ai-train_platform/util/yolo2pix_new.py

import os
import zipfile
import shutil
import cv2
import numpy as np
from collections import defaultdict
import smb
import pandas as pd
import glob
from datetime import datetime

# ---------------- 常量 ----------------
CELL_AREA = 0.0036       # 每格面积 (平方米)
GRID_WIDTH = 108         # 网格像素宽
GRID_HEIGHT = 102        # 网格像素高
COVER_RATIO = 0.01       # mask 覆盖比例阈值

# ---------------- 路面类别映射 ----------------
CLASS_MAP_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
}
CLASS_MAP_CEMENT = {
    "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
}
CLASS_MAP_GRAVEL = {
    "坑槽":0,"沉陷":1,"车辙":2,"波浪搓板":3
}

ROAD_TYPE_EN_TO_CN = {
    "asphalt":"沥青",
    "cement":"水泥",
    "gravel":"砾石"
}

# ---------------- 工具函数 ----------------
def num_to_coord(num, cols, cell_w, cell_h):
    n = num - 1
    r, c = divmod(n, cols)
    x1, y1 = c * cell_w, r * cell_h
    x2, y2 = x1 + cell_w, y1 + cell_h
    return x1, y1, x2, y2

def draw_grid_on_image(image_path, grid_cells, cell_size=(GRID_WIDTH, GRID_HEIGHT), save_path=None):
    image = cv2.imread(image_path)
    if image is None: return
    h, w = image.shape[:2]
    cell_w, cell_h = cell_size
    cols = w // cell_w
    overlay = image.copy()
    for cname, nums in grid_cells.items():
        color = (np.random.randint(64,255),np.random.randint(64,255),np.random.randint(64,255))
        for num in nums:
            x1,y1,x2,y2 = num_to_coord(num, cols, cell_w, cell_h)
            cv2.rectangle(overlay,(x1,y1),(x2,y2),color,-1)
    cv2.addWeighted(overlay,0.4,image,0.6,0,image)
    for i in range(0, w, cell_w):
        cv2.line(image,(i,0),(i,h),(100,100,100),1)
    for j in range(0, h, cell_h):
        cv2.line(image,(0,j),(w,j),(100,100,100),1)
    if save_path: cv2.imwrite(save_path,image)
    return image

def detect_road_type_from_content(label_file):
    """根据标签内容判断路面类型"""
    try:
        with open(label_file,'r',encoding='utf-8') as f:
            content = f.read()
    except:
        return "gravel"
    for kw in CLASS_MAP_ASPHALT.keys():
        if kw in content: return "asphalt"
    for kw in CLASS_MAP_CEMENT.keys():
        if kw in content: return "cement"
    for kw in CLASS_MAP_GRAVEL.keys():
        if kw in content: return "gravel"
    return "gravel"

def get_road_info(road_dict, pile_dict, img_file_name):
    """获取路线信息"""
    parts = pile_dict.get(img_file_name)
    if parts :
        road_code = parts[0]
        road_info = road_dict.get(road_code)
        if road_info :
            data = road_info['data']
            return data
    return {}

def detect_road_type_from_road_dict(road_dict, pile_dict, img_file_name):
    """根据读取的excel内容内容判断路面类型"""
    road_code = 'xxxxxxxxxxx'
    pile_no = "xxxxx"
    road_type = "asphalt"
    parts = pile_dict.get(img_file_name)
    if parts :
        road_code = parts[0]
        pile_no = parts[1]
        road_info = road_dict.get(road_code)
        if road_info :
            data = road_info['data']
            pile_no = parts[1]
            road_type_cn = data['路面类型（沥青/水泥/砂石）']
            identify_width = data['识别宽度（米）']
            if road_type_cn == '沥青' :
                road_type = "asphalt"
            elif road_type_cn == '水泥' :
                road_type = "cement"
            elif road_type_cn == '砾石' :
                road_type = "gravel"
    return road_code, pile_no, road_type

def yoloseg_to_grid_share_dir(road_dict,pile_dict,image_path,label_file,cover_ratio=COVER_RATIO):
    """将YOLO-Seg标签转换成格子编号和类别"""
    img_file_name = os.path.basename(image_path)
    road_code, pile_no, road_type = detect_road_type_from_road_dict(road_dict, pile_dict, img_file_name)
    if road_type=="asphalt": class_map = CLASS_MAP_ASPHALT
    elif road_type=="cement": class_map = CLASS_MAP_CEMENT
    else: class_map = CLASS_MAP_GRAVEL
    class_names = list(class_map.keys())

