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python numpy 顯示圖像陣列的實(shí)例

更新時間：2018年07月02日 14:57:35 作者：vola9527

今天小編就為大家分享一篇python numpy 顯示圖像陣列的實(shí)例，具有很好的參考價值，希望對大家有所幫助。一起跟隨小編過來看看吧

每次要顯示圖像陣列的時候，使用自帶的 matplotlib 或者cv2 都要設(shè)置一大堆東西，subplot，fig等等，突然想起可以利用numpy 的htstack() 和 vstack()　將圖片對接起來組成一張新的圖片。因此寫了寫了下面的函數(shù)。做了部分注釋，一些比較繞的地方可以自行體會。

大致流程包括：

1、輸入圖像列表　img_list

2、show_type : 最終的顯示方式，輸入為行數(shù)列數(shù)?。ɡ纭how_type=22 ，則最終顯示圖片為兩行兩列）

3、basic_shape, 圖片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

最終輸入情況和結(jié)果如下圖：

第一組結(jié)果圖：自行看輸入