共享目录AI识别

predict/predict_yolo11seg.py

@@ -17,7 +17,9 @@ import matplotlib.pyplot as plt
 from ultralytics import YOLO
 
 from middleware.minio_util import upload_file, downFile, check_zip_size, upload_folder
-from util.yolo2pix import yoloseg_to_grid_cells_fixed_v5, draw_grid_on_image
+from util.yolo2pix_new import *
+from util.smb import *
+import threading
 
 # 定义红白蓝颜色 (BGR格式)
 RED = (0, 0, 255)
@@ -183,6 +185,71 @@ class YOLOSegmentationInference:
             print(f"图片预处理失败: {e}")
             return None, None
 
+    def perform_inference_share_dir(self, image, image_path, conf_threshold: float = 0.25,
+                          iou_threshold: float = 0.5) -> InferenceResult:
+        """
+        执行推理
+
+        Args:
+            image_path: 图片数据
+            image_path: 图片路径
+            conf_threshold: 置信度阈值
+            iou_threshold: IOU阈值
+
+        Returns:
+            推理结果
+        """
+        result = InferenceResult(image_path)
+
+        try:
+            if self.model is None:
+                raise ValueError("模型未加载，请先调用load_model()")
+
+            # 转换为RGB格式
+            original_image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+            result.original_image = original_image_rgb
+
+            # 执行推理
+            print(f"正在处理图片: {os.path.basename(image_path)}")
+            start_time = time.time()
+
+            # 使用YOLO模型进行推理
+            # predictions = self.model(image_path, conf=conf_threshold, iou=iou_threshold)[0]
+            predictions = self.model(original_image_rgb, conf=conf_threshold, iou=iou_threshold)[0]
+
+            result.inference_time = time.time() - start_time
+
+            # 处理结果
+            if predictions.masks is not None:
+                # 处理掩码
+                masks = predictions.masks.data.cpu().numpy()
+
+                # 处理边界框
+                boxes = predictions.boxes.data.cpu().numpy()
+
+                # 处理类别和置信度
+                class_ids = predictions.boxes.cls.cpu().numpy().astype(int)
+                scores = predictions.boxes.conf.cpu().numpy()
+
+                # 获取类别名称
+                class_names = [self.model.names[i] for i in class_ids]
+
+                # 存储结果
+                result.masks = masks
+                result.boxes = boxes
+                result.classes = class_ids
+                result.scores = scores
+                result.class_names = class_names
+
+                print(f"检测到 {len(masks)} 个对象，推理时间: {result.inference_time:.3f} 秒")
+
+            return result
+
+        except Exception as e:
+            print(f"推理失败: {e}")
+            return result
+
     def perform_inference(self, image_path: str, conf_threshold: float = 0.25,
                           iou_threshold: float = 0.5) -> InferenceResult:
         """
@@ -323,7 +390,7 @@ class YOLOSegmentationInference:
 
         try:
             base_name = os.path.splitext(os.path.basename(result.image_path))[0]
-            output_dir = output_dir + "-" + base_name
+            # output_dir = output_dir + "/" + base_name
             # 创建输出目录
             os.makedirs(output_dir, exist_ok=True)
 
@@ -334,14 +401,15 @@ class YOLOSegmentationInference:
                 "result_dir": output_dir
             }
             # 保存结果图片
-            result_path = os.path.join(output_dir, f"{base_name}_result.jpg")
+            result_path = os.path.join(output_dir, f"{base_name}.jpg")
             result_image_bgr = cv2.cvtColor(result.result_image, cv2.COLOR_RGB2BGR)
             cv2.imwrite(result_path, result_image_bgr)
             print(f"结果图片已保存: {result_path}")
 
             # 保存单独的掩码文件
             if save_mask and result.masks is not None and len(result.masks) > 0:
-                mask_dir = os.path.join(output_dir, "masks")
+                # mask_dir = os.path.join(output_dir, "masks")
+                mask_dir = output_dir
                 os.makedirs(mask_dir, exist_ok=True)
 
                 for i in range(len(result.masks)):
@@ -356,7 +424,8 @@ class YOLOSegmentationInference:
 
             # 保存YOLO格式的标签文件
             if save_label and result.masks is not None and len(result.masks) > 0 and len(result.boxes) > 0:
-                label_dir = os.path.join(output_dir, "labels")
+                # label_dir = os.path.join(output_dir, "labels")
+                label_dir = output_dir
                 os.makedirs(label_dir, exist_ok=True)
 
                 label_path = os.path.join(label_dir, f"{base_name}.txt")
@@ -463,6 +532,45 @@ class YOLOSegmentationInference:
         #     self.show_results(result)
 