    img = cv2.imread(image_path)
    if img is None: return "", {}
    h, w = img.shape[:2]
    cols = max(1, w//GRID_WIDTH)
    rows = max(1, h//GRID_HEIGHT)

    result_lines = []
    all_class_cells = {}
    with open(label_file,'r',encoding='utf-8') as f:
        for line in f:
            parts = line.strip().split()
            if len(parts)<5: continue
            cls_id = int(parts[0])
            coords = [float(x) for x in parts[1:]]
            if len(coords)%2!=0: coords=coords[:-1]
            if len(coords)<6: continue
            poly = np.array(coords,dtype=np.float32).reshape(-1,2)
            poly[:,0]*=w
            poly[:,1]*=h
            mask = np.zeros((h,w),dtype=np.uint8)
            cv2.fillPoly(mask,[poly.astype(np.int32)],255)
            cell_info = []
            covered_cells=[]
            min_x = cols
            min_y = rows
            max_x = 0
            max_y = 0
            for r in range(rows):
                for c in range(cols):
                    x1,y1 = c*GRID_WIDTH, r*GRID_HEIGHT
                    x2,y2 = min(w,x1+GRID_WIDTH), min(h,y1+GRID_HEIGHT)
                    region = mask[y1:y2, x1:x2]
                    if np.count_nonzero(region)/region.size>cover_ratio:
                        covered_cells.append(r*cols+c+1)
                        # 最小x坐标，y坐标
                        if min_x > c :
                            min_x = c
                        if min_y > r :
                            min_y = r
                        # 最大x坐标，y坐标
                        if max_x < c + 1 :
                            max_x = c + 1
                        if max_y < r + 1 :
                            max_y = r + 1

            if not covered_cells: continue
            # min_cell = covered_cells[0]
            # max_cell = covered_cells[len(covered_cells)-1]

            cname = class_names[cls_id] if cls_id<len(class_names) else str(cls_id)
            ids_str = '-'.join(map(str,sorted(covered_cells)))+'-'
            result_lines.append(f"{cname}   {f"桩号:K000{pile_no}"}   {ROAD_TYPE_EN_TO_CN.get(road_type, 'xx')}   {ids_str}")
            if cname not in all_class_cells: all_class_cells[cname]=set()
            cell_info.append(covered_cells)
            cell_info.append([max_x - min_x, max_y - min_y])
            all_class_cells[cname] = cell_info
    return '\n'.join(result_lines), all_class_cells, road_type, rows * cols

def yoloseg_to_grid(image_path,label_file,cover_ratio=COVER_RATIO):
    """将YOLO-Seg标签转换成格子编号和类别"""
    road_type = detect_road_type_from_content(label_file)
    if road_type=="asphalt": class_map = CLASS_MAP_ASPHALT
    elif road_type=="cement": class_map = CLASS_MAP_CEMENT
    else: class_map = CLASS_MAP_GRAVEL
    class_names = list(class_map.keys())

    img = cv2.imread(image_path)
    if img is None: return "", {}
    h, w = img.shape[:2]
    cols = max(1, w//GRID_WIDTH)
    rows = max(1, h//GRID_HEIGHT)

    result_lines = []
    all_class_cells = {}
    with open(label_file,'r',encoding='utf-8') as f:
        for line in f:
            parts = line.strip().split()
            if len(parts)<5: continue
            cls_id = int(parts[0])
            coords = [float(x) for x in parts[1:]]
            if len(coords)%2!=0: coords=coords[:-1]
            if len(coords)<6: continue
            poly = np.array(coords,dtype=np.float32).reshape(-1,2)
            poly[:,0]*=w
            poly[:,1]*=h
            mask = np.zeros((h,w),dtype=np.uint8)
            cv2.fillPoly(mask,[poly.astype(np.int32)],255)
            covered_cells=[]
            for r in range(rows):
                for c in range(cols):
                    x1,y1 = c*GRID_WIDTH, r*GRID_HEIGHT
                    x2,y2 = min(w,x1+GRID_WIDTH), min(h,y1+GRID_HEIGHT)
                    region = mask[y1:y2, x1:x2]
                    if np.count_nonzero(region)/region.size>cover_ratio:
                        covered_cells.append(r*cols+c+1)
            if not covered_cells: continue
            cname = class_names[cls_id] if cls_id<len(class_names) else str(cls_id)
            ids_str = '-'.join(map(str,sorted(covered_cells)))+'-'
            result_lines.append(f"{cname}   {ids_str}")
            if cname not in all_class_cells: all_class_cells[cname]=set()
            all_class_cells[cname].update(covered_cells)
    return '\n'.join(result_lines), all_class_cells, road_type

