苏州市相城区建设局网站,重庆公共资源交易中心网,wordpress做自建站,青岛做网站企业深度学习——划分自定义数据集 以人脸表情数据集raf_db为例#xff0c;初始目录如下#xff1a; 需要经过处理后返回 train_images, train_label, val_images, val_label 定义 read_split_data(root: str, val_rate: float 0.2) 方法来解决#xff0c;代码如下#xff1a… 深度学习——划分自定义数据集 以人脸表情数据集raf_db为例初始目录如下 需要经过处理后返回 train_images, train_label, val_images, val_label 定义 read_split_data(root: str, val_rate: float 0.2) 方法来解决代码如下
# root数据集所在路径
# val_rate划分测试集的比例def read_split_data(root: str, val_rate: float 0.2):random.seed(0) # 保证随机结果可复现assert os.path.exists(root), dataset root: {} does not exist..format(root)# 遍历文件夹一个文件夹对应一个类别file_class [cla for cla in os.listdir(root) if os.path.isdir(os.path.join(root, cla))]# 排序保证各平台顺序一致file_class.sort()# 生成类别名称以及对应的数字索引class_indices dict((k, v) for v, k in enumerate(file_class))json_str json.dumps(dict((val, key) for key, val in class_indices.items()), indent4)with open(class_indices.json, w) as json_file:json_file.write(json_str)train_images [] # 存储训练集的所有图片路径train_label [] # 存储训练集图片对应索引信息val_images [] # 存储验证集的所有图片路径val_label [] # 存储验证集图片对应索引信息every_class_num [] # 存储每个类别的样本总数supported [.jpg, .JPG, .png, .PNG] # 支持的文件后缀类型# 遍历每个文件夹下的文件for cla in file_class:cla_path os.path.join(root, cla)# 遍历获取supported支持的所有文件路径images [os.path.join(root, cla, i) for i in os.listdir(cla_path)if os.path.splitext(i)[-1] in supported]# 排序保证各平台顺序一致images.sort()# 获取该类别对应的索引image_class class_indices[cla]# 记录该类别的样本数量every_class_num.append(len(images))# 按比例随机采样验证样本val_path random.sample(images, kint(len(images) * val_rate))for img_path in images:if img_path in val_path: # 如果该路径在采样的验证集样本中则存入验证集val_images.append(img_path)val_label.append(image_class)else: # 否则存入训练集train_images.append(img_path)train_label.append(image_class)print({} images were found in the dataset..format(sum(every_class_num)))print({} images for training..format(len(train_images)))print({} images for validation..format(len(val_images)))assert len(train_images) 0, number of training images must greater than 0.assert len(val_images) 0, number of validation images must greater than 0.return train_images, train_label, val_images, val_label此时可通过以下代码获得训练集和测试集数据
train_images, train_label, val_images, val_label read_split_data(data_path)完结撒花。
