[概述]
ZXing ("zebra crossing") is an open-source, multi-format 1D/2D barcode image processing library implemented in Java, with ports to other languages.
开源地址：
https://github.com/zxing/zxing
[工程结构]
以ZXing.Net.Source.0.14.0.0版本为例，此文件目录下对应两个工程：
Base和WinMD，我们主要分析Base工程，其中：
ZXing.Net.Source.0.14.0.0\Base\Source\lib目录下的工程为源码工程，zxing.vs2012为源码工程Solution文件；
ZXing.Net.Source.0.14.0.0\Base\Clients\WindowsFormsDemo目录下的工程为ZXING输出类库的应用工程，WindowsFormsDemo为应用工程Solution文件。
[应用工程分析]
WindowsFormsDemo有三个Tab，分别为Decoder/Encoder/WebCam，分别实现图片读码/二维码生成/网络摄像头采样读码（主要调用了avicap32.dll，它是Windows API应用程序接口相关模块，用于对摄像头和其它视频硬件进行AⅥ电影和视频的截取，详见工程文件WebCam.cs）。
Decoder(图片读码)：
private void btnStartDecoding_Click(object sender, EventArgs e)
{
var fileName = txtBarcodeImageFile.Text;
if (!File.Exists(fileName))
{
MessageBox.Show(this, String.Format("File not found: {0}", fileName), "Error", MessageBoxButtons.OK,
MessageBoxIcon.Error);
return;
}
using (var bitmap = (Bitmap)Bitmap.FromFile(fileName))
{
if (TryOnlyMultipleQRCodes)
Decode(bitmap, TryMultipleBarcodes, new List<BarcodeFormat> { BarcodeFormat.QR_CODE });
else
Decode(bitmap, TryMultipleBarcodes, null);
}
}
private void Decode(Bitmap image, bool tryMultipleBarcodes, IList<BarcodeFormat> possibleFormats)
{
resultPoints.Clear();
lastResults.Clear();
txtContent.Text = String.Empty;
var timerStart = DateTime.Now.Ticks;
Result[] results = null;
barcodeReader.Options.PossibleFormats = possibleFormats;
if (tryMultipleBarcodes)
results = barcodeReader.DecodeMultiple(image);
else
{
var result = barcodeReader.Decode(image);
if (result != null)
{
results = new[] {result};
}
}
var timerStop = DateTime.Now.Ticks;
if (results == null)
{
txtContent.Text = "No barcode recognized";
}
labDuration.Text = new TimeSpan(timerStop - timerStart).Milliseconds.ToString("0 ms");
if (results != null)
{
foreach (var result in results)
{
if (result.ResultPoints.Length > 0)
{
var rect = new Rectangle((int) result.ResultPoints[0].X, (int) result.ResultPoints[0].Y, 1, 1);
foreach (var point in result.ResultPoints)
{
if (point.X < rect.Left)
rect = new Rectangle((int) point.X, rect.Y, rect.Width + rect.X - (int) point.X, rect.Height);
if (point.X > rect.Right)
rect = new Rectangle(rect.X, rect.Y, rect.Width + (int) point.X - rect.X, rect.Height);
if (point.Y < rect.Top)
rect = new Rectangle(rect.X, (int) point.Y, rect.Width, rect.Height + rect.Y - (int) point.Y);
if (point.Y > rect.Bottom)
rect = new Rectangle(rect.X, rect.Y, rect.Width, rect.Height + (int) point.Y - rect.Y);
}
using (var g = picBarcode.CreateGraphics())
{
g.DrawRectangle(Pens.Green, rect);
}
}
}
}
}
Encoder(二维码生成)：
（待续）
WebCam(网络摄像头采样读码)：
private void btnDecodeWebCam_Click(object sender, EventArgs e)
{
if (wCam == null)
{
wCam = new WebCam {Container = picWebCam};
wCam.OpenConnection();
webCamTimer = new Timer();
webCamTimer.Tick += webCamTimer_Tick;
webCamTimer.Interval = 200; // Image derivation interval
webCamTimer.Start();
btnDecodeWebCam.Text = "Decoding..."; // Update UI
}
else
{
webCamTimer.Stop();
webCamTimer = null;
wCam.Dispose();
wCam = null;
btnDecodeWebCam.Text = "Decode"; // Update UI
}
}
void webCamTimer_Tick(object sender, EventArgs e)
{
var bitmap = wCam.GetCurrentImage(); // Derive a imaghe
if (bitmap == null)
return;
Console.WriteLine("Bitmap width is:{0}, height is{1}. Camera is: {2} mega-pixel.", bitmap.Width, bitmap.Height, bitmap.Width* bitmap.Height/10000);
var reader = new BarcodeReader();
var result = reader.Decode(bitmap); // Decode the image
if (result != null)
{
txtTypeWebCam.Text = result.BarcodeFormat.ToString();
txtContentWebCam.Text = result.Text;
}
}
其中WebCam对象定义的各类对摄像头的参数设置和操作详见WebCam.cs。
[源码工程分析]
1.图像解码（Qrcode为例）
Qrcode解码流程为检测定位->解码，涉及的几个主要文件为：BarcodeReader.cs(createBinarizer)->BarcodeReaderGeneric.cs(createBinarizer)->HybridBinarizer.cs(createBinarizer)、QRCodeReader.cs，Detector.cs和FinderPatternFinder.cs，Decoder.cs。
HybridBinarizer.cs(createBinarizer)类实现位图的二值化处理，核心代码段为：
/// <summary>
/// Calculates the final BitMatrix once for all requests. This could be called once from the
/// constructor instead, but there are some advantages to doing it lazily, such as making
/// profiling easier, and not doing heavy lifting when callers don‘t expect it.
