u

code/email/Email-Springboot/src/main/java/com/email/Test.java

@@ -1,6 +1,46 @@
 package com.email;
 
+import com.email.core.fetch.EmailFetcher;
+import com.email.core.fetch.ImapJavaMailEmailFetcher;
+import com.email.domain.entity.UserAccount;
+
+import javax.mail.*;
+import javax.mail.Flags;
+import javax.mail.search.FlagTerm;
+import javax.net.ssl.SSLSocket;
+import javax.net.ssl.SSLSocketFactory;
+import java.io.*;
+import java.util.Properties;
 
 public class Test {
+        public static void main(String[] args) throws Exception {
+            UserAccount userAccount = new UserAccount();
+            userAccount.setEmail("717406575@qq.com");
+            userAccount.setPassword("dhqesrwzgblrbcac");
+            userAccount.setImapHost("imap.qq.com");
+            userAccount.setImapPort("993");
 
+            EmailFetcher emailFetcher = new ImapJavaMailEmailFetcher();
+
+            emailFetcher.fetchAll(
+                    userAccount,
+
+                    // progressConsumer: 打印进度
+                    progress -> System.out.printf("📩 [%s] 已拉取 %d / %d 封邮件%n",
+                            progress.getFolderName(),
+                            progress.getCurrent(),
+                            progress.getTotal()
+                    ),
+
+                    // folderConsumer: 开始处理某个文件夹
+                    folder -> {
+
+                    },
+
+                    // fetchDataConsumer: 每封邮件拉取完成后处理
+                    emailData -> {
+
+                    }
+            );
+        }
 }

code/email/Email-Springboot/src/main/java/com/email/client/EmailImapClient.java

@@ -35,7 +35,7 @@ public class EmailImapClient {
         props.put("mail.imaps.partialfetch", "false");
 
 
-// 在连接参数中强制指定ID信息
+    // 在连接参数中强制指定ID信息
         props.put("mail.imap.id.mechanism", "OAUTH2");
         props.put("mail.imap.id.name", "your_client_name");
         props.put("mail.imap.id.version", "1.0.0");

code/email/Email-Springboot/src/main/java/com/email/client/EmailImapClient2.java

@@ -0,0 +1,36 @@
+package com.email.client;
+
+import com.email.domain.entity.UserAccount;
+
+import javax.mail.Session;
+import java.util.HashMap;
+import java.util.Properties;
+
+public class EmailImapClient2 {
+    private  UserAccount userAccount;
+
+    public void connect(){
+        Properties props = new Properties();
+        props.put("mail.store.protocol", "imap");
+        props.put("mail.imap.host", userAccount.getImapHost());
+        props.put("mail.imap.port", userAccount.getImapPort());
+        props.put("mail.imap.ssl.enable", "true");
+        props.put("mail.imap.starttls.enable", "true");
+        props.put("mail.imap.partialfetch", "false");
+        props.put("mail.imaps.partialfetch", "false");
+
+
+        // 在连接参数中强制指定ID信息
+        props.put("mail.imap.id.mechanism", "OAUTH2");
+        props.put("mail.imap.id.name", userAccount.getDisplayName());
+        props.put("mail.imap.id.version", "1.0.0");
+
+        props.put("mail.imap.fetchsize", "4194304");
+        HashMap<String, String> imapId = new HashMap<>();
+        imapId.put("name", String.valueOf(userAccount.getId()));
+        imapId.put("version", "1.0.0");
+        imapId.put("vendor", "vendor");
+        imapId.put("support-email", userAccount.getEmail());
+        Session.getInstance(props);
+    }
+}

code/email/Email-Springboot/src/main/java/com/email/client/ImapUtf7.java

@@ -1,36 +0,0 @@
-package com.email.client;
-
-import java.nio.charset.Charset;
-import java.nio.charset.StandardCharsets;
-import java.util.Base64;
-
-public class ImapUtf7 {
-    public static String decode(String encoded) {
-        if (encoded == null) return null;
-
-        StringBuilder sb = new StringBuilder();
-        int i = 0;
-        while (i < encoded.length()) {
-            char ch = encoded.charAt(i);
-            if (ch == '&') {
-                int start = ++i;
-                while (i < encoded.length() && encoded.charAt(i) != '-') {
-                    i++;
-                }
-                if (i == start) {
-                    sb.append('&');
-                    i++; // 跳过 '-'
-                } else {
-                    String base64 = encoded.substring(start, i).replace(',', '/');
-                    byte[] utf16Bytes = Base64.getDecoder().decode(base64);
-                    sb.append(new String(utf16Bytes, StandardCharsets.UTF_16BE));
-                    i++; // 跳过 '-'
-                }
-            } else {
-                sb.append(ch);
-                i++;
-            }
-        }
-        return sb.toString();
-    }
-}

code/email/Email-Springboot/src/main/java/com/email/core/fetch/EmailFetcher.java

@@ -0,0 +1,15 @@
+package com.email.core.fetch;
+
+import com.email.core.fetch.model.EmailFetchData;
+import com.email.core.fetch.model.EmailFetchProgress;
+import com.email.domain.entity.EmailFolder;
+import com.email.domain.entity.UserAccount;
+
+import java.util.function.Consumer;
+
+public interface EmailFetcher {
+    void fetchAll(UserAccount userAccount,
+                  Consumer<EmailFetchProgress> emailFetchProgressConsumer,
+                  Consumer<EmailFolder> emailFolderConsumer,
+                  Consumer<EmailFetchData> emailFetchDataConsumer);
+}

code/email/Email-Springboot/src/main/java/com/email/core/fetch/ImapCommandEmailFetcher.java

@@ -0,0 +1,4 @@
+package com.email.core.fetch;
+
+public class ImapCommandEmailFetcher {
+}

code/email/Email-Springboot/src/main/java/com/email/core/fetch/ImapJavaMailEmailFetcher.java

