一、OpenCV在Android中的集成與配置

1.1 OpenCV簡介

OpenCV（Open Source Computer Vision Library）是一個開源的計算機視覺和機器學習軟件庫，包含超過2500種優化算法，涵蓋圖像處理、模式識別、機器學習等眾多領域。在Android平臺上，OpenCV為開發者提供了強大的圖像處理能力，可以用于實現各種復雜的計算機視覺應用。

1.2 在Android Studio中集成OpenCV

1.2.1 通過Gradle依賴集成

最簡單的方式是通過Gradle添加OpenCV依賴：

dependencies {implementation 'org.opencv:opencv-android:4.5.5'
}

1.2.2 通過模塊方式集成

更靈活的方式是下載OpenCV Android SDK并將其作為模塊導入：

1. 從OpenCV官網下載Android SDK
2. 在Android Studio中選擇File -> New -> Import Module
3. 選擇OpenCV SDK中的java文件夾
4. 修改模塊的build.gradle文件，確保與主項目兼容

1.2.3 初始化OpenCV

在應用啟動時需要初始化OpenCV庫：

public class MainActivity extends AppCompatActivity {private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {@Overridepublic void onManagerConnected(int status) {if (status == LoaderCallbackInterface.SUCCESS) {Log.i("OpenCV", "OpenCV loaded successfully");} else {super.onManagerConnected(status);}}};@Overrideprotected void onResume() {super.onResume();if (!OpenCVLoader.initDebug()) {OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION_3_0_0, this, mLoaderCallback);} else {mLoaderCallback.onManagerConnected(LoaderCallbackInterface.SUCCESS);}}
}

1.3 OpenCV基礎類介紹

在Android中使用OpenCV主要涉及以下幾個核心類：

1. Mat：OpenCV的基礎矩陣類，用于存儲圖像數據
2. Bitmap：Android的位圖類，需要與Mat相互轉換
3. Imgproc：圖像處理類，包含各種圖像處理算法
4. Core：核心功能類，提供基本數學運算和矩陣操作
5. Feature2d：特征檢測與描述類

二、指定區域圖像抓取與對比

2.1 圖像抓取基礎

在Android中獲取圖像主要有以下幾種方式：

1. 從相機捕獲
2. 從圖庫選擇
3. 從資源文件加載
4. 從網絡下載

2.2 指定區域圖像抓取實現

2.2.1 從Bitmap中截取指定區域

public static Bitmap cropBitmap(Bitmap srcBitmap, Rect region) {if (srcBitmap == null || region == null) return null;try {// 確保區域在圖像范圍內int x = Math.max(0, region.left);int y = Math.max(0, region.top);int width = Math.min(srcBitmap.getWidth() - x, region.width());int height = Math.min(srcBitmap.getHeight() - y, region.height());return Bitmap.createBitmap(srcBitmap, x, y, width, height);} catch (Exception e) {e.printStackTrace();return null;}
}

2.2.2 使用OpenCV截取指定區域

public static Mat cropImage(Mat srcMat, Rect roi) {if (srcMat.empty() || roi == null) return null;try {// 確保ROI在圖像范圍內Rect adjustedRoi = new Rect(Math.max(0, roi.x),Math.max(0, roi.y),Math.min(srcMat.cols() - roi.x, roi.width),Math.min(srcMat.rows() - roi.y, roi.height));return new Mat(srcMat, adjustedRoi);} catch (Exception e) {e.printStackTrace();return new Mat();}
}

2.3 圖像對比技術

2.3.1 均方誤差(MSE)對比

public static double compareImagesMSE(Mat img1, Mat img2) {if (img1.rows() != img2.rows() || img1.cols() != img2.cols()) {throw new IllegalArgumentException("Images must have same dimensions");}Mat diff = new Mat();Core.absdiff(img1, img2, diff);diff.convertTo(diff, CvType.CV_32F);diff = diff.mul(diff);Scalar mse = Core.mean(diff);return (mse.val[0] + mse.val[1] + mse.val[2]) / 3;
}