/// </summary>
private void binarizeEntireImage()
{
if (matrix == null)
{
LuminanceSource source = LuminanceSource;
int width = source.Width;
int height = source.Height;
if (width >= MINIMUM_DIMENSION && height >= MINIMUM_DIMENSION)
{
byte[] luminances = source.Matrix;
int subWidth = width >> BLOCK_SIZE_POWER;
if ((width & BLOCK_SIZE_MASK) != 0)
{
subWidth++;
}
int subHeight = height >> BLOCK_SIZE_POWER;
if ((height & BLOCK_SIZE_MASK) != 0)
{
subHeight++;
}
int[][] blackPoints = calculateBlackPoints(luminances, subWidth, subHeight, width, height);
var newMatrix = new BitMatrix(width, height);
calculateThresholdForBlock(luminances, subWidth, subHeight, width, height, blackPoints, newMatrix);
matrix = newMatrix;
}
else
{
// If the image is too small, fall back to the global histogram approach.
matrix = base.BlackMatrix;
}
}
}
/// <summary>
/// For each 8x8 block in the image, calculate the average black point using a 5x5 grid
/// of the blocks around it. Also handles the corner cases (fractional blocks are computed based
/// on the last 8 pixels in the row/column which are also used in the previous block).
/// PS(Jay):This algrithm has big issue!!! Should be enhanced!!!
/// </summary>
/// <param name="luminances">The luminances.</param>
/// <param name="subWidth">Width of the sub.</param>
/// <param name="subHeight">Height of the sub.</param>
/// <param name="width">The width.</param>
/// <param name="height">The height.</param>
/// <param name="blackPoints">The black points.</param>
/// <param name="matrix">The matrix.</param>
private static void calculateThresholdForBlock(byte[] luminances, int subWidth, int subHeight, int width, int height, int[][] blackPoints, BitMatrix matrix)
{
for (int y = 0; y < subHeight; y++)
{
int yoffset = y << BLOCK_SIZE_POWER;
int maxYOffset = height - BLOCK_SIZE;
if (yoffset > maxYOffset)
{
yoffset = maxYOffset;
}
for (int x = 0; x < subWidth; x++)
{
int xoffset = x << BLOCK_SIZE_POWER;
int maxXOffset = width - BLOCK_SIZE;
if (xoffset > maxXOffset)
{
xoffset = maxXOffset;
}
int left = cap(x, 2, subWidth - 3);
int top = cap(y, 2, subHeight - 3);
int sum = 0;
for (int z = -2; z <= 2; z++)
{
int[] blackRow = blackPoints[top + z];
sum += blackRow[left - 2];
sum += blackRow[left - 1];
sum += blackRow[left];
sum += blackRow[left + 1];
sum += blackRow[left + 2];
}
int average = sum / 25;
thresholdBlock(luminances, xoffset, yoffset, average, width, matrix);
}
}
}
private static int cap(int value, int min, int max)
{
return value < min ? min : value > max ? max : value;
}
/// <summary>
/// Applies a single threshold to an 8x8 block of pixels.
/// </summary>
/// <param name="luminances">The luminances.</param>
/// <param name="xoffset">The xoffset.</param>
/// <param name="yoffset">The yoffset.</param>
/// <param name="threshold">The threshold.</param>
/// <param name="stride">The stride.</param>
/// <param name="matrix">The matrix.</param>
private static void thresholdBlock(byte[] luminances, int xoffset, int yoffset, int threshold, int stride, BitMatrix matrix)
{
int offset = (yoffset * stride) + xoffset;
for (int y = 0; y < BLOCK_SIZE; y++, offset += stride)
{
for (int x = 0; x < BLOCK_SIZE; x++)
{
int pixel = luminances[offset + x] & 0xff;
// Comparison needs to be <=, so that black == 0 pixels are black, even if the threshold is 0.
matrix[xoffset + x, yoffset + y] = (pixel <= threshold);
}
}
}
/// <summary>
/// Calculates a single black point for each 8x8 block of pixels and saves it away.
/// See the following thread for a discussion of this algorithm:
/// http://groups.google.com/group/zxing/browse_thread/thread/d06efa2c35a7ddc0
/// </summary>
/// <param name="luminances">The luminances.</param>
/// <param name="subWidth">Width of the sub.</param>
/// <param name="subHeight">Height of the sub.</param>
/// <param name="width">The width.</param>
/// <param name="height">The height.</param>
/// <returns></returns>
private static int[][] calculateBlackPoints(byte[] luminances, int subWidth, int subHeight, int width, int height)
{
int[][] blackPoints = new int[subHeight][];
for (int i = 0; i < subHeight; i++)
{
blackPoints[i] = new int[subWidth];
}
for (int y = 0; y < subHeight; y++)
{
int yoffset = y << BLOCK_SIZE_POWER;
int maxYOffset = height - BLOCK_SIZE;
if (yoffset > maxYOffset)
{
yoffset = maxYOffset;
}
for (int x = 0; x < subWidth; x++)
{
int xoffset = x << BLOCK_SIZE_POWER;
int maxXOffset = width - BLOCK_SIZE;
if (xoffset > maxXOffset)
{
xoffset = maxXOffset;
}
int sum = 0;
int min = 0xFF;
int max = 0;
for (int yy = 0, offset = yoffset * width + xoffset; yy < BLOCK_SIZE; yy++, offset += width)
{
for (int xx = 0; xx < BLOCK_SIZE; xx++)
{
int pixel = luminances[offset + xx] & 0xFF;
// still looking for good contrast
sum += pixel;
if (pixel < min)
{
min = pixel;
}
if (pixel > max)
{
max = pixel;
}
}
// short-circuit min/max tests once dynamic range is met
if (max - min > MIN_DYNAMIC_RANGE)
{
// finish the rest of the rows quickly
for (yy++, offset += width; yy < BLOCK_SIZE; yy++, offset += width)
{
for (int xx = 0; xx < BLOCK_SIZE; xx++)
{
sum += luminances[offset + xx] & 0xFF;
}
}
}
}
// The default estimate is the average of the values in the block.
