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package com.crane.test;
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import okhttp3.*;
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import com.fasterxml.jackson.databind.ObjectMapper;
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import com.fasterxml.jackson.databind.JsonNode;
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import com.fasterxml.jackson.databind.node.ObjectNode;
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import java.io.File;
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import java.io.FileOutputStream;
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import java.io.IOException;
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import java.awt.*;
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import java.awt.image.BufferedImage;
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import java.io.*;
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import java.util.Base64;
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import javax.imageio.*;
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import javax.imageio.stream.ImageOutputStream;
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import com.fasterxml.jackson.databind.JsonNode;
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import com.fasterxml.jackson.databind.ObjectMapper;
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import com.fasterxml.jackson.databind.node.ObjectNode;
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import okhttp3.*;
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public class Test {
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public static byte[] compressImageToTargetSize(BufferedImage image, float initialQuality, int maxSizeKB) throws IOException {
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ByteArrayOutputStream baos = new ByteArrayOutputStream();
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float quality = initialQuality;
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int maxBytes = maxSizeKB * 1024;
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while (quality > 0.05f) {
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baos.reset();
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ImageWriter jpgWriter = ImageIO.getImageWritersByFormatName("jpg").next();
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ImageWriteParam jpgWriteParam = jpgWriter.getDefaultWriteParam();
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jpgWriteParam.setCompressionMode(ImageWriteParam.MODE_EXPLICIT);
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jpgWriteParam.setCompressionQuality(quality); // 0.0 ~ 1.0
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ImageOutputStream ios = ImageIO.createImageOutputStream(baos);
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jpgWriter.setOutput(ios);
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jpgWriter.write(null, new IIOImage(image, null, null), jpgWriteParam);
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jpgWriter.dispose();
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ios.close();
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if (baos.size() <= maxBytes) {
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break;
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}
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quality -= 0.05f;
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}
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return baos.toByteArray();
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}
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public static void saveCompressedImageToFile(byte[] imageBytes, String outputPath) throws IOException {
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try (FileOutputStream fos = new FileOutputStream(outputPath)) {
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fos.write(imageBytes);
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}
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System.out.println("✅ 压缩图像已保存到: " + outputPath);
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}
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public class Test{
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public static void main(String[] args) throws IOException {
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String API_URL = "http://localhost:8866/ocr";
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String imagePath = "D:\\workspace\\code\\crane\\crane-api\\crane-test\\src\\main\\java\\com\\crane\\test\\namorni-preprava-namorni-doprava-kamionova-doprava-kontejneru-2-1024x768.jpg";
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String compressedImagePath = "D:\\workspace\\code\\crane\\crane-api\\crane-test\\compressed.jpg";
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File file = new File(imagePath);
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long base64StartTime = System.nanoTime();
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byte[] fileContent = java.nio.file.Files.readAllBytes(file.toPath());
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String base64Image = Base64.getEncoder().encodeToString(fileContent);
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long base64EndTime = System.nanoTime();
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long durationMs1 = (base64EndTime - base64StartTime) / 1_000_000; // 毫秒
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// 1. 加载原始图像
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BufferedImage originalImage = ImageIO.read(new File(imagePath));
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System.out.println("图片转 BASE64 耗时: " + durationMs1 + " ms");
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BufferedImage resizedImage = originalImage; // 保持原图尺寸不变
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// 3. 压缩图片到小于 500KB
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byte[] compressedBytes = compressImageToTargetSize(resizedImage, 1f, 5000);
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System.out.println("压缩后图片大小: " + (compressedBytes.length / 1024) + " KB");
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// 👉 保存压缩图像到本地
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saveCompressedImageToFile(compressedBytes, compressedImagePath);
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// 4. 转为 Base64 编码
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String base64Image = Base64.getEncoder().encodeToString(compressedBytes);
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long base64EndTime = System.nanoTime();
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long durationMs1 = (base64EndTime - base64StartTime) / 1_000_000;
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System.out.println("图片压缩 + BASE64 编码耗时: " + durationMs1 + " ms");
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// 5. 构建请求
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ObjectMapper objectMapper = new ObjectMapper();
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ObjectNode payload = objectMapper.createObjectNode();
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payload.put("file", base64Image);