2.3.2 結構相似性(SSIM)對比

public static double compareImagesSSIM(Mat img1, Mat img2) {// 轉換為灰度圖像Mat gray1 = new Mat();Mat gray2 = new Mat();Imgproc.cvtColor(img1, gray1, Imgproc.COLOR_BGR2GRAY);Imgproc.cvtColor(img2, gray2, Imgproc.COLOR_BGR2GRAY);// 參數設置final double C1 = 6.5025, C2 = 58.5225;int d = CvType.CV_32F;Mat I1 = new Mat(), I2 = new Mat();gray1.convertTo(I1, d);gray2.convertTo(I2, d);Mat I1_2 = I1.mul(I1);Mat I2_2 = I2.mul(I2);Mat I1_I2 = I1.mul(I2);// 計算均值Mat mu1 = new Mat(), mu2 = new Mat();Imgproc.GaussianBlur(I1, mu1, new Size(11, 11), 1.5);Imgproc.GaussianBlur(I2, mu2, new Size(11, 11), 1.5);Mat mu1_2 = mu1.mul(mu1);Mat mu2_2 = mu2.mul(mu2);Mat mu1_mu2 = mu1.mul(mu2);// 計算方差Mat sigma1_2 = new Mat(), sigma2_2 = new Mat(), sigma12 = new Mat();Imgproc.GaussianBlur(I1_2, sigma1_2, new Size(11, 11), 1.5);Core.subtract(sigma1_2, mu1_2, sigma1_2);Imgproc.GaussianBlur(I2_2, sigma2_2, new Size(11, 11), 1.5);Core.subtract(sigma2_2, mu2_2, sigma2_2);Imgproc.GaussianBlur(I1_I2, sigma12, new Size(11, 11), 1.5);Core.subtract(sigma12, mu1_mu2, sigma12);// 計算SSIMMat t1 = new Mat(), t2 = new Mat(), t3 = new Mat();Core.multiply(mu1_mu2, new Scalar(2), t1);Core.add(t1, new Scalar(C1), t1);Core.multiply(sigma12, new Scalar(2), t2);Core.add(t2, new Scalar(C2), t2);Core.add(mu1_2, mu2_2, t3);Core.add(t3, new Scalar(C1), t3);Mat ssim_map = new Mat();Core.multiply(t1, t2, ssim_map);Core.divide(ssim_map, t3, ssim_map);Scalar mssim = Core.mean(ssim_map);return mssim.val[0];
}

2.3.3 特征點匹配對比

public static double compareImagesFeatureMatching(Mat img1, Mat img2) {// 轉換為灰度圖像Mat gray1 = new Mat();Mat gray2 = new Mat();Imgproc.cvtColor(img1, gray1, Imgproc.COLOR_BGR2GRAY);Imgproc.cvtColor(img2, gray2, Imgproc.COLOR_BGR2GRAY);// 檢測ORB特征點ORB orb = ORB.create();MatOfKeyPoint keypoints1 = new MatOfKeyPoint();MatOfKeyPoint keypoints2 = new MatOfKeyPoint();Mat descriptors1 = new Mat();Mat descriptors2 = new Mat();orb.detectAndCompute(gray1, new Mat(), keypoints1, descriptors1);orb.detectAndCompute(gray2, new Mat(), keypoints2, descriptors2);// 使用BFMatcher進行匹配BFMatcher matcher = BFMatcher.create(BFMatcher.BRUTEFORCE_HAMMING);MatOfDMatch matches = new MatOfDMatch();matcher.match(descriptors1, descriptors2, matches);// 計算匹配質量List<DMatch> matchesList = matches.toList();double maxDist = 0;double minDist = 100;for (DMatch match : matchesList) {double dist = match.distance;if (dist < minDist) minDist = dist;if (dist > maxDist) maxDist = dist;}// 篩選好的匹配點List<DMatch> goodMatches = new ArrayList<>();for (DMatch match : matchesList) {if (match.distance <= Math.max(2 * minDist, 30.0)) {goodMatches.add(match);}}// 計算匹配率double matchRatio = (double)goodMatches.size() / matchesList.size();return matchRatio;
}