int average = sum >> (BLOCK_SIZE_POWER * 2);
if (max - min <= MIN_DYNAMIC_RANGE)
{
// If variation within the block is low, assume this is a block with only light or only
// dark pixels. In that case we do not want to use the average, as it would divide this
// low contrast area into black and white pixels, essentially creating data out of noise.
//
// The default assumption is that the block is light/background. Since no estimate for
// the level of dark pixels exists locally, use half the min for the block.
average = min >> 1;
if (y > 0 && x > 0)
{
// Correct the "white background" assumption for blocks that have neighbors by comparing
// the pixels in this block to the previously calculated black points. This is based on
// the fact that dark barcode symbology is always surrounded by some amount of light
// background for which reasonable black point estimates were made. The bp estimated at
// the boundaries is used for the interior.
// The (min < bp) is arbitrary but works better than other heuristics that were tried.
int averageNeighborBlackPoint = (blackPoints[y - 1][x] + (2 * blackPoints[y][x - 1]) +
blackPoints[y - 1][x - 1]) >> 2;
if (min < averageNeighborBlackPoint)
{
average = averageNeighborBlackPoint;
}
}
}
blackPoints[y][x] = average;
}
}
return blackPoints;
}
这一段算法有存在改进的必要。在HybridBinarizer继承的GlobalHistogramBinarizer类中，是从图像中均匀取5行（覆盖整个图像高度），每行取中间五分之四作为样本；以灰度值为X轴，每个灰度值的像素个数为Y轴建立一个直方图，从直方图中取点数最多的一个灰度值，然后再去给其他的灰度值进行分数计算，按照点数乘以与最多点数灰度值的距离的平方来进行打分，选分数最高的一个灰度值。接下来在这两个灰度值中间选取一个区分界限，取的原则是尽量靠近中间并且要点数越少越好。界限有了以后就容易了，与整幅图像的每个点进行比较，如果灰度值比界限小的就是黑，在新的矩阵中将该点置1，其余的就是白，为0。此部分具体代码见GlobalHistogramBinarizer类的BlackMatrix()重写方法。这个算法的劣势是由于是全局计算阈值点，所以应对局部阴影不太理想（However, because it picks a global black point, it cannot handle difficult shadows and gradients.）。
QRCodeReader类实现了接口Reader，核心段代码为：
/// <summary>
/// Locates and decodes a barcode in some format within an image. This method also accepts
/// hints, each possibly associated to some data, which may help the implementation decode.
/// </summary>
/// <param name="image">image of barcode to decode</param>
/// <param name="hints">passed as a <see cref="IDictionary{TKey, TValue}"/> from <see cref="DecodeHintType"/>
/// to arbitrary data. The
/// meaning of the data depends upon the hint type. The implementation may or may not do
/// anything with these hints.</param>
/// <returns>
/// String which the barcode encodes
/// </returns>
public Result decode(BinaryBitmap image, IDictionary<DecodeHintType, object> hints)
{
DecoderResult decoderResult;
ResultPoint[] points;
if (image == null || image.BlackMatrix == null)
{
// something is wrong with the image
return null;
}
if (hints != null && hints.ContainsKey(DecodeHintType.PURE_BARCODE)) // 纯barcode图片
{
var bits = extractPureBits(image.BlackMatrix);
if (bits == null)
return null;
decoderResult = decoder.decode(bits, hints);
points = NO_POINTS;
}
else
{
var detectorResult = new Detector(image.BlackMatrix).detect(hints); // 检测barcode
if (detectorResult == null)
return null;
decoderResult = decoder.decode(detectorResult.Bits, hints); // 解码barcode
points = detectorResult.Points;
}
if (decoderResult == null)
return null;
// If the code was mirrored: swap the bottom-left and the top-right points.
var data = decoderResult.Other as QRCodeDecoderMetaData;
if (data != null)
{
data.applyMirroredCorrection(points);
}
var result = new Result(decoderResult.Text, decoderResult.RawBytes, points, BarcodeFormat.QR_CODE);
var byteSegments = decoderResult.ByteSegments;
if (byteSegments != null)
{
result.putMetadata(ResultMetadataType.BYTE_SEGMENTS, byteSegments);
}
var ecLevel = decoderResult.ECLevel;
if (ecLevel != null)
{
result.putMetadata(ResultMetadataType.ERROR_CORRECTION_LEVEL, ecLevel);
}
if (decoderResult.StructuredAppend)
{
result.putMetadata(ResultMetadataType.STRUCTURED_APPEND_SEQUENCE, decoderResult.StructuredAppendSequenceNumber);
result.putMetadata(ResultMetadataType.STRUCTURED_APPEND_PARITY, decoderResult.StructuredAppendParity);
}
return result;
}
qrcode->detector目录下的Detector类：
namespace ZXing.QrCode.Internal
{
/// <summary>
/// <p>Encapsulates logic that can detect a QR Code in an image, even if the QR Code
/// is rotated or skewed, or partially obscured.</p>
/// </summary>
/// <author>Sean Owen</author>
public class Detector
{
private readonly BitMatrix image;
private ResultPointCallback resultPointCallback;
/// <summary>
/// Initializes a new instance of the <see cref="Detector"/> class.
