import pandas as pd import numpy as np from scipy.stats import spearmanr, kendalltau, pearsonr import os import argparse def read_txt_scores(file_path): """读取txt文件中的分数""" scores = {} video_keys = set() video_scores_list = [] # 保持原始顺序的列表 try: with open(file_path, 'r') as f: for line in f: parts = line.strip().split(',') if len(parts) == 2: video_name = parts[0] try: score = float(parts[1]) scores[video_name] = score video_keys.add(video_name) video_scores_list.append((video_name, score)) # 同时存储不带路径的视频名（用于匹配） base_name = os.path.basename(video_name) if base_name.endswith('.mp4'): scores[base_name] = score video_keys.add(base_name) # 不带扩展名的版本 scores[base_name[:-4]] = score video_keys.add(base_name[:-4]) except ValueError: print(f"跳过无效分数: {parts[1]} for {video_name}") except Exception as e: print(f"读取txt文件时出错: {e}") print(f"从{file_path}读取了{len(video_keys)}个独特视频的分数") return scores, video_scores_list def extract_video_name(name): """从视频路径中提取基本视频名，以便更好地匹配""" if isinstance(name, str): # 尝试不同的格式化方式 base_name = os.path.basename(name) # 移除扩展名 if base_name.endswith('.mp4'): return base_name[:-4] return base_name return str(name) def calculate_correlations(txt_scores_list, xlsx_df): """计算相关系数""" # 获取xlsx文件中的模型名称列表 models = xlsx_df.columns.tolist() # 初始化结果字典 results = { 'SRCC': {}, 'KRCC': {}, 'PLCC': {} } # 创建一个仅包含txt分数的列表 txt_scores = [score for _, score in txt_scores_list] # 检查数量是否匹配 print(f"TXT分数数量: {len(txt_scores)}") print(f"XLSX文件行数: {xlsx_df.shape[0]}") if len(txt_scores) != xlsx_df.shape[0]: print(f"警告: TXT分数数量({len(txt_scores)})与XLSX行数({xlsx_df.shape[0]})不匹配") # 如果不匹配，我们只使用最小的那个 min_count = min(len(txt_scores), xlsx_df.shape[0]) print(f"使用前{min_count}个数据点进行计算") txt_scores = txt_scores[:min_count] xlsx_df = xlsx_df.iloc[:min_count, :] # 为每个模型计算相关系数 for model in models: print(f"\n处理模型: {model}") # 获取当前模型的所有有效分数 model_series = xlsx_df[model] # 跳过NaN值 valid_indices = model_series.dropna().index model_scores = model_series.dropna().values.tolist() # 提取对应的txt分数 txt_model_scores = [txt_scores[i] for i in valid_indices if i < len(txt_scores)] valid_count = len(txt_model_scores) print(f"模型 {model}: 有效数据点数量 = {valid_count}") # 检查是否有足够的有效数据点 if valid_count > 1: # 至少需要2个点来计算相关系数 # 计算SRCC - Spearman相关系数（秩相关） srcc, p_srcc = spearmanr(txt_model_scores, model_scores) results['SRCC'][model] = srcc # 计算KRCC - Kendall相关系数（秩相关） krcc, p_krcc = kendalltau(txt_model_scores, model_scores) results['KRCC'][model] = krcc # 计算PLCC - Pearson相关系数（线性相关） plcc, p_plcc = pearsonr(txt_model_scores, model_scores) results['PLCC'][model] = plcc print(f" SRCC={srcc:.4f} (p={p_srcc:.4f}), KRCC={krcc:.4f} (p={p_krcc:.4f}), PLCC={plcc:.4f} (p={p_plcc:.4f})") # 打印前几个数据点，帮助验证 print(f" 前5个数据点示例 (TXT分数 vs {model}分数):") for i in range(min(5, valid_count)): print(f" {txt_model_scores[i]:.2f} vs {model_scores[i]:.2f}") else: print(f"警告: 模型 {model} 没有足够的有效数据点进行相关性计算") results['SRCC'][model] = np.nan results['KRCC'][model] = np.nan results['PLCC'][model] = np.nan return results def main(): # 设置命令行参数 parser = argparse.ArgumentParser(description='计算TXT文件和XLSX文件之间的相关系数') parser.add_argument('--txt', type=str, default='text.txt', help='TXT文件路径') parser.add_argument('--xlsx', type=str, default='score.xlsx', help='XLSX文件路径') parser.add_argument('--output', type=str, default='correlation_results.csv', help='输出CSV文件路径') args = parser.parse_args() # 读取txt文件 txt_file = args.txt txt_scores, txt_scores_list = read_txt_scores(txt_file) # 检查是否接近3857 