def generate_header(road_type):
    if road_type=="asphalt": return "起点桩号(km),识别宽度(m),破损率DR(%),龟裂,块状裂缝,纵向裂缝,横向裂缝,沉陷,车辙,波浪拥包,坑槽,松散,泛油,修补"
    if road_type=="cement": return "起点桩号(km),识别宽度(m),破损率DR(%),破碎板,裂缝,板角断裂,错台,拱起,边角剥落,接缝料损坏,坑洞,唧泥,露骨,修补"
    if road_type=="gravel": return "起点桩号(km),识别宽度(m),破损率DR(%),坑槽,沉陷,车辙,波浪搓板"
    return ""

def get_min_max_pile(group) :
    min_pile = float(99.000)
    max_pile = float(0.000)
    for g in group :
        tmp_pile = convert_special_format(g[0])
        if min_pile > tmp_pile :
            min_pile = tmp_pile
        if max_pile < tmp_pile :
            max_pile = tmp_pile
    
    return min_pile, max_pile

def convert_special_format(input_str):
    """
    将特殊格式字符串转换为浮点数
    支持格式: "0+022" -> 0.022, "1+234" -> 1.234
    """
    if '+' in input_str:
        # 分割整数部分和小数部分
        parts = input_str.split('+')
        if len(parts) == 2:
            integer_part = parts[0]
            decimal_part = parts[1]
            
            # 构建标准小数格式
            standard_format = f"{integer_part}.{decimal_part}"
            return float(standard_format)
        else:
            raise ValueError(f"无效的格式: {input_str}")
    else:
        # 如果没有 '+'，直接转换
        return float(input_str)

# 是否在区间内
def in_interval(increment, cur_pile_no, tmp_start, tmp_end) :
    if increment > 0 :
        if cur_pile_no >= tmp_start and cur_pile_no < tmp_end :
            return True
        else :
            return False
    else :
        if cur_pile_no > tmp_end and cur_pile_no <= tmp_start :
            return True
        else :
            return False


# ---------------- 主函数-共享目录 ----------------
def process_dir(road_dict,pile_dict,dir="output",cell_area=CELL_AREA,grid_width=GRID_WIDTH,grid_height=GRID_HEIGHT):
    os.makedirs(dir,exist_ok=True)
    # 解压
    # 读取桩号映射
    # 遍历图片
    summary_data = []
    for root,_,files in os.walk(dir):
        for file in files:
            if file.lower().endswith((".jpg",".png",".jpeg",".bmp")) :
                image_path = os.path.join(root,file)
                label_file = os.path.splitext(image_path)[0]+".txt"
                if not os.path.exists(label_file):
                    print(f"⚠️ 找不到标签: {label_file}")
                    continue
                out_txt, class_cells, road_type, all_cell_num = yoloseg_to_grid_share_dir(road_dict,pile_dict,image_path,label_file)
                # 写每张图独立 _grid.txt
                grid_txt_path = os.path.splitext(image_path)[0]+"_grid.txt"
                with open(grid_txt_path,'w',encoding='utf-8') as f:
                    f.write(out_txt)
                # 生成网格可视化
                # draw_grid_on_image(image_path,class_cells,save_path=os.path.splitext(image_path)[0]+"_grid.jpg")
                # 统计各类面积
                counts = {k:[len(v[0])*cell_area, v[1][0], v[1][1]] for k,v in class_cells.items()}
                # total_area = sum(counts.values())
                # 灾害总面积比例
                merged_set = set([])
                for k,v in class_cells.items() :
                    merged_set = merged_set.union(v[0])
                total_area = len(merged_set)
                
                # 桩号 路线编号
                parts = pile_dict.get(file)
                pile_no = "0+000"
                if parts :
                    pile_no = parts[1]
                
                # 破损率 DR (%) = total_area / 总面积
                # DR = total_area/ (total_area if total_area>0 else 1) *100  # 简化为100%或者0
                DR= total_area / all_cell_num * 100
                summary_data.append((pile_no, DR, counts, road_type))

    current_time = datetime.now().strftime("%Y%m%d%H%M%S")
    # 写桩号问题列表.txt
    if summary_data:
        img_file_name = os.path.basename(image_path)
        road_data = get_road_info(road_dict, pile_dict, img_file_name)
        road_code = pile_dict.get(img_file_name)[0]
        road_type = summary_data[0][3]

        min_pile, max_pile = get_min_max_pile(summary_data)
        out_file = os.path.join(dir,f"{road_code}-DR-{min_pile:0.3f}-{max_pile:0.3f}-detail-{current_time}.txt")
        header = generate_header(road_type)
        with open(out_file,'w',encoding='utf-8') as f:
            f.write(header+'\n')
            for pile_no,DR,counts,rt in summary_data:
                row = [pile_no,"3.6",f"{DR:.2f}"]
                if road_type=="asphalt":
                    keys = list(CLASS_MAP_ASPHALT.keys())
                elif road_type=="cement":
                    keys = list(CLASS_MAP_CEMENT.keys())
                else:
                    keys = list(CLASS_MAP_GRAVEL.keys())
                for k in keys:
                    row.append(f"{counts.get(k,[0,0,0])[0]:.2f}")
                f.write(','.join(row)+'\n')
        print(f"输出完成: {out_file}")

    # 写灾害数据.txt
    if summary_data:
        img_file_name = os.path.basename(image_path)
        road_data = get_road_info(road_dict, pile_dict, img_file_name)
        identify_width = road_data.get('识别宽度（米）', '3.6')
        up_or_down = road_data.get('方向(上行/下行)', '上行')
        road_code = pile_dict.get(img_file_name)[0]
        group_by_road_type = {}
        for data in summary_data:
            group_by_road_type.setdefault(data[3], []).append(data)


        for road_type, group in group_by_road_type.items():
            min_pile, max_pile = get_min_max_pile(group)
            out_file = os.path.join(dir,f"{road_code}-DR-{min_pile:0.3f}-{max_pile:0.3f}-{current_time}.txt")
            header = generate_header(road_type)

            group_list = group
            increment = float(0.010)
            pile_no_start = float(0.000)
            pile_no_end = float(0.000) + increment
            # 上行/下行
            if up_or_down == '下行' :
                group_list = list(group)[::-1]
                increment = float(-0.010)
                tmp_pile_no = convert_special_format(group[len(group)-1][1])
                pile_no_start = tmp_pile_no
                if tmp_pile_no % increment != 0 :
                    pile_no_end = tmp_pile_no + (tmp_pile_no % increment)
                else :
                    pile_no_end = tmp_pile_no + increment

            with open(out_file,'w',encoding='utf-8') as f:
                f.write(header+'\n')
                index = 0
                tmp_start = pile_no_start
                tmp_end = pile_no_end
                while True :
                    # 每10m一个区间，在区间内进行灾害计算
                    pile_no, DR, counts, road_type = summary_data[index]
                    cur_pile_no = convert_special_format(pile_no)
                    if not in_interval(increment, cur_pile_no, tmp_start, tmp_end) :
                        # 没在刻度内直接输出无病害数据
                        pile_no = f"{tmp_start:0.3f}"
                        row = [pile_no,identify_width,f"{0:.2f}"]
                        if road_type=="asphalt":
                            keys = list(CLASS_MAP_ASPHALT.keys())
                        elif road_type=="cement":
                            keys = list(CLASS_MAP_CEMENT.keys())
                        else:
                            keys = list(CLASS_MAP_GRAVEL.keys())
                        for k in keys:
                            row.append(f"{0:.2f}")
                        f.write(','.join(row)+'\n')
                    else :
                        row = [f"{tmp_start:0.3f}", identify_width]
                        subRows = []
                        while index < len(group_list):
                            pile_no, DR, counts, road_type = summary_data[index]
                            cur_pile_no = convert_special_format(pile_no)
                            
                            tmp_row = []
                            if in_interval(increment, cur_pile_no, tmp_start, tmp_end) :
                                pile_no = f"{tmp_start:0.3f}"
                                tmp_row = [DR]
                                if road_type=="asphalt":
                                    keys = list(CLASS_MAP_ASPHALT.keys())
                                elif road_type=="cement":
                                    keys = list(CLASS_MAP_CEMENT.keys())
                                else:
                                    keys = list(CLASS_MAP_GRAVEL.keys())
                                for k in keys:
                                    tmp_row.append(counts.get(k, [0,0,0])[0])
                                subRows.append(tmp_row)
                                index = index + 1
                            else :
                                break
                        
