add_argument（...，type = argparse.filetype（＆＃x27; r＆＃x27;））但是arg是无

Question

我想从ImageNet数据集恢复Tiny Imagenet数据集的大尺寸图像。我发现代码在此存储库中。问题是什么，我该如何解决此问题？

  import argparse, os, os.path, glob, random, sys, json
from collections import defaultdict
from lxml import objectify

from matplotlib.pyplot import imread
from imageio import imwrite


import cv2
#from scipy.misc import  imresize
from matplotlib.patches import Rectangle
import matplotlib.pyplot as plt
import numpy as np

train_anns_path = '/home/user/tiny_imagenet/LOC_train_solution.csv'
train_image_dir = '/data/imagenet/Data/CLS-LOC/train'
val_anns_path = '/home/user/tiny_imagenet/LOC_val_solution.csv'
val_image_dir = '/data/imagenet/Data/CLS-LOC/val'


def get_synset_stats():
  with open('words.txt') as f:
    wnid_to_words = dict(line.strip().split('\t') for line in f)

  wnids = os.listdir(train_anns_path)
  wnid_to_stats = {wnid: {} for wnid in wnids}
  for i, wnid in enumerate(wnids):
    synset_dir = os.path.join(train_anns_path, wnid)
    bbox_files = os.listdir(synset_dir)
    bbox_files = [os.path.join(synset_dir, x) for x in bbox_files]

    glob_str = '%s_*.JPEG' % wnid
    img_files = glob.glob(os.path.join(train_image_dir, glob_str))

    wnid_to_stats[wnid]['bbox_files'] = bbox_files
    wnid_to_stats[wnid]['img_files'] = img_files
    wnid_to_stats[wnid]['num_imgs_train'] = len(img_files)
    wnid_to_stats[wnid]['num_loc_train'] = len(bbox_files)
    wnid_to_stats[wnid]['words'] = wnid_to_words[wnid]

    #print >> sys.stderr, i
    #print '%d\t%s\t%s\t%d\t%d' % (
       # i, wnid, wnid_to_words[wnid], len(bbox_files), len(img_files))

    
def parse_xml_file(filename):
  with open(filename, 'r') as f:
    xml = f.read()
  ann = objectify.fromstring(xml)
  img_filename = '%s.JPEG' % ann.filename
  bbox = ann.object.bndbox
  bbox = [bbox.xmin, bbox.ymin, bbox.xmax, bbox.ymax]
  bbox = [int(x) for x in bbox]
  name = str(ann.object.name)
  return img_filename, bbox, name


def resize_image(img, size, bbox=None, crop=True, show=False):
  """
  Resize an image and its bounding box to a square.
  img - A numpy array with pixel data for the image to resize.
  size - Integer giving the height and width of the resized image.
  bbox - Optionally, a list [xmin, ymin, xmax, ymax] giving the coordinates
         of a bounding box in the original image.
  crop - If true, center crop the original image before resizing; this avoids
         distortion in images with nonunit aspect ratio, but may also crop out
         part of the object.
  show - If true, show the original and resized image and bounding box.
  Returns:
  If bbox was passed: (img_resized, bbox_resized)
  otherwise: img_resized
  """

  def draw_rect(coords):
    width = coords[2] - coords[0]
    height = coords[3] - coords[1]
    rect = Rectangle((coords[0], coords[1]), width, height, 
                     fill=False, linewidth=2.0, ec='green')
    plt.gca().add_patch(rect)

  img_resized = img
  if bbox is not None:
    bbox_resized = [x for x in bbox]
  if crop:
    h, w = img.shape[0], img.shape[1]
    if h > w:
      h0 = (h - w) / 2
      if bbox is not None:
        bbox_resized[1] -= h0
        bbox_resized[3] -= h0
      img_resized = img[h0:h0+w, :]
    elif w > h:
      w0 = (w - h) / 2
      if bbox is not None:
        bbox_resized[0] -= w0
        bbox_resized[2] -= w0
      img_resized = img[:, w0:w0+h]

  if bbox is not None:
    h_ratio = float(size) / img_resized.shape[0]
    w_ratio = float(size) / img_resized.shape[1]
    ratios = [w_ratio, h_ratio, w_ratio, h_ratio]
    bbox_resized = [int(1 + r * (x - 1)) for x, r in zip(bbox_resized, ratios)]
    bbox_resized = np.clip(bbox_resized, 0, size - 1)
  img_resized = cv2.resize(img_resized, (size, size))

  if show:
    plt.subplot(1, 2, 1)
    plt.imshow(img)
    if bbox is not None:
      draw_rect(bbox)
    plt.subplot(1, 2, 2)
    plt.imshow(img_resized)
    if bbox is not None:
      draw_rect(bbox_resized)
    plt.show()

  if bbox is None:
    return img_resized
  else:
    return img_resized, bbox_resized


def write_data_in_synset_folders(part_data, part, out_dir, image_size):
  part_dir = os.path.join(out_dir, part)
  os.mkdir(part_dir)
  num_wnids = len(part_data)
  for i, (wnid, wnid_data) in enumerate(part_data.iteritems()):
    #print 'Writing images for synset %d / %d of %s' % (i + 1, num_wnids, part)
    wnid_dir = os.path.join(part_dir, wnid)
    os.mkdir(wnid_dir)
    image_dir = os.path.join(wnid_dir, 'images')
    os.mkdir(image_dir)
    boxes_filename = os.path.join(wnid_dir, '%s_boxes.txt' % wnid)
    boxes_file = open(boxes_filename, 'w')
    for i, (img_filename, bbox) in enumerate(wnid_data):
      out_img_filename = '%s_%d.JPEG' % (wnid, i)
      full_out_img_filename = os.path.join(image_dir, out_img_filename)
      img = imread(img_filename)
      img_resized, bbox_resized = resize_image(img, image_size, bbox)
      imwrite(full_out_img_filename, img_resized)
      boxes_file.write('%s\t%d\t%d\t%d\t%d\n' % (out_img_filename,
                       bbox_resized[0], bbox_resized[1], bbox_resized[2], bbox_resized[3]))
    boxes_file.close()


def write_data_in_one_folder(part_data, part, out_dir, image_size):
  part_dir = os.path.join(out_dir, part)
  os.mkdir(part_dir)