2.4 圖像對比應用實例

2.4.1 圖像相似度檢測

public class ImageComparator {private static final double MSE_THRESHOLD = 1000;private static final double SSIM_THRESHOLD = 0.8;private static final double FEATURE_MATCH_THRESHOLD = 0.5;public enum ComparisonResult {VERY_SIMILAR,SIMILAR,DIFFERENT,INVALID}public static ComparisonResult compareImages(Bitmap bmp1, Bitmap bmp2, Rect roi1, Rect roi2) {try {// 轉換Bitmap為MatMat mat1 = new Mat();Mat mat2 = new Mat();Utils.bitmapToMat(bmp1, mat1);Utils.bitmapToMat(bmp2, mat2);// 裁剪指定區域Mat cropped1 = cropImage(mat1, roi1);Mat cropped2 = cropImage(mat2, roi2);if (cropped1.empty() || cropped2.empty()) {return ComparisonResult.INVALID;}// 調整大小一致if (cropped1.size().width != cropped2.size().width || cropped1.size().height != cropped2.size().height) {Imgproc.resize(cropped2, cropped2, cropped1.size());}// 計算各種相似度指標double mse = compareImagesMSE(cropped1, cropped2);double ssim = compareImagesSSIM(cropped1, cropped2);double featureMatch = compareImagesFeatureMatching(cropped1, cropped2);// 綜合判斷if (mse < MSE_THRESHOLD && ssim > SSIM_THRESHOLD && featureMatch > FEATURE_MATCH_THRESHOLD) {return ComparisonResult.VERY_SIMILAR;} else if (ssim > SSIM_THRESHOLD || featureMatch > FEATURE_MATCH_THRESHOLD) {return ComparisonResult.SIMILAR;} else {return ComparisonResult.DIFFERENT;}} catch (Exception e) {e.printStackTrace();return ComparisonResult.INVALID;}}
}

2.4.2 圖像差異可視化

public static Bitmap visualizeImageDifference(Bitmap bmp1, Bitmap bmp2) {try {// 轉換Bitmap為MatMat mat1 = new Mat();Mat mat2 = new Mat();Utils.bitmapToMat(bmp1, mat1);Utils.bitmapToMat(bmp2, mat2);// 確保圖像大小一致if (mat1.size().width != mat2.size().width || mat1.size().height != mat2.size().height) {Imgproc.resize(mat2, mat2, mat1.size());}// 計算差異Mat diff = new Mat();Core.absdiff(mat1, mat2, diff);// 增強差異可視化Core.normalize(diff, diff, 0, 255, Core.NORM_MINMAX);Imgproc.cvtColor(diff, diff, Imgproc.COLOR_BGR2RGB);// 轉換回BitmapBitmap result = Bitmap.createBitmap(diff.cols(), diff.rows(), Bitmap.Config.ARGB_8888);Utils.matToBitmap(diff, result);return result;} catch (Exception e) {e.printStackTrace();return null;}
}

三、指定點顏色提取與對比

3.1 顏色空間基礎

OpenCV支持多種顏色空間，常用的有：

1. RGB/BGR：默認顏色空間
2. HSV/HSL：色調、飽和度、亮度/明度
3. Lab：感知均勻的顏色空間
4. YCrCb：亮度和色度分量
5. Grayscale：灰度圖像

3.2 指定點顏色提取

3.2.1 從Bitmap中獲取像素顏色

public static int getPixelColor(Bitmap bitmap, int x, int y) {if (bitmap == null || x < 0 || y < 0 || x >= bitmap.getWidth() || y >= bitmap.getHeight()) {return 0;}return bitmap.getPixel(x, y);
}public static int[] getPixelColorARGB(Bitmap bitmap, int x, int y) {int pixel = getPixelColor(bitmap, x, y);return new int[] {(pixel >> 24) & 0xff, // Alpha(pixel >> 16) & 0xff, // Red(pixel >> 8) & 0xff,  // Greenpixel & 0xff          // Blue};
}