         return result
+    
+    def process_single_image_share_dir(self, image_path, user_name, pwd, output_dir: Optional[str] = None,
+                             conf_threshold: float = 0.25, iou_threshold: float = 0.5,
+                             save_mask: bool = False, save_label: bool = False, show: bool = True,
+                             result_save: [] = None) -> InferenceResult:
+        """
+        处理单张图片
+
+        Args:
+            image_path: 图片路径
+            output_dir: 输出目录，如果为None则不保存
+            conf_threshold: 置信度阈值
+            iou_threshold: IOU阈值
+            save_mask: 是否保存单独的掩码文件
+            save_label: 是否保存YOLO格式的标签文件
+            show: 是否显示结果
+
+        Returns:
+            推理结果
+        """
+        # 执行推理
+        config = get_conf(image_path, user_name, pwd)
+        scanner = get_scanner(image_path, user_name=user_name, pwd=pwd)
+        image = scanner.read_img_file(image_path)
+        result = self.perform_inference_share_dir(image, image_path, conf_threshold, iou_threshold)
+
+        # 绘制结果
+        if result.masks is not None and len(result.masks) > 0:
+            self.draw_results(result, conf_threshold)
+
+        # 保存结果
+        if output_dir is not None:
+            self.save_results(result, output_dir, save_mask, save_label, result_save)
+
+        # # 显示结果
+        # if show:
+        #     self.show_results(result)
+
+        return result
 
     def process_image_directory(self, input_dir: str, output_dir: Optional[str] = None,
                                 conf_threshold: float = 0.25, iou_threshold: float = 0.5,
@@ -528,6 +636,83 @@ class YOLOSegmentationInference:
             print(f"处理目录失败: {e}")
             return results
 
+    def process_image_directory_share_dir(self, input_dir, user_name, pwd, output_dir: Optional[str] = None,
+                                conf_threshold: float = 0.25, iou_threshold: float = 0.5,
+                                save_mask: bool = False, save_label: bool = False, show: bool = False,
+                                result_save: [] = None) -> List[
+        InferenceResult]:
+        """
+        处理目录中的所有图片
+
+        Args:
+            input_dir: 输入目录
+            output_dir: 输出目录，如果为None则不保存
+            conf_threshold: 置信度阈值
+            iou_threshold: IOU阈值
+            save_mask: 是否保存单独的掩码文件
+            save_label: 是否保存YOLO格式的标签文件
+            show: 是否显示结果
+
+        Returns:
+            推理结果列表
+        """
+        results = []
+
+        try:
+            # 检查目录是否存在
+            config = get_conf(zip_url=input_dir, user_name=user_name, pwd=pwd)
+            scanner = get_scanner(zip_url=input_dir, user_name=user_name, pwd=pwd);
+            if not scanner.directory_exists(input_dir):
+                print(f"错误: {input_dir} 不是有效的目录")
+                return results
+
+            # 获取所有图片文件
+            image_files = scanner.get_smb_images(input_dir)
+
+            if not image_files:
+                print(f"在目录 {input_dir} 中未找到图片文件")
+                return results
+
+            print(f"找到 {len(image_files)} 个图片文件")
+
+            # 处理每张图片
+            for image_path in image_files:
+                result = self.process_single_image_share_dir(
+                    image_path=image_path,
+                    user_name=user_name,
+                    pwd=pwd,
+                    output_dir=output_dir,
+                    conf_threshold=conf_threshold,
+                    iou_threshold=iou_threshold,
+                    save_mask=save_mask,
+                    save_label=save_label,
+                    show=show,
+                    result_save=result_save
+                )
+                results.append(result)
+
+            # 推送识别数据到共享目录
+            pile_dict = get_pile_dict(image_path, user_name, pwd)
+            process_dir(pile_dict, output_dir)
+            # 找到 图像类 文件夹
+            found_paths = scanner.find_folders_by_name(
+                share_path=config['share'],
+                folder_name='图像类'
+            )
+            if len(found_paths) > 0 :
+                tmpConf = get_conf(found_paths[0], user_name, pwd)
+                scanner.upload_directory(output_dir, config['share'], remote_dir=tmpConf['dir']+"_识别")
+            else :
+                print(f"错误: 远程共享目录 找不到【图像类】目录")
+
+            return results
+
+        except PermissionError:
+            print(f"权限错误: 无法访问目录 {input_dir}")
+            return results
+        except Exception as e:
+            print(f"处理目录失败: {e}")
+            return results
 
 def predict_images(pt_name, zip_url, output_dir="predictions", conf_threshold=0.25, save_json=False):
     zip_save_path = "dataset/zip_file"
@@ -596,8 +781,8 @@ def predict_images(pt_name, zip_url, output_dir="predictions", conf_threshold=0.
     input_path = zip_local_dir_save
     result_save = []
 
-    conf_threshold = 0.25,
+    conf_threshold = 0.25
-    iou_threshold = 0.5,
+    iou_threshold = 0.5
     save_mask = True,
     save_label = True,
     show = True
@@ -673,3 +858,85 @@ def predict_images(pt_name, zip_url, output_dir="predictions", conf_threshold=0.
         os.remove(zip_dir_path)
 
