如何把deepfashion数据集进行数据处理，请帮我编写一个示例

好的，以下是一个DeepFashion数据集的数据处理示例，包括数据清洗、数据预处理、数据增强、数据划分、标注处理和数据存储：

import os
import numpy as np
import cv2
import json
from sklearn.model_selection import train_test_split

# 定义DeepFashion数据集路径和预处理参数
data_dir = '/path/to/deepfashion/dataset'
img_size = (256, 256)
train_val_ratio = 0.9
val_test_ratio = 0.5
max_rotation_angle = 10

# 读取标注文件，并进行数据清洗
with open(os.path.join(data_dir, 'Anno/list_bbox.txt'), 'r') as f:
    lines = f.readlines()[2:]  # 跳过前两行
    lines = [line.strip().split() for line in lines]
    lines = [[line[0], *map(int, line[1:])] for line in lines]
    lines = [line for line in lines if line[3] > 0 and line[4] > 0]  # 去掉宽度或高度为0的标注

# 划分训练集、验证集和测试集
train_lines, val_test_lines = train_test_split(lines, train_size=train_val_ratio, random_state=42)
val_lines, test_lines = train_test_split(val_test_lines, train_size=val_test_ratio, random_state=42)

# 处理训练集数据
X_train = []
y_train = []
for line in train_lines:
    img_path = os.path.join(data_dir, 'Img', 'train', line[0])
    bbox = line[1:]
    img = cv2.imread(img_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = cv2.resize(img, img_size)
    rotation_angle = np.random.uniform(-max_rotation_angle, max_rotation_angle)
    M = cv2.getRotationMatrix2D((img_size[0] / 2, img_size[1] / 2), rotation_angle, 1.0)
    img = cv2.warpAffine(img, M, img_size)
    X_train.append(img)
    y_train.append(bbox)

# 处理验证集数据
X_val = []
y_val = []
for line in val_lines:
    img_path = os.path.join(data_dir, 'Img', 'train', line[0])
    bbox = line[1:]
    img = cv2.imread(img_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = cv2.resize(img, img_size)
    X_val.append(img)
    y_val.append(bbox)

# 处理测试集数据
X_test = []
y_test = []
for line in test_lines:
    img_path = os.path.join(data_dir, 'Img', 'test', line[0])
    bbox = line[1:]
    img = cv2.imread(img_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = cv2.resize(img, img_size)
    X_test.append(img)
    y_test.append(bbox)

# 将训练集、验证集和测试集数据保存到本地文件中
np.save(os.path.join(data_dir, 'X_train.npy'), np.array(X_train))
np.save(os.path.join(data_dir, 'y_train.npy'), np.array(y_train))
np.save(os.path.join(data_dir, 'X_val.npy'), np.array(X_val))
np.save(os.path.join(data_dir, 'y_val.npy'), np.array(y_val))
np.save(os.path.join(data_dir, 'X_test.npy'), np.array(X_test))
np.save(os.path.join(data_dir, 'y_test.npy'), np.array(y_test))

以上是一个简单的DeepFashion数据集的数据处理示例，其中包括了数据清洗、数据预处理、数据增强、数据划分、标注处理和数据存储等步骤。具体实现可以根据实际需求和数据集特点进行调整。