3.2.2 使用OpenCV獲取像素顏色

public static double[] getPixelColor(Mat mat, int x, int y) {if (mat.empty() || x < 0 || y < 0 || x >= mat.cols() || y >= mat.rows()) {return new double[]{0, 0, 0};}double[] pixel = mat.get(y, x); // OpenCV中使用(row, col)順序return pixel;
}public static Scalar getPixelColorScalar(Mat mat, int x, int y) {double[] pixel = getPixelColor(mat, x, y);if (pixel == null || pixel.length < 3) {return new Scalar(0, 0, 0);}return new Scalar(pixel[0], pixel[1], pixel[2]);
}

3.3 顏色空間轉換

public static Mat convertColorSpace(Mat src, int conversionCode) {if (src.empty()) return new Mat();Mat dst = new Mat();try {Imgproc.cvtColor(src, dst, conversionCode);} catch (Exception e) {e.printStackTrace();}return dst;
}// 常用顏色空間轉換代碼
public static final int COLOR_BGR2RGB = Imgproc.COLOR_BGR2RGB;
public static final int COLOR_BGR2HSV = Imgproc.COLOR_BGR2HSV;
public static final int COLOR_BGR2Lab = Imgproc.COLOR_BGR2Lab;
public static final int COLOR_BGR2YCrCb = Imgproc.COLOR_BGR2YCrCb;
public static final int COLOR_BGR2GRAY = Imgproc.COLOR_BGR2GRAY;

3.4 顏色對比技術

3.4.1 歐氏距離顏色對比

public static double colorDistanceEuclidean(Scalar color1, Scalar color2) {double diffR = color1.val[0] - color2.val[0];double diffG = color1.val[1] - color2.val[1];double diffB = color1.val[2] - color2.val[2];return Math.sqrt(diffR * diffR + diffG * diffG + diffB * diffB);
}

3.4.2 CIEDE2000顏色差異算法

// 需要實現Lab顏色空間的Delta E計算
public static double colorDistanceCIEDE2000(Scalar lab1, Scalar lab2) {// 簡化的Delta E 2000計算double L1 = lab1.val[0];double a1 = lab1.val[1];double b1 = lab1.val[2];double L2 = lab2.val[0];double a2 = lab2.val[1];double b2 = lab2.val[2];double deltaL = L2 - L1;double meanL = (L1 + L2) / 2;double C1 = Math.sqrt(a1 * a1 + b1 * b1);double C2 = Math.sqrt(a2 * a2 + b2 * b2);double meanC = (C1 + C2) / 2;double G = 0.5 * (1 - Math.sqrt(Math.pow(meanC, 7) / (Math.pow(meanC, 7) + Math.pow(25, 7))));double a1Prime = a1 * (1 + G);double a2Prime = a2 * (1 + G);double C1Prime = Math.sqrt(a1Prime * a1Prime + b1 * b1);double C2Prime = Math.sqrt(a2Prime * a2Prime + b2 * b2);double meanCPrime = (C1Prime + C2Prime) / 2;double h1Prime = Math.toDegrees(Math.atan2(b1, a1Prime));if (h1Prime < 0) h1Prime += 360;double h2Prime = Math.toDegrees(Math.atan2(b2, a2Prime));if (h2Prime < 0) h2Prime += 360;double deltaHPrime;if (Math.abs(h1Prime - h2Prime) <= 180) {deltaHPrime = h2Prime - h1Prime;} else if (h2Prime <= h1Prime) {deltaHPrime = h2Prime - h1Prime + 360;} else {deltaHPrime = h2Prime - h1Prime - 360;}double deltaH = 2 * Math.sqrt(C1Prime * C2Prime) * Math.sin(Math.toRadians(deltaHPrime / 2));double meanHPrime;if (Math.abs(h1Prime - h2Prime) <= 180) {meanHPrime = (h1Prime + h2Prime) / 2;} else if (h1Prime + h2Prime < 360) {meanHPrime = (h1Prime + h2Prime + 360) / 2;} else {meanHPrime = (h1Prime + h2Prime - 360) / 2;}double T = 1 - 0.17 * Math.cos(Math.toRadians(meanHPrime - 30))+ 0.24 * Math.cos(Math.toRadians(2 * meanHPrime))+ 0.32 * Math.cos(Math.toRadians(3 * meanHPrime + 6))- 0.20 * Math.cos(Math.toRadians(4 * meanHPrime - 63));double SL = 1 + (0.015 * Math.pow(meanL - 50, 2)) / Math.sqrt(20 + Math.pow(meanL - 50, 2));double SC = 1 + 0.045 * meanCPrime;double SH = 1 + 0.015 * meanCPrime * T;double RT = -2 * Math.sqrt(Math.pow(meanCPrime, 7) / (Math.pow(meanCPrime, 7) + Math.pow(25, 7)))* Math.sin(Math.toRadians(60 * Math.exp(-Math.pow((meanHPrime - 275) / 25, 2))));double deltaE = Math.sqrt(Math.pow(deltaL / SL, 2)+ Math.pow(deltaHPrime / SC, 2)+ Math.pow(deltaH / SH, 2)+ RT * (deltaCPrime / SC) * (deltaH / SH));return deltaE;
}

