src/backend/app/algorithms/processors/coords_processor.py

@@ -1,132 +0,0 @@
-import pandas as pd
-from pathlib import Path
-import sys
-import logging
-import numpy as np
-import statsmodels.api as sm 
-
-# 配置日志
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-def apply_lowess_and_clipping_scaling(input_csv_path, output_csv_path, lowess_frac, target_range, clipping_percentile):
-    """
-    应用 Lowess 局部加权回归进行平滑（提取总体趋势），然后使用百分位数裁剪后的 Min-Max 边界来缩放。
-    目标：生成最平滑、最接近单调下降的客观趋势。
-    """
-    input_path = Path(input_csv_path)
-    output_path = Path(output_csv_path)
-
-    if not input_path.exists():
-        logging.error(f"错误：未找到输入文件 {input_csv_path}")
-        return
-
-    logging.info(f"读取原始数据: {input_csv_path}")
-    df = pd.read_csv(input_path)
-    
-    df = df.loc[:,~df.columns.duplicated()].copy()
-
-    # 定义原始数据列名
-    raw_x_col = 'X_Feature_L2_Norm'
-    raw_y_col = 'Y_Feature_Variance'
-    raw_z_col = 'Z_LDM_Loss'
-    
-    # --------------------------- 1. Lowess 局部加权回归平滑 (提取总体趋势) ---------------------------
-    logging.info(f"应用 Lowess 局部加权回归，平滑因子 frac={lowess_frac}。")
-    
-    x_coords = df['step'].values
-    
-    for raw_col in [raw_x_col, raw_y_col, raw_z_col]:
-        y_coords = df[raw_col].values
-        
-        smoothed_data = sm.nonparametric.lowess(
-            endog=y_coords, 
-            exog=x_coords, 
-            frac=lowess_frac, 
-            it=0
-        )
-        df[f'{raw_col}_LOWESS'] = smoothed_data[:, 1]
-
-
-    # --------------------------- 2. 百分位数边界缩放与方向统一 ---------------------------
-    p = clipping_percentile 
-    logging.info(f"应用百分位数边界 (p={p}) 进行线性缩放，目标范围 [0, {target_range:.2f}]")
-    
-    scale_cols_map = {
-        'X_Feature_L2_Norm': f'{raw_x_col}_LOWESS', 
-        'Y_Feature_Variance': f'{raw_y_col}_LOWESS', 
-        'Z_LDM_Loss': f'{raw_z_col}_LOWESS'
-    }
-    
-    for final_col, lowess_col in scale_cols_map.items():
-        
-        data = df[lowess_col]
-        
-        # 裁剪：计算裁剪后的 min/max (定义缩放窗口)
-        lower_bound = data.quantile(p)
-        upper_bound = data.quantile(1.0 - p)
-        
-        min_val = lower_bound
-        max_val = upper_bound
-        data_range = max_val - min_val
-        
-        if data_range <= 0 or data_range == np.nan: 
-            df[final_col] = 0.0
-            logging.warning(f"列 {final_col} 裁剪后的范围为 {data_range:.4f}，跳过缩放。")
-            continue
-            
-        # 归一化: (data - Min_window) / Range_window
-        normalized_data = (data - min_val) / data_range
-        
-        # **优化方向统一逻辑 (所有指标都应是越小越好):**
-        if final_col in ['X_Feature_L2_Norm', 'Y_Feature_Variance']:
-            # X/Y 反转：将 Max 映射到 0，Min 映射到 TargetRange
-            final_scaled_data = (1.0 - normalized_data) * target_range
-        else: # Z_LDM_Loss
-            # Z 标准缩放：Min 映射到 0，Max 映射到 TargetRange
-            final_scaled_data = normalized_data * target_range
-            
-        # 保留负值，以确保平滑过渡
-        df[final_col] = final_scaled_data
-        
-        logging.info(f"  - 列 {final_col}：裁剪边界: [{min_val:.4f}, {max_val:.4f}]。缩放后范围不再严格约束 [0, {target_range:.2f}]，以保留趋势。")
-
-
-    # --------------------------- 3. 最终保存 ---------------------------
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    
-    final_cols = ['step', 'X_Feature_L2_Norm', 'Y_Feature_Variance', 'Z_LDM_Loss']
-    
-
-    df[final_cols].to_csv(
-        output_path, 
-        index=False, 
-        float_format='%.3f'
-    )
-    
-    logging.info(f"Lowess平滑和缩放后的数据已保存到: {output_csv_path}")
-
-
-if __name__ == '__main__':
-    if len(sys.argv) != 6:
-        logging.error("使用方法: python smooth_coords.py <输入CSV路径> <输出CSV路径> <Lowess 平滑因子 frac (例如 0.4)> <目标视觉范围 (例如 30)> <离散点裁剪百分比 (例如 0.15)>")
-    else:
-        input_csv = sys.argv[1]
-        output_csv = sys.argv[2]
-        try:
-            lowess_frac = float(sys.argv[3])
-            target_range = float(sys.argv[4])
-            clipping_p = float(sys.argv[5])
-            
-            if not (0.0 < lowess_frac <= 1.0):
-                raise ValueError("Lowess 平滑因子 frac 必须在 (0.0, 1.0] 之间。")
-            if target_range <= 0:
-                 raise ValueError("目标视觉范围必须大于 0。")
-            if not (0 <= clipping_p < 0.5):
-                 raise ValueError("裁剪百分比必须在 [0, 0.5) 之间。")
-            
-            if not Path(output_csv).suffix:
-                 output_csv = str(Path(output_csv) / "scaled_coords.csv")
-
-            apply_lowess_and_clipping_scaling(input_csv, output_csv, lowess_frac, target_range, clipping_p)
-        except ValueError as e:
-            logging.error(f"参数错误: {e}")

