德国交通标志识别

数据集官网上有，可以自行下载

代码

数据集导入
import pickle
training_file = './train.p'
testing_file = './test.p'
with open(training_file, mode='rb') as f:train = pickle.load(f)
with open(testing_file, mode='rb') as f:test = pickle.load(f)
X_train, y_train = train['features'], train['labels']
X_test, y_test = test['features'], test['labels']

探索和可视化数据集
n_train = X_train.shape[0]
n_test = X_test.shape[0]
image_shape = X_train.shape[1:]
n_classes = len(set(y_train))
print("Number of training examples =", n_train)
print("Number of testing examples =", n_test)
print()
print("Image data shape =", image_shape)
print("Number of classes =", n_classes)

数据预处理
import numpy as np
X_train_rgb = X_train
X_train_gry = np.sum(X_train/3, axis=3, keepdims=True)
X_test_rgb = X_test
X_test_gry = np.sum(X_test/3, axis=3, keepdims=True)
 print('RGB shape:', X_train_rgb.shape)
 print('Grayscale shape:', X_train_gry.shape)
X_train_normalized = (X_train - 128.)/128. 
X_test_normalized = (X_test - 128.)/128.

from scipy import ndimage
def expend_training_data(X_train, y_train):
    """
    Augment training data
    """
    expanded_images = np.zeros([X_train.shape[0] * 5, X_train.shape[1], X_train.shape[2]])
    expanded_labels = np.zeros([X_train.shape[0] * 5])
 
    counter = 0
    for x, y in zip(X_train, y_train):
 
        # register original data
        expanded_images[counter, :, :] = x
        expanded_labels[counter] = y
        counter = counter + 1
 
        # get a value for the background
        # zero is the expected value, but median() is used to estimate background's value
        bg_value = np.median(x)  # this is regarded as background's value
 
        for i in range(4):
            # rotate the image with random degree
            angle = np.random.randint(-15, 15, 1)
            new_img = ndimage.rotate(x, angle, reshape=False, cval=bg_value)
 
            # shift the image with random distance
            shift = np.random.randint(-2, 2, 2)
            new_img_ = ndimage.shift(new_img, shift, cval=bg_value)
 
            # register new training data
            expanded_images[counter, :, :] = new_img_
            expanded_labels[counter] = y
            counter = counter + 1
 
    return expanded_images, expanded_labels
 
X_train_normalized = np.reshape(X_train_normalized,(-1, 32, 32))
agument_x, agument_y = expend_training_data(X_train_normalized[:], y_train[:])
agument_x = np.reshape(agument_x, (-1, 32, 32, 1))
 
print(agument_y.shape)
print(agument_x.shape)
 
print('agument_y mean:', np.mean(agument_y))
print('agument_x mean:',np.mean(agument_x))
#print(y_train.shape)