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, divisionimport SimpleITK as sitkimport numpy as npimport osimport csvfrom glob import globimport pandas as pdtry:    from tqdm import tqdm # 进度条插件except:    print('TQDM does make much nicer wait bars...')    tqdm = lambda x: xdef make_mask(center,diam,z,width,height,spacing,origin):    '''Center : centers of circles px -- list of coordinates x,y,zdiam : diameters of circles px -- diameterwidthXheight : pixel dim of imagespacing = mm/px conversion rate np array x,y,zorigin = x,y,z mm np.arrayz = 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 NXNReturns 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 filesluna_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 filedef get_filename(file_list, case):    for f in file_list:        if case in f:            return(f)## The locations of the nodesdf_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)