Java验证码识别源代码实现
在网络爬虫、自动化测试等领域，经常需要识别网页上的验证码。本文将介绍使用Java语言实现的验证码识别源代码，以帮助开发者实现自动化验证码识别功能。
1. 获取验证码图片
首先，我们需要从网页中获取验证码图片。可以使用Java中的HttpClient库发送HTTP请求获取网页内容，并使用Jsoup库解析网页元素。
```java
import org.apache.http.client.methods.HttpGet;
import org.apache.http.impl.client.CloseableHttpClient;
import org.apache.http.impl.client.HttpClients;
import org.jsoup.Jsoup;
import org.jsoup.nodes.Document;
import org.jsoup.nodes.Element;
String url = "http://example.com"; // 网页URL
String imageUrl = ""; // 验证码图片URL
// 发送HTTP请求获取网页内容
CloseableHttpClient httpClient = HttpClients.createDefault();
HttpGet httpGet = new HttpGet(url);
CloseableHttpResponse response = httpClient.execute(httpGet);
HttpEntity entity = response.getEntity();
String htmlContent = EntityUtils.toString(entity);
// 解析网页元素，获取验证码图片URL
Document doc = Jsoup.parse(htmlContent);
Element imgElement = doc.select("img#captcha").first();
if (imgElement != null) {
imageUrl = imgElement.attr("src");
}
response.close();
httpClient.close();
```
2. 下载验证码图片
获取到验证码图片URL后，我们需要将其下载到本地进行后续处理。可以使用Java中的IO流读取远程图片，并保存到指定路径。
```java
URL imgUrl = new URL(imageUrl); // 验证码图片URL
String savePath = "captcha.png"; // 下载保存路径
// 下载验证码图片到本地
InputStream inputStream = imgUrl.openStream();
BufferedInputStream bufferedInputStream = new BufferedInputStream(inputStream);
OutputStream outputStream = new FileOutputStream(savePath);
byte[] buffer = new byte[1024];
int length;
while ((length = bufferedInputStream.read(buffer)) != -1) {
outputStream.write(buffer, 0, length);
}
outputStream.close();
bufferedInputStream.close();
inputStream.close();
```
3. 图片预处理
为了提高识别的准确率，我们需要对验证码图片进行预处理。可以使用Java中的第三方图像处理库，如OpenCV、JAI等。
```java
import org.opencv.core.Mat;
import org.opencv.core.CvType;
import org.opencv.core.Size;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfInt;
import org.opencv.core.Canny;
import org.opencv.core.MatOfPoint;
import org.opencv.core.Scalar;
import org.opencv.core.Point;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
String captchaPath = "captcha.png"; // 验证码图片路径
String processedPath = "processed.png"; // 预处理后图片路径
// 加载原始验证码图片
Mat src = Imgcodecs.imread(captchaPath);
// 转换为灰度图像
Mat gray = new Mat();
Imgproc.cvtColor(src, gray, Imgproc.COLOR_BGR2GRAY);
// 二值化处理
Mat binary = new Mat();
Imgproc.threshold(gray, binary, 0, 255, Imgproc.THRESH_BINARY_INV | Imgproc.THRESH_OTSU);
// 边缘检测
Mat edges = new Mat();
Imgproc.Canny(binary, edges, 50, 150);
// 填充内部空白
Mat closed = new Mat();
Mat kernel = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(5, 5));
Imgproc.morphologyEx(edges, closed, Imgproc.MORPH_CLOSE, kernel);
// 保存预处理后的图片
Imgcodecs.imwrite(processedPath, closed);
```
4. 验证码识别
经过预处理的验证码图片通常会更易于识别。可以使用Java中的第三方OCR库，如Tesseract、SikuliX等，进行验证码的文字识别。
```java
import net.sourceforge.tess4j.Tesseract;
import net.sourceforge.tess4j.TesseractException;
String processedPath = "processed.png"; // 预处理后图片路径
// 创建OCR引擎实例
Tesseract tesseract = new Tesseract();
// 设置语言库路径
tesseract.setDatapath("tessdata");
try {
// 识别验证码文字