                        # 同列汇总 10m一个区间--对应5张图
                        column_sums = [f"{(sum(column)/5):0.2f}" for column in zip(*subRows)]
                        row += column_sums
                        f.write(','.join(row)+'\n')

                    tmp_start = tmp_end
                    tmp_end = tmp_start + increment

                    print(f"tmp_start={tmp_start}, tmp_end={tmp_end}, index={index}, len(group_list)={len(group_list)}")
                    if index >= len(group_list) :
                        break
        
        print(f"输出完成: {out_file}")

    # 病害明显列表.xlsx
    headers = ['序号','路线编码','方向','桩号','路面类型','病害名称','程度','长度(m)',' 宽度(m)',' 面积(㎡)',' 横向位置','备注']
    data_list = []
    if summary_data:
        img_file_path = os.path.dirname(image_path)
        img_file_name = os.path.basename(image_path)
        road_data = get_road_info(road_dict, pile_dict, img_file_name)
        road_code, pile_no, road_type = detect_road_type_from_road_dict(road_dict, pile_dict, img_file_name)
        identify_width = road_data.get('识别宽度（米）', '3.6')
        up_or_down = road_data.get('方向(上行/下行)', '上行')
        excel_index = 1
        for data in summary_data:
            damage_data = data[2]
            for attr_name, attr_value in damage_data.items():
                excel_data = [excel_index, road_code, up_or_down, f"K000{data[0]}", ROAD_TYPE_EN_TO_CN.get(road_type), attr_name, '', attr_value[1]*cell_area, attr_value[2]*cell_area, attr_value[0], '', '']
                data_list.append(excel_data)

    all_data = [headers] + data_list
    smb.write_to_excel_pandas(all_data, img_file_path + '/病害明显列表.xlsx')

# ---------------- 主函数 ----------------
def process_zip(zip_path,pile_map_file,output_dir="output",cell_area=CELL_AREA,grid_width=GRID_WIDTH,grid_height=GRID_HEIGHT):
    if not os.path.exists(zip_path):
        raise FileNotFoundError(f"{zip_path} 不存在")
    os.makedirs(output_dir,exist_ok=True)
    # 解压
    with zipfile.ZipFile(zip_path,'r') as zip_ref:
        zip_ref.extractall(output_dir)

    # 读取桩号映射
    pile_dict = {}
    with open(pile_map_file,'r',encoding='utf-8') as f:
        for line in f:
            parts = line.strip().split("->")
            if len(parts)>=4:
                pile_dict[parts[3]]=parts  # filename -> 桩号

    # 遍历图片
    summary_data = []
    for root,_,files in os.walk(output_dir):
        for file in files:
            if file.lower().endswith((".jpg",".png",".jpeg",".bmp")) :
                image_path = os.path.join(root,file)
                label_file = os.path.splitext(image_path)[0]+".txt"
                if not os.path.exists(label_file):
                    print(f"⚠️ 找不到标签: {label_file}")
                    continue
                out_txt, class_cells, road_type, = yoloseg_to_grid(image_path,label_file)
                # 写每张图独立 _grid.txt
                grid_txt_path = os.path.splitext(image_path)[0]+"_grid.txt"
                with open(grid_txt_path,'w',encoding='utf-8') as f:
                    f.write(out_txt)
                # 生成网格可视化
                draw_grid_on_image(image_path,class_cells,save_path=os.path.splitext(image_path)[0]+"_grid.jpg")
                # 统计各类面积
                counts = {k:len(v)*cell_area for k,v in class_cells.items()}
                total_area = sum(counts.values())
                # 桩号
                pile_no = pile_dict.get(file,"0+000")
                # 破损率 DR (%) = total_area / 总面积
                DR = total_area / (total_area if total_area > 0 else 1) * 100  # 简化为100%或者0
                summary_data.append((pile_no, DR, counts, road_type))