@@ -0,0 +1,163 @@
+package com.email.core.fetch;
+
+import com.email.constants.ResponseStatusConstants;
+import com.email.core.fetch.model.EmailFetchData;
+import com.email.core.fetch.model.EmailFetchProgress;
+import com.email.domain.entity.EmailFolder;
+import com.email.domain.entity.UserAccount;
+import com.email.enums.ResponseEnum;
+import com.email.exception.auth.AuthException;
+import com.sun.javaws.progress.Progress;
+import com.sun.mail.imap.IMAPFolder;
+import lombok.extern.slf4j.Slf4j;
+
+import javax.mail.*;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.List;
+import java.util.Properties;
+import java.util.concurrent.CountDownLatch;
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+import java.util.function.Consumer;
+
+@Slf4j
+public class ImapJavaMailEmailFetcher implements EmailFetcher {
+
+    private final ExecutorService folderExecutor = Executors.newFixedThreadPool(10);
+
+
+    @Override
+    public void fetchAll(UserAccount userAccount, Consumer<EmailFetchProgress> progressConsumer, Consumer<EmailFolder> folderConsumer, Consumer<EmailFetchData> fetchDataConsumer) {
+        Store store = null;
+        try {
+            store = connect(userAccount); // connect 内部已处理异常
+
+            List<Folder> allFolders = listAllFolders(store.getDefaultFolder());
+
+            CountDownLatch folderLatch = new CountDownLatch(allFolders.size());
+
+            for (Folder folder : allFolders) {
+                folderExecutor.submit(() -> {
+                    try {
+                        processFolder(folder, progressConsumer, folderConsumer, fetchDataConsumer);
+                    } catch (Exception e) {
+                        log.error("处理文件夹 [{}] 失败：{}", folder.getFullName(), e.getMessage(), e);
+                    } finally {
+                        folderLatch.countDown();
+                    }
+                });
+            }
+
+            folderLatch.await(); // 等待所有文件夹处理完
+        }  catch (Exception e) {
+            throw new RuntimeException("抓取邮件失败：" + e.getMessage(), e);
+        } finally {
+            if (store != null && store.isConnected()) {
+                try {
+                    store.close();
+                } catch (MessagingException closeEx) {
+                    log.warn("关闭邮箱连接时异常：{}", closeEx.getMessage());
+                }
+            }
+        }
+    }
+
+
+
+    // 递归获取所有文件夹（包括子文件夹）
+    private List<Folder> listAllFolders(Folder folder) throws MessagingException {
+        List<Folder> allFolders = new ArrayList<>();
+
+        if ((folder.getType() & Folder.HOLDS_MESSAGES) != 0) {
+            allFolders.add(folder);
+        }
+
+        if ((folder.getType() & Folder.HOLDS_FOLDERS) != 0) {
+            Folder[] subFolders = folder.list();
+            for (Folder sub : subFolders) {
+                allFolders.addAll(listAllFolders(sub));
+            }
+        }
+        return allFolders;
+    }
+
+    private void processFolder(Folder folder,
+                             Consumer<EmailFetchProgress> progressConsumer,
+                             Consumer<EmailFolder> folderConsumer,
+                             Consumer<EmailFetchData> fetchDataConsumer) throws Exception {
+        if (!(folder instanceof IMAPFolder)) return;
+
+        folder.open(Folder.READ_ONLY);
+
+        int totalMessages = folder.getMessageCount();
+        if (totalMessages == 0) {
+            folder.close(false);
+            return;
+        }
+
+        EmailFolder emailFolder = new EmailFolder();
+
+        folderConsumer.accept(emailFolder);
+
+        FetchProfile fetchProfile = new FetchProfile();
+        fetchProfile.add(FetchProfile.Item.ENVELOPE);
+        fetchProfile.add(FetchProfile.Item.CONTENT_INFO);
+
+        Message[] messages = folder.getMessages();
+        folder.fetch(messages, fetchProfile);
+
+        for (int i = 0; i < messages.length; i++) {
+            Message message = messages[i];
+            try {
+                progressConsumer.accept(new EmailFetchProgress(folder.getFullName(), i + 1, totalMessages));
+            } catch (Exception e) {
+                log.warn("处理邮件失败 folder={} index={} error={}", folder.getFullName(), i, e.getMessage(), e);
+            }
+        }
+
+        folder.close(false);
+    }
+
+    private Store connect(UserAccount account) {
+        Properties props = new Properties();
+        props.put("mail.store.protocol", "imaps");
+        props.put("mail.imap.ssl.enable", "true");
+        props.put("mail.imap.connectiontimeout", "10000");
+        props.put("mail.imap.timeout", "10000");
+
+        try {
+            Session session = Session.getInstance(props);
+            Store store = session.getStore("imaps");
+
+            store.connect(
+                    account.getImapHost(),
+                    Integer.parseInt(account.getImapPort()),
+                    account.getEmail(),
+                    account.getPassword()
+            );
+
+            return store;
+        } catch (AuthenticationFailedException e) {
+            log.warn("邮箱认证失败：账号={}，原因={}", account.getEmail(), e.getMessage(), e);
+            throw new AuthException(ResponseEnum.AUTH_EMAIL_INVALID_CREDENTIALS);
+        } catch (NoSuchProviderException e) {
+            log.warn("邮箱协议不支持：账号={}，原因={}", account.getEmail(), e.getMessage(), e);
+            throw new AuthException(ResponseEnum.AUTH_EMAIL_PROTOCOL_UNSUPPORTED);
+        } catch (MessagingException e) {
+            log.warn("邮箱服务器连接失败：账号={}，Host={}，Port={}，SSL={}，原因={}",
+                    account.getEmail(),
+                    account.getImapHost(),
+                    account.getImapPort(),
+                    account.getImapSsl(),
+                    e.getMessage(),
+                    e
+            );
+            throw new AuthException(ResponseEnum.AUTH_EMAIL_CONNECTION_FAILED);
+        } catch (Exception e) {
+            log.error("邮箱连接发生未知错误：账号={}，原因={}", account.getEmail(), e.getMessage(), e);
+            throw new AuthException(ResponseEnum.AUTH_EMAIL_UNKNOWN_ERROR);
+        }
+    }
+
+}

code/email/Email-Springboot/src/main/java/com/email/core/fetch/model/EmailFetchData.java

