python - 'numpy.ndarray' 对象没有属性 'read'

Question

我试图将函数中声明的变量用于另一个函数。但是当我这样做时，我收到了这种错误:

Exception in Tkinter callback
Traceback (most recent call last):
File "C:\Users\HP\AppData\Local\Enthought\Canopy32\App\appdata\canopy-1.0.3.1262.win-x86\lib\lib-tk\Tkinter.py", line 1410, in __call__
return self.func(*args)
File "D:\6th sem\Major project\Code\frame.py", line 198, in result
im = Image.open(resizelist[val])
File "E:\Canopy\System\lib\site-packages\PIL\Image.py", line 1956, in open
prefix = fp.read(16)
AttributeError: 'numpy.ndarray' object has no attribute 'read'

我的代码是:

def messageWindow():
  win = Toplevel()
  path = 'C:\Users\HP\Desktop\dataset'
  COLUMNS = 12
  image_count = 0
  for infile in glob.glob(os.path.join(path, '*.jpg')):
    image_count += 1
    r, c = divmod(image_count, COLUMNS)
    im = Image.open(infile)
    resized = im.resize((100, 100), Image.ANTIALIAS)
    tkimage = ImageTk.PhotoImage(resized)
    myvar = Label(win, image=tkimage)
    myvar.image = tkimage
    myvar.grid(row=r, column=c)
  i=0
  cont_list = list()
  ene_list = list()
  homo_list = list()
  cor_list = list()
  dis_list = list()
  B_mean = list()
  G_mean = list()
  R_mean = list()
  piclist = list()
  graylist = list()
  resizelist = list()
  eq_graylist = list()

 for infile in glob.glob(os.path.join(path,'*.jpg')):
    imge = cv2.imread(infile)
    arr = array(imge)
    piclist.append(imge)

    g_img = cv2.imread(infile,0)
    gray_re_img = cv2.resize(g_img,(256,256))
    graylist.append(gray_re_img)

    equ = cv2.equalizeHist(gray_re_img)
    eq_graylist.append(equ)

    re_img = cv2.resize(imge,(256,256))
    resizelist.append(imge)
    i = i + 1

 for infiles in glob.glob(os.path.join(path,'*.jpg')):
    img = cv2.imread(infiles)
    blue, green, red = cv2.split(img)
    total = img.size
    B = sum(blue) / total
    G = sum(green) / total
    R = sum(red) / total
    B_mean.append(B)
    G_mean.append(G)
    R_mean.append(R)

    im = skimage.io.imread(infile, as_grey=True)
    im = skimage.img_as_ubyte(im)
    im /= 32
    g = skimage.feature.greycomatrix(im, [1], [0], levels=8, symmetric=False, normed=True)
    cont = skimage.feature.greycoprops(g, 'contrast')[0][0]
    cont_list.append(cont)
    ene = skimage.feature.greycoprops(g, 'energy')[0][0]
    ene_list.append(ene)
    homo = skimage.feature.greycoprops(g, 'homogeneity')[0][0]
    homo_list.append(homo)
    cor = skimage.feature.greycoprops(g, 'correlation')[0][0]
    cor_list.append(cor)
    dis = skimage.feature.greycoprops(g, 'dissimilarity')[0][0]
    dis_list.append(dis)

 feature_matrix_db = zip( B_mean , G_mean , R_mean, cont_list , ene_list , homo_list , cor_list, dis_list)
 blue2.set(B_mean)
 green2.set(G_mean)
 red2.set(R_mean)
 con2.set(cont_list)
 ene2.set(ene_list)
 homo2.set(homo_list)
 corr2.set(cor_list)
 diss2.set(dis_list)        
 return(feature_matrix_db,resizelist)

def OPEN():
  path=tkFileDialog.askopenfilename(filetypes=[("Image File",'.jpg')])
  custName.set(path)
  im = Image.open(path)
  resized = im.resize((200, 200),Image.ANTIALIAS)
  tkimage = ImageTk.PhotoImage(resized)
  myvar=Label(root,image = tkimage)
  myvar.image = tkimage
  myvar.pack()
  myvar.place(x = 30, y = 100) 
  graylist1 = list()
  resizelist1 = list()
  eq_graylist1 = list()
  cont_list1 = list()
  ene_list1 = list()
  homo_list1 = list()
  cor_list1 = list()
  B_mean1 = list()
  G_mean1 = list()
  R_mean1 = list()
  dis_list1 = list()

