Question

在我开始解释之前，这是实际情况。看看图片......

我按顺序：

MNIST_data：此文件夹包含MNIST_data
.jpg格式的图像系列（数字）
需要运行程序的2.py文件。 predict_2.py是重要的一个
3个模型文件（不太重要）

您可以找到我使用的项目here。

所以，这就是我需要做的事情：一旦Tensorflow使用predict_2.py文件启动（通过CMD），它将返回（再次，在CMD命令之后）我发送给它的图像中的数字。

所以，我有图像（1），发送命令（2）TF进行识别，数字打印在CMD（3）中。

现在，正如您所看到的，文件夹中有更多的数字/整数/数字。第一个问题：我怎么能扔掉所有这些？第二：在CMD中输出/取而代之后，如何在文件中打印/保存它们（所有数字，而不仅仅是一个）？（

）

predict_2.py代码如下：

"""
Predict a handwritten integer (MNIST expert).

Script requires
1) saved model (model2.ckpt file) in the same location as the script is run from.
(requried a model created in the MNIST expert tutorial)
2) one argument (png file location of a handwritten integer)

Documentation at:
http://niektemme.com/ @@to do
"""

#import modules
import sys
import tensorflow as tf
from PIL import Image, ImageFilter
import os
from datetime import datetime

def predictint(imvalue):
    """
    This function returns the predicted integer.
    The input is the pixel values from the imageprepare() function.
    """

    # Define the model (same as when creating the model file)
    x = tf.placeholder(tf.float32, [None, 784])
    W = tf.Variable(tf.zeros([784, 10]))
    b = tf.Variable(tf.zeros([10]))

    def weight_variable(shape):
      initial = tf.truncated_normal(shape, stddev=0.1)
      return tf.Variable(initial)

    def bias_variable(shape):
      initial = tf.constant(0.1, shape=shape)
      return tf.Variable(initial)

    def conv2d(x, W):
      return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

    def max_pool_2x2(x):
      return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')   

    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])

    x_image = tf.reshape(x, [-1,28,28,1])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)

    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])

    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)

    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])

    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])

    y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

    init_op = tf.initialize_all_variables()
    saver = tf.train.Saver()

    """
    Load the model2.ckpt file
    file is stored in the same directory as this python script is started
    Use the model to predict the integer. Integer is returend as list.

    Based on the documentatoin at
    https://www.tensorflow.org/versions/master/how_tos/variables/index.html
    """
    with tf.Session() as sess:
        sess.run(init_op)
        saver.restore(sess, "model2.ckpt")
        #print ("Model restored.")

        prediction=tf.argmax(y_conv,1)
        return prediction.eval(feed_dict={x: [imvalue],keep_prob: 1.0}, session=sess)


def imageprepare(argv):
    """
    This function returns the pixel values.
    The input is a png file location.
    """
    im = Image.open(argv).convert('L')
    width = float(im.size[0])
    height = float(im.size[1])
    newImage = Image.new('L', (28, 28), (255)) #creates white canvas of 28x28 pixels

    if width > height: #check which dimension is bigger
        #Width is bigger. Width becomes 20 pixels.
        nheight = int(round((20.0/width*height),0)) #resize height according to ratio width
        if (nheight == 0): #rare case but minimum is 1 pixel
            nheigth = 1  
        # resize and sharpen
        img = im.resize((20,nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)
        wtop = int(round(((28 - nheight)/2),0)) #caculate horizontal pozition
        newImage.paste(img, (4, wtop)) #paste resized image on white canvas
    else:
        #Height is bigger. Heigth becomes 20 pixels. 
        nwidth = int(round((20.0/height*width),0)) #resize width according to ratio height
        if (nwidth == 0): #rare case but minimum is 1 pixel
            nwidth = 1
         # resize and sharpen
        img = im.resize((nwidth,20), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)
        wleft = int(round(((28 - nwidth)/2),0)) #caculate vertical pozition
        newImage.paste(img, (wleft, 4)) #paste resized image on white canvas

    #newImage.save("sample.png")

    tv = list(newImage.getdata()) #get pixel values

    #normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.
    tva = [ (255-x)*1.0/255.0 for x in tv] 
    return tva
    #print(tva)

def main(argv):
    """
    Main function.
    """
    imvalue = imageprepare(argv)
    predint = predictint(imvalue)
    print (predint[0]) #first value in list

if __name__ == "__main__":
    main(sys.argv[1])

需要修改什么？我已经有了可以自动创建文件的代码，但它是空的（即使有一个检测到的数字也没有。）

提前致谢。对我来说，这不是一件容易的事，真的，非常感谢你的帮助。

使用TensorFlow（Python）读取多个整数（并将它们保存在文件中）

0 个答案: