Android平台二維碼之生成，掃描 & 識別，android平台

Android平台二維碼之生成，掃描 & 識別，android平台

1.二維碼的前世今生

“二維條碼/二維碼（2-dimensional bar code）是用某種特定的幾何圖形按一定規律在平面（二維方向上）分布的黑白相間的圖形記錄數據符號信息的；在代碼編制上巧妙地利用構成計算機內部邏輯基礎的“0”、“1”比特流的概念，使用若干個與二進制相對應的幾何形體來表示文字數值信息，通過圖象輸入設備或光電掃描設備自動識讀以實現信息自動處理：它具有條碼技術的一些共性：每種碼制有其特定的字符集；每個字符占有一定的寬度；具有一定的校驗功能等。同時還具有對不同行的信息自動識別功能、及處理圖形旋轉變化點。 [1] ”

上面是百度百科的解釋。既然有二維碼，那麼肯定有一維碼。

一維碼。最為常見的就是食品 & 書本後面的條碼。

條碼起源與20世紀40年代，後來在1970年 UPC碼發明，並開始廣泛應用與食品包裝。

具體的介紹可以看百度百科 一維碼。

其實二維碼與一維碼本質上是類似的，就跟一維數組和二維數組一樣。

 

2.二維碼的java支持庫

為了讓java或者說android方便繼承條碼的功能，google就開發了一個zxing的庫：

https://github.com/zxing/zxing

 

3.生成二維碼

public class EncodeThread {

    public static void encode(final String url, final int width, final int height, final EncodeResult result) {

        if (result == null) {
            return;
        }

        if (TextUtils.isEmpty(url)) {
            result.onEncodeResult(null);
            return;
        }

        new Thread() {
            @Override
            public void run() {
                try {
                    MultiFormatWriter writer = new MultiFormatWriter();
                    Hashtable<EncodeHintType, String> hints = new Hashtable<>();
                    hints.put(EncodeHintType.CHARACTER_SET, "utf-8");
                    BitMatrix bitMatrix = writer.encode(url, BarcodeFormat.QR_CODE, width, height, hints);
                    Bitmap bitmap = parseBitMatrix(bitMatrix);
                    result.onEncodeResult(bitmap);
                    return;
                } catch (WriterException e) {
                    e.printStackTrace();
                }
                result.onEncodeResult(null);
            }
        }.start();

    }

    /**
     * 生成二維碼內容<br>
     *
     * @param matrix
     * @return
     */
    public static Bitmap parseBitMatrix(BitMatrix matrix) {
        final int QR_WIDTH = matrix.getWidth();
        final int QR_HEIGHT = matrix.getHeight();
        int[] pixels = new int[QR_WIDTH * QR_HEIGHT];
        //this we using qrcode algorithm
        for (int y = 0; y < QR_HEIGHT; y++) {
            for (int x = 0; x < QR_WIDTH; x++) {
                if (matrix.get(x, y)) {
                    pixels[y * QR_WIDTH + x] = 0xff000000;
                } else {
                    pixels[y * QR_WIDTH + x] = 0xffffffff;
                }
            }
        }
        Bitmap bitmap = Bitmap.createBitmap(QR_WIDTH, QR_HEIGHT, Bitmap.Config.ARGB_8888);
        bitmap.setPixels(pixels, 0, QR_WIDTH, 0, 0, QR_WIDTH, QR_HEIGHT);
        return bitmap;
    }

    public interface EncodeResult {
        void onEncodeResult(Bitmap bitmap);
    }
}

zxing 支持很多條碼格式：我們這裡使用QR_CODE碼。也就是我們常見的微信裡面的二維碼。

我們先來分析下這段代碼：

 MultiFormatWriter writer = new MultiFormatWriter();

這個是一個工具類，把所有支持的幾個write寫在裡面了。

public BitMatrix encode(String contents,
                          BarcodeFormat format,
                          int width, int height,
                          Map<EncodeHintType,?> hints) throws WriterException {

    Writer writer;
    switch (format) {
      case EAN_8:
        writer = new EAN8Writer();
        break;
      case UPC_E:
        writer = new UPCEWriter();
        break;
      case EAN_13:
        writer = new EAN13Writer();
        break;
      case UPC_A:
        writer = new UPCAWriter();
        break;
      case QR_CODE:
        writer = new QRCodeWriter();
        break;
      case CODE_39:
        writer = new Code39Writer();
        break;
      case CODE_93:
        writer = new Code93Writer();
        break;
      case CODE_128:
        writer = new Code128Writer();
        break;
      case ITF:
        writer = new ITFWriter();
        break;
      case PDF_417:
        writer = new PDF417Writer();
        break;
      case CODABAR:
        writer = new CodaBarWriter();
        break;
      case DATA_MATRIX:
        writer = new DataMatrixWriter();
        break;
      case AZTEC:
        writer = new AztecWriter();
        break;
      default:
        throw new IllegalArgumentException("No encoder available for format " + format);
    }
    return writer.encode(contents, format, width, height, hints);
  }

這是官方最新支持的格式，具體看引入的jar裡面支持的格式。

對與bitmatrix的結果，通過摸個算法，設置每個點白色，或者黑色。

最後創建一張二維碼的圖片。

4.識別二維碼

如何從一張圖片上面，識別二維碼呢：

public class ReDecodeThread { public static void encode(final Bitmap bitmap, final ReDecodeThreadResult listener) { if (listener == null) { return; } if (bitmap == null) { listener.onReDecodeResult(null); return; } new Thread() { @Override public void run() { try { MultiFormatReader multiFormatReader = new MultiFormatReader(); BitmapLuminanceSource source = new BitmapLuminanceSource(bitmap); BinaryBitmap bitmap1 = new BinaryBitmap(new HybridBinarizer(source)); Result result1 = multiFormatReader.decode(bitmap1); listener.onReDecodeResult(result1.getText()); return; } catch (NotFoundException e) { e.printStackTrace(); } listener.onReDecodeResult(null); } }.start(); } public interface ReDecodeThreadResult { void onReDecodeResult(String url); } } View Code

過程也是很簡單，使用MultiFormatReader來分析圖片，這裡不需要缺人圖片的條碼格式。

如果分析下源碼，就是依次使用每種格式的reader來分析，直到找到合適的為止。

當然回了能夠把Bitmap轉化成Bitmatrix，然後在分析。

public final class BitmapLuminanceSource extends LuminanceSource{ private final byte[] luminances; public BitmapLuminanceSource(String path) throws FileNotFoundException { this(loadBitmap(path)); } public BitmapLuminanceSource(Bitmap bitmap) { super(bitmap.getWidth(), bitmap.getHeight()); int width = bitmap.getWidth(); int height = bitmap.getHeight(); int[] pixels = new int[width * height]; bitmap.getPixels(pixels, 0, width, 0, 0, width, height); // In order to measure pure decoding speed, we convert the entire image // to a greyscale array // up front, which is the same as the Y channel of the // YUVLuminanceSource in the real app. luminances = new byte[width * height]; for (int y = 0; y < height; y++) { int offset = y * width; for (int x = 0; x < width; x++) { int pixel = pixels[offset + x]; int r = (pixel >> 16) & 0xff; int g = (pixel >> 8) & 0xff; int b = pixel & 0xff; if (r == g && g == b) { // Image is already greyscale, so pick any channel. luminances[offset + x] = (byte) r; } else { // Calculate luminance cheaply, favoring green. luminances[offset + x] = (byte) ((r + g + g + b) >> 2); } } } } @Override public byte[] getRow(int y, byte[] row) { if (y < 0 || y >= getHeight()) { throw new IllegalArgumentException("Requested row is outside the image: " + y); } int width = getWidth(); if (row == null || row.length < width) { row = new byte[width]; } System.arraycopy(luminances, y * width, row, 0, width); return row; } // Since this class does not support cropping, the underlying byte array // already contains // exactly what the caller is asking for, so give it to them without a copy. @Override public byte[] getMatrix() { return luminances; } private static Bitmap loadBitmap(String path) throws FileNotFoundException { Bitmap bitmap = BitmapFactory.decodeFile(path); if (bitmap == null) { throw new FileNotFoundException("Couldn't open " + path); } return bitmap; } } BitmapLuminanceSource