/// </summary>
/// <param name="image">The image.</param>
public Detector(BitMatrix image)
{
this.image = image;
}
/// <summary>
/// Gets the image.
/// </summary>
virtual protected internal BitMatrix Image
{
get
{
return image;
}
}
/// <summary>
/// Gets the result point callback.
/// </summary>
virtual protected internal ResultPointCallback ResultPointCallback
{
get
{
return resultPointCallback;
}
}
/// <summary>
/// <p>Detects a QR Code in an image, simply.</p>
/// </summary>
/// <returns>
/// <see cref="DetectorResult"/> encapsulating results of detecting a QR Code
/// </returns>
public virtual DetectorResult detect()
{
return detect(null);
}
/// <summary>
/// <p>Detects a QR Code in an image, simply.</p>
/// </summary>
/// <param name="hints">optional hints to detector</param>
/// <returns>
/// <see cref="DetectorResult"/> encapsulating results of detecting a QR Code
/// </returns>
public virtual DetectorResult detect(IDictionary<DecodeHintType, object> hints)
{
resultPointCallback = hints == null || !hints.ContainsKey(DecodeHintType.NEED_RESULT_POINT_CALLBACK) ? null : (ResultPointCallback)hints[DecodeHintType.NEED_RESULT_POINT_CALLBACK];
FinderPatternFinder finder = new FinderPatternFinder(image, resultPointCallback);
FinderPatternInfo info = finder.find(hints);
if (info == null)
return null;
return processFinderPatternInfo(info);
}
/// <summary>
/// Processes the finder pattern info.
/// </summary>
/// <param name="info">The info.</param>
/// <returns></returns>
protected internal virtual DetectorResult processFinderPatternInfo(FinderPatternInfo info)
{
FinderPattern topLeft = info.TopLeft;
FinderPattern topRight = info.TopRight;
FinderPattern bottomLeft = info.BottomLeft;
float moduleSize = calculateModuleSize(topLeft, topRight, bottomLeft);
if (moduleSize < 1.0f)
{
return null;
}
int dimension;
if (!computeDimension(topLeft, topRight, bottomLeft, moduleSize, out dimension))
return null;
Internal.Version provisionalVersion = Internal.Version.getProvisionalVersionForDimension(dimension);
if (provisionalVersion == null)
return null;
int modulesBetweenFPCenters = provisionalVersion.DimensionForVersion - 7;
AlignmentPattern alignmentPattern = null;
// Anything above version 1 has an alignment pattern
if (provisionalVersion.AlignmentPatternCenters.Length > 0)
{
// Guess where a "bottom right" finder pattern would have been
float bottomRightX = topRight.X - topLeft.X + bottomLeft.X;
float bottomRightY = topRight.Y - topLeft.Y + bottomLeft.Y;
// Estimate that alignment pattern is closer by 3 modules
// from "bottom right" to known top left location
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index=‘!DefaultContextWindowIndex‘&keyword=‘jlca1042‘"
float correctionToTopLeft = 1.0f - 3.0f / (float)modulesBetweenFPCenters;
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index=‘!DefaultContextWindowIndex‘&keyword=‘jlca1042‘"
int estAlignmentX = (int)(topLeft.X + correctionToTopLeft * (bottomRightX - topLeft.X));
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index=‘!DefaultContextWindowIndex‘&keyword=‘jlca1042‘"
int estAlignmentY = (int)(topLeft.Y + correctionToTopLeft * (bottomRightY - topLeft.Y));
// Kind of arbitrary -- expand search radius before giving up
for (int i = 4; i <= 16; i <<= 1)
{
alignmentPattern = findAlignmentInRegion(moduleSize, estAlignmentX, estAlignmentY, (float)i);
if (alignmentPattern == null)
continue;
break;
}
// If we didn‘t find alignment pattern... well try anyway without it
}
PerspectiveTransform transform = createTransform(topLeft, topRight, bottomLeft, alignmentPattern, dimension);
BitMatrix bits = sampleGrid(image, transform, dimension);
if (bits == null)
return null;
ResultPoint[] points;
if (alignmentPattern == null)
{
points = new ResultPoint[] { bottomLeft, topLeft, topRight };
}
else
{
points = new ResultPoint[] { bottomLeft, topLeft, topRight, alignmentPattern };
}
return new DetectorResult(bits, points);
}
private static PerspectiveTransform createTransform(ResultPoint topLeft, ResultPoint topRight, ResultPoint bottomLeft, ResultPoint alignmentPattern, int dimension)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index=‘!DefaultContextWindowIndex‘&keyword=‘jlca1042‘"
float dimMinusThree = (float)dimension - 3.5f;
float bottomRightX;
float bottomRightY;
float sourceBottomRightX;
float sourceBottomRightY;
if (alignmentPattern != null)
{
bottomRightX = alignmentPattern.X;
bottomRightY = alignmentPattern.Y;
sourceBottomRightX = sourceBottomRightY = dimMinusThree - 3.0f;
}
else
{
// Don‘t have an alignment pattern, just make up the bottom-right point
bottomRightX = (topRight.X - topLeft.X) + bottomLeft.X;
bottomRightY = (topRight.Y - topLeft.Y) + bottomLeft.Y;
sourceBottomRightX = sourceBottomRightY = dimMinusThree;
}