@@ -0,0 +1,19 @@
+package com.email.core.fetch.model;
+
+import com.email.domain.entity.EmailAddress;
+import com.email.domain.entity.EmailFile;
+import com.email.domain.entity.EmailSummary;
+import lombok.AllArgsConstructor;
+import lombok.Data;
+import lombok.NoArgsConstructor;
+
+import java.util.List;
+
+@Data
+@AllArgsConstructor
+@NoArgsConstructor
+public class EmailFetchData {
+    private EmailSummary emailSummary;
+    private List<EmailAddress> emailAddressList;
+    private List<EmailFile> emailFileList;
+}

code/email/Email-Springboot/src/main/java/com/email/core/fetch/model/EmailFetchProgress.java

@@ -0,0 +1,15 @@
+package com.email.core.fetch.model;
+
+import lombok.AllArgsConstructor;
+import lombok.Data;
+import lombok.NoArgsConstructor;
+
+@Data
+@AllArgsConstructor
+@NoArgsConstructor
+public class EmailFetchProgress {
+    private String folderName; // 文件夹名称
+
+    private Integer current; // 当前页
+    private Integer total; // 总数
+}

code/email/Email-Springboot/src/main/java/com/email/core/send/EmailSender.java

@@ -0,0 +1,4 @@
+package com.email.core.send;
+
+public interface EmailSender {
+}

code/email/Email-Springboot/src/main/java/com/email/core/send/SmtpCommandEmailSender.java

@@ -0,0 +1,4 @@
+package com.email.core.send;
+
+public class SmtpCommandEmailSender {
+}

code/email/Email-Springboot/src/main/java/com/email/core/send/SmtpJavaMailEmailSender.java

@@ -0,0 +1,4 @@
+package com.email.core.send;
+
+public class SmtpJavaMailEmailSender {
+}

code/email/Email-Springboot/src/main/java/com/email/domain/dto/EmailSentDTO.java

@@ -19,7 +19,7 @@ public class EmailSentDTO {
 
     private String body;                       // 邮件正文内容
     private String from;              // 发件人
-    private String fromName;              // 发件人
+    private String fromName;              // 发件人名称
 
     private Integer isSingSent;         // 是否群发单显（1 是，0 否）
     private Integer isHtml;

code/email/Email-Springboot/src/main/java/com/email/domain/entity/EmailFile.java

@@ -15,14 +15,14 @@ import lombok.experimental.Accessors;
 @NoArgsConstructor
 @TableName("email_file")
 @Accessors(chain = true)
-public class   EmailFile {
+public class EmailFile {
     @TableId
     @JsonSerialize(using = ToStringSerializer.class)
     private Long id;                    // 主键ID
     private String fileName;            // 文件名（不含路径）
     private String fileExtension;       // 文件扩展名，例如：jpg、pdf
     private Long fileSize;              // 文件大小，单位字节
-    private String filePath;            // 文件路径（建议存相对路径）
+    private String filePath;            // 文件路径（相对路径）
     private Integer fileType;           // 文件类型：0-临时，1-附件，2-内嵌资源，3-模板等
 
     private Long emailId;               // 关联的邮件ID
@@ -31,4 +31,3 @@ public class   EmailFile {
     @TableField(exist = false)
     private String fileUrl;
 }
-

code/email/Email-Springboot/src/main/java/com/email/enums/ResponseEnum.java

@@ -18,7 +18,13 @@ public enum ResponseEnum {
     AUTH_SMTP_PROTOCOL_ERROR(ResponseStatusConstants.UNAUTHORIZED, "SMTP 协议不支持或配置错误"),
     AUTH_IMAP_PROTOCOL_ERROR(ResponseStatusConstants.UNAUTHORIZED, "IMAP 协议不支持或配置错误"),
     AUTH_SERVER_CONNECTION_FAILED(ResponseStatusConstants.UNAUTHORIZED, "邮箱服务器连接失败，请检查是否已经开通 IMAP/SMTP 协议"),
-    AUTH_UNKNOWN_ERROR(ResponseStatusConstants.UNAUTHORIZED,"邮箱服务器连接失败，请重新登录");
+    AUTH_UNKNOWN_ERROR(ResponseStatusConstants.UNAUTHORIZED,"邮箱服务器连接失败，请重新登录"),
+
+    AUTH_EMAIL_INVALID_CREDENTIALS(ResponseStatusConstants.UNAUTHORIZED,"邮箱账户或密码错误"),
+    AUTH_EMAIL_PROTOCOL_UNSUPPORTED(ResponseStatusConstants.UNAUTHORIZED,"邮箱协议不支持"),
+    AUTH_EMAIL_CONNECTION_FAILED(ResponseStatusConstants.UNAUTHORIZED,"邮箱服务器连接失败，请检查配置"),
+    AUTH_EMAIL_UNKNOWN_ERROR(ResponseStatusConstants.UNAUTHORIZED,"连接邮箱时发生未知错误");
+
 