  imge = cv2.imread(path)
  arr = array(imge)
  g_img = cv2.imread(path,0)
  gray_re_img = cv2.resize(g_img,(256,256))
  graylist1.append(gray_re_img)

  equ = cv2.equalizeHist(gray_re_img)
  eq_graylist1.append(equ)

  re_img = cv2.resize(imge,(256,256))
  resizelist1.append(re_img)

  blue, green, red = cv2.split(re_img)
  total = re_img.size
  B = sum(blue) / total
  G = sum(green) / total
  R = sum(red) / total
  B_mean1.append(B)
  G_mean1.append(G)
  R_mean1.append(R)

  im = skimage.io.imread(path, as_grey=True)
  im = skimage.img_as_ubyte(im)
  im /= 32
  g = skimage.feature.greycomatrix(im, [1], [0], levels=8, symmetric=False, normed=True)
  cont = skimage.feature.greycoprops(g, 'contrast')[0][0]
  cont_list1.append(cont)
  ene = skimage.feature.greycoprops(g, 'energy')[0][0]
  ene_list1.append(ene)
  homo = skimage.feature.greycoprops(g, 'homogeneity')[0][0]
  homo_list1.append(homo)
  cor = skimage.feature.greycoprops(g, 'correlation')[0][0]
  cor_list1.append(cor)
  dis = skimage.feature.greycoprops(g, 'dissimilarity')[0][0]
  dis_list1.append(dis)

 feature_matrix_ip = zip( B_mean1 , G_mean1 , R_mean1, cont_list1 , ene_list1 , homo_list1 , cor_list1 , dis_list1)
 blue1.set(B_mean1)
 green1.set(G_mean1)
 red1.set(R_mean1)
 con1.set(cont_list1)
 ene1.set(ene_list1)
 homo1.set(homo_list1)
 corr1.set(cor_list1)
 diss1.set(dis_list1)
 return(feature_matrix_ip)

def result():
  COLUMNS = 12
  image_count = 0
  resultlist_key = []
  result_list = list()
  i = 0
  a_list = list()
  b_list = list()
  a_list.append(feature_matrix_ip)
  while i < 70:
     b_list.append(feature_matrix_db[i])
     dist = distance.euclidean(a_list,b_list[i])
     result_list.append(dist)
     resultlist_key = OrderedDict(sorted(enumerate(result_list),key=lambda x: x[0])).keys()
     i = i + 1 

 res_lst_srt = {'values': result_list,'keys':resultlist_key}
 res_lst_srt['values'], res_lst_srt['keys'] = zip(*sorted(zip(res_lst_srt['values'], res_lst_srt['keys'])))
 key = res_lst_srt['keys']

 for i1,val in enumerate(key):
     if i1 < 4:
         image_count += 1
         r, c = divmod(image_count, COLUMNS)
         im = Image.open(resizelist[val]) # <---- This is where the error is coming
         tkimage = ImageTk.PhotoImage(resized)
         myvar = Label(win, image=tkimage)
         myvar.image = tkimage
         myvar.grid(row=r, column=c) 

即使经过 return(feature_matrix_db, resizelist)它给出了同样的错误。有没有办法解决这个问题？或者我需要更改我的代码。我已经初始化的一切。正在调用/导入每个必需的 header 。

提前致谢!

Answer 1

所以来自 http://effbot.org/imagingbook/image.htm

Image.open(file) ⇒ image

Image.open(file, mode) ⇒ image

Opens and identifies the given image file. This is a lazy operation; the function reads the file header, but the actual image data is not read from the file until you try to process the data (call the load method to force loading). If the mode argument is given, it must be “r”.

You can use either a string (representing the filename) or a file object as the file argument. In the latter case, the file object must implement read, seek, and tell methods, and be opened in binary mode.

from PIL import Image im = Image.open("lenna.jpg") from PIL import image from StringIO import StringIO

read data from string im = Image.open(StringIO(data))

正如文档所说，参数传递给 Image.open必须实现 read , seek和 tell方法。您正在传递由 OpenCv 生成的 numpy 数组，当它需要文件名、StringIO 实例或文件对象时。

我认为你可以更换违规 Image.open调用 Image.fromarray这将把 numpy 数组作为输入。 IE。:

im = Image.fromarray(resizelist[val])