return PerspectiveTransform.quadrilateralToQuadrilateral(
3.5f,
3.5f,
dimMinusThree,
3.5f,
sourceBottomRightX,
sourceBottomRightY,
3.5f,
dimMinusThree,
topLeft.X,
topLeft.Y,
topRight.X,
topRight.Y,
bottomRightX,
bottomRightY,
bottomLeft.X,
bottomLeft.Y);
}
private static BitMatrix sampleGrid(BitMatrix image, PerspectiveTransform transform, int dimension)
{
GridSampler sampler = GridSampler.Instance;
return sampler.sampleGrid(image, dimension, dimension, transform);
}
/// <summary> <p>Computes the dimension (number of modules on a size) of the QR Code based on the position
/// of the finder patterns and estimated module size.</p>
/// </summary>
private static bool computeDimension(ResultPoint topLeft, ResultPoint topRight, ResultPoint bottomLeft, float moduleSize, out int dimension)
{
int tltrCentersDimension = MathUtils.round(ResultPoint.distance(topLeft, topRight) / moduleSize);
int tlblCentersDimension = MathUtils.round(ResultPoint.distance(topLeft, bottomLeft) / moduleSize);
dimension = ((tltrCentersDimension + tlblCentersDimension) >> 1) + 7;
switch (dimension & 0x03)
{
// mod 4
case 0:
dimension++;
break;
// 1? do nothing
case 2:
dimension--;
break;
case 3:
return true;
}
return true;
}
/// <summary> <p>Computes an average estimated module size based on estimated derived from the positions
/// of the three finder patterns.</p>
/// </summary>
protected internal virtual float calculateModuleSize(ResultPoint topLeft, ResultPoint topRight, ResultPoint bottomLeft)
{
// Take the average
return (calculateModuleSizeOneWay(topLeft, topRight) + calculateModuleSizeOneWay(topLeft, bottomLeft)) / 2.0f;
}
/// <summary> <p>Estimates module size based on two finder patterns -- it uses
/// {@link #sizeOfBlackWhiteBlackRunBothWays(int, int, int, int)} to figure the
/// width of each, measuring along the axis between their centers.</p>
/// </summary>
private float calculateModuleSizeOneWay(ResultPoint pattern, ResultPoint otherPattern)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index=‘!DefaultContextWindowIndex‘&keyword=‘jlca1042‘"
float moduleSizeEst1 = sizeOfBlackWhiteBlackRunBothWays((int)pattern.X, (int)pattern.Y, (int)otherPattern.X, (int)otherPattern.Y);
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index=‘!DefaultContextWindowIndex‘&keyword=‘jlca1042‘"
float moduleSizeEst2 = sizeOfBlackWhiteBlackRunBothWays((int)otherPattern.X, (int)otherPattern.Y, (int)pattern.X, (int)pattern.Y);
if (Single.IsNaN(moduleSizeEst1))
{
return moduleSizeEst2 / 7.0f;
}
if (Single.IsNaN(moduleSizeEst2))
{
return moduleSizeEst1 / 7.0f;
}
// Average them, and divide by 7 since we‘ve counted the width of 3 black modules,
// and 1 white and 1 black module on either side. Ergo, divide sum by 14.
return (moduleSizeEst1 + moduleSizeEst2) / 14.0f;
}
/// <summary> See {@link #sizeOfBlackWhiteBlackRun(int, int, int, int)}; computes the total width of
/// a finder pattern by looking for a black-white-black run from the center in the direction
/// of another point (another finder pattern center), and in the opposite direction too.
/// </summary>
private float sizeOfBlackWhiteBlackRunBothWays(int fromX, int fromY, int toX, int toY)
{
float result = sizeOfBlackWhiteBlackRun(fromX, fromY, toX, toY);
// Now count other way -- don‘t run off image though of course
float scale = 1.0f;
int otherToX = fromX - (toX - fromX);
if (otherToX < 0)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index=‘!DefaultContextWindowIndex‘&keyword=‘jlca1042‘"
scale = (float)fromX / (float)(fromX - otherToX);
otherToX = 0;
}
else if (otherToX >= image.Width)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index=‘!DefaultContextWindowIndex‘&keyword=‘jlca1042‘"
scale = (float)(image.Width - 1 - fromX) / (float)(otherToX - fromX);
otherToX = image.Width - 1;
}
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index=‘!DefaultContextWindowIndex‘&keyword=‘jlca1042‘"
int otherToY = (int)(fromY - (toY - fromY) * scale);
scale = 1.0f;
if (otherToY < 0)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index=‘!DefaultContextWindowIndex‘&keyword=‘jlca1042‘"
scale = (float)fromY / (float)(fromY - otherToY);
otherToY = 0;
}
else if (otherToY >= image.Height)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index=‘!DefaultContextWindowIndex‘&keyword=‘jlca1042‘"
scale = (float)(image.Height - 1 - fromY) / (float)(otherToY - fromY);
otherToY = image.Height - 1;
}
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index=‘!DefaultContextWindowIndex‘&keyword=‘jlca1042‘"
otherToX = (int)(fromX + (otherToX - fromX) * scale);
result += sizeOfBlackWhiteBlackRun(fromX, fromY, otherToX, otherToY);
return result - 1.0f; // -1 because we counted the middle pixel twice
}
/// <summary> <p>This method traces a line from a point in the image, in the direction towards another point.