     private final Integer code;   //响应码
     private final String message; //相应信息

notes/后端开发/python/集装箱号识别.md

@@ -1,663 +0,0 @@
-# 识别结果
-
-参数基本采用默认参数，都训练 **10轮** 。
-
-
-
-
-
-## PP-OCRv5_server (极好)
-
-识别效果：**极好** ，基本都是别出来了，并且正确。
-
-![image-20250630141231138](./assets/%E9%9B%86%E8%A3%85%E7%AE%B1%E5%8F%B7%E8%AF%86%E5%88%AB/image-20250630141231138.png)
-
-### PP-OCRv5_server_det.yml
-
-```yaml
-Global:
-  model_name: PP-OCRv5_server_det # To use static model for inference.
-  debug: false
-  use_gpu: true
-  epoch_num: &epoch_num 10
-  log_smooth_window: 20
-  print_batch_step: 10
-  save_model_dir: ./output-v5server/PP-OCRv5_server_det
-  save_epoch_step: 200
-  eval_batch_step:
-  - 0
-  - 200
-  cal_metric_during_train: false
-  checkpoints:
-  pretrained_model: ./pretrain_models/PP-OCRv5_server_det_pretrained.pdparams
-  save_inference_dir: null
-  use_visualdl: false
-  infer_img: doc/imgs_en/img_10.jpg
-  save_res_path: ./checkpoints/det_db/predicts_db.txt
-  distributed: false
-
-Architecture:
-  model_type: det
-  algorithm: DB
-  Transform: null
-  Backbone:
-    name: PPHGNetV2_B4
-    det: True
-  Neck:
-    name: LKPAN
-    out_channels: 256
-    intracl: true
-  Head:
-    name: PFHeadLocal
-    k: 50
-    mode: "large"
-    
-
-Loss:
-  name: DBLoss
-  balance_loss: true
-  main_loss_type: DiceLoss
-  alpha: 5
-  beta: 10
-  ohem_ratio: 3
-
-Optimizer:
-  name: Adam
-  beta1: 0.9
-  beta2: 0.999
-  lr:
-    name: Cosine
-    learning_rate: 0.001 #(8*8c)
-    warmup_epoch: 2
-  regularizer:
-    name: L2
-    factor: 1e-6
-
-PostProcess:
-  name: DBPostProcess
-  thresh: 0.3
-  box_thresh: 0.6
-  max_candidates: 1000
-  unclip_ratio: 1.5
-
-Metric:
-  name: DetMetric
-  main_indicator: hmean
-
-Train:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./data/input0/images #图片文件夹路径
-    label_file_list:
-      - ./det_train_label.txt #标签路径
-    ratio_list: [1.0]
-    transforms:
-    - DecodeImage:
-        img_mode: BGR
-        channel_first: false
-    - DetLabelEncode: null
-    - CopyPaste: null
-    - IaaAugment:
-        augmenter_args:
-        - type: Fliplr
-          args:
-            p: 0.5
-        - type: Affine
-          args:
-            rotate:
-            - -10
-            - 10
-        - type: Resize
-          args:
-            size:
-            - 0.5
-            - 3
-    - EastRandomCropData:
-        size:
-        - 640
-        - 640
-        max_tries: 50
-        keep_ratio: true
-    - MakeBorderMap:
-        shrink_ratio: 0.4
-        thresh_min: 0.3
-        thresh_max: 0.7
-        total_epoch: *epoch_num
-    - MakeShrinkMap:
-        shrink_ratio: 0.4
-        min_text_size: 8
-        total_epoch: *epoch_num
-    - NormalizeImage:
-        scale: 1./255.
-        mean:
-        - 0.485
-        - 0.456
-        - 0.406
-        std:
-        - 0.229
-        - 0.224
-        - 0.225
-        order: hwc
-    - ToCHWImage: null
-    - KeepKeys:
-        keep_keys:
-        - image
-        - threshold_map
-        - threshold_mask
-        - shrink_map
-        - shrink_mask
-  loader:
-    shuffle: true
-    drop_last: false
-    batch_size_per_card: 10
-    num_workers: 1
-
-Eval:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./data/input0/images
-    label_file_list:
-      - ./det_eval_label.txt
-    transforms:
-    - DecodeImage:
-        img_mode: BGR
-        channel_first: false
-    - DetLabelEncode: null
-    - DetResizeForTest:
-    - NormalizeImage:
-        scale: 1./255.
-        mean:
-        - 0.485
-        - 0.456
-        - 0.406
-        std:
-        - 0.229
-        - 0.224
-        - 0.225
-        order: hwc
-    - ToCHWImage: null
-    - KeepKeys:
-        keep_keys:
-        - image
-        - shape
-        - polys
-        - ignore_tags
-  loader:
-    shuffle: false
-    drop_last: false
-    batch_size_per_card: 1
-    num_workers: 1
-profiler_options: null
-```
-
-### PP-OCRv5_server_rec.yml
-
-```yaml
-Global:
-  model_name: PP-OCRv5_server_rec # To use static model for inference.
-  debug: false
-  use_gpu: true
-  epoch_num: 10
-  log_smooth_window: 20
-  print_batch_step: 10
-  save_model_dir: ./output-v5server/PP-OCRv5_server_rec
-  save_epoch_step: 200
-  eval_batch_step: [0, 200]
-  cal_metric_during_train: true
-  calc_epoch_interval: 1
-  pretrained_model: ./pretrain_models/PP-OCRv5_server_rec_pretrained.pdparams
-  checkpoints:
-  save_inference_dir:
-  use_visualdl: false
-  infer_img: doc/imgs_words/ch/word_1.jpg
-  character_dict_path: ./ppocr/utils/dict/ppocrv5_dict.txt
-  max_text_length: &max_text_length 25
-  infer_mode: false
