AI_python_yoooger/south_west_computer_club/crack/yolo2pix copy.py

import os
import re
import cv2
import zipfile
import shutil
import numpy as np
from concurrent.futures import ThreadPoolExecutor

# ------------------ 路面类别映射集 ------------------
CLASS_MAPS_5010 = {
    "asphalt": {
        "龟裂": 0, "块状裂缝": 1, "纵向裂缝": 2, "横向裂缝": 3,
        "沉陷": 4, "车辙": 5, "波浪拥包": 6, "坑槽": 7,
        "松散": 8, "泛油": 9, "修补": 10,
    },
    "cream": {"破碎板": 0, "裂缝": 1, "坑洞": 2, "露骨": 3, "错台": 4, "拱起": 5},
    "gravel": {"坑槽": 0, "沉陷": 1, "车辙": 2, "波浪搓板": 3},
}

CLASS_MAPS_5011 = {
    "asphalt": {
        "龟裂": 0, "块状裂缝": 1, "纵向裂缝": 2, "横向裂缝": 3,
        "沉陷": 4, "车辙": 5, "波浪拥包": 6, "坑槽": 7,
        "松散": 8, "泛油": 9, "修补": 10,
    },
    "cream": {
        "破碎板": 0, "裂缝": 1, "板角断裂": 2, "错台": 3,
        "拱起": 4, "边角剥落": 5, "接缝料损坏": 6,
        "坑洞": 7, "唧泥": 8, "露骨": 9, "修补": 10,
    },
    "gravel": {},  # 可扩展
}

# ------------------ 工具函数 ------------------
def detect_road_type(text: str) -> str:
    """检测路面类型"""
    low = text.lower()
    if "沥青" in low:
        return "asphalt"
    if "水泥" in low:
        return "cream"
    if "石子" in low or "gravel" in low:
        return "gravel"
    return "asphalt"

def detect_section_type(path: str) -> str:
    """判断属于5010还是5011"""
    name = os.path.basename(path)
    if "5011" in name:
        return "5011"
    if "5010" in name:
        return "5010"
    # 若未明确，默认5010
    return "5010"

def clean_name(name):
    return re.sub(r'[\s\r\n\t]+', '', name)

# ------------------ YOLO-Seg 主函数 ------------------
def yoloseg_to_grid_cells_fixed_v6(image_path, label_path, output_dir, class_map, extra_info=None):
    img = cv2.imread(image_path)
    if img is None:
        return
    h, w = img.shape[:2]

    txt_name = os.path.splitext(os.path.basename(label_path))[0]
    output_txt = os.path.join(output_dir, f"{txt_name}.txt")

    with open(label_path, "r", encoding="utf-8") as f:
        lines = f.readlines()

    with open(output_txt, "w", encoding="utf-8") as out:
        for line in lines:
            parts = line.strip().split()
            if len(parts) < 2:
                continue
            cls_name = parts[-1]
            cls_id = class_map.get(cls_name, -1)
            if cls_id == -1:
                print(f"未识别类别: {cls_name}")
                continue
            coords = list(map(float, parts[:-1]))
            coords = [round(c, 4) for c in coords]
            out.write(f"{cls_id} {' '.join(map(str, coords))} {extra_info or ''}\n")

# ------------------ 批处理函数 ------------------
def process_zip_yoloseg_with_draw(zip_path, output_root):
    temp_dir = "temp_extract"
    if os.path.exists(temp_dir):
        shutil.rmtree(temp_dir)
    os.makedirs(temp_dir, exist_ok=True)

    # 解压
    with zipfile.ZipFile(zip_path, 'r') as zf:
        zf.extractall(temp_dir)

    image_files = []
    for root, _, files in os.walk(temp_dir):
        for f in files:
            if f.lower().endswith((".jpg", ".png", ".jpeg")):
                image_files.append(os.path.join(root, f))

    print(f"检测到 {len(image_files)} 张图片")

    def process_one(img_path):
        name = os.path.splitext(os.path.basename(img_path))[0]
        label_path = os.path.join(os.path.dirname(img_path), f"{name}.txt")
        if not os.path.exists(label_path):
            print(f"缺少标签: {img_path}")
            return

        # 判断5010或5011
        section_type = detect_section_type(img_path)

        # 读取标签判断路面类型
        with open(label_path, "r", encoding="utf-8") as f:
            txt = f.read()
        road_type = detect_road_type(txt)

        # 选择对应类别映射表
        if section_type == "5011":
            class_map = CLASS_MAPS_5011.get(road_type, {})
        else:
            class_map = CLASS_MAPS_5010.get(road_type, {})

        if not class_map:
            print(f"无映射表: {road_type} ({section_type})")
            return

        out_dir = os.path.join(output_root, road_type, name)
        os.makedirs(out_dir, exist_ok=True)

        extra_info = f"{section_type}_{road_type}"
        yoloseg_to_grid_cells_fixed_v6(img_path, label_path, out_dir, class_map, extra_info=extra_info)

    with ThreadPoolExecutor(max_workers=6) as ex:
        list(ex.map(process_one, image_files))

    shutil.rmtree(temp_dir)
    print("处理完成")

# ------------------ 主入口 ------------------
if __name__ == "__main__":
    zip_path = r"D:\work\data\road_seg.zip"   # 输入zip路径
    output_root = r"D:\work\data\output"      # 输出根目录
    process_zip_yoloseg_with_draw(zip_path, output_root)