python numpy 显示图像阵列

每次要显示图像阵列的时候，使用自带的 matplotlib 或者cv2 都要设置一大堆东西，subplot，fig等等，突然想起 可以利用numpy 的htstack() 和 vstack()　将图片对接起来组成一张新的图片。因此写了写了下面的函数。做了部分注释，一些比较绕的地方可以自行体会。

大致流程包括：

１．　输入图像列表　img_list

 2.    show_type : 最终的显示方式，输入为行数列数　（例如　show_type=22 ，则最终显示图片为两行两列）

３．　basic_shape, 图片ｒｅｓｉｚｅ的尺寸。

def image_show( img_list, show_type, basic_size=[300,500]):    '''        img_list contains the images that need to be stitched,        the show_typ contains the final shape of the stitched one, ie, 12 for 1 row 2 cols.        basic_size : all input image need to be reshaped first.         '''    # reshap row and col number.     n_row, n_col = basic_size    #print n_row,n_col        # num of pixels need to be filled vertically and horizontally.    h_filling = 10    v_filling = 10        # image resize.     resize_list=[]    for i in img_list:        temp_img = cv2.resize( i, ( n_col, n_row ), interpolation = cv2. INTER_CUBIC )        resize_list.append( temp_img )        # resolve the final stitched image 's shape.    n_row_img, n_col_img = show_type/10, show_type%10    #print n_row_img, n_col_img        # the blank_img and the image need to be filled should be defined firstly.    blank_img= np.ones([n_row,n_col])*255    blank_img= np.array( blank_img, np.uint8 )    v_img=  np.array( np.ones([n_row,v_filling])*255, np.uint8)    h_img= np.array( np.ones ([ h_filling, n_col_img*n_col+(n_col_img-1)*h_filling])*255, np.uint8)                    # images in the image list should be dispatched into different sub-list    # in each sub list the images will be connected horizontally.    recombination_list=[]    temp_list=[]    n_list= len(resize_list)    for index,  i in enumerate ( xrange (n_list)):        if index!= 0 and index % n_col_img==0 :            recombination_list.append(temp_list)            temp_list = []            if len(resize_list)> n_col_img:                pass            else:                recombination_list.append(resize_list)                break        temp_list.append( resize_list.pop(0))    if n_list== n_col_img:        recombination_list.append(temp_list)    #print len(temp_list)    #print temp_list        # stack the images horizontally.    h_temp=[]    for i in recombination_list:        #print len(i)        if len(i)==n_col_img:                        temp_new_i=[ [j,v_img]  if index+1 != len(i) else j for index, j in enumerate (i)  ]            new_i=[ j   for i in temp_new_i[:-1] for j in i ]            new_i.append( temp_new_i[-1])            h_temp.append(np.hstack(new_i))        else:                        add_n= n_col_img - len(i)            for k in range(add_n):                i.append(blank_img)                            temp_new_i=[ [j,v_img]  if index+1 != len(i) else j for index, j in enumerate (i)  ]            new_i=[ j   for i in temp_new_i[:-1] for j in i ]            new_i.append( temp_new_i[-1])                        h_temp.append(np.hstack(new_i))                            #print len(h_temp)    #print h_temp                temp_full_img= [ [j, h_img ] if index+1 != len(h_temp) else j for index, j in enumerate(h_temp)  ]    if len(temp_full_img) > 2:        full_img= [  j  for i in temp_full_img[:-1] for j in i ]        full_img.append(temp_full_img[-1])    else:        full_img= [  j  for i in temp_full_img for j in i ]        #full_img.append(temp_full_img[-1])                    if len(full_img)>1:        return np.vstack( full_img)     else:        return full_img

最终输入情况和结果如下图：

第一组结果图：自行看输入

第二组结果图。