-  use_space_char: true
-  distributed: true
-  save_res_path: ./output-v5server/rec/predicts_ppocrv5.txt
-  d2s_train_image_shape: [3, 48, 320]
-
-
-Optimizer:
-  name: Adam
-  beta1: 0.9
-  beta2: 0.999
-  lr:
-    name: Cosine
-    learning_rate: 0.0005
-    warmup_epoch: 1
-  regularizer:
-    name: L2
-    factor: 3.0e-05
-
-
-Architecture:
-  model_type: rec
-  algorithm: SVTR_HGNet
-  Transform:
-  Backbone:
-    name: PPHGNetV2_B4
-    text_rec: True
-  Head:
-    name: MultiHead
-    head_list:
-      - CTCHead:
-          Neck:
-            name: svtr
-            dims: 120
-            depth: 2
-            hidden_dims: 120
-            kernel_size: [1, 3]
-            use_guide: True
-          Head:
-            fc_decay: 0.00001
-      - NRTRHead:
-          nrtr_dim: 384
-          max_text_length: *max_text_length
-
-Loss:
-  name: MultiLoss
-  loss_config_list:
-    - CTCLoss:
-    - NRTRLoss:
-
-PostProcess:  
-  name: CTCLabelDecode
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-
-Train:
-  dataset:
-    name: MultiScaleDataSet
-    ds_width: false
-    data_dir: ./RecTrainData/
-    ext_op_transform_idx: 1
-    label_file_list:
-    - ./rec_train_label.txt
-    transforms:
-    - DecodeImage:
-        img_mode: BGR
-        channel_first: false
-    - RecAug:
-    - MultiLabelEncode:
-        gtc_encode: NRTRLabelEncode
-    - KeepKeys:
-        keep_keys:
-        - image
-        - label_ctc
-        - label_gtc
-        - length
-        - valid_ratio
-  sampler:
-    name: MultiScaleSampler
-    scales: [[320, 32], [320, 48], [320, 64]]
-    first_bs: &bs 12
-    fix_bs: false
-    divided_factor: [8, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: true
-    batch_size_per_card: 10
-    drop_last: true
-    num_workers: 1
-Eval:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./RecEvalData/
-    label_file_list:
-    - ./rec_eval_label.txt
-    transforms:
-    - DecodeImage:
-        img_mode: BGR
-        channel_first: false
-    - MultiLabelEncode:
-        gtc_encode: NRTRLabelEncode
-    - RecResizeImg:
-        image_shape: [3, 48, 320]
-    - KeepKeys:
-        keep_keys:
-        - image
-        - label_ctc
-        - label_gtc
-        - length
-        - valid_ratio
-  loader:
-    shuffle: false
-    drop_last: false
-    batch_size_per_card: 1
-    num_workers: 1
-```
-
-
-
-## PP-OCRv5_mobile
-
-### PP-OCRv5_mobile_det.yml
-
-```yaml
-Global:
-  model_name: PP-OCRv5_mobile_det # To use static model for inference.
-  debug: false
-  use_gpu: true
-  epoch_num: &epoch_num 100
-  log_smooth_window: 20
-  print_batch_step: 100
-  save_model_dir: ./output-v5mobile/PP-OCRv5_mobile_det
-  save_epoch_step: 200
-  eval_batch_step:
-  - 0
-  - 200
-  cal_metric_during_train: false
-  checkpoints:
-  pretrained_model: https://paddleocr.bj.bcebos.com/pretrained/PPLCNetV3_x0_75_ocr_det.pdparams
-  save_inference_dir: null
-  use_visualdl: false
-  infer_img: doc/imgs_en/img_10.jpg
-  save_res_path: ./checkpoints/det_db/predicts_db.txt
-  d2s_train_image_shape: [3, 640, 640]
-  distributed: true
-
-Architecture:
-  model_type: det
-  algorithm: DB
-  Transform: null
-  Backbone:
-    name: PPLCNetV3
-    scale: 0.75
-    det: True
-  Neck:
-    name: RSEFPN
-    out_channels: 96
-    shortcut: True
-  Head:
-    name: DBHead
-    k: 50
-    fix_nan: True
-
-Loss:
-  name: DBLoss
-  balance_loss: true
-  main_loss_type: DiceLoss
-  alpha: 5
-  beta: 10
-  ohem_ratio: 3
-
-Optimizer:
-  name: Adam
-  beta1: 0.9
-  beta2: 0.999
-  lr:
-    name: Cosine
-    learning_rate: 0.001 #(8*8c)
-    warmup_epoch: 2
-  regularizer:
-    name: L2
-    factor: 5.0e-05
-
-PostProcess:
-  name: DBPostProcess
-  thresh: 0.3
-  box_thresh: 0.6
-  max_candidates: 1000
-  unclip_ratio: 1.5
-
-Metric:
-  name: DetMetric
-  main_indicator: hmean
-
-Train:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./data/input0/images #图片文件夹路径
-    label_file_list:
-      - ./det_train_label.txt #标签路径
-    ratio_list: [1.0]
-    transforms:
-    - DecodeImage:
-        img_mode: BGR
-        channel_first: false
-    - DetLabelEncode: null
-    - CopyPaste: null
-    - IaaAugment:
-        augmenter_args:
-        - type: Fliplr
-          args:
-            p: 0.5
-        - type: Affine
-          args:
-            rotate:
-            - -10
-            - 10
-        - type: Resize
-          args:
-            size:
-            - 0.5
-            - 3
-    - EastRandomCropData:
-        size:
-        - 640
-        - 640
-        max_tries: 50
-        keep_ratio: true
-    - MakeBorderMap:
-        shrink_ratio: 0.4