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payload.put("fileType", 1);
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OkHttpClient client = new OkHttpClient.Builder()
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.connectTimeout(300, java.util.concurrent.TimeUnit.SECONDS) // 连接超时，建议20-30秒
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.writeTimeout(300, java.util.concurrent.TimeUnit.SECONDS) // 写请求体超时
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.readTimeout(300, java.util.concurrent.TimeUnit.SECONDS) // 读取响应超时，OCR服务响应较慢可设置长点
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.connectTimeout(300, java.util.concurrent.TimeUnit.SECONDS)
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.writeTimeout(300, java.util.concurrent.TimeUnit.SECONDS)
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.readTimeout(300, java.util.concurrent.TimeUnit.SECONDS)
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.build();
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MediaType JSON = MediaType.get("application/json; charset=utf-8");
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@@ -44,26 +94,21 @@ public class Test{
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.build();
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long startTime = System.nanoTime();
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try (Response response = client.newCall(request).execute()) {
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long endTime = System.nanoTime();
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long durationMs = (endTime - startTime) / 1_000_000; // 毫秒
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long durationMs = (endTime - startTime) / 1_000_000;
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System.out.println("⏱️ 请求耗时: " + durationMs + " ms");
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if (response.isSuccessful()) {
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String responseBody = response.body().string();
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JsonNode root = objectMapper.readTree(responseBody);
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JsonNode result = root.get("result");
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JsonNode ocrResults = result.get("ocrResults");
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for (int i = 0; i < ocrResults.size(); i++) {
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JsonNode item = ocrResults.get(i);
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JsonNode prunedResult = item.get("prunedResult");
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System.out.println(prunedResult);
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// 获取 rec_texts 并打印
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JsonNode recTexts = prunedResult.get("rec_texts");
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if (recTexts != null && recTexts.isArray()) {
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System.out.println("📦 rec_texts:");
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# 相关网站
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- github：https://github.com/PaddlePaddle/PaddleNLP/tree/develop
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- 官网：
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# 报错
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## AttributeError: module 'h11' has no attribute 'Event'. Did you mean: '_events'?
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```bash
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pip install "h11==0.14.0" --force-reinstall
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```
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## ImportError: cannot import name 'download' from 'aistudio_sdk.hub'
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```bash
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pip install aistudio-sdk==0.2.6
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```
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## ValueError: Unknown argument: show_log
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```
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pip install paddleocr==2.10
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```
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@@ -29,7 +29,7 @@ PaddleX 3.0 是基于飞桨框架构建的低代码开发工具，它集成了
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# CPU 版本
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python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
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# GPU 版本，需显卡驱动程序版本 ≥450.80.02（Linux）或 ≥452.39（Windows）
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# GPU 版本，需显卡驱动程序版本 ≥450.80.02（Linux）或 ≥452.39（Windows）（当前使用的是这个版本测试的）
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python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
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# GPU 版本，需显卡驱动程序版本 ≥550.54.14（Linux）或 ≥550.54.14（Windows）
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+33
-1
@@ -149,9 +149,41 @@ pip install pyinstaller
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pyrcc5 -o libs/resources.py resources.qrc
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# 打包可执行程序
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pyinstaller -c PPOCRLabel.py --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
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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
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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
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# 运行dist中的可执行程序，以windows为例
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PPOCRLabel.exe --lang ch
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```
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## 报错
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### DLL load failed while importing aggregations
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**conda虚拟环境下**报这个错误，解决方案
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```bash
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conda install pandas
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```
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验证
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```python
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import pandas as pd
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print(pd.__version__)
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df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
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print(df)
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```
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输出下面结果，代表成功
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```
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2.3.0
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a b
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0 1 3
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1 2 4
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```
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