String captchaText = tesseract.doOCR(new File(processedPath));
System.out.println("识别结果：" + captchaText);
} catch (TesseractException e) {
e.printStackTrace();
}
```
5. 完整代码示例
```java
import org.apache.http.client.methods.HttpGet;
import org.apache.http.impl.client.CloseableHttpClient;
import org.apache.http.impl.client.HttpClients;
import org.jsoup.Jsoup;
import org.jsoup.nodes.Document;
import org.jsoup.nodes.Element;
import net.sourceforge.tess4j.Tesseract;
import net.sourceforge.tess4j.TesseractException;
import org.opencv.core.Mat;
import org.opencv.core.CvType;
import org.opencv.core.Size;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfInt;
import org.opencv.core.Canny;
import org.opencv.core.MatOfPoint;
import org.opencv.core.Scalar;
import org.opencv.core.Point;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
public class CaptchaRecognition {
public static void main(String[] args) {
String url = "http://example.com"; // 网页URL
String imageUrl = ""; // 验证码图片URL
String captchaPath = "captcha.png"; // 验证码图片路径
String processedPath = "processed.png"; // 预处理后图片路径
try {
// 发送HTTP请求获取网页内容
CloseableHttpClient httpClient = HttpClients.createDefault();
HttpGet httpGet = new HttpGet(url);
CloseableHttpResponse response = httpClient.execute(httpGet);
HttpEntity entity = response.getEntity();
String htmlContent = EntityUtils.toString(entity);
// 解析网页元素，获取验证码图片URL
Document doc = Jsoup.parse(htmlContent);
Element imgElement = doc.select("img#captcha").first();
if (imgElement != null) {
imageUrl = imgElement.attr("src");
}
response.close();
httpClient.close();
// 下载验证码图片到本地
URL imgUrl = new URL(imageUrl);
InputStream inputStream = imgUrl.openStream();
BufferedInputStream bufferedInputStream = new BufferedInputStream(inputStream);
OutputStream outputStream = new FileOutputStream(captchaPath);
byte[] buffer = new byte[1024];
int length;
while ((length = bufferedInputStream.read(buffer)) != -1) {
outputStream.write(buffer, 0, length);
}
outputStream.close();
bufferedInputStream.close();
inputStream.close();
// 加载原始验证码图片
Mat src = Imgcodecs.imread(captchaPath);
// 转换为灰度图像
Mat gray = new Mat();
Imgproc.cvtColor(src, gray, Imgproc.COLOR_BGR2GRAY);
// 二值化处理
Mat binary = new Mat();
Imgproc.threshold(gray, binary, 0, 255, Imgproc.THRESH_BINARY_INV | Imgproc.THRESH_OTSU);
// 边缘检测
Mat edges = new Mat();
Imgproc.Canny(binary, edges, 50, 150);
// 填充内部空白
Mat closed = new Mat();
Mat kernel = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(5, 5));
Imgproc.morphologyEx(edges, closed, Imgproc.MORPH_CLOSE, kernel);
// 保存预处理后的图片
Imgcodecs.imwrite(processedPath, closed);
// 创建OCR引擎实例
Tesseract tesseract = new Tesseract();
// 设置语言库路径
tesseract.setDatapath("tessdata");
try {
// 识别验证码文字
String captchaText = tesseract.doOCR(new File(processedPath));
System.out.println("识别结果：" + captchaText);
} catch (TesseractException e) {
e.printStackTrace();
}
} catch (IOException e) {
e.printStackTrace();
}
}
}
```
以上就是使用Java语言实现验证码识别的源代码分享，通过获取验证码图片、下载验证码图片、图片预处理以及验证码识别等步骤，可以实现自动化验证码识别功能。开发者可以根据具体需求和验证码特点进行优
