_temp_.py

# import os
# render2real_path = "/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/spotlight_sketch/epoch0"
# sketch_enhance_body_path = "/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/spotlight_sketch/GT"
# render2real_files = set(os.listdir(render2real_path))
# sketch_enhance_body_files = set(os.listdir(sketch_enhance_body_path))
# for file in render2real_files:
#     if file not in sketch_enhance_body_files:
#         print(f"Removing {file} from render2real_path")
#         os.remove(os.path.join(render2real_path, file))
# for file in sketch_enhance_body_files:
#     if file not in render2real_files:
#         print(f"Removing {file} from sketch_enhance_body_path")
#         os.remove(os.path.join(sketch_enhance_body_path, file))

# import os
# input_path = "/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/mbti/Realistic"
# for file in os.listdir(input_path):
#     # 把文件名称最后的_Realistic去掉
#     new_name = file.replace("_Realistic", "")
#     os.rename(os.path.join(input_path, file), os.path.join(input_path, new_name))

# import os
# for file in os.listdir("dataset/spotlight_sketch_cat/GT"):
#     with open("dataset/spotlight_sketch_cat/pairs_t2t.txt", "a") as f:  
#         # 目标图 原图          
#         f.write(f"GT/{file}\tepoch0/{file}\n")


# import os
# import json
# from tqdm import tqdm
# input_txt = "/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/spotlight_sketch_cat/spotlight_nano_comprehension_1203.txt"
# with open(input_txt, "r") as f:
#     lines = f.readlines()
#     for i in tqdm(range(len(lines))):
#         data = json.loads(lines[i])   
#         fig_id = f"{data['Image_Name']}.png"
#         del data["Image_Name"]
#         input_dir = "dataset/spotlight_sketch_cat/epoch0"
#         GT_dir = "dataset/spotlight_sketch_cat/GT"
#         for file in os.listdir(input_dir): 
#             if fig_id in file:
#                 with open("dataset/spotlight_sketch_cat/pairs_i2i.txt", "a") as f:
#                     # 目标 原图 prompt
#                     f.write(f"{GT_dir}/{file}\t{input_dir}/{file}\t{data}\n")

# 把文件夹中的图片每六张拼成一个3行两列的大图，保存到另一个文件夹中，原图拼接不要截图
import os
from PIL import Image
from tqdm import tqdm
import numpy as np
base_dirs = ["/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/the roses","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/nouvelle","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/legs","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/frankenstein"]
# 核心配置参数（按需求定义）
crop_size = (1920, 800)    # 目标CenterCrop尺寸 (宽, 高)
resize_size = (477, 188)   # 下采样后的单张尺寸 (宽, 高)
line_width = 6             # 黑线宽度（6像素）
target_merge_size = (960, 576)  # 最终拼接目标尺寸 (宽, 高)


def center_crop_to_size(img, target_size):
    """
    对图片进行CenterCrop到指定尺寸，不足部分用黑色像素填充
    :param img: PIL Image对象
    :param target_size: (target_w, target_h) 目标裁剪尺寸
    :return: crop+补黑后的PIL Image
    """
    target_w, target_h = target_size
    img_w, img_h = img.size
    
    # Step1: 计算CenterCrop的区域（中心对齐）
    # 水平方向裁剪
    if img_w >= target_w:
        left = (img_w - target_w) // 2
        right = left + target_w
    else:
        left = 0
        right = img_w
    # 垂直方向裁剪
    if img_h >= target_h:
        top = (img_h - target_h) // 2
        bottom = top + target_h
    else:
        top = 0
        bottom = img_h
    
    # Step2: 执行CenterCrop
    cropped = img.crop((left, top, right, bottom))
    
    # Step3: 不足目标尺寸的部分用黑色填充
    if cropped.size != (target_w, target_h):
        new_img = Image.new("RGB", (target_w, target_h), (0, 0, 0))  # 黑色背景
        new_img.paste(cropped, ((target_w - cropped.width) // 2, (target_h - cropped.height) // 2))
        cropped = new_img
    
    return cropped
for k in range(len(base_dirs)):
    save_path = f"{base_dirs[k]}_dedup_cat" 
    os.makedirs(save_path, exist_ok=True)
    input_path = f"{base_dirs[k]}_dedup"
# 获取并排序文件列表
    files = [f for f in os.listdir(input_path) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]
    files.sort()