3.4.3 顏色相似度判斷

public static boolean areColorsSimilar(Scalar color1, Scalar color2, double threshold, int colorSpace) {// 轉換為指定顏色空間Mat mat1 = new Mat(1, 1, CvType.CV_8UC3, color1);Mat mat2 = new Mat(1, 1, CvType.CV_8UC3, color2);if (colorSpace != Imgproc.COLOR_BGR2RGB) {Imgproc.cvtColor(mat1, mat1, colorSpace);Imgproc.cvtColor(mat2, mat2, colorSpace);}Scalar c1 = new Scalar(mat1.get(0, 0));Scalar c2 = new Scalar(mat2.get(0, 0));// 根據顏色空間選擇合適的比較方法if (colorSpace == Imgproc.COLOR_BGR2Lab) {double deltaE = colorDistanceCIEDE2000(c1, c2);return deltaE < threshold;} else {double distance = colorDistanceEuclidean(c1, c2);return distance < threshold;}
}

3.5 顏色對比應用實例

3.5.1 屏幕取色器實現

public class ColorPickerView extends View {private Bitmap mBitmap;private int mSelectedX = -1;private int mSelectedY = -1;private OnColorSelectedListener mListener;public interface OnColorSelectedListener {void onColorSelected(int color);}public ColorPickerView(Context context) {super(context);}public ColorPickerView(Context context, AttributeSet attrs) {super(context, attrs);}public void setBitmap(Bitmap bitmap) {mBitmap = bitmap;invalidate();}public void setOnColorSelectedListener(OnColorSelectedListener listener) {mListener = listener;}@Overrideprotected void onDraw(Canvas canvas) {super.onDraw(canvas);if (mBitmap != null) {// 繪制圖像Rect src = new Rect(0, 0, mBitmap.getWidth(), mBitmap.getHeight());Rect dst = new Rect(0, 0, getWidth(), getHeight());canvas.drawBitmap(mBitmap, src, dst, null);// 繪制選擇點標記if (mSelectedX >= 0 && mSelectedY >= 0) {Paint paint = new Paint();paint.setColor(Color.WHITE);paint.setStyle(Paint.Style.STROKE);paint.setStrokeWidth(3);canvas.drawCircle(mSelectedX, mSelectedY, 20, paint);// 獲取并顯示顏色值int color = getPixelColor(mBitmap, mSelectedX, mSelectedY);paint.setStyle(Paint.Style.FILL);paint.setColor(color);canvas.drawRect(getWidth() - 100, 20, getWidth() - 20, 100, paint);paint.setColor(Color.BLACK);paint.setTextSize(30);canvas.drawText(String.format("#%06X", (0xFFFFFF & color)), getWidth() - 180, 70, paint);}}}@Overridepublic boolean onTouchEvent(MotionEvent event) {if (mBitmap == null) return super.onTouchEvent(event);int x = (int)(event.getX() * mBitmap.getWidth() / getWidth());int y = (int)(event.getY() * mBitmap.getHeight() / getHeight());if (x >= 0 && x < mBitmap.getWidth() && y >= 0 && y < mBitmap.getHeight()) {mSelectedX = x;mSelectedY = y;if (mListener != null) {int color = getPixelColor(mBitmap, x, y);mListener.onColorSelected(color);}invalidate();return true;}return super.onTouchEvent(event);}private int getPixelColor(Bitmap bitmap, int x, int y) {if (x < 0 || y < 0 || x >= bitmap.getWidth() || y >= bitmap.getHeight()) {return Color.TRANSPARENT;}return bitmap.getPixel(x, y);}
}