     return file_save_dir, "success"
+
+
+
+
+def predict_images_share_dir(pt_name, zip_url, user_name, pwd, output_dir="predictions", conf_threshold=0.25, save_json=False):
+    # 本地测试模式 - 请根据实际情况修改以下路径
+    # local_model_path = r"D:\project\verification\ultralytics-main\model\script\seg\pt\test.pttest.pt"
+    local_model_path = r"../pt_save/road_crack.pt"
+    local_output_dir = output_dir
+    # zip_url = "meta_data/ai_train_platform/train.zip"
+
+    try:
+        # 加载模型
+        print(f"正在加载模型: {local_model_path}")
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"模型已加载到 {device}")
+    except Exception as e:
+        print(f"处理目录失败: {e}")
+
+    inference = YOLOSegmentationInference(
+        model_path=local_model_path,
+        device=device
+    )
+
+    # 加载模型
+    if not inference.load_model():
+        return
+
+    
+
+    # zip_url = r"D:\project\verification\ultralytics-main\model\script\seg\test_seg_pic"
+
+    result_save = []
+    conf_threshold = 0.25
+    iou_threshold = 0.5
+    save_mask = False
+    save_label = True
+    show = True
+
+    # 查找指定文件夹 图像类
+    config = get_conf(zip_url, user_name, pwd)
+    scanner = get_scanner(zip_url, user_name, pwd)
+    found_paths = scanner.find_folders_by_name(
+        share_path=config['share'],
+        folder_name='图像类'
+    )
+
+    target_path = ""
+    report_data_path = ""
+    if len(found_paths) > 0:
+        # 处理目录
+        report_data_path = found_paths[0]
+        tmpConfig = get_conf(report_data_path, user_name, pwd)
+        found_paths = scanner.find_folders_by_name(
+            share_path=config['share'],
+            folder_name='Images',
+            start_dir=tmpConfig['dir']
+        )
+        if len(found_paths) > 0:
+            target_path = found_paths[0]
+            # inference.process_image_directory_share_dir(
+            #     input_dir=target_path,
+            #     user_name=user_name,
+            #     pwd=pwd,
+            #     output_dir=output_dir,
+            #     conf_threshold=conf_threshold,
+            #     iou_threshold=iou_threshold,
+            #     save_mask=save_mask,
+            #     save_label=save_label,
+            #     show=show, 
+            #     result_save=result_save
+            # )
+
+            # 创建并启动线程
+            thread1 = threading.Thread(target=inference.process_image_directory_share_dir, args=(target_path,user_name,pwd,output_dir,conf_threshold,iou_threshold,save_mask,save_label,show,result_save))
+
+            # 启动线程
+            thread1.start()
+    else:
+        print(f"错误: 输入 {zip_url} 不是有效的文件或目录")
+
+    return f"{report_data_path}_识别", "success"

