神经网络之路标识别

落日映苍穹つ 2022-06-03 08:58 219阅读 0赞

参考了一下链接的博客  
[https://yq.aliyun.com/articles/67167][https_yq.aliyun.com_articles_67167]  
[https://www.jianshu.com/p/d8feaddc7bdf][https_www.jianshu.com_p_d8feaddc7bdf]  
成功实现了神经网络模型的训练和路标图片的识别。文末附有源码。

#### 1、环境问题 ####

原作者在docker里配有环境，所以为了简便我直接用作者的环境。

yum install docker
    docker search waleedka

![这里写图片描述][SouthEast]  
然后pull下来

docker pull docker.io/waleedka/modern-deep-learning

我是之前pull的忘了，pull后的字段是什么了，大致就是docker.io/waleedka/modern-deep-learning，然后会进行镜像的下载，挺费时间的，漫长的等待。  
在本地创建一个文件夹叫traffic，并赋予权限

mkdir traffic
    chmod 777 traffic

然后把训练数据集合测试数据集考过来。数据集下载地址：[http://btsd.ethz.ch/shareddata/][http_btsd.ethz.ch_shareddata]  
最好这样建立文件夹便于代码运行。  
![这里写图片描述][SouthEast 1]  
/root/traffic/datasets/BelgiumTS下有两个文件夹，一个是训练的数据集，一个是测试的数据集。  
训练集里文件长这样：  
![这里写图片描述][SouthEast 2]  
接下来就是运行镜像了

docker run -it -p 8888:8888 -p 6006:6006 -v ~/traffic:/traffic waleedka/modern-deep-learning

**注意：我的工程目录是在~/traffic下，我在我的docker中将其映射到了/traffic目录下**  
在镜像运行后的 /traffic 文件夹里可以看到和linux对应的文件

#### 2、编写python脚本,， vi traffic.py ####

import os
    import tensorflow as tf
    import random
    import skimage
    from skimage import data, io, filters
    import skimage.transform
    import numpy as np
    
    #%matplotlib inline
    
    def load_data(data_dir):
        # Get all subdirectories of data_dir. Each represents a label.
        directories = [d for d in os.listdir(data_dir) 
                       if os.path.isdir(os.path.join(data_dir, d))]
        # Loop through the label directories and collect the data in
        # two lists, labels and images.
        labels = []
        images = []
        for d in directories:
            label_dir = os.path.join(data_dir, d)
            file_names = [os.path.join(label_dir, f) 
                          for f in os.listdir(label_dir) 
                          if f.endswith(".ppm")]
            for f in file_names:
                images.append(skimage.data.imread(f))
                labels.append(int(d))
        return images, labels
    
    images, labels = load_data("/traffic/datasets/BelgiumTS/Training/")
    
    for image in images[:5]:
        print("shape: {0}, min: {1}, max: {2}".format(
              image.shape, image.min(), image.max()))
    
    
    # zip size 32*32
    images32 = [skimage.transform.resize(image, (32, 32)) for image in images]
    labels_a = np.array(labels)
    images_a = np.array(images32)
    
    print("labels: ", labels_a.shape, "\nimages: ", images_a.shape)
    
    
    graph = tf.Graph()
    
    with graph.as_default():
        # Placeholders for inputs and labels.
        images_ph = tf.placeholder(tf.float32, [None, 32, 32, 3])
        labels_ph = tf.placeholder(tf.int32, [None])
    
        # Flatten input from: [None, height, width, channels]
        # To: [None, height * width * channels] == [None, 3072]
        images_flat = tf.contrib.layers.flatten(images_ph)
    
        # Fully connected layer. 
        # Generates logits of size [None, 62]
        logits = tf.contrib.layers.fully_connected(images_flat, 62, tf.nn.relu)
    
        # Convert logits to label indexes (int).
        # Shape [None], which is a 1D vector of length == batch_size.
        predicted_labels = tf.argmax(logits, 1)
    
        # Define the loss function. 
        # Cross-entropy is a good choice for classification.
        # labels=..., logits=..., ...
        loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,labels=labels_ph))
    
        # Create training op.
        train = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
    
        # And, finally, an initialization op to execute before training.
        # TODO: rename to tf.global_variables_initializer() on TF 0.12.
        init = tf.initialize_all_variables()
    
    print("images_flat: ", images_flat)
    print("logits: ", logits)
    print("loss: ", loss)
    print("predicted_labels: ", predicted_labels)
    
    
    print("begin train")
    session = tf.Session(graph=graph)
    
    # First step is always to initialize all variables. 
    # We don't care about the return value, though. It's None.
    _ = session.run([init])
    
    
    for i in range(201):
        _, loss_value = session.run([train, loss], 
                                    feed_dict={images_ph: images_a, labels_ph: labels_a})
        if i % 10 == 0:
            print("Loss: ", loss_value)
    
    print("use model")
    sample_indexes = random.sample(range(len(images32)), 10)
    sample_images = [images32[i] for i in sample_indexes]
    sample_labels = [labels[i] for i in sample_indexes]
    
    # Run the "predicted_labels" op.
    predicted = session.run([predicted_labels],feed_dict={images_ph: sample_images})[0]
    print(sample_labels)
    print(predicted)

运行脚本python traffic.py

[https_yq.aliyun.com_articles_67167]: https://yq.aliyun.com/articles/67167
[https_www.jianshu.com_p_d8feaddc7bdf]: https://www.jianshu.com/p/d8feaddc7bdf
[SouthEast]: /images/20220603/ab497dad1ba24408bc3e005778bca5af.png
[http_btsd.ethz.ch_shareddata]: http://btsd.ethz.ch/shareddata/
[SouthEast 1]: /images/20220603/c3e5961cb87a4b76b402c9da17596f8e.png
[SouthEast 2]: /images/20220603/e8f558c74e574e1d9af01c9b0e6a2547.png