    # 遍历文件，每6张拼接一次
    for i in tqdm(range(0, len(files), 6), desc="拼接图片"):
        # 初始化最终拼接画布（目标尺寸960×576，黑色背景）
        merged_image = np.zeros((target_merge_size[1], target_merge_size[0], 3), dtype=np.uint8)
        
        # 逐个处理6张图片
        valid_imgs = []  # 存储处理后的有效图片
        for j in range(6):
            if i + j >= len(files):
                # 不足6张时，break
                img_np = np.zeros((resize_size[1], resize_size[0], 3), dtype=np.uint8)
                valid_imgs.append(img_np)
                continue
            
            img_path = os.path.join(input_path, files[i + j])
            try:
                # 读取图片（确保RGB格式）
                img = Image.open(img_path).convert("RGB")
                img_w, img_h = img.size
                
                # 过滤条件：原始宽度不足1800则跳过并打印
                if img_w < 1800:
                    print(f"跳过文件 {files[i+j]}: 原始宽度 {img_w} < 1800")
                    # 用黑色图片填充该位置
                    img_np = np.zeros((resize_size[1], resize_size[0], 3), dtype=np.uint8)
                    valid_imgs.append(img_np)
                    continue
                
                # Step1: CenterCrop到1920×800，不足补黑
                cropped_img = center_crop_to_size(img, crop_size)
                
                # Step2: 下采样到477×188（LANCZOS插值，保持画质）
                resized_img = cropped_img.resize(resize_size, resample=Image.LANCZOS)
                
                # 转为numpy数组
                img_np = np.array(resized_img)
                valid_imgs.append(img_np)
            
            except Exception as e:
                print(f"处理文件 {files[i+j]} 出错: {str(e)}")
                # 出错时用黑色图片填充
                img_np = np.zeros((resize_size[1], resize_size[0], 3), dtype=np.uint8)
                valid_imgs.append(img_np)
        
        # Step3: 计算每张图在拼接画布中的位置（3行2列 + 6像素黑线）
        # 验证拼接尺寸兼容性（防止配置错误）
        assert len(valid_imgs) == 6, "有效图片数量必须为6张"
        # 计算图片+黑线的总占位，确保适配960×576
        total_col = 2 * resize_size[0] + 1 * line_width  # 2列+1条竖线
        total_row = 3 * resize_size[1] + 2 * line_width  # 3行+2条横线
        # 计算画布中的偏移（居中放置，保证最终尺寸960×576）
        offset_x = (target_merge_size[0] - total_col) // 2
        offset_y = (target_merge_size[1] - total_row) // 2
        
        # 逐个放置图片到拼接画布
        for idx, img_np in enumerate(valid_imgs):
            row = idx // 2  # 0/1/2行
            col = idx % 2   # 0/1列
            
            # 计算当前图片的起始位置（含黑线+整体偏移）
            x_start = offset_x + col * (resize_size[0] + line_width)
            y_start = offset_y + row * (resize_size[1] + line_width)
            x_end = x_start + resize_size[0]
            y_end = y_start + resize_size[1]
            
            # 确保不超出画布边界
            x_end = min(x_end, target_merge_size[0])
            y_end = min(y_end, target_merge_size[1])
            x_start = max(x_start, 0)
            y_start = max(y_start, 0)
            
            # 放置图片到画布
            merged_image[y_start:y_end, x_start:x_end, :] = img_np[:y_end-y_start, :x_end-x_start, :]
        
        # Step4: 保存最终拼接图片
        save_name = f'merged_{i//6}.png'
        save_full_path = os.path.join(save_path, save_name)
        Image.fromarray(merged_image).save(save_full_path)

    print(f"所有图片处理完成！结果保存至: {save_path}")