    # 写桩号问题列表.txt
    if summary_data:
        road_type = summary_data[0][3]
        out_file = os.path.join(output_dir,"桩号问题列表.txt")
        header = generate_header(road_type)
        with open(out_file,'w',encoding='utf-8') as f:
            f.write(header+'\n')
            for pile_no,DR,counts,rt in summary_data:
                row = [pile_no,"3.6",f"{DR:.2f}"]
                if road_type=="asphalt":
                    keys = list(CLASS_MAP_ASPHALT.keys())
                elif road_type=="cement":
                    keys = list(CLASS_MAP_CEMENT.keys())
                else:
                    keys = list(CLASS_MAP_GRAVEL.keys())
                for k in keys:
                    row.append(f"{counts.get(k,0):.2f}")
                f.write(','.join(row)+'\n')
        print(f"✅ 输出完成: {out_file}")


# 路线编码 -> 路线信息
def get_road_dict(local_dir):
    """
    从本地目录读取Excel文件，构建路线字典
    
    Args:
        local_dir: 本地目录路径
        
    Returns:
        dict: 路线编码到路线信息的映射字典
    """
    # 查找匹配的Excel文件
    pattern = os.path.join(local_dir, '每公里指标明细表*.xls')
    found_paths = glob.glob(pattern)
    
    print(f"\n找到 {len(found_paths)} 个 '每公里指标明细表*.xls' 文件:")
    for i, path in enumerate(found_paths, 1):
        print(f"{i}. {path}")

    road_dict = {}
    if len(found_paths) > 0:
        # 读取第一个匹配的Excel文件
        df = pd.read_excel(found_paths[0])
        
        # 处理所有行（这里需要根据实际情况调整处理逻辑）
        for index, row in df.iterrows():
            data = row.to_dict()
            if pd.notna(data.get('线路编码', None)):
                up_or_down = 'A'
                if data.get('方向(上行/下行)', '') == '下行':
                    up_or_down = 'B'
                
                # 构建key，确保区划代码为整数
                area_code = data.get('区划代码', '')
                if pd.notna(area_code):
                    area_code = str(int(float(area_code))) if str(area_code).replace('.', '').isdigit() else str(area_code)
                else:
                    area_code = ''
                
                key = f"{data['线路编码']}{area_code}{up_or_down}"
                
                if key in road_dict:
                    road_dict[key].append({'index': index, 'data': data})
                else:
                    road_dict[key] = [{'index': index, 'data': data}]
    
    return road_dict

# filename -> 桩号
def get_pile_dict(local_dir):
    """
    从本地目录读取fileindex.txt文件，构建桩号字典
    
    Args:
        local_dir: 本地目录路径
        
    Returns:
        dict: 文件名到桩号信息的映射字典
    """
    # 查找fileindex.txt文件
    pattern = os.path.join(local_dir, 'fileindex.txt')
    found_paths = glob.glob(pattern)
    
    print(f"\n找到 {len(found_paths)} 个 'fileindex.txt' 文件:")
    for i, path in enumerate(found_paths, 1):
        print(f"{i}. {path}")

    pile_dict = {}
    if len(found_paths) > 0:
        # 读取第一个匹配的txt文件
        with open(found_paths[0], 'r', encoding='utf-8') as file:
            lines = file.readlines()
        
        for i, line in enumerate(lines, 1):
            parts = line.strip().split("->")
            if len(parts) >= 4:
                pile_dict[parts[3]] = parts  # filename -> 桩号
    
    return pile_dict

# ---------------- 示例调用 ----------------
if __name__=="__main__":
    # zip_path = "D:/devForBdzlWork/ai-train_platform/predict/inferenceResult.zip"          # 输入 ZIP 文件
    # pile_map_file = "D:/devForBdzlWork/ai-train_platform/predict/pile_map.txt"    # 图片名 -> 桩号
    # process_zip(zip_path=zip_path,pile_map_file=pile_map_file,output_dir="output")

    # output_dir = "D:/devForBdzlWork/ai-train_platform/predictions/7"
    # pile_dict = smb.get_pile_dict("192.168.110.114/share_File/西南计算机", "administrator", "abc@1234")
    # road_dict = smb.get_road_dict("192.168.110.114/share_File/西南计算机", "administrator", "abc@1234")
    # process_dir(road_dict, pile_dict, output_dir)

    output_dir = "D:/devForBdzlWork/ai-train_platform/predictions/7"
    pile_dict = get_pile_dict(output_dir)
    road_dict = get_road_dict(output_dir)
    process_dir(road_dict, pile_dict, output_dir)