-        thresh_min: 0.3
-        thresh_max: 0.7
-        total_epoch: *epoch_num
-    - MakeShrinkMap:
-        shrink_ratio: 0.4
-        min_text_size: 8
-        total_epoch: *epoch_num
-    - NormalizeImage:
-        scale: 1./255.
-        mean:
-        - 0.485
-        - 0.456
-        - 0.406
-        std:
-        - 0.229
-        - 0.224
-        - 0.225
-        order: hwc
-    - ToCHWImage: null
-    - KeepKeys:
-        keep_keys:
-        - image
-        - threshold_map
-        - threshold_mask
-        - shrink_map
-        - shrink_mask
-  loader:
-    shuffle: true
-    drop_last: false
-    batch_size_per_card: 14
-    num_workers: 1
-
-Eval:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./data/input0/images
-    label_file_list:
-      - ./det_eval_label.txt
-    transforms:
-    - DecodeImage:
-        img_mode: BGR
-        channel_first: false
-    - DetLabelEncode: null
-    - DetResizeForTest:
-    - NormalizeImage:
-        scale: 1./255.
-        mean:
-        - 0.485
-        - 0.456
-        - 0.406
-        std:
-        - 0.229
-        - 0.224
-        - 0.225
-        order: hwc
-    - ToCHWImage: null
-    - KeepKeys:
-        keep_keys:
-        - image
-        - shape
-        - polys
-        - ignore_tags
-  loader:
-    shuffle: false
-    drop_last: false
-    batch_size_per_card: 1
-    num_workers: 1
-profiler_options: null
-```
-
-
-
-### PP-OCRv5_mobile_rec.yml
-
-```yaml
-Global:
-  model_name: PP-OCRv5_mobile_rec # To use static model for inference.
-  debug: false
-  use_gpu: true
-  epoch_num: 75
-  log_smooth_window: 20
-  print_batch_step: 10
-  save_model_dir: ./output/PP-OCRv5_mobile_rec
-  save_epoch_step: 10
-  eval_batch_step: [0, 2000]
-  cal_metric_during_train: true
-  pretrained_model: https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_mobile_rec_pretrained.pdparams 
-  checkpoints:
-  save_inference_dir:
-  use_visualdl: false
-  infer_img: doc/imgs_words/ch/word_1.jpg
-  character_dict_path: ./ppocr/utils/dict/ppocrv5_dict.txt
-  max_text_length: &max_text_length 25
-  infer_mode: false
-  use_space_char: true
-  distributed: true
-  save_res_path: ./output/rec/predicts_ppocrv5.txt
-  d2s_train_image_shape: [3, 48, 320]
-
-
-Optimizer:
-  name: Adam
-  beta1: 0.9
-  beta2: 0.999
-  lr:
-    name: Cosine
-    learning_rate: 0.0005
-    warmup_epoch: 5
-  regularizer:
-    name: L2
-    factor: 3.0e-05
-
-
-Architecture:
-  model_type: rec
-  algorithm: SVTR_LCNet
-  Transform:
-  Backbone:
-    name: PPLCNetV3
-    scale: 0.95
-  Head:
-    name: MultiHead
-    head_list:
-      - CTCHead:
-          Neck:
-            name: svtr
-            dims: 120
-            depth: 2
-            hidden_dims: 120
-            kernel_size: [1, 3]
-            use_guide: True
-          Head:
-            fc_decay: 0.00001
-      - NRTRHead:
-          nrtr_dim: 384
-          max_text_length: *max_text_length
-
-Loss:
-  name: MultiLoss
-  loss_config_list:
-    - CTCLoss:
-    - NRTRLoss:
-
-PostProcess:  
-  name: CTCLabelDecode
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-
-Train:
-  dataset:
-    name: MultiScaleDataSet
-    ds_width: false
-    data_dir: ./RecTrainData/
-    ext_op_transform_idx: 1
-    label_file_list:
-    - ./rec_train_label.txt
-    transforms:
-    - DecodeImage:
-        img_mode: BGR
-        channel_first: false
-    - RecConAug:
-        prob: 0.5
-        ext_data_num: 2
-        image_shape: [48, 320, 3]
-        max_text_length: *max_text_length
-    - RecAug:
-    - MultiLabelEncode:
-        gtc_encode: NRTRLabelEncode
-    - KeepKeys:
-        keep_keys:
-        - image
-        - label_ctc
-        - label_gtc
-        - length
-        - valid_ratio
-  sampler:
-    name: MultiScaleSampler
-    scales: [[320, 32], [320, 48], [320, 64]]
-    first_bs: &bs 128
-    fix_bs: false
-    divided_factor: [8, 16] # w, h
-    is_training: True
-  loader:
-    shuffle: true
-    batch_size_per_card: 14
-    drop_last: true
-    num_workers: 1
-Eval:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./RecEvalData/
-    label_file_list:
-    - ./rec_eval_label.txt
-    transforms:
-    - DecodeImage:
-        img_mode: BGR
-        channel_first: false
-    - MultiLabelEncode:
-        gtc_encode: NRTRLabelEncode
-    - RecResizeImg:
-        image_shape: [3, 48, 320]
-    - KeepKeys:
-        keep_keys:
-        - image
-        - label_ctc
-        - label_gtc
-        - length
-        - valid_ratio
-  loader:
-    shuffle: false
-    drop_last: false
-    batch_size_per_card: 14
-    num_workers: 1
-```
-