/// It begins in a black region, and keeps going until it finds white, then black, then white again.
/// It reports the distance from the start to this point.</p>
///
/// <p>This is used when figuring out how wide a finder pattern is, when the finder pattern
/// may be skewed or rotated.</p>
/// </summary>
private float sizeOfBlackWhiteBlackRun(int fromX, int fromY, int toX, int toY)
{
// Mild variant of Bresenham‘s algorithm;
// see http://en.wikipedia.org/wiki/Bresenham‘s_line_algorithm
bool steep = Math.Abs(toY - fromY) > Math.Abs(toX - fromX);
if (steep)
{
int temp = fromX;
fromX = fromY;
fromY = temp;
temp = toX;
toX = toY;
toY = temp;
}
int dx = Math.Abs(toX - fromX);
int dy = Math.Abs(toY - fromY);
int error = -dx >> 1;
int xstep = fromX < toX ? 1 : -1;
int ystep = fromY < toY ? 1 : -1;
// In black pixels, looking for white, first or second time.
int state = 0;
// Loop up until x == toX, but not beyond
int xLimit = toX + xstep;
for (int x = fromX, y = fromY; x != xLimit; x += xstep)
{
int realX = steep ? y : x;
int realY = steep ? x : y;
// Does current pixel mean we have moved white to black or vice versa?
// Scanning black in state 0,2 and white in state 1, so if we find the wrong
// color, advance to next state or end if we are in state 2 already
if ((state == 1) == image[realX, realY])
{
if (state == 2)
{
return MathUtils.distance(x, y, fromX, fromY);
}
state++;
}
error += dy;
if (error > 0)
{
if (y == toY)
{
break;
}
y += ystep;
error -= dx;
}
}
// Found black-white-black; give the benefit of the doubt that the next pixel outside the image
// is "white" so this last point at (toX+xStep,toY) is the right ending. This is really a
// small approximation; (toX+xStep,toY+yStep) might be really correct. Ignore this.
if (state == 2)
{
return MathUtils.distance(toX + xstep, toY, fromX, fromY);
}
// else we didn‘t find even black-white-black; no estimate is really possible
return Single.NaN;
}
/// <summary>
/// <p>Attempts to locate an alignment pattern in a limited region of the image, which is
/// guessed to contain it. This method uses {@link AlignmentPattern}.</p>
/// </summary>
/// <param name="overallEstModuleSize">estimated module size so far</param>
/// <param name="estAlignmentX">x coordinate of center of area probably containing alignment pattern</param>
/// <param name="estAlignmentY">y coordinate of above</param>
/// <param name="allowanceFactor">number of pixels in all directions to search from the center</param>
/// <returns>
/// <see cref="AlignmentPattern"/> if found, or null otherwise
/// </returns>
protected AlignmentPattern findAlignmentInRegion(float overallEstModuleSize, int estAlignmentX, int estAlignmentY, float allowanceFactor)
{
// Look for an alignment pattern (3 modules in size) around where it
// should be
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index=‘!DefaultContextWindowIndex‘&keyword=‘jlca1042‘"
int allowance = (int)(allowanceFactor * overallEstModuleSize);
int alignmentAreaLeftX = Math.Max(0, estAlignmentX - allowance);
int alignmentAreaRightX = Math.Min(image.Width - 1, estAlignmentX + allowance);
if (alignmentAreaRightX - alignmentAreaLeftX < overallEstModuleSize * 3)
{
return null;
}
int alignmentAreaTopY = Math.Max(0, estAlignmentY - allowance);
int alignmentAreaBottomY = Math.Min(image.Height - 1, estAlignmentY + allowance);
var alignmentFinder = new AlignmentPatternFinder(
image,
alignmentAreaLeftX,
alignmentAreaTopY,
alignmentAreaRightX - alignmentAreaLeftX,
alignmentAreaBottomY - alignmentAreaTopY,
overallEstModuleSize,
resultPointCallback);
return alignmentFinder.find();
}
}
}
qrcode->detector目录下的FinderPatternFinder类：
/*
* Copyright 2007 ZXing authors
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
using System;
using System.Collections.Generic;
using ZXing.Common;
namespace ZXing.QrCode.Internal
{
/// <summary>
/// <p>This class attempts to find finder patterns in a QR Code. Finder patterns are the square
/// markers at three corners of a QR Code.</p>
///
/// <p>This class is thread-safe but not reentrant. Each thread must allocate its own object.
/// </summary>
/// <author>Sean Owen</author>
public class FinderPatternFinder
{
private const int CENTER_QUORUM = 2;
/// <summary>
/// 1 pixel/module times 3 modules/center
/// </summary>
protected internal const int MIN_SKIP = 3;
/// <summary>
/// support up to version 10 for mobile clients
/// </summary>
protected internal const int MAX_MODULES = 57;
private const int INTEGER_MATH_SHIFT = 8;
private readonly BitMatrix image;
private List<FinderPattern> possibleCenters; // Records the alignment patterns cordination information
private bool hasSkipped;
private readonly int[] crossCheckStateCount;
private readonly ResultPointCallback resultPointCallback;
/// <summary>
/// <p>Creates a finder that will search the image for three finder patterns.</p>
/// </summary>
/// <param name="image">image to search</param>
public FinderPatternFinder(BitMatrix image)
: this(image, null)
{
}
/// <summary>
/// Initializes a new instance of the <see cref="FinderPatternFinder"/> class.