util/smb.py

@@ -0,0 +1,859 @@
+import os
+from smbclient import (
+    register_session, 
+    listdir, 
+    scandir, 
+    stat,
+    makedirs,     # 递归创建目录
+    open_file
+)
+from datetime import datetime
+import numpy as np
+import cv2
+import pandas as pd
+import io
+
+class SMBScanner:
+    def __init__(self, ip, username, password, domain=''):
+        self.ip = ip
+        self.username = username
+        self.password = password
+        self.domain = domain
+        
+    def connect(self):
+        """连接 SMB 共享"""
+        try:
+            register_session(
+                self.ip, 
+                username=self.username, 
+                password=self.password
+            )
+            print(f"成功连接到 {self.ip}")
+            return True
+        except Exception as e:
+            print(f"连接失败: {e}")
+            return False
+        
+    def directory_exists(self, full_path):
+        """
+        检查目录是否存在
+        
+        Args:
+            full_path: 全路径
+            
+        Returns:
+            bool: 目录是否存在
+        """
+        if not self.connect():
+            return False
+        
+        try:
+            # 尝试获取目录信息
+            dir_stat = stat(full_path)
+            return True
+        except Exception as e:
+            print(f"未知错误: {e}")
+            return False
+    
+    def read_excel(self, smb_path, sheet_name=0):
+        """读取Excel文件"""
+        if not self.connect():
+            return False
+        
+        try:
+            with open_file(smb_path, mode='rb') as smb_file:
+                file_content = smb_file.read()
+            
+            excel_data = io.BytesIO(file_content)
+            df = pd.read_excel(excel_data, sheet_name=sheet_name)
+            return df
+            
+        except Exception as e:
+            print(f"读取Excel失败: {e}")
+            return None
+
+    def process_all_rows(self, df):
+        """
+        处理所有行数据
+        """
+        if df is None or df.empty:
+            print("没有数据可处理")
+            return
+        
+        print("开始处理每行数据:")
+        print("=" * 60)
+        
+        results = []
+        
+        for row_number, (index, row) in enumerate(df.iterrows(), 1):
+            print(f"\n处理第 {row_number} 行:")
+            print("-" * 40)
+            
+            # 显示行数据
+            for col_name in df.columns:
+                value = row[col_name]
+                print(f"  {col_name}: {value}")
+            
+            # 处理逻辑（根据实际需求修改）
+            processed_row = {
+                'row_number': row_number,
+                'original_index': index,
+                'data': row.to_dict(),
+                'summary': f"处理了 {len(df.columns)} 个字段"
+            }
+            
+            results.append(processed_row)
+            
+            # 进度显示
+            if row_number % 10 == 0 or row_number == len(df):
+                print(f"\n 进度: {row_number}/{len(df)} ({row_number/len(df)*100:.1f}%)")
+        
+        print("\n" + "=" * 60)
+        print(f"处理完成！共处理 {len(results)} 行数据")
+        
+        return results
+
+    def get_smb_images(self, full_path):
+        """SMB 图片文件获取"""
+        image_extensions = ['.jpg', '.jpeg', '.png', '.bmp', '.tiff']
+        image_files = []
+        
+        try:
+            for entry in scandir(full_path):
+                if entry.is_file():
+                    _, ext = os.path.splitext(entry.name)
+                    if ext.lower() in image_extensions:
+                        image_files.append(entry.path)
+                elif entry.is_dir():
+                    imgs = self.get_smb_images(entry.path)
+                    image_files.extend(imgs)
+        except Exception as e:
+            print(f"错误: {e}")
+        
+        return image_files
+
+    def build_full_path(self, share_path, file_path):
+        """构建完整的 SMB 路径"""
+        # 清理路径中的多余斜杠
+        share_path = share_path.strip('\\')
+        file_path = file_path.lstrip('\\')
+        return f"\\\\{self.ip}\\{share_path}\\{file_path}"
+    
+    def read_txt_by_line(self, full_path):
+        """逐行读取，适合大文件"""
+        if not self.connect():
+            return None
+        
+        print(f"读取 TXT 文件: {full_path}")
+        
+        try:
+            with open_file(full_path, mode='rb') as file_obj:
+                content_bytes = file_obj.read()
+            
+            # 使用 StringIO 逐行处理
+            text_content = content_bytes.decode('utf-8', errors='ignore')
+            string_io = io.StringIO(text_content)
+            
+            lines = []
+            line_number = 0
+            
+            while True:
+                line = string_io.readline()
+                if not line:  # 读到文件末尾
+                    break
+                
+                line_number += 1
+                line = line.strip()
+                # print(f"行 {line_number}: {line}")
+                lines.append(line)
+            
+            print(f"总共读取 {line_number} 行")
+            return lines
+            
+        except Exception as e:
+            print(f"读取文件时出错: {e}")
+            return None
+
+    def read_img_file(self, full_path):
+        """读取文件并返回 OpenCV 图像"""
+        if not self.connect():
+            return None
+        
+        print(f"读取文件: {full_path}")
+        
+        file_obj = None
+        try:
+            # 以二进制模式读取文件
+            file_obj = open_file(full_path, mode='rb')
+            content = b""
+            
+            # 分块读取文件内容
+            while True:
+                chunk = file_obj.read(8192)  # 8KB 块
+                if not chunk:
+                    break
+                content += chunk
+            
+            print(f"成功读取 {len(content)} 字节")
+            
+            # 解码图像
+            if len(content) == 0:
+                print("文件为空")
+                return None
+                
+            image_array = np.frombuffer(content, np.uint8)
+            image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
+            
+            if image is None:
+                print("图像解码失败 - 可能不是有效的图像文件")
+                return None
+                
+            print(f"图像解码成功: {image.shape}")
+            return image
+            
+        except Exception as e:
+            print(f"读取文件失败: {e}")
+            return None
+        finally:
+            if file_obj:
+                file_obj.close()
+    
+    def writeFile(self, share_path, file_path, data, chunk_size=8192):
+        """写入文件到 SMB 共享"""
+        if not self.connect():
+            return False
+        
+        full_path = self.build_full_path(share_path, file_path)
+        file_obj = None
+        
+        try:
+            # 确保目录存在
+            dir_path = os.path.dirname(full_path)
+            try:
+                makedirs(dir_path, exist_ok=True)
+            except:
+                pass  # 目录可能已存在
+            
+            file_obj = open_file(full_path, mode='wb')
+            