notes/后端开发/深度学习/paddlepaddle/paddleocr.md

@@ -0,0 +1,193 @@
+注：这里以 **识别集装箱号** 为案例进行演示
+
+# 相关网站
+
+- paddlepaddle：https://www.paddlepaddle.org.cn/
+- github：https://github.com/PaddlePaddle/PaddleOCR/tree/main
+
+
+
+# 1 环境配置
+
+当前使用的是虚拟环境：
+
+- python：3.11
+- paddlepaddle：3.1
+- paddleocr：3.1.0
+
+## 1.1 安装paddlepaddle
+
+网站：https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/develop/install/pip/linux-pip.html
+
+需要注意自己的 CUDA 版本，不要过高即可，查看命令：
+
+```bash
+nvidia-smi 
+```
+
+输出如下：
+
+```
++-----------------------------------------------------------------------------+
+| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |
+|-------------------------------+----------------------+----------------------+
+| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
+|                               |                      |               MIG M. |
+|===============================+======================+======================|
+|   0  Tesla T4            On   | 00000000:00:09.0 Off |                    0 |
+| N/A   27C    P8     8W /  70W |      4MiB / 15360MiB |      0%      Default |
+|                               |                      |                  N/A |
++-------------------------------+----------------------+----------------------+
+                                                                               
++-----------------------------------------------------------------------------+
+| Processes:                                                                  |
+|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
+|        ID   ID                                                   Usage      |
+|=============================================================================|
+|  No running processes found                                                 |
++-----------------------------------------------------------------------------+
+```
+
+当前电脑的 CUDA 版本为 12.0，所以下载 小于等于 12.0版本的paddlepaddle 即可
+
+```bash
+python -m pip install paddlepaddle-gpu==3.1.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
+```
+
+检查是否安装成功：
+
+```bash
+python -c "import paddle; paddle.utils.run_check()"
+```
+
+输出以下内容即代表成功：
+
+```
+(ocr) root@VM-0-80-ubuntu:/workspace# python -c "import paddle; paddle.utils.run_check()"
+/root/miniforge3/envs/ocr/lib/python3.11/site-packages/paddle/utils/cpp_extension/extension_utils.py:715: UserWarning: No ccache found. Please be aware that recompiling all source files may be required. You can download and install ccache from: https://github.com/ccache/ccache/blob/master/doc/INSTALL.md
+  warnings.warn(warning_message)
+Running verify PaddlePaddle program ... 
+I0710 06:19:32.810492  2651 pir_interpreter.cc:1524] New Executor is Running ...
+W0710 06:19:32.813128  2651 gpu_resources.cc:114] Please NOTE: device: 0, GPU Compute Capability: 7.5, Driver API Version: 12.0, Runtime API Version: 11.7
+I0710 06:19:35.382279  2651 pir_interpreter.cc:1547] pir interpreter is running by multi-thread mode ...
+PaddlePaddle works well on 1 GPU.
+PaddlePaddle is installed successfully! Let's start deep learning with PaddlePaddle now.
+```
+
+## 1.2 下载paddleocr
+
+下载网站：https://github.com/PaddlePaddle/PaddleOCR/releases
+
+当前使用的版本是 3.1.0
+
+下载后解压
+
+```bash
+unzip PaddleOCR-3.1.0.zip 
+```
+
+安装依赖
+
+```bash
+cd PaddleOCR-3.1.0
+
+pip install -r requirements.txt  --user
+```
+
+测试是否能正常运行：
+
+```bash
+python tools/infer/predict_system.py --image_dir="/workspace/img/" --use_angle_cls=True --use_space_char=True
+```
+
+输出以下内容即成功：
+```
+ppocr INFO: not find det model file path None
+```
+
+
+
+# 2 准备数据集
+
+## 2.1 标注工具
+
+### 2.1.1 PPOcrLabel
+
+> 存在问题：EXE 安装包运行会报错。但是该工具在本地运行标注的话还是比较推荐的。
+
+下载地址：
+
+- 专门为 PPOCR 制作的标注工具
+
+
+
+### 2.1.2 X-AnyLabeling
+
+下载地址：
+
+- 支持多种导出格式，其中就支持 PPOCR 的格式
+
+
+
+##  2.2 数据集结构
+
+示例：
+
+```
+conno/1b4e2845-e9ba-4233-9268-bf07801ace0e.jpg	[{"transcription": "RKSU5020243", "points": [[233, 317], [299, 317], [258, 833], [181, 839]], "difficult": false}]
+conno/6ffd244b-1d27-4084-998b-e4d7ac076c5f.jpg	[{"transcription": "ZGXU6173701", "points": [[717, 448], [781, 419], [835, 980], [751, 996]], "difficult": false}]
+conno/0ba730a8-052b-4662-88fa-bfd3c7dbd118.jpg	[{"transcription": "WHLU5706937", "points": [[565, 567], [915, 485], [915, 551], [578, 635]], "difficult": false}]
+conno/8f50bf55-f825-43ac-87a5-f744aa3c2099.jpg	[{"transcription": "EISU9420010", "points": [[295, 744], [624, 747], [618, 813], [299, 806]], "difficult": false}]
+conno/33e22c52-64b6-4715-87d9-d3a7823b97f7.jpg	[{"transcription": "SSKU1302201", "points": [[660, 676], [960, 671], [966, 724], [662, 737]], "difficult": false}]
+conno/34a24249-7670-4be2-981f-19c502ee7da2.jpg	[{"transcription": "TKRU4507940", "points": [[660, 395], [724, 395], [717, 882], [619, 902]], "difficult": false}]
+conno/67ead2d0-89c0-455c-aef0-49a76a3492aa.jpg	[{"transcription": "待识别", "points": [[585, 415], [589, 415], [593, 501], [917, 341], [893, 263]], "difficult": false}]
+conno/78c58cfa-ff0f-4890-9fd9-07a0b639b9ca.jpg	[{"transcription": "LYGU3568255", "points": [[553, 393], [587, 491], [972, 334], [983, 245]], "difficult": false}]
+conno/481aee74-f9ce-4145-a8c7-f4c1f45fcc75.jpg	[{"transcription": "RKSU4005584", "points": [[56, 298], [19, 935], [101, 937], [115, 280]], "difficult": false}]
+conno/630d6e72-f99d-4922-8886-ea14f9e1a9bd.jpg	[{"transcription": "FCIU2682384", "points": [[562, 737], [572, 803], [867, 712], [858, 655]], "difficult": false}]
+conno/9751b358-0b48-4006-8a73-89e816d37b67.jpg	[{"transcription": "CNIU2452605", "points": [[608, 801], [606, 867], [908, 837], [906, 773]], "difficult": false}]
+```
+
+# 3 准备预训练模型
+
+> 如果已经有预训练模型的话，可以忽略这一步。
+
+下载地址：https://github.com/PaddlePaddle/PaddleOCR/blob/release/3.1/docs/version3.x/model_list.md
+
+如果报 404 的话，根据 版本 + 路径 进行查找即可。
+
+## 3.1 文本检测训练模型
+
+PP-OCRv5_mobile_det
+
+![image-20250710162314463](./assets/paddleocr/image-20250710162314463.png)
+
+## 3.2 文本识别训练模型
+
+PP-OCRv5_mobile_rec
+
+![image-20250710162351772](./assets/paddleocr/image-20250710162351772.png)
+
+# 4 数据整理
+
+## 4.1 检测模型所需数据准备
+
+
+
+# 报错处理
+
+##  ImportError: libGL.so.1: cannot open shared object file: No such file or directory
+
+### 问题原因
+
+- 缺少 `libGL.so.1` 库
+
+### 解决方案
+
+```bash
+sudo apt update
+sudo apt install libgl1-mesa-glx
+```
+
+
+