/// </summary>
/// <param name="image">The image.</param>
/// <param name="resultPointCallback">The result point callback.</param>
public FinderPatternFinder(BitMatrix image, ResultPointCallback resultPointCallback)
{
this.image = image;
this.possibleCenters = new List<FinderPattern>();
this.crossCheckStateCount = new int[5];
this.resultPointCallback = resultPointCallback;
}
/// <summary>
/// Gets the image.
/// </summary>
virtual protected internal BitMatrix Image
{
get
{
return image;
}
}
/// <summary>
/// Gets the possible centers.
/// </summary>
virtual protected internal List<FinderPattern> PossibleCenters
{
get
{
return possibleCenters;
}
}
internal virtual FinderPatternInfo find(IDictionary<DecodeHintType, object> hints)
{
bool tryHarder = hints != null && hints.ContainsKey(DecodeHintType.TRY_HARDER);
bool pureBarcode = hints != null && hints.ContainsKey(DecodeHintType.PURE_BARCODE);
int maxI = image.Height;
int maxJ = image.Width;
// We are looking for black/white/black/white/black modules in
// 1:1:3:1:1 ratio; this tracks the number of such modules seen so far
// Let‘s assume that the maximum version QR Code we support takes up 1/4 the height of the
// image, and then account for the center being 3 modules in size. This gives the smallest
// number of pixels the center could be, so skip this often. When trying harder, look for all
// QR versions regardless of how dense they are.
int iSkip = (3 * maxI) / (4 * MAX_MODULES);
if (iSkip < MIN_SKIP || tryHarder)
{
iSkip = MIN_SKIP;
}
bool done = false;
int[] stateCount = new int[5];
for (int i = iSkip - 1; i < maxI && !done; i += iSkip)
{
// Get a row of black/white values
stateCount[0] = 0;
stateCount[1] = 0;
stateCount[2] = 0;
stateCount[3] = 0;
stateCount[4] = 0;
int currentState = 0;
for (int j = 0; j < maxJ; j++)
{
if (image[j, i])
{
// Black pixel
if ((currentState & 1) == 1)
{
// Counting white pixels
currentState++;
}
stateCount[currentState]++;
}
else
{
// White pixel
if ((currentState & 1) == 0)
{
// Counting black pixels
if (currentState == 4)
{
// A winner?
if (foundPatternCross(stateCount))
{
// Yes(possible alignment pattern was found)
bool confirmed = handlePossibleCenter(stateCount, i, j, pureBarcode); // Check whether the alignment pattern is true or fake
if (confirmed)
{
// Start examining every other line. Checking each line turned out to be too
// expensive and didn‘t improve performance.
iSkip = 2;
if (hasSkipped) // If at least two alignment patterns were found and the skip parameter has been calculated
{
done = haveMultiplyConfirmedCenters(); // Check whether we have found at least 3 finder patterns
}
else
{
int rowSkip = findRowSkip(); // Calculate number of rows we could safely skip during scanning, based on the first two finder patterns
if (rowSkip > stateCount[2])
{
// Skip rows between row of lower confirmed center and top of presumed third confirmed center
// but back up a bit to get a full chance of detecting it, entire width of center of finder pattern
// Skip by rowSkip, but back off by stateCount[2] (size of last center of pattern we saw)
// to be conservative, and also back off by iSkip which is about to be re-added
i += rowSkip - stateCount[2] - iSkip;
j = maxJ - 1;
}
}
}
else
{
stateCount[0] = stateCount[2];
stateCount[1] = stateCount[3];
stateCount[2] = stateCount[4];
stateCount[3] = 1;
stateCount[4] = 0;
currentState = 3;
continue;
}
// Clear state to start looking again
currentState = 0;
stateCount[0] = 0;
stateCount[1] = 0;
stateCount[2] = 0;
stateCount[3] = 0;
stateCount[4] = 0;
}
else
{
// No, shift counts back by two
stateCount[0] = stateCount[2];
stateCount[1] = stateCount[3];
stateCount[2] = stateCount[4];
stateCount[3] = 1;
stateCount[4] = 0;
currentState = 3;
}
}
else
{
stateCount[++currentState]++;
}
}
else
{
// Counting white pixels
stateCount[currentState]++;
}
}
}
if (foundPatternCross(stateCount))
{
bool confirmed = handlePossibleCenter(stateCount, i, maxJ, pureBarcode);
if (confirmed)
{
iSkip = stateCount[0];
if (hasSkipped)
{
// Found a third one
done = haveMultiplyConfirmedCenters();
}
}
}
}
FinderPattern[] patternInfo = selectBestPatterns();
if (patternInfo == null)
return null;
ResultPoint.orderBestPatterns(patternInfo);
return new FinderPatternInfo(patternInfo);
}
/// <summary> Given a count of black/white/black/white/black pixels just seen and an end position,
/// figures the location of the center of this run.