+            if isinstance(data, bytes):
+                total_size = len(data)
+                written = 0
+                
+                for i in range(0, total_size, chunk_size):
+                    chunk = data[i:i + chunk_size]
+                    file_obj.write(chunk)
+                    written += len(chunk)
+                    print(f"写入进度: {written}/{total_size} 字节 ({written/total_size*100:.1f}%)")
+                
+            elif hasattr(data, '__iter__'):
+                total_written = 0
+                for chunk in data:
+                    if isinstance(chunk, str):
+                        chunk = chunk.encode('utf-8')
+                    file_obj.write(chunk)
+                    total_written += len(chunk)
+                    print(f"已写入: {total_written} 字节")
+            else:
+                file_obj.write(bytes(data))
+            
+            print(f"文件写入完成: {full_path}")
+            return True
+            
+        except Exception as e:
+            print(f"写入文件失败: {e}")
+            return False
+        finally:
+            if file_obj:
+                file_obj.close()
+    
+    def writeImageToFile(self, share_path, file_path, image, image_format='.jpg', quality=95):
+        """将 OpenCV 图像写入 SMB 文件"""
+        if not self.connect():
+            return False
+        
+        full_path = f"{file_path}{image_format}"
+        file_obj = None
+        
+        try:
+            if image_format.lower() == '.jpg':
+                encode_params = [cv2.IMWRITE_JPEG_QUALITY, quality]
+                success, encoded_image = cv2.imencode(image_format, image, encode_params)
+            else:
+                success, encoded_image = cv2.imencode(image_format, image)
+            
+            if not success:
+                print("图像编码失败")
+                return False
+            
+            image_data = encoded_image.tobytes()
+            return self.writeFile(share_path, f"{file_path}{image_format}", image_data)
+            
+        except Exception as e:
+            print(f"写入图像失败: {e}")
+            return False
+
+    def _ensure_remote_directory(self, share_name, remote_dir):
+        """确保远程目录存在"""
+        if not remote_dir:
+            return
+        
+        try:
+            # 构建完整远程路径
+            full_remote_path = self.build_full_path(share_name, remote_dir)
+            
+            # 使用 makedirs 递归创建目录（如果不存在）
+            makedirs(full_remote_path, exist_ok=True)
+            print(f"确保远程目录存在: {remote_dir}")
+                    
+        except Exception as e:
+            print(f"创建远程目录失败: {e}")
+            raise
+
+    def upload_directory(self, local_dir, share_name, remote_dir="", overwrite=True):
+        """
+        将本地目录推送到远程共享目录
+        """
+        if not self.connect():
+            return False
+        
+        print(f"开始上传目录: {local_dir} -> {share_name}/{remote_dir}")
+        
+        if not os.path.exists(local_dir):
+            print(f"本地目录不存在: {local_dir}")
+            return False
+        
+        try:
+            # 确保远程目录存在
+            self._ensure_remote_directory(share_name, remote_dir)
+            
+            # 递归上传目录内容
+            success = self._upload_directory_recursive(local_dir, share_name, remote_dir, overwrite)
+            
+            if success:
+                print("目录上传完成")
+            else:
+                print("目录上传过程中出现错误")
+                
+            return success
+            
+        except Exception as e:
+            print(f"上传目录失败: {e}")
+            return False
+
+    def _upload_directory_recursive(self, local_path, share_name, remote_path, overwrite):
+        """递归上传目录内容"""
+        try:
+            success = True
+            
+            for item_name in os.listdir(local_path):
+                local_item_path = os.path.join(local_path, item_name)
+                remote_item_path = f"{remote_path}/{item_name}" if remote_path else item_name
+                
+                if os.path.isdir(local_item_path):
+                    # 处理子目录
+                    print(f"上传子目录: {item_name}")
+                    
+                    # 确保远程子目录存在
+                    self._ensure_remote_directory(share_name, remote_item_path)
+                    
+                    # 递归上传子目录
+                    sub_success = self._upload_directory_recursive(local_item_path, share_name, remote_item_path, overwrite)
+                    if not sub_success:
+                        success = False
+                        
+                else:
+                    # 上传文件
+                    file_success = self._upload_single_file(local_item_path, share_name, remote_item_path, overwrite)
+                    if not file_success:
+                        success = False
+            
+            return success
+            
+        except Exception as e:
+            print(f"上传目录内容失败 {local_path}: {e}")
+            return False
+
+    def _upload_single_file(self, local_file_path, share_name, remote_file_path, overwrite):
+        """上传单个文件"""
+        file_obj = None
+        try:
+            # 构建远程完整路径
+            full_remote_path = self.build_full_path(share_name, remote_file_path)
+            
+            # 检查文件是否已存在
+            if not overwrite:
+                try:
+                    stat(full_remote_path)
+                    print(f"文件已存在，跳过: {remote_file_path}")
+                    return True
+                except FileNotFoundError:
+                    # 文件不存在，继续上传
+                    pass
+            
+            # 上传文件
+            print(f"上传文件: {os.path.basename(local_file_path)}")
+            
+            # 读取本地文件
+            with open(local_file_path, 'rb') as local_file:
+                local_content = local_file.read()
+            
+            # 写入远程文件
+            with open_file(full_remote_path, mode='wb') as remote_file:
+                remote_file.write(local_content)
+            
+            file_size = len(local_content)
+            print(f"文件上传成功: {remote_file_path} ({file_size} 字节)")
+            return True
+            
+        except Exception as e:
+            print(f"上传文件失败 {local_file_path}: {e}")
+            return False
+
+    def upload_file(self, local_file_path, share_name, remote_file_path, overwrite=True):
+        """
+        上传单个文件到远程共享目录
+        """
+        if not self.connect():
+            return False
+        
+        print(f"上传文件: {local_file_path} -> {share_name}/{remote_file_path}")
+        
+        file_obj = None
+        try:
+            # 构建远程完整路径
+            full_remote_path = self.build_full_path(share_name, remote_file_path)
+            
+            # 检查文件是否已存在
+            if not overwrite:
+                try:
+                    stat(full_remote_path)
+                    print(f"文件已存在，跳过: {remote_file_path}")
+                    return True
+                except FileNotFoundError:
+                    # 文件不存在，继续上传
+                    pass
+            
+            # 以二进制模式读取本地文件
+            with open(local_file_path, 'rb') as local_file:
+                content = b""
+                
+                # 分块读取文件内容
+                while True:
+                    chunk = local_file.read(8192)  # 8KB 块
+                    if not chunk:
+                        break
+                    content += chunk
+            
+            print(f"成功读取 {len(content)} 字节")
+            
+            if len(content) == 0:
+                print("文件为空")
+                return False
+                
+            # 写入远程文件
+            with open_file(full_remote_path, mode='wb') as remote_file:
+                remote_file.write(content)
+                
+            print(f"文件上传成功")
+            return True
+            
+        except Exception as e:
+            print(f"上传文件失败: {e}")
+            return False
+
+    def find_folders_by_name(self, share_path, folder_name, start_dir="", max_depth=10):
+        """专门查找文件夹"""
+        return self.find_items_by_name(
+            share_path=share_path,
+            target_name=folder_name,
+            item_type="folder",
+            start_dir=start_dir,
+            max_depth=max_depth
+        )
+
+    def find_files_by_name(self, share_path, file_name, start_dir="", max_depth=10):
+        """专门查找文件"""
+        return self.find_items_by_name(
+            share_path=share_path,
+            target_name=file_name,
+            item_type="file",
+            start_dir=start_dir,
+            max_depth=max_depth
+        )
+    
+    def find_items_by_name(self, share_path, target_name, item_type="both", start_dir="", max_depth=10):
+        """
+        递归查找指定名称的文件夹和/或文件
+        
+        Args:
+            share_path: 共享名称
+            target_name: 目标名称（支持通配符 * 和 ?）
+            item_type: 查找类型 - "folder", "file", "both"
+            start_dir: 起始目录
+            max_depth: 最大搜索深度
+            
+        Returns:
+            list: 找到的完整路径列表
+        """
+        if not self.connect():
+            return []
+        
+        found_paths = []
+        start_path = self.build_full_path(share_path, start_dir)
+        
+        try:
+            self._search_recursive(
+                share_path=share_path,
+                current_path=start_path,
+                target_name=target_name,
+                item_type=item_type,
+                found_paths=found_paths,
+                current_depth=0,
+                max_depth=max_depth
+            )
+        except Exception as e:
+            print(f"搜索过程中出错: {e}")
+        
+        return found_paths
+
+    def _search_recursive(self, share_path, current_path, target_name, item_type, found_paths, current_depth, max_depth):
+        """递归搜索文件夹和文件"""
+        if current_depth > max_depth:
+            return
+        
+        try:
+            for entry in scandir(current_path):
+                try:
+                    # 检查文件夹
+                    if entry.is_dir():
+                        if self._is_match(entry.name, target_name) and item_type in ["both", "folder"]:
+                            found_paths.append(entry.path)
+                            print(f"找到目标文件夹: {entry.path}")
+                        
+                        # 递归搜索子目录
+                        self._search_recursive(
+                            share_path=share_path,
+                            current_path=entry.path,
+                            target_name=target_name,
+                            item_type=item_type,
+                            found_paths=found_paths,
+                            current_depth=current_depth + 1,
+                            max_depth=max_depth
+                        )
+                    
+                    # 检查文件
+                    elif entry.is_file():
+                        if self._is_match(entry.name, target_name) and item_type in ["both", "file"]:
+                            found_paths.append(entry.path)
+                            print(f"找到目标文件: {entry.path}")
+                            
+                except Exception as e:
+                    print(f"处理条目 {entry.path} 时出错: {e}")
+                    
+        except Exception as e:
+            print(f"搜索目录 {current_path} 时出错: {e}")
+
+    def _is_match(self, name, pattern):
+        """
+        检查名称是否匹配模式（支持简单通配符）
+        
+        Args:
+            name: 实际名称
+            pattern: 匹配模式（支持 * 和 ?）
+            
+        Returns:
+            bool: 是否匹配
+        """
+        # 如果没有通配符，直接比较
+        if '*' not in pattern and '?' not in pattern:
+            return name.lower() == pattern.lower()
+        
+        # 通配符匹配
+        import fnmatch
+        return fnmatch.fnmatch(name.lower(), pattern.lower())
+
+    def list_directory(self, share_path, dir, recursive=False, max_depth=3):
+        """列出目录内容"""
+        if not self.connect():
+            return []
+        
+        try:
+            full_path = f"\\\\{self.ip}\\{share_path}\\{dir}"
+            print(f"开始遍历: {full_path}")
+            result = []
+            self._walk_directory(full_path, recursive, max_depth, 0, result)
+            
+        except Exception as e:
+            print(f"遍历失败: {e}")
+
+        return result
+    
+    def _walk_directory(self, path, recursive, max_depth, current_depth, result):
+        """递归遍历目录"""
+        if current_depth > max_depth:
+            return
+            
+        try:
+            for entry in scandir(path):
+                try:
+                    file_stat = stat(entry.path)
+                    indent = "  " * current_depth
+                    # 创建条目信息字典
+                    item = {
+                        'name': entry.name,
+                        'path': entry.path,
+                        'depth': current_depth,
+                        'indent': indent,
+                        'is_dir': entry.is_dir(),
+                        'size': file_stat.st_size if not entry.is_dir() else 0,
+                        'modified_time': datetime.fromtimestamp(file_stat.st_mtime).strftime('%Y-%m-%d %H:%M:%S')
+                    }
+                    
+                    if entry.is_dir():
+                        # print(f"{indent}文件夹：{entry.name}/")
+                        result.append(item)
+                        if recursive and current_depth < max_depth:
+                            sub_items = self._walk_directory(
+                                entry.path, 
+                                recursive, 
+                                max_depth, 
+                                current_depth + 1
+                            )
+                            result.extend(sub_items)
+                    else:
+                        file_size = self._format_size(file_stat.st_size)
+                        mod_time = datetime.fromtimestamp(
+                            file_stat.st_mtime