src/backend/app/algorithms/processors/image_processor.py

@@ -1,149 +0,0 @@
-"""
-图片处理功能，用于把原始图片剪裁为中心正方形，指定分辨率，并保存为指定格式，还可以选择是否序列化改名。
-"""
-
-import argparse
-import os
-from pathlib import Path
-from PIL import Image
-
-# --- 1. 参数解析 ---
-def parse_args(input_args=None):
-    """
-    解析命令行参数。
-    """
-    parser = argparse.ArgumentParser(description="Image Processor for Centering, Resizing, and Format Conversion.")
-    
-    # 路径和分辨率参数
-    parser.add_argument(
-        "--input_dir",
-        type=str,
-        required=True,
-        help="A folder containing the original images to be processed and overwritten.",
-    )
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help="The target resolution (width and height) for the output images (e.g., 512 for 512x512).",
-    )
-    # 格式参数
-    parser.add_argument(
-        "--target_format",
-        type=str,
-        default="png",
-        choices=["jpeg", "png", "webp", "jpg"],
-        help="The target format for the saved images (e.g., 'png', 'jpg', 'webp'). The original file will be overwritten, potentially changing the file extension.",
-    )
-    
-    # 序列化数字重命名参数
-    parser.add_argument(
-        "--rename_sequential",
-        action="store_true", # 当这个参数存在时，其值为 True
-        help="If set, images will be sequentially renamed (e.g., 001.jpg, 002.jpg...) instead of preserving the original filename. WARNING: This WILL delete the originals.",
-    )
-    
-    if input_args is not None:
-        args = parser.parse_args(input_args)
-    else:
-        args = parser.parse_args()
-    
-    return args
-
-# --- 2. 核心图像处理逻辑 ---
-def process_image(image_path: Path, output_path: Path, resolution: int, target_format: str, delete_original: bool):
-    """
-    加载图像，居中取最大正方形，升降分辨率，并保存为目标格式。
-    
-    Args:
-        image_path: 原始图片路径。
-        output_path: 最终保存路径。
-        resolution: 目标分辨率。
-        target_format: 目标文件格式。
-        delete_original: 是否删除原始文件。
-    """
-    try:
-        # 加载图像并统一转换为 RGB 模式
-        img = Image.open(image_path).convert("RGB")
-        
-        # 居中取最大正方形
-        width, height = img.size
-        min_dim = min(width, height)
-
-        # 计算裁剪框 (以最短边为尺寸的中心正方形)
-        left = (width - min_dim) // 2
-        top = (height - min_dim) // 2
-        right = left + min_dim
-        bottom = top + min_dim
-
-        # 裁剪中心正方形
-        img = img.crop((left, top, right, bottom))
-
-        # 升降分辨率到指定 resolution
-        # 使用 LANCZOS 高质量重采样方法
-        img = img.resize((resolution, resolution), resample=Image.Resampling.LANCZOS)
-        
-        # 准备输出格式
-        save_format = target_format.upper().replace('JPEG', 'JPG')
-        
-        # 保存图片
-        # 对于 JPEG/JPG，设置 quality 参数
-        if save_format == 'JPG':
-             img.save(output_path, format='JPEG', quality=95)
-        else:
-             img.save(output_path, format=save_format)
-        
-        # 根据标记决定是否删除原始文件
-        if delete_original and image_path.resolve() != output_path.resolve():
-             os.remove(image_path)
-        
-        print(f"Processed: {image_path.name} -> {output_path.name} ({resolution}x{resolution} {save_format})")
-
-    except Exception as e:
-        print(f"Error processing {image_path.name}: {e}")
-
-# --- 3. 主函数 ---
-def main(args):
-    # 路径准备
-    input_dir = Path(args.input_dir)
-
-    if not input_dir.is_dir():
-        print(f"Error: Input directory not found at {input_dir}")
-        return
-
-    # 查找所有图片文件 (支持 jpg, jpeg, png, webp)
-    valid_suffixes = ['.jpg', '.jpeg', '.png', '.webp']
-    image_paths = sorted([p for p in input_dir.iterdir() if p.suffix.lower() in valid_suffixes]) # 排序以确保重命名顺序一致
-    
-    if not image_paths:
-        print(f"No image files found in {input_dir}")
-        return
-
-    print(f"Found {len(image_paths)} images in {input_dir}. Starting processing...")
-
-    # 准备目标格式的扩展名
-    extension = args.target_format.lower().replace('jpeg', 'jpg')
-    
-    # 迭代处理图片
-    for i, image_path in enumerate(image_paths):
-        
-        # 决定输出路径
-        if args.rename_sequential:
-            # 顺序重命名逻辑：001, 002, 003... (至少三位数字)
-            new_name = f"{i + 1:03d}.{extension}"
-            output_path = input_dir / new_name
-            # 如果原始文件与新文件名称不同，则需要删除原始文件
-            delete_original = True
-        else:
-            # 保持原始文件名，但修改后缀
-            output_path = image_path.with_suffix(f'.{extension}')
-            # 只有当原始后缀与目标后缀不同时，才需要删除原始文件（防止遗留旧格式）
-            delete_original = (image_path.suffix.lower() != f'.{extension}')
-
-        process_image(image_path, output_path, args.resolution, args.target_format, delete_original)
-    
-    print("Processing complete.")
-
-if __name__ == "__main__":
-    args = parse_args()
-    main(args)