  # First flatten the part data so we can shuffle it
  part_data_flat = []
  for wnid, wnid_data in part_data.iteritems():
    for (img_filename, bbox) in wnid_data:
      part_data_flat.append((wnid, img_filename, bbox))

  random.shuffle(part_data_flat)
  image_dir = os.path.join(part_dir, 'images')
  os.mkdir(image_dir)

  annotations_filename = os.path.join(part_dir, '%s_annotations.txt' % part)
  annotations_file = open(annotations_filename, 'w')
  for i, (wnid, img_filename, bbox) in enumerate(part_data_flat):
    if i % 100 == 0:
      print ('Finished writing %d / %d %s images' % (i, len(part_data_flat), part))
    out_img_filename = '%s_%s.JPEG' % (part, i)
    full_out_img_filename = os.path.join(image_dir, out_img_filename)
    img = imread(img_filename)
    img_resized, bbox_resized = resize_image(img, image_size, bbox)
    imwrite(full_out_img_filename, img_resized)
    annotations_file.write('%s\t%s\t%d\t%d\t%d\t%d\n' % (
        out_img_filename, wnid,
        bbox_resized[0], bbox_resized[1], bbox_resized[2], bbox_resized[3]))
  annotations_file.close()


def make_tiny_imagenet(wnids, num_train, num_val, out_dir, image_size=50, test=False):
  if os.path.isdir(out_dir):
   # print 'Output directory already exists'
    return

  # dataset['train']['n123'][0] = (filename, (xmin, ymin, xmax, xmax))
  # gives one example of an image and bbox for synset n123 of the training subset
  dataset = defaultdict(lambda: defaultdict(list))
  for i, wnid in enumerate(wnids):
    #print 'Choosing train and val images for synset %d / %d' % (i + 1, len(wnids))

    # TinyImagenet train and val images come from ILSVRC-2012 train images
    train_synset_dir = os.path.join(train_anns_path, wnid)
    orig_train_bbox_files = os.listdir(train_synset_dir)
    orig_train_bbox_files = {os.path.join(train_synset_dir, x) for x in orig_train_bbox_files}

    train_bbox_files = random.sample(orig_train_bbox_files, num_train)
    orig_train_bbox_files -= set(train_bbox_files)
    val_bbox_files = random.sample(orig_train_bbox_files, num_val)

    for bbox_file in train_bbox_files:
      img_filename, bbox, _ =  parse_xml_file(bbox_file)
      img_filename = os.path.join(train_image_dir, img_filename)
      dataset['train'][wnid].append((img_filename, bbox))

    for bbox_file in val_bbox_files:
      img_filename, bbox, _ = parse_xml_file(bbox_file)
      img_filename = os.path.join(train_image_dir, img_filename)
      dataset['val'][wnid].append((img_filename, bbox))
    
  # All the validation XML files are all mixed up in one folder, so we need to
  # iterate over all of them. Since this takes forever, guard it behind a flag.
  # The name field of the validation XML files gives the synset of that image.
  if test:
    val_xml_files = os.listdir(val_anns_path)
    for i, val_xml_file in enumerate(val_xml_files):
      if i % 200 == 0:
       print ('Processed %d / %d val xml files so far' % (i, len(val_xml_files)))
      val_xml_file = os.path.join(val_anns_path, val_xml_file)
      img_filename, bbox, wnid = parse_xml_file(val_xml_file)
      if wnid in wnids:
        img_filename = os.path.join(val_image_dir, img_filename)
        dataset['test'][wnid].append((img_filename, bbox))

  # Now that we have selected the images for the dataset, we need to actually
  # create it on disk
  os.mkdir(out_dir)
  write_data_in_synset_folders(dataset['train'], 'train', out_dir, image_size)
  write_data_in_one_folder(dataset['val'], 'val', out_dir, image_size)
  write_data_in_one_folder(dataset['test'], 'test', out_dir, image_size)


parser = argparse.ArgumentParser()
parser.add_argument('--wnid_file', type=argparse.FileType('r'))
parser.add_argument('--num_train', type=int, default=100)
parser.add_argument('--num_val', type=int, default=100)
parser.add_argument('--image_size', type=int, default=64)
parser.add_argument('--out_dir')
args = parser.parse_args()

if __name__ == '__main__':
  wnids = [line.strip() for line in args.wnid_file]
  print (len(wnids))
  # wnids = ['n02108089', 'n09428293', 'n02113799']
  make_tiny_imagenet(wnids, args.num_train, args.num_val, args.out_dir, 
                     image_size=args.image_size, test=True)
  sys.exit(0)

  train_synsets = os.listdir(train_anns_path)

  get_synset_stats()
  sys.exit(0)

错误是：

file“/home/user/tiny_imagenet/make_tiny_imagenet.py”，第238行，in wnids = [line.strip（）for Args.wnid_file中的行] typeError：“非型”对象不是iTable

Answer 1

根据您的错误：

wnids = [line.strip（）for args.wnid_file] typeError：'nontype'对象不是

我猜问题是args.wnid_file是 none 。
您需要验证是否将WNID_FILE正确传递到Python脚本。