tensorflow，如何从张量中获取价值

Question

我使用 image_dataset_from_directory 方法 BatchDataset 上传数据

seed = 64
images = tf.keras.utils.image_dataset_from_directory(
    '/content/drive/MyDrive/DATA_PYTHON/Recognize_Alphabet/Recognize_Alphabet', 
    validation_split=0.2, 
    image_size=(34, 34),
    color_mode='rgb',
    interpolation='nearest',
    subset='training',
    seed=seed)

我想反转我上传的所有图片的颜色。为此，我尝试编写一个方法：

def invertColor(im, b):

  sess2 = tf2.Session()
  im = sess2.run(im)

  imI = PIL.ImageOps.invert(im)
  imIN = np.asarray(imI)
  imINC = cv2.cvtColor(imIN, cv2.COLOR_BGR2RGB)
  bI = Image.fromarray(imINC, 'RGB')
  return bI

当我用这个 invertColor 方法调用 map 时

images2 = images.map(invertColor)

我收到以下错误：

InvalidArgumentError: in user code:
File "<ipython-input-19-1f1e09851e25>", line 5, in invertColor  *
    sess2.run(im)

InvalidArgumentError: Graph execution error:

Detected at node 'args_0' defined at (most recent call last):
    File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
      "__main__", mod_spec)

如何在invertColor方法中获取im元素的值？（或者如何反转 BatchDataset 中的颜色？）

Answer 1

您可以尝试使用tf.py_function 以图形模式集成PIL 操作。这是一个批量大小为 1 的示例，以保持简单（您可以在之后更改批量大小）：

之前

import tensorflow as tf
import matplotlib.pyplot as plt
import pathlib
import PIL
import cv2
from PIL import Image
import PIL.ImageOps    
import numpy as np

dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)

batch_size = 1

train_ds = tf.keras.utils.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="training",
  seed=123,
  shuffle= False,
  image_size=(180, 180),
  batch_size=batch_size)

image, _ = next(iter(train_ds.take(1)))
plt.imshow(image[0].numpy() / 255)

之后

def invert_color(image):
  im = Image.fromarray(image[0].numpy().astype('uint8'), 'RGB')
  imI = PIL.ImageOps.invert(im)
  imIN = np.asarray(imI)
  imINC = cv2.cvtColor(imIN, cv2.COLOR_BGR2RGB)
  return imINC / 255

def change_data(image, label):
  return  tf.py_function(invert_color, [image], Tout=[tf.float32]), label

train_ds = train_ds.map(change_data)

image, _ = next(iter(train_ds.take(1)))
plt.imshow(image[0].numpy())

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