今天主要介紹無人駕駛當中深度學習技術的應用。

本文是根據博客專家AdamShan的文章整理而來，在此表示感謝。

關于深度學習的圖像分類技術，網上已有很多關于深度學習的課程（如吳恩達老師的深度學習專項課程），故本文不對理論部分進行闡述，只關注實踐部分，用到的深度學習框架是keras。關于keras框架的學習，之前轉載了一篇黃海廣博士的文章，是很不錯的學習書籍。

首發：深度學習入門寶典-《python深度學習》原文代碼中文注釋版及電子書

(2021-03-03補充) 本文中使用數據集下載地址：動手學無人駕駛（1）：交通標志識別》訓練集和測試集

目錄

1.導入庫

2. 數據處理（重點）

2.1 讀取數據集

2.2 顯示圖片

2.3 圖片尺寸處理

2.4 將圖片轉換為numpy 數組

2.5 將圖像轉為灰度圖

2.6 數據集增強

3. 訓練神經網絡

4. 測試驗證

5. 使用模型

6. 總結

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關于圖像分類的介紹，可以參考以下視頻（CS231n：用于視覺識別的卷積神經網絡（2017年秋季）1080p_嗶哩嗶哩_bilibili）。

CS231n：用于視覺識別的卷積神經網絡（2017年秋季）1080p

1.導入庫

在編寫代碼時，首先是加載本文中所需要的庫，如下：

import os import random import scipy import skimage import matplotlib import matplotlib.pyplot as plt import numpy as np from scipy import ndimage from skimage import colorimport keras from keras.models import Sequential from keras.layers import Dense, Flatten from keras.optimizers import SGD from keras.layers import Dropout from keras.optimizers import RMSprop# Allow image embeding in notebook %matplotlib inline

注意：在Jupyter Notebook運行上述代碼時scipy庫和skimage庫會沖突，解決辦法是通過pip包安裝scipy，如下：

sudo pip3 install scipy

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2. 數據處理（重點）

本文中用到的交通標志數據集是BelgiumTS數據集，將數據集解壓位于notebook同一目錄下，

• data/Training/
• data/Testing/

每個文件夾包含了62個類，編號從 00000 到 00061.

2.1 讀取數據集

def load_data(data_dir):"""Loads a data set and returns two lists:（加載數據，返回圖像列表和標簽列表）images: a list of Numpy arrays, each representing an image.labels: a list of numbers that represent the images labels."""# Get all subdirectories of data_dir. Each represents a label.#獲取data_dir所有子文件夾，每一個包含一種標簽#os.listdir(絕對路徑)，返回文件名和目錄名組成的列表#os.path.isdir()，發【判斷是否為目錄#os.path.join(),將多個路徑組合為一個路徑directories = [d for d in os.listdir(data_dir) if os.path.isdir(os.path.join(data_dir, d))]#print(directories)# Loop through the label directories and collect the data in(循環遍歷每一個子目錄，收集數據)# 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")]#print(file_names)# For each label, load it's images and add them to the images list.# And add the label number (i.e. directory name) to the labels list.for f in file_names:images.append(skimage.data.imread(f))labels.append(int(d))return images, labels# Load training and testing datasets. ROOT_PATH = "data" train_data_dir = os.path.join(ROOT_PATH, "Training") test_data_dir = os.path.join(ROOT_PATH, "Testing")images, labels = load_data(train_data_dir)

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2.2 顯示圖片

加載完數據集后，顯示每個交通標識類別的第一張圖片。

def display_images_and_labels(images, labels):"""Display the first image of each label."""unique_labels = set(labels)plt.figure(figsize=(15, 15))i = 1for label in unique_labels:# Pick the first image for each label.image = images[labels.index(label)]plt.subplot(8, 8, i) # A grid of 8 rows x 8 columnsplt.axis('off')plt.title("Label {0} ({1})".format(label, labels.count(label)))i += 1_ = plt.imshow(image)plt.show()display_images_and_labels(images, labels)

效果如下，共有62類交通標志。

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2.3 圖片尺寸處理

數據集圖片大小尺寸并不統一，為了更好的訓練神經網絡，需要將所有圖片調整到一個相同的尺寸，這里將圖片尺寸調整到（32，32）。

# Resize images(調整到（32,32）) images32 = [skimage.transform.resize(image, (32, 32))for image in images] display_images_and_labels(images32, labels)

調整后的圖片如下圖所示，此時圖片大小尺寸已經統一。

顯示調整后的圖片尺寸信息：

for image in images32[:5]:print("shape: {0}, min: {1}, max: {2}".format(image.shape, image.min(), image.max())) shape: (32, 32, 3), min: 0.03529411764705882, max: 0.996078431372549 shape: (32, 32, 3), min: 0.03395373774509821, max: 0.996078431372549 shape: (32, 32, 3), min: 0.03694182751225482, max: 0.996078431372549 shape: (32, 32, 3), min: 0.06460056678921586, max: 0.9191425398284314 shape: (32, 32, 3), min: 0.060355392156862725, max: 0.9028492647058823