5.掃描二維碼

掃描二維碼，其實比上面只多了一步，就是把camera獲取的東西直接轉換，然後進行識別。

  public void requestPreviewFrame(Handler handler, int message) {
    if (camera != null && previewing) {
      previewCallback.setHandler(handler, message);
      if (useOneShotPreviewCallback) {
        camera.setOneShotPreviewCallback(previewCallback);
      } else {
        camera.setPreviewCallback(previewCallback);
      }
    }
  }

首先把camera預覽的數據放入previewCallback中。

final class PreviewCallback implements Camera.PreviewCallback

public void onPreviewFrame(byte[] data, Camera camera) {
    Point cameraResolution = configManager.getCameraResolution();
    if (!useOneShotPreviewCallback) {
      camera.setPreviewCallback(null);
    }
    if (previewHandler != null) {
      Message message = previewHandler.obtainMessage(previewMessage, cameraResolution.x,
          cameraResolution.y, data);
      message.sendToTarget();
      previewHandler = null;
    } else {
      Log.d(TAG, "Got preview callback, but no handler for it");
    }
  }

可以看到，預覽的數據data，回傳遞過來，然後handler的方式傳遞出去。

接收data的地方：

  @Override
  public void handleMessage(Message message) {
    switch (message.what) {
      case R.id.decode:
        //Log.d(TAG, "Got decode message");
        decode((byte[]) message.obj, message.arg1, message.arg2);
        break;
      case R.id.quit:
        Looper.myLooper().quit();
        break;
    }
  }

然後是decode data

private void decode(byte[] data, int width, int height) {
    long start = System.currentTimeMillis();
    Result rawResult = null;
    
    //modify here
    byte[] rotatedData = new byte[data.length];
    for (int y = 0; y < height; y++) {
        for (int x = 0; x < width; x++)
            rotatedData[x * height + height - y - 1] = data[x + y * width];
    }
    int tmp = width; // Here we are swapping, that's the difference to #11
    width = height;
    height = tmp;
    
    PlanarYUVLuminanceSource source = CameraManager.get().buildLuminanceSource(rotatedData, width, height);
    BinaryBitmap bitmap = new BinaryBitmap(new HybridBinarizer(source));
    try {
      rawResult = multiFormatReader.decodeWithState(bitmap);
    } catch (ReaderException re) {
      // continue
    } finally {
      multiFormatReader.reset();
    }

    if (rawResult != null) {
      long end = System.currentTimeMillis();
      Log.d(TAG, "Found barcode (" + (end - start) + " ms):\n" + rawResult.toString());
      Message message = Message.obtain(activity.getHandler(), R.id.decode_succeeded, rawResult);
      Bundle bundle = new Bundle();
      bundle.putParcelable(DecodeThread.BARCODE_BITMAP, source.renderCroppedGreyscaleBitmap());
      message.setData(bundle);
      //Log.d(TAG, "Sending decode succeeded message...");
      message.sendToTarget();
    } else {
      Message message = Message.obtain(activity.getHandler(), R.id.decode_failed);
      message.sendToTarget();
    }
  }

當把camera上的圖片轉換成BinaryBitmap以後，剩下的事情，就更直接從圖片識別是一樣的。

PlanarYUVLuminanceSource source = CameraManager.get().buildLuminanceSource(rotatedData, width, height);
    BinaryBitmap bitmap = new BinaryBitmap(new HybridBinarizer(source));

參考：

http://www.cnblogs.com/weixing/archive/2013/08/28/3287120.html