+                        ).strftime('%Y-%m-%d %H:%M:%S')
+                        # print(f"{indent}文件：{entry.name} [{file_size}] [{mod_time}]")
+                        item['formatted_size'] = file_size
+                        result.append(item)
+
+                except Exception as e:
+                    print(f"{indent} 无法访问: {entry.name} - {e}")
+                    
+        except Exception as e:
+            print(f"无法读取目录 {path}: {e}")
+        
+        return result
+    
+    def _format_size(self, size_bytes):
+        """格式化文件大小"""
+        if size_bytes == 0:
+            return "0 B"
+        
+        size_names = ["B", "KB", "MB", "GB", "TB"]
+        i = 0
+        while size_bytes >= 1024 and i < len(size_names) - 1:
+            size_bytes /= 1024.0
+            i += 1
+            
+        return f"{size_bytes:.1f} {size_names[i]}"
+    
+    def get_file_info(self, share_path, file_path):
+        """获取文件详细信息"""
+        if not self.connect():
+            return None
+            
+        try:
+            full_path = f"\\\\{self.ip}\\{share_path}\\{file_path}"
+            file_stat = stat(full_path)
+            
+            return {
+                'name': os.path.basename(file_path),
+                'path': full_path,
+                'size': file_stat.st_size,
+                'size_formatted': self._format_size(file_stat.st_size),
+                'create_time': datetime.fromtimestamp(file_stat.st_ctime),
+                'modify_time': datetime.fromtimestamp(file_stat.st_mtime),
+                'access_time': datetime.fromtimestamp(file_stat.st_atime),
+                'is_dir': False  # 需要额外判断
+            }
+        except Exception as e:
+            print(f"获取文件信息失败: {e}")
+            return None
+    
+    def display_image(self, image, window_name="Image"):
+        """
+        显示图像
+        
+        Args:
+            image: OpenCV图像
+            window_name: 窗口名称
+        """
+        # 创建窗口
+        cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
+        
+        # 调整窗口大小适应屏幕
+        screen_width = 1920  # 可根据实际屏幕调整
+        screen_height = 1080
+        
+        img_height, img_width = image.shape[:2]
+        
+        # 计算缩放比例
+        scale = min(screen_width / img_width, screen_height / img_height, 1.0)
+        
+        if scale < 1.0:
+            new_width = int(img_width * scale)
+            new_height = int(img_height * scale)
+            image = cv2.resize(image, (new_width, new_height))
+            print(f"图像已缩放: {img_width}x{img_height} -> {new_width}x{new_height}")
+        
+        # 显示图像
+        cv2.imshow(window_name, image)
+        print("图像显示中... 按任意键关闭窗口")
+        
+        # 等待按键
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+        print("窗口已关闭")
+
+# 从传入的路径中提取ip，共享目录，目标访问目录
+def get_conf(zip_url, user_name, pwd) :
+    zip_url = zip_url.replace('\\\\', '/')
+    zip_url = zip_url.replace('\\', '/')
+    if zip_url.startswith("/"):
+        zip_url = zip_url.replace('/', '', 1)
+
+    parts = zip_url.split('/')
+    if len(parts) < 2 :
+        print(f"传入的共享目录格式错误: {zip_url}")
+        return "", "fail"
+    
+    dir = ''
+    if len(parts) > 2:
+        new_parts = parts[2:]
+        dir = '/'.join(new_parts)
+
+    # 配置信息
+    config = {
+        'ip': parts[0],
+        'username': user_name,
+        'password': pwd,
+        'domain': '',  # 工作组留空
+        'share': parts[1],
+        'dir': dir
+    }
+
+    return config
+
+
+def get_scanner(zip_url, user_name, pwd) :
+    config = get_conf(zip_url, user_name, pwd)
+    
+    # 创建扫描器
+    scanner = SMBScanner(
+        ip=config['ip'],
+        username=config['username'],
+        password=config['password'],
+        domain=config['domain']
+    )
+    return scanner
+
+# filename -> 桩号
+def get_pile_dict(dir,user_name,pwd) :
+    config = get_conf(dir, user_name, pwd)
+    scanner = get_scanner(dir, user_name=user_name, pwd=pwd)
+    found_paths = scanner.find_files_by_name(
+            share_path=config['share'],
+            file_name='fileindex.txt'
+        )
+    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}")
+    
+    lines = scanner.read_txt_by_line(full_path=found_paths[0])
+
+    pile_dict = {}
+    for i, line in enumerate(lines, 1):
+        parts = line.strip().split("->")
+        if len(parts)>=4:
+            pile_dict[parts[3]]=parts[1]  # filename -> 桩号
+    
+    return pile_dict
+
+def main():
+    # 配置信息
+    config = {
+        'ip': '192.168.110.114',
+        'username': 'administrator',
+        'password': 'abc@1234',
+        'domain': '',  # 工作组留空
+        'share': 'share_File',
+        'dir': '西南计算机'
+    }
+    
+    # 创建扫描器
+    scanner = SMBScanner(
+        ip=config['ip'],
+        username=config['username'],
+        password=config['password'],
+        domain=config['domain']
+    )
+    
+    # 遍历共享目录
+    # scanner.list_directory(
+    #     share_path=config['share'],
+    #     dir=config['dir'],
+    #     recursive=True,      # 递归遍历
+    #     max_depth=9         # 最大深度
+    # )
+
+    # 读取文件
+    # full_path = scanner.build_full_path(
+    #     share_path=config['share'],
+    #     file_path= f"{config['dir']}/AA县/报送数据/图像类/CD45500155A/Images/20250508131651/01/20250508-131712-644.jpg"
+    # )
+    # image = scanner.read_img_file(full_path=full_path)
+
+    # scanner.display_image(image)
+
+    # # 写入文件
+    # scanner.writeImageToFile(
+    #     share_path=config['share'], 
+    #     file_path= f"{config['dir']}/AA县/报送数据/图像类_识别/CD45500155A/Images/20250508131651/01/20250508-131712-644.jpg",
+    #     image=image
+    # )
+
+    # # 查找指定文件夹 报送数据
+    # found_paths = scanner.find_folders_by_name(
+    #     share_path=config['share'],
+    #     folder_name='报送数据'
+    # )
+    # print(f"\n找到 {len(found_paths)} 个 '报送数据' 文件夹:")
+    # for i, path in enumerate(found_paths, 1):
+    #     print(f"{i}. {path}")
+
+
+    # # 查找指定目录中的所有图片
+    # full_path = scanner.build_full_path(share_path=config['share'], file_path='西南计算机\\AA县\\报送数据')
+    # imgPaths = scanner.get_smb_images(full_path)
+    # for i, path in enumerate(imgPaths, 1):
+    #     print(f"{i}. {path}")
+    
+    # # 读取excel
+    # full_path = scanner.build_full_path(share_path=config['share'], file_path='西南计算机\\AA县\\24年年报.xlsx')
+    # df = scanner.read_excel(full_path)
+    # scanner.process_all_rows(df)
+
+
+    # 读取txt
+    # found_paths = scanner.find_files_by_name(
+    #     share_path=config['share'],
+    #     file_name='fileindex.txt'
+    # )
+    # 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}")
+    
+    # lines = scanner.read_txt_by_line(full_path=found_paths[0])
+    # for i, line in enumerate(lines, 1):
+    #         print(f"{i}. {line}")
+
+    output_dir = "D:/devForBdzlWork/ai-train_platform/predictions"
+    scanner.upload_directory(output_dir, config['share'], remote_dir="西南计算机/AA县/报送数据_识别")
+
+
+if __name__ == "__main__":
+    main()