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2.4 將圖片轉換為numpy 數組

labels_a = np.array(labels) images_a = np.array(images32) print("labels: ", labels_a.shape, "\nimages: ", images_a.shape)

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2.5 將圖像轉為灰度圖

對圖像進行一定的預處理，在這里將原來的RGB三通道的圖像轉換為灰度圖。

images_a = color.rgb2gray(images_a) display_images_and_labels(images_a, labels)

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2.6 數據集增強

這里將數據集擴充為5倍，并顯示其中的幾類。

def expend_training_data(train_x, train_y):"""Augment training data擴充5被"""expanded_images = np.zeros([train_x.shape[0] * 5, train_x.shape[1], train_x.shape[2]])expanded_labels = np.zeros([train_x.shape[0] * 5])counter = 0for x, y in zip(train_x, train_y):# register original data（加載原始數據）expanded_images[counter, :, :] = xexpanded_labels[counter] = ycounter = counter + 1# get a value for the background# zero is the expected value, but median() is used to estimate background's valuebg_value = np.median(x) # this is regarded as background's valuefor i in range(4):# rotate the image with random degreeangle = np.random.randint(-15, 15, 1)#new_img = ndimage.rotate(x, angle, reshape=False, cval=bg_value)# shift the image with random distanceshift = np.random.randint(-2, 2, 2)new_img_ = ndimage.shift(x, shift, cval=bg_value)# register new training dataexpanded_images[counter, :, :] = new_img_expanded_labels[counter] = ycounter = counter + 1return expanded_images, expanded_labelsimages_a, labels_a = expend_training_data(images_a, labels_a) print(images_a.shape, labels_a.shape) labels = labels_a.tolist() print(len(labels)) def plot_agument(images, labels):plt.figure(figsize=(16, 9))unique_labels = set(labels)i = 1for label in unique_labels:# Pick the first image for each label.if i > 3:breakimg_index = labels.index(label)for j in range(5):image = images_a[img_index+j]plt.subplot(3, 5, (i-1)*5 + j+1) # A grid of 8 rows x 8 columnsplt.axis('off')plt.title("Label {0} ({1})".format(label, labels.count(label)))_ = plt.imshow(image, cmap='gray')i += 1plot_agument(images_a, labels)

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3. 訓練神經網絡

這里用到keras搭建神經網絡，模式為Sequential模式。

from sklearn.utils import shuffleindx = np.arange(0, len(labels_a)) indx = shuffle(indx) images_a = images_a[indx] labels_a = labels_a[indx]#總樣本數為22875 train_x, val_x = images_a[:20000], images_a[20000:] train_y, val_y = labels_a[:20000], labels_a[20000:]train_y = keras.utils.to_categorical(train_y, 62) val_y = keras.utils.to_categorical(val_y, 62)#構建神經網絡 model = Sequential() model.add(Flatten(input_shape=(32, 32))) model.add(Dense(512, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(512, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(62, activation='softmax'))model.summary()model.compile(loss='categorical_crossentropy',optimizer=RMSprop(),metrics=['accuracy'])history = model.fit(train_x, train_y,batch_size=128,epochs=20,verbose=1,validation_data=(val_x, val_y))### print the keys contained in the history object #print(history.history.keys()) model.save('model.json')

網絡的訓練誤差和驗證誤差如圖所示：

def plot_training(history):### plot the training and validation loss for each epochplt.plot(history.history['loss'])plt.plot(history.history['val_loss'])plt.title('model mean squared error loss')plt.ylabel('mean squared error loss')plt.xlabel('epoch')plt.legend(['training set', 'validation set'], loc='upper right')plt.show()plot_training(history=history)

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4. 測試驗證

加載測試集：

# Load the test dataset. test_images, test_labels = load_data(test_data_dir) # Transform the images, just like we did with the training set. test_images32 = [skimage.transform.resize(image, (32, 32))for image in test_images]test_images_a = np.array(test_images32) test_labels_a = np.array(test_labels)test_images_a = color.rgb2gray(test_images_a)display_images_and_labels(test_images_a, test_labels)

test_x = test_images_a test_y = keras.utils.to_categorical(test_labels_a, 62) score = model.evaluate(test_x, test_y, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1])

測試精度為：88%。

Test loss: 0.49647544848156117 Test accuracy: 0.8789682388305664

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5. 使用模型測試

這里選取幾張圖片樣本，進行測試，測試結果如圖所示，其中有2張圖片測試結構與標簽不一致。

# Display the predictions and the ground truth visually. fig = plt.figure(figsize=(10, 10)) j = 1 for i in range(0, 1000, 100):truth = test_labels_a[i]prediction = predicted[i]plt.subplot(5, 2, j)j = j+1 plt.axis('off')color='green' if truth == prediction else 'red'plt.text(40, 10, "Truth: {0}\nPrediction: {1}".format(truth, prediction), fontsize=12, color=color)plt.imshow(test_x[i], cmap='gray')

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6. 總結

本文利用深度學習進行了交通標志識別，首先進行了數據的處理，然后訓練了神經網絡，最后進行了測試，測試精度達到88%。主要的工作是圖像數據處理。為進一步提高精度，可考慮使用卷積神經網絡。

總結

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