data_processing_code/Augumentation.py

import pandas as pd
import numpy as np
from concurrent.futures import ThreadPoolExecutor
max_timesteps=299
feature_num=16
label_nums=9

def generate_partial_sequences(row, max_timesteps=max_timesteps, features_per_timestep=feature_num, fill_value=-10):
    # 确定实际长度
    actual_length_indices = np.where(row[:-label_nums] != fill_value)[0]  # 排除最后的标签列
    if len(actual_length_indices) > 0:
        actual_length = (actual_length_indices[-1] // features_per_timestep) + 1
    else:
        actual_length = 0
    partial_sequences = []
    # step_size = max(1, int(actual_length * 0.1))  # 步长为实际长度的10%，至少为1
    step_size =5

    for end_length in range(step_size, actual_length + step_size, step_size):
        # 计算结束点，不超过实际长度
        end_length = min(end_length, actual_length)
        partial_sequence_list = row[:end_length * features_per_timestep].tolist()

        selected_features_list = [partial_sequence_list[i:i + 10] for i in
                                  range(0, len(partial_sequence_list), features_per_timestep)]
        selected_features_list = [item for sublist in selected_features_list for item in sublist]

        ProgressOfTask= end_length/actual_length

        hand_rotation_axis = partial_sequence_list[-6:-3]
        hand_direction = partial_sequence_list[-3:]

        padding_length = (max_timesteps - end_length) * (features_per_timestep-6)  # 计算填充长度
        selected_features_list.extend([fill_value] * padding_length)  # 添加填充

        selected_features_list.extend(row[-9:])  # 添加标签
        selected_features_list.extend(hand_rotation_axis)  # 添加HandRotationAxis和HandDirection
        selected_features_list.extend(hand_direction)
        selected_features_list.append(ProgressOfTask)  # 添加ProgressOfTask

        partial_sequences.append(selected_features_list)
    return partial_sequences


def process_row(index, df):
    """Wrapper function to handle the DataFrame row."""
    row=df.iloc[index]
    return generate_partial_sequences(row)


def main(df, num_threads=20):
    """Process the DataFrame using multiple threads."""
    with ThreadPoolExecutor(max_workers=num_threads) as executor:
        # 创建一个future列表，对每一行数据并行应用 process_row 函数
        futures = [executor.submit(process_row, index, df) for index in range(len(df))]
        # 使用 as_completed 来获取已完成的future结果
        results = []
        for future in futures:
            results.extend(future.result())
    # 将结果转换为 DataFrame

    columns = df.columns
    # 选择不以'HandRotationAxis'和'HandDirection'开头的列
    columns_to_keep = [column for column in columns if
                       not column.startswith('HandRotationAxis') and not column.startswith('HandDirection')]

    # 现在添加具体的旋转轴和方向列到列表的末尾
    # 如果有特定的顺序要求，这里按照特定顺序添加
    columns_to_keep.extend([
        'HandRotationAxis_X', 'HandRotationAxis_Y', 'HandRotationAxis_Z',
        'HandDirection_X', 'HandDirection_Y', 'HandDirection_Z',
        'ProgressOfTask'
    ])
    #print(len(columns_to_keep))
    partial_sequences_df = pd.DataFrame(results, columns=columns_to_keep)
    return partial_sequences_df

if __name__ == '__main__':
    #加载数据集
    for i in range(79, 80):
        # if i ==3 or i ==6 or i ==15 or i ==19 or i== 22:
        #     continue
        file_path = f'../Data/Study2Evaluation/Supervised/{i}_train_data_preprocessed_evaluation.csv'
        df = pd.read_csv(file_path)
        partial_sequences_df = main(df)
        save_path_csv = f'../Data/Study2Evaluation/Dataset/{i}_traindataset.csv'
        partial_sequences_df.to_csv(save_path_csv, index=False)

        file_path = f'../Data/Study2Evaluation/Supervised/{i}_test_data_preprocessed_evaluation.csv'
        df = pd.read_csv(file_path)
        partial_sequences_df = main(df)
        save_path_csv = f'../Data/Study2Evaluation/Dataset/{i}_testdataset.csv'
        partial_sequences_df.to_csv(save_path_csv, index=False)