python numpy 显示图像阵列的实例

Python  /  管理员 发布于 7年前   165

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。

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