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，当我尝试编译张量流代码时，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，谢谢大家提前！

\n\n

这是我的编译结果：

\n\n

D:\\Python\\Anaconda2\\envs\\tensorflow\\python.exe D:/Python/pycharm_project/test/mnist_chuji\nTraceback (most recent call last):\n    File "D:/Python/pycharm_project/test/mnist_chuji", line 52, in <module>\n      DisplayArray(u_init, rng=[-0.1, 0.1])\n    File "D:/Python/pycharm_project/test/mnist_chuji", line 15, in DisplayArray\n      PIL.Image.fromarray(a).save(f, fmt)\nNameError: name \'PIL\' is not defined\n\nProcess finished with exit code 1 \n

\n\n

这是我的代码，我标记了我的错误发生的行号，以便您轻松找到它：

\n\n

#\xe5\xaf\xbc\xe5\x85\xa5\xe6\xa8\xa1\xe6\x8b\x9f\xe4\xbb\xbf\xe7\x9c\x9f\xe9\x9c\x80\xe8\xa6\x81\xe7\x9a\x84\xe5\xba\x93\nimport tensorflow as tf\nimport numpy as np\n\n#\xe5\xaf\xbc\xe5\x85\xa5\xe5\x8f\xaf\xe8\xa7\x86\xe5\x8c\x96\xe9\x9c\x80\xe8\xa6\x81\xe7\x9a\x84\xe5\xba\x93\nfrom PIL import Image\nfrom io import StringIO #python3 \xe4\xbd\xbf\xe7\x94\xa8\xe4\xba\x86io\xe4\xbb\xa3\xe6\x9b\xbf\xe4\xba\x86sStringIO\nfrom IPython.display import clear_output, Image, display\n\ndef DisplayArray(a, fmt=\'jpeg\', rng=[0,1]):\n  """Display an array as a picture."""\n  a = (a - rng[0])/float(rng[1] - rng[0])*255\n  a = np.uint8(np.clip(a, 0, 255))\n  f = StringIO()\n  PIL.Image.fromarray(a).save(f, fmt) #line 15\n  display(Image(data=f.getvalue()))\n\nsess = tf.InteractiveSession()\n\ndef make_kernel(a):\n  """Transform a 2D array into a convolution kernel"""\n  a = np.asarray(a)\n  a = a.reshape(list(a.shape) + [1,1])\n  return tf.constant(a, dtype=1)\n\ndef simple_conv(x, k):\n  """A simplified 2D convolution operation"""\n  x = tf.expand_dims(tf.expand_dims(x, 0), -1)\n  y = tf.nn.depthwise_conv2d(x, k, [1, 1, 1, 1], padding=\'SAME\')\n  return y[0, :, :, 0]\n\ndef laplace(x):\n  """Compute the 2D laplacian of an array"""\n  laplace_k = make_kernel([[0.5, 1.0, 0.5],\n                           [1.0, -6., 1.0],\n                           [0.5, 1.0, 0.5]])\n  return simple_conv(x, laplace_k)\n\nN = 500\n\n# Initial Conditions -- some rain drops hit a pond\n\n# Set everything to zero\nu_init = np.zeros([N, N], dtype="float32")\nut_init = np.zeros([N, N], dtype="float32")\n\n# Some rain drops hit a pond at random points\nfor n in range(40):\n  a,b = np.random.randint(0, N, 2)\n  u_init[a,b] = np.random.uniform()\n\nDisplayArray(u_init, rng=[-0.1, 0.1]) #line 52\n\n# Parameters:\n# eps -- time resolution\n# damping -- wave damping\neps = tf.placeholder(tf.float32, shape=())\ndamping = tf.placeholder(tf.float32, shape=())\n\n# Create variables for simulation state\nU  = tf.Variable(u_init)\nUt = tf.Variable(ut_init)\n\n# Discretized PDE update rules\nU_ = U + eps * Ut\nUt_ = Ut + eps * (laplace(U) - damping * Ut)\n\n# Operation to update the state\nstep = tf.group(\n  U.assign(U_),\n  Ut.assign(Ut_))\n\n# Initialize state to initial conditions\ntf.initialize_all_variables().run()\n\n# Run 1000 steps of PDE\nfor i in range(1000):\n  # Step simulation\n  step.run({eps: 0.03, damping: 0.04})\n  # Visualize every 50 steps\n  if i % 50 == 0:\n    clear_output()\n    DisplayArray(U.eval(), rng=[-0.1, 0.1])\n

\n\n

我已经在我的张量流环境（python 3.5.2）中安装了枕头。

\n\n

非常感谢大家！

\n

Answer 1

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