About "PIL" error, NameError: name 'PIL' is not defined

I am a new python user and new one in "Stack Overflow", when I try to compile a tensorflow code I met some question, and I can't found answer from the website, so I want get some helps from here, thank everyone in advance!

And this is my compiling result:

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 

Here is my code, and I marked the line number that my errors happened to make you finding it easily:

#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]) 

And I have install the pillow in my tensorflow environment(python 3.5.2).

Thank you everyone very much!

1 Answer

Use Image.fromarray, since Image was imported from PIL but PIL itself was never imported.