3.5.2 顏色匹配檢測

public class ColorMatchDetector {private Scalar mTargetColor;private double mThreshold;private int mColorSpace;public ColorMatchDetector(Scalar targetColor, double threshold, int colorSpace) {mTargetColor = targetColor;mThreshold = threshold;mColorSpace = colorSpace;}public Mat findColorRegions(Mat inputImage) {// 轉換為目標顏色空間Mat converted = new Mat();Imgproc.cvtColor(inputImage, converted, mColorSpace);// 創建目標顏色矩陣Mat targetMat = new Mat(inputImage.size(), converted.type(), mTargetColor);// 計算差異Mat diff = new Mat();Core.absdiff(converted, targetMat, diff);// 分割通道List<Mat> channels = new ArrayList<>();Core.split(diff, channels);// 計算總差異Mat totalDiff = new Mat(channels.get(0).size(), CvType.CV_32F);for (Mat channel : channels) {Mat floatChannel = new Mat();channel.convertTo(floatChannel, CvType.CV_32F);Core.add(totalDiff, floatChannel.mul(floatChannel), totalDiff);}Core.sqrt(totalDiff, totalDiff);// 創建掩碼Mat mask = new Mat();Core.inRange(totalDiff, new Scalar(0), new Scalar(mThreshold), mask);// 清理小區域Mat kernel = Imgproc.getStructuringElement(Imgproc.MORPH_ELLIPSE, new Size(5, 5));Imgproc.morphologyEx(mask, mask, Imgproc.MORPH_OPEN, kernel);Imgproc.morphologyEx(mask, mask, Imgproc.MORPH_CLOSE, kernel);return mask;}public List<Rect> findColorContours(Mat inputImage) {Mat mask = findColorRegions(inputImage);// 查找輪廓List<MatOfPoint> contours = new ArrayList<>();Mat hierarchy = new Mat();Imgproc.findContours(mask, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);// 轉換為矩形List<Rect> rects = new ArrayList<>();for (MatOfPoint contour : contours) {Rect rect = Imgproc.boundingRect(contour);if (rect.area() > 100) { // 忽略小區域rects.add(rect);}}return rects;}
}

四、指定區域OCR內容提取

4.1 OCR技術簡介

OCR（Optical Character Recognition，光學字符識別）是將圖像中的文字轉換為可編輯文本的技術。在Android中實現OCR通常有以下幾種方式：

1. 使用Tesseract OCR引擎
2. 使用Google ML Kit文本識別API
3. 使用第三方OCR服務API

4.2 Tesseract OCR集成

4.2.1 添加Tesseract依賴

在build.gradle中添加：

dependencies {implementation 'com.rmtheis:tess-two:9.1.0'
}

4.2.2 初始化Tesseract

public class TessOCR {private TessBaseAPI mTess;public TessOCR(Context context, String language) {mTess = new TessBaseAPI();// 訓練數據路徑String datapath = context.getFilesDir() + "/tesseract/";File dir = new File(datapath + "tessdata/");if (!dir.exists()) {dir.mkdirs();}// 檢查訓練數據文件是否存在File tessdataFile = new File(datapath + "tessdata/" + language + ".traineddata");if (!tessdataFile.exists()) {try {// 從assets復制訓練數據InputStream in = context.getAssets().open("tessdata/" + language + ".traineddata");OutputStream out = new FileOutputStream(tessdataFile);byte[] buffer = new byte[1024];int read;while ((read = in.read(buffer)) != -1) {out.write(buffer, 0, read);}in.close();out.flush();out.close();} catch (IOException e) {e.printStackTrace();}}mTess.init(datapath, language);}public String recognizeText(Bitmap bitmap) {if (bitmap == null) return "";mTess.setImage(bitmap);return mTess.getUTF8Text();}public String recognizeText(Mat mat) {if (mat.empty()) return "";Bitmap bitmap = Bitmap.createBitmap(mat.cols(), mat.rows(), Bitmap.Config.ARGB_8888);Utils.matToBitmap(mat, bitmap);return recognizeText(bitmap);}public void destroy() {if (mTess != null) {mTess.end();}}
}

