = df_node[df_node["file"]==img_file] #get all nodules associate with file if mini_df.shape[0]>0: # some files may not have a nodule--skipping those  # load the data once itk_img = sitk.ReadImage(img_file) img_array = sitk.GetArrayFromImage(itk_img) # indexes are z,y,x (notice the ordering) num_z, height, width = img_array.shape #heightXwidth constitute the transverse plane origin = np.array(itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm) spacing = np.array(itk_img.GetSpacing()) # spacing of voxels in world coor. (mm) # go through all nodes (why just the biggest?) for node_idx, cur_row in mini_df.iterrows(): node_x = cur_row["coordX"] node_y = cur_row["coordY"] node_z = cur_row["coordZ"] diam = cur_row["diameter_mm"] # just keep 3 slices imgs = np.ndarray([3,height,width],dtype=np.float32) masks = np.ndarray([3,height,width],dtype=np.uint8) center = np.array([node_x, node_y, node_z]) # nodule center v_center = np.rint((center-origin)/spacing) # nodule center in voxel space (still x,y,z ordering) for i, i_z in enumerate(np.arange(int(v_center[2])-1, int(v_center[2])+2).clip(0, num_z-1)): # clip prevents going out of bounds in Z mask = make_mask(center, diam, i_z*spacing[2]+origin[2], width, height, spacing, origin) masks[i] = mask imgs[i] = img_array[i_z] np.save(os.path.join(output_path,"images_%04d_%04d.npy" % (fcount, node` idx)),imgs) np.save(os.path.join(output_path,"masks_%04d_%04d.npy" % (fcount, node_idx)),masks)