python - 关于 "PIL"错误，NameError : name 'PIL' is not defined

Question

我是一名新的Python用户，也是“Stack Overflow”中的新用户，当我尝试编译 tensorflow 代码时，我遇到了一些问题，并且我无法从网站上找到答案，所以我想从这里获得一些帮助，先谢谢大家了!

这是我的编译结果:

D:\Python\Anaconda2\envs\tensorflow\python.exe D:/Python/pycharm_project/test/mnist_chuji
Traceback (most recent call last):
    File "D:/Python/pycharm_project/test/mnist_chuji", line 52, in <module>
      DisplayArray(u_init, rng=[-0.1, 0.1])
    File "D:/Python/pycharm_project/test/mnist_chuji", line 15, in DisplayArray
      PIL.Image.fromarray(a).save(f, fmt)
NameError: name 'PIL' is not defined

Process finished with exit code 1 

这是我的代码，我标记了我的错误发生的行号，方便大家查找:

#导入模拟仿真需要的库
import tensorflow as tf
import numpy as np

#导入可视化需要的库
from PIL import Image
from io import StringIO #python3 使用了io代替了sStringIO
from IPython.display import clear_output, Image, display

def DisplayArray(a, fmt='jpeg', rng=[0,1]):
  """Display an array as a picture."""
  a = (a - rng[0])/float(rng[1] - rng[0])*255
  a = np.uint8(np.clip(a, 0, 255))
  f = StringIO()
  PIL.Image.fromarray(a).save(f, fmt) #line 15
  display(Image(data=f.getvalue()))

sess = tf.InteractiveSession()

def make_kernel(a):
  """Transform a 2D array into a convolution kernel"""
  a = np.asarray(a)
  a = a.reshape(list(a.shape) + [1,1])
  return tf.constant(a, dtype=1)

def simple_conv(x, k):
  """A simplified 2D convolution operation"""
  x = tf.expand_dims(tf.expand_dims(x, 0), -1)
  y = tf.nn.depthwise_conv2d(x, k, [1, 1, 1, 1], padding='SAME')
  return y[0, :, :, 0]

def laplace(x):
  """Compute the 2D laplacian of an array"""
  laplace_k = make_kernel([[0.5, 1.0, 0.5],
                           [1.0, -6., 1.0],
                           [0.5, 1.0, 0.5]])
  return simple_conv(x, laplace_k)

N = 500

# Initial Conditions -- some rain drops hit a pond

# Set everything to zero
u_init = np.zeros([N, N], dtype="float32")
ut_init = np.zeros([N, N], dtype="float32")

# Some rain drops hit a pond at random points
for n in range(40):
  a,b = np.random.randint(0, N, 2)
  u_init[a,b] = np.random.uniform()

DisplayArray(u_init, rng=[-0.1, 0.1]) #line 52

# Parameters:
# eps -- time resolution
# damping -- wave damping
eps = tf.placeholder(tf.float32, shape=())
damping = tf.placeholder(tf.float32, shape=())

# Create variables for simulation state
U  = tf.Variable(u_init)
Ut = tf.Variable(ut_init)

# Discretized PDE update rules
U_ = U + eps * Ut
Ut_ = Ut + eps * (laplace(U) - damping * Ut)

# Operation to update the state
step = tf.group(
  U.assign(U_),
  Ut.assign(Ut_))

# Initialize state to initial conditions
tf.initialize_all_variables().run()

# Run 1000 steps of PDE
for i in range(1000):
  # Step simulation
  step.run({eps: 0.03, damping: 0.04})
  # Visualize every 50 steps
  if i % 50 == 0:
    clear_output()
    DisplayArray(U.eval(), rng=[-0.1, 0.1])

我已经在我的tensorflow环境中安装了pillow(python 3.5.2)。

非常感谢大家!

Answer 1

使用Image.fromarray，因为Image是从PIL导入的，但PIL本身从未导入。