人工智能-项目实践-智慧交通-使用cNN对交通标志进行分类.zip

#设置matplotlib将图片保存起来 import matplotlib matplotlib.use('Agg') #导包 import argparse import os import cv2 from imutils import paths import random import numpy as np import matplotlib.pyplot as plt from keras.preprocessing.image import img_to_array from keras.utils import to_categorical from keras.optimizers import Adam from keras.preprocessing.image import ImageDataGenerator from Lenet import LeNet #定义参数解析器 def args_parse(): ap = argparse.ArgumentParser() ap.add_argument("-train", "--data_train", required=True, help="path to input data_train") ap.add_argument("-test", "--data_test", required=True, help="path to input data_test") ap.add_argument("-m", "--model", required=True, help='path to output the model') ap.add_argument("-p", "--plot", type=str, default="plot.png", help="path to output accuracy/loss") args = vars(ap.parse_args()) return args #参数初始化 EPOCHS = 35 INIT_LR = 1e-3 BATCH_SIZES = 32 NUM_CLASS = 62 NORM_SIZE = 32 #载入数据 def load_data(path): print("begin to load iamges...") data = [] labels = [] imagePaths = sorted(list(paths.list_images(path))) random.seed(250) random.shuffle(imagePaths) for ip in imagePaths: image = cv2.imread(ip) image = cv2.resize(image, (NORM_SIZE, NORM_SIZE)) image = img_to_array(image) data.append(image) label = int(ip.split(os.path.sep)[-2]) labels.append(label) #归一化 data = np.array(data, dtype='float') / 255.0 labels = np.array(labels) #标签转化成哑编码形式 labels = to_categorical(labels, num_classes=NUM_CLASS) return data, labels #训练函数 def train(idg, X_train, X_test, y_train, y_test, args): print("compiling model ...") model = LeNet.build(width=NORM_SIZE, height=NORM_SIZE, depth=3, classes=NUM_CLASS) opt = Adam(lr=INIT_LR, decay=INIT_LR/EPOCHS) model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=['accuracy']) #训练网络参数 print("start to train network...") H = model.fit_generator(idg.flow(X_train, y_train, batch_size=BATCH_SIZES), validation_data=(X_test, y_test), steps_per_epoch=len(X_train)/BATCH_SIZES, epochs=EPOCHS, verbose=1) #保存网络模型 print('save network...') model.save(args['model']) #绘制训练集的代价和准确率 print("start to plot...") plt.style.use('ggplot') plt.figure() N = EPOCHS plt.plot(np.arange(0, N), H.history['loss'], label='train_loss') plt.plot(np.arange(0, N), H.history['val_loss'], label='val_loss') plt.plot(np.arange(0, N), H.history['acc'], label='train_acc') plt.plot(np.arange(0, N), H.history['val_acc'], label='val_acc') plt.title("Training Loss and Accuracy on traffic-sign classifier") plt.xlabel("Epoch#") plt.ylabel("Loss/Accuracy") plt.legend(loc="lower left") plt.savefig(args['plot']) if __name__=='__main__': args = args_parse() train_path = args['data_train'] test_path = args['data_test'] X_train, y_train = load_data(train_path) X_test, y_test = load_data(test_path) #数据增强 idg = ImageDataGenerator(rotation_range=30, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode="nearest") train(idg, X_train, X_test, y_train, y_test, args)