unique_videos = set() for key in txt_scores.keys(): if '/' in key or '\\' in key: unique_videos.add(key) print(f"TXT文件中的唯一视频数量: {len(unique_videos)}") if abs(len(unique_videos) - 3857) > 10: print(f"警告: txt文件中的唯一视频数量({len(unique_videos)})与预期的3857相差较大") # 读取xlsx文件 xlsx_file = args.xlsx try: # 读取xlsx文件，设置第一行为列名 xlsx_df = pd.read_excel(xlsx_file) print(f"从{xlsx_file}读取了{xlsx_df.shape[0]}行和{xlsx_df.shape[1]}列") # 检查xlsx文件格式 if xlsx_df.shape[1] < 6: print(f"警告: xlsx文件应该有6列模型，但只发现了{xlsx_df.shape[1]}列") # 前几行和列的预览 print("\n前5行数据预览:") print(xlsx_df.head()) # 列名称列表 all_columns = xlsx_df.columns.tolist() print(f"\n所有列名称: {all_columns}") # 检查是否包含6个模型列 print(f"发现{len(all_columns)}个模型列: {', '.join(all_columns)}") # 分析缺失值 na_counts = xlsx_df.isna().sum() print("\n各模型缺失值数量:") for col in xlsx_df.columns: print(f" {col}: {na_counts[col]}") # 计算相关系数 correlations = calculate_correlations(txt_scores_list, xlsx_df) # 创建结果DataFrame result_df = pd.DataFrame({ 'Model': [], 'SRCC': [], 'KRCC': [], 'PLCC': [] }) # 显示结果 print("\n=== 相关系数结果 ===") for model in correlations['SRCC'].keys(): srcc = correlations['SRCC'].get(model, np.nan) krcc = correlations['KRCC'].get(model, np.nan) plcc = correlations['PLCC'].get(model, np.nan) result_df = pd.concat([result_df, pd.DataFrame({ 'Model': [model], 'SRCC': [srcc], 'KRCC': [krcc], 'PLCC': [plcc] })], ignore_index=True) # 打印详细结果 print(result_df.to_string(index=False, float_format=lambda x: f"{x:.4f}" if not np.isnan(x) else "NaN")) # 保存结果到CSV result_df.to_csv(args.output, index=False) print(f"\n结果已保存到 {args.output}") except Exception as e: print(f"处理xlsx文件时出错: {e}") import traceback traceback.print_exc() def process_all_txt_files(): """处理所有TXT文件与xlsx文件的相关性""" txt_files = [f for f in os.listdir('.') if f.endswith('.txt')] xlsx_file = 'score.xlsx' if not os.path.exists(xlsx_file): print(f"错误: 找不到XLSX文件 {xlsx_file}") return all_results = {} for txt_file in txt_files: print(f"\n===== 处理文件: {txt_file} =====") try: # 临时修改sys.argv以传递参数给main函数 import sys old_argv = sys.argv output_file = f"correlation_results_{os.path.splitext(txt_file)[0]}.csv" sys.argv = ['', f'--txt={txt_file}', f'--xlsx={xlsx_file}', f'--output={output_file}'] # 运行主函数 main() # 恢复sys.argv sys.argv = old_argv # 读取结果并合并 if os.path.exists(output_file): results = pd.read_csv(output_file) all_results[txt_file] = results print(f"已加载结果文件: {output_file}") else: print(f"警告: 未找到结果文件 {output_file}") except Exception as e: print(f"处理文件 {txt_file} 时出错: {e}") import traceback traceback.print_exc() # 合并所有结果 if all_results: print(f"\n合并 {len(all_results)} 个结果文件") combined_results = pd.DataFrame() for txt_file, results in all_results.items(): file_base = os.path.splitext(txt_file)[0] if 'Model' in results.columns: # 重命名列以区分不同txt文件的结果 renamed_cols = {col: f'{col}_{file_base}' for col in results.columns if col != 'Model'} tmp_results = results.rename(columns=renamed_cols) if combined_results.empty: combined_results = tmp_results else: combined_results = pd.merge(combined_results, tmp_results, on='Model') # 保存合并结果 if not combined_results.empty: combined_results.to_csv('all_correlation_results.csv', index=False) print("\n所有结果已合并保存到 all_correlation_results.csv") else: print("\n警告: 没有可合并的结果") else: print("\n警告: 没有可用的结果文件进行合并") if __name__ == "__main__": # 检查是否存在多个txt文件 txt_files = [f for f in os.listdir('.') if f.endswith('.txt')] if len(txt_files) > 1: print(f"检测到多个TXT文件: {txt_files}") process_all_txt_files() else: main()