notes/软件工具/标注工具.md

@@ -1,189 +0,0 @@
-# 通用错误
-
-## Microsoft Visual C++ 14.0 or greater is required.
-
-### 1. 下载Microsoft C++ Build Tools
-打开网址：[Microsoft C++ Build Tools](https://visualstudio.microsoft.com/zh-hans/visual-cpp-build-tools/)
-
-点击下载【下载生成工具】，并打开。
-
-![在这里插入图片描述](./assets/%E6%A0%87%E6%B3%A8%E5%B7%A5%E5%85%B7/ccf75e7381f40c3d3c0438abf4b4bc83.png)
-
-接着在Workloads中，点击选择【C++ build tools】
-
-![在这里插入图片描述](./assets/%E6%A0%87%E6%B3%A8%E5%B7%A5%E5%85%B7/18e76d4498f8da5b0113fe89da2a3809.png)
-
-![在这里插入图片描述](./assets/%E6%A0%87%E6%B3%A8%E5%B7%A5%E5%85%B7/0cea5d0928af789071db2756e1651a25.png)
-
-并选择单个组件的C++工具开始下载。
-
-### 2. 重启
-显示下载完成后，重启机器即可。
-
-# [xclabel](https://gitee.com/Vanishi/xclabel)
-
-## 环境
-
-- python：3.10
-
-## 安装
-
-```bash
-git clone https://gitee.com/Vanishi/xclabel.git
-```
-
-创建虚拟环境
-
-```bash
-conda create -n xclabel python=3.10
-
-conda activate xclabel
-
-//更新pip
-python -m pip install --upgrade pip -i https://pypi.tuna.tsinghua.edu.cn/simple
-
-//安装requirements
-python -m pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
-```
-
-## 运行
-
-- 环境安装完成后，启动服务： python manage.py runserver 0.0.0.0:9924
-- 访问服务：在浏览器输入 [http://127.0.0.1:9924](https://gitee.com/link?target=http%3A%2F%2F127.0.0.1%3A9924) 就可以开始了，默认账号 admin admin888
-
-
-
-## 报错
-
-### FileNotFoundError: [WinError 3] 系统找不到指定的路径。: 'E:\\'
-
-修改 config.json 配置路径即可
-
-```json
-{
-  "host": "127.0.0.1",
-  "port": 9924,
-  "ffmpeg": "ffmpeg",
-  "storageDir": "D:\\project\\bxc\\gitee\\xclabel\\static\\storage",
-  "yolo8": {
-    "install_dir": "D:\\project\\ai\\yolov8",
-    "venv": "venv\\Scripts\\activate.bat",
-    "name": "yolo",
-    "model": "yolov8n.pt"
-  },
-  "yolo11": {
-    "install_dir": "D:\\project\\ai\\yolov11",
-    "venv": "venv\\Scripts\\activate.bat",
-    "name": "yolo",
-    "model": "yolo11n.pt"
-  }
-}
-```
-
-
-
-# [PPOCRLabel](https://github.com/PFCCLab/PPOCRLabel)
-
-## 环境
-
-- python：3.9
-- paddlepadle：3.0.0
-- ppocrlabel：3.1.1
-
-## 安装
-
-创建虚拟环境
-
-```bash
-conda create -n ppocrlabel python=3.9 
-conda activate ppocrlabel 
-```
-
-安装PaddlePaddle
-
-```bash
-pip3 install --upgrade pip
-
-python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
-```
-
-下载 [PPOCRLabel](https://github.com/PFCCLab/PPOCRLabel)
-
-```bash
-git clone https://github.com/PFCCLab/PPOCRLabel.git
-```
-
-安装相关依赖
-
-```bash
-pip install pyproject.toml
-
-
-pip install openpyxl 
-pip install opencv-python 
-pip install PyQt5  
-pip install pandas       
-pip install paddleocr     
-```
-
-
-
-## 启动
-
-```bash
-python PPOCRLabel.py --lang ch
-```
-
-出现以下页面即代表启动成功
-
-![image-20250702110516138](./assets/%E6%A0%87%E6%B3%A8%E5%B7%A5%E5%85%B7/image-20250702110516138.png)
-
-## 打包 exe
-
-```bash
-cd ./PPOCRLabel
-# 安装pyinstaller
-pip install pyinstaller
-
-# 重新生成资源
-pyrcc5 -o libs/resources.py resources.qrc
-
-# 打包可执行程序
-pyinstaller -c PPOCRLabel.py   --add-data "D:\\devtool/anaconda3\\envs\\ppocrlabel\\Lib\\site-packages\\paddlex;./paddlex" --collect-all paddleocr --collect-all pyclipper --collect-all imghdr --collect-all skimage --collect-all imgaug --collect-all scipy.io --collect-all lmdb --collect-all paddle --hidden-import=pyqt5  -p ./libs -p ./ -p ./data -p ./resources -F
-
-pyinstaller -F -w --clean --add-data "D:/devtool/anaconda3/envs/ppocrlabel/Lib/site-packages/paddlex;./paddlex" --collect-all paddleocr --collect-all pyclipper --collect-all imghdr --collect-all skimage --collect-all imgaug --collect-all scipy.io --collect-all lmdb --collect-all paddle --collect-submodules paddleocr --collect-submodules paddlex --collect-submodules paddlex.inference.pipelines --collect-submodules paddlex.utils --collect-submodules paddlex.visualize --collect-submodules paddlex.cv.models --hidden-import=pyqt5 --hidden-import=shapely --hidden-import=visualdl --hidden-import=skimage.metrics -p ./libs -p ./ -p ./data -p ./resources PPOCRLabel.py
-
-
-
-# 运行dist中的可执行程序，以windows为例
-PPOCRLabel.exe --lang ch
-```
-
-## 报错
-
-### DLL load failed while importing aggregations
-
-**conda虚拟环境下**报这个错误，解决方案
-
-```bash
-conda install pandas
-```
-
-验证
-
-```python
-import pandas as pd
-print(pd.__version__)
-df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
-print(df)
-```
-
-输出下面结果，代表成功
-
-```
-2.3.0
-   a  b
-0  1  3
-1  2  4
-```
-