4.3 圖像預處理優化OCR結果

4.3.1 基本預處理流程

public static Mat prepareImageForOCR(Mat src) {if (src.empty()) return new Mat();Mat processed = new Mat();// 1. 轉換為灰度圖像Imgproc.cvtColor(src, processed, Imgproc.COLOR_BGR2GRAY);// 2. 應用自適應閾值Imgproc.adaptiveThreshold(processed, processed, 255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY, 11, 2);// 3. 降噪Imgproc.medianBlur(processed, processed, 3);// 4. 銳化Mat kernel = new Mat(3, 3, CvType.CV_32F) {{put(0, 0, 0);put(0, 1, -1);put(0, 2, 0);put(1, 0, -1);put(1, 1, 5);put(1, 2, -1);put(2, 0, 0);put(2, 1, -1);put(2, 2, 0);}};Imgproc.filter2D(processed, processed, -1, kernel);return processed;
}

4.3.2 高級預處理技術

public static Mat advancedOCRPreprocessing(Mat src) {if (src.empty()) return new Mat();Mat processed = new Mat();// 1. 轉換為灰度圖像Imgproc.cvtColor(src, processed, Imgproc.COLOR_BGR2GRAY);// 2. 應用CLAHE (對比度受限的自適應直方圖均衡化)CLAHE clahe = Imgproc.createCLAHE();clahe.setClipLimit(2);clahe.apply(processed, processed);// 3. 非局部均值去噪Photo.fastNlMeansDenoising(processed, processed, 10, 7, 21);// 4. 邊緣增強Mat edges = new Mat();Imgproc.Sobel(processed, edges, CvType.CV_8U, 1, 1);Core.add(processed, edges, processed);// 5. 局部自適應閾值Imgproc.adaptiveThreshold(processed, processed, 255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,Imgproc.THRESH_BINARY, 15, 5);// 6. 形態學操作去除小噪點Mat kernel = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(2, 2));Imgproc.morphologyEx(processed, processed, Imgproc.MORPH_OPEN, kernel);return processed;
}

4.4 指定區域OCR實現

4.4.1 從指定區域提取文本

public class RegionOCR {private TessOCR mTessOCR;public RegionOCR(Context context, String language) {mTessOCR = new TessOCR(context, language);}public String extractTextFromRegion(Bitmap srcBitmap, Rect region) {if (srcBitmap == null || region == null) return "";// 裁剪指定區域Bitmap cropped = Bitmap.createBitmap(srcBitmap, region.left, region.top,region.width(), region.height());// 預處理圖像Mat mat = new Mat();Utils.bitmapToMat(cropped, mat);mat = prepareImageForOCR(mat);// 執行OCRreturn mTessOCR.recognizeText(mat);}public String extractTextFromRegion(Mat srcMat, Rect region) {if (srcMat.empty() || region == null) return "";// 調整區域確保在圖像范圍內Rect adjusted = new Rect(Math.max(0, region.x),Math.max(0, region.y),Math.min(srcMat.cols() - region.x, region.width),Math.min(srcMat.rows() - region.y, region.height));// 裁剪指定區域Mat cropped = new Mat(srcMat, adjusted);// 預處理圖像Mat processed = prepareImageForOCR(cropped);// 執行OCRreturn mTessOCR.recognizeText(processed);}public void destroy() {if (mTessOCR != null) {mTessOCR.destroy();}}
}