util/yolo2pix_new.py

@@ -0,0 +1,243 @@
+import os
+import zipfile
+import shutil
+import cv2
+import numpy as np
+from collections import defaultdict
+import smb
+
+# ---------------- 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
+}
+
+# ---------------- 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 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 ""
+
+
+# ---------------- 主函数-共享目录 ----------------
+def process_dir(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 = 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,"未知")
+                # 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(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}")
+
+# ---------------- 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[1]  # 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,"未知")
+                # 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}")
+
+# ---------------- 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/1"
+    pile_dict = smb.get_pile_dict("192.168.110.114/share_File/西南计算机", "administrator", "abc@1234")
+    process_dir(pile_dict, output_dir)

yolo_api.py

@@ -178,6 +178,7 @@ from pathlib import Path
 
 from download_train import download_train
 from predict.predict_yolo11seg import predict_images
+from predict.predict_yolo11seg import predict_images_share_dir
 from query_process_status import get_process_status
 
 
@@ -487,6 +488,52 @@ async def query_train_task(request: Request):
 
 
 
+# 接收前端实时流，进行任务推理-共享目录
+@app.post("/ai/project/inference4ShareDir")
+async def start_inference_share_dir(request):
+    try:
+        # 解析并验证请求数据
+        request_json = request.json
+        task_id = request_json["task_id"]
+        pt_name = request_json["pt_name"]
+        zip_url = request_json["zip_url"]
+        user_name = request_json["user_name"]
+        pwd = request_json["pwd"]
+        time_ns = time.time_ns()
+        # pt_name = f"{time_ns}-{task_id}.pt"
+        # model_path=r"pt_save\best.pt"
+        print(f"task_id  {task_id}")
+
+        if user_name == "":
+            user_name = "administrator"
+        if pwd == "":
+            pwd = "abc@1234"
+
+        output_dir = f"predictions/{task_id}"
+        inference_zip_url,message=predict_images_share_dir(pt_name, zip_url, user_name, pwd, output_dir=output_dir, conf_threshold=0.25, save_json=False)
+        if inference_zip_url:
+            return response.json({
+                "status": "success",
+                "task_id": task_id,
+                "inference_zip_url":inference_zip_url,
+                "message": "predict request successfully"
+            })
+        else:
+            return response.json({
+                "status": "fail",
+                "task_id": task_id,
+                "inference_zip_url":inference_zip_url,
+                "message": message
+            })
+    except ValueError as e:
+        print(f"Validation error: {str(e)}")
+        return response.json({"status": "error", "message": str(e)}, status=400)
+    except Exception as e:
+        print(f"Unexpected error: {str(e)}")
+        return response.json({"status": "error", "message": f"Internal server error: {str(e)}"}, status=500)
+
+
+
 # 接收前端实时流，进行任务推理
 @app.post("/ai/project/inference")
 async def start_inference(request):