基于python+CNN+opencv实现的驾驶员分心驾驶状态识别源码+数据+使用说明.zip

import os import paddlex as pdx import paddlehub as hub import paddle.fluid as fluid import numpy as np from paddlex.cls import transforms os.environ['CUDA_VISIBLE_DEVICES'] = '0' # 加载数据 def load_data(data_base_path): datas = [] for i in range(10): data_path = data_base_path + '/imgs/train/c{}/'.format(i) for im in os.listdir(data_path): pt = os.path.join(data_base_path+'/imgs/train/c{}/'.format(i), im) line = '{} {}'.format(pt, i) datas.append(line) np.random.seed(10) np.random.shuffle(datas) total_num = len(datas) train_num = int(0.8*total_num) test_num = int(0.1*total_num) valid_num = total_num - train_num - test_num print('train:', train_num) print('valid:', valid_num) print('test:', test_num) with open('train_list.txt', 'w') as f: for v in datas[:train_num]: f.write(v+'\n') with open('test_list.txt', 'w') as f: for v in datas[-test_num:]: f.write(v+'\n') with open('val_list.txt', 'w') as f: for v in datas[train_num:-test_num]: f.write(v+'\n') with open('labels.txt', 'w') as f: for i in range(10): f.write('ch{}\n'.format(i)) train_transforms = transforms.Compose([ transforms.ResizeByShort(short_size=256), transforms.RandomCrop(crop_size=224), transforms.RandomDistort(), transforms.Normalize() ]) eval_transforms = transforms.Compose([ transforms.ResizeByShort(short_size=256), transforms.CenterCrop(crop_size=224), transforms.Normalize() ]) train_dataset = pdx.datasets.ImageNet( data_dir='', file_list='train_list.txt', label_list='labels.txt', transforms=train_transforms, shuffle=True) eval_dataset = pdx.datasets.ImageNet( data_dir='', file_list='val_list.txt', label_list='labels.txt', transforms=eval_transforms) num_classes = len(train_dataset.labels) print(num_classes) return train_dataset, eval_dataset, num_classes def model_train(train_dataset, eval_dataset, num_classes): model = pdx.cls.MobileNetV3_small_ssld(num_classes=num_classes) model.train(num_epochs=20, train_dataset=train_dataset, train_batch_size=32, log_interval_steps=20, eval_dataset=eval_dataset, lr_decay_epochs=[1], save_interval_epochs=1, learning_rate=0.01, save_dir='output/mobilenetv3') def model_eval(eval_dataset): save_dir = 'output/mobilenetv3/best_model' model = pdx.load_model(save_dir) model.evaluate(eval_dataset, batch_size=1, epoch_id=None, return_details=False) if __name__ == "__main__": train_dataset, eval_dataset, num_classes=load_data("../data") model_train(train_dataset, eval_dataset, num_classes) model_eval(eval_dataset)