/// </summary>
private static float? centerFromEnd(int[] stateCount, int end)
{
var result = (end - stateCount[4] - stateCount[3]) - stateCount[2] / 2.0f;
if (Single.IsNaN(result))
return null;
return result;
}
/// <param name="stateCount">count of black/white/black/white/black pixels just read
/// </param>
/// <returns> true iff the proportions of the counts is close enough to the 1/1/3/1/1 ratios
/// used by finder patterns to be considered a match
/// </returns>
protected internal static bool foundPatternCross(int[] stateCount)
{
int totalModuleSize = 0;
for (int i = 0; i < 5; i++)
{
int count = stateCount[i];
if (count == 0)
{
return false;
}
totalModuleSize += count;
}
if (totalModuleSize < 7)
{
return false;
}
int moduleSize = (totalModuleSize << INTEGER_MATH_SHIFT) / 7; // 1+1+3+1+1=7, at least 7 modules
int maxVariance = moduleSize / 2;
// Allow less than 50% variance from 1-1-3-1-1 proportions
return Math.Abs(moduleSize - (stateCount[0] << INTEGER_MATH_SHIFT)) < maxVariance &&
Math.Abs(moduleSize - (stateCount[1] << INTEGER_MATH_SHIFT)) < maxVariance &&
Math.Abs(3 * moduleSize - (stateCount[2] << INTEGER_MATH_SHIFT)) < 3 * maxVariance &&
Math.Abs(moduleSize - (stateCount[3] << INTEGER_MATH_SHIFT)) < maxVariance &&
Math.Abs(moduleSize - (stateCount[4] << INTEGER_MATH_SHIFT)) < maxVariance;
}
private int[] CrossCheckStateCount
{
get
{
crossCheckStateCount[0] = 0;
crossCheckStateCount[1] = 0;
crossCheckStateCount[2] = 0;
crossCheckStateCount[3] = 0;
crossCheckStateCount[4] = 0;
return crossCheckStateCount;
}
}
/// <summary>
/// After a vertical and horizontal scan finds a potential finder pattern, this method
/// "cross-cross-cross-checks" by scanning down diagonally through the center of the possible
/// finder pattern to see if the same proportion is detected.
/// </summary>
/// <param name="startI">row where a finder pattern was detected</param>
/// <param name="centerJ">center of the section that appears to cross a finder pattern</param>
/// <param name="maxCount">maximum reasonable number of modules that should be observed in any reading state, based on the results of the horizontal scan</param>
/// <param name="originalStateCountTotal">The original state count total.</param>
/// <returns>true if proportions are withing expected limits</returns>
private bool crossCheckDiagonal(int startI, int centerJ, int maxCount, int originalStateCountTotal)
{
int maxI = image.Height;
int maxJ = image.Width;
int[] stateCount = CrossCheckStateCount;
// Start counting up, left from center finding black center mass
int i = 0;
while (startI - i >= 0 && image[centerJ - i, startI - i])
{
stateCount[2]++;
i++;
}
if ((startI - i < 0) || (centerJ - i < 0))
{
return false;
}
// Continue up, left finding white space
while ((startI - i >= 0) && (centerJ - i >= 0) && !image[centerJ - i, startI - i] && stateCount[1] <= maxCount)
{
stateCount[1]++;
i++;
}
// If already too many modules in this state or ran off the edge:
if ((startI - i < 0) || (centerJ - i < 0) || stateCount[1] > maxCount)
{
return false;
}
// Continue up, left finding black border
while ((startI - i >= 0) && (centerJ - i >= 0) && image[centerJ - i, startI - i] && stateCount[0] <= maxCount)
{
stateCount[0]++;
i++;
}
if (stateCount[0] > maxCount)
{
return false;
}
// Now also count down, right from center
i = 1;
while ((startI + i < maxI) && (centerJ + i < maxJ) && image[centerJ + i, startI + i])
{
stateCount[2]++;
i++;
}
// Ran off the edge?
if ((startI + i >= maxI) || (centerJ + i >= maxJ))
{
return false;
}
while ((startI + i < maxI) && (centerJ + i < maxJ) && !image[centerJ + i, startI + i] && stateCount[3] < maxCount)
{
stateCount[3]++;
i++;
}
if ((startI + i >= maxI) || (centerJ + i >= maxJ) || stateCount[3] >= maxCount)
{
return false;
}
while ((startI + i < maxI) && (centerJ + i < maxJ) && image[centerJ + i, startI + i] && stateCount[4] < maxCount)
{
stateCount[4]++;
i++;
}
if (stateCount[4] >= maxCount)
{
return false;
}
// If we found a finder-pattern-like section, but its size is more than 100% different than
// the original, assume it‘s a false positive
int stateCountTotal = stateCount[0] + stateCount[1] + stateCount[2] + stateCount[3] + stateCount[4];
return Math.Abs(stateCountTotal - originalStateCountTotal) < 2*originalStateCountTotal &&
foundPatternCross(stateCount);
}
/// <summary>
/// <p>After a horizontal scan finds a potential finder pattern, this method
/// "cross-checks" by scanning down vertically through the center of the possible
/// finder pattern to see if the same proportion is detected.</p>
/// </summary>
/// <param name="startI">row where a finder pattern was detected</param>
/// <param name="centerJ">center of the section that appears to cross a finder pattern</param>
/// <param name="maxCount">maximum reasonable number of modules that should be
/// observed in any reading state, based on the results of the horizontal scan</param>
/// <param name="originalStateCountTotal">The original state count total.</param>
/// <returns>
/// vertical center of finder pattern, or null if not found
/// </returns>
private float? crossCheckVertical(int startI, int centerJ, int maxCount, int originalStateCountTotal)
{
int maxI = image.Height;
int[] stateCount = CrossCheckStateCount;
// Start counting up from center
int i = startI;
while (i >= 0 && image[centerJ, i])
{
stateCount[2]++;
i--;
}
if (i < 0)
{
return null;
}
while (i >= 0 && !image[centerJ, i] && stateCount[1] <= maxCount)
{
stateCount[1]++;
i--;
}
// If already too many modules in this state or ran off the edge:
if (i < 0 || stateCount[1] > maxCount)
{
return null;
}
while (i >= 0 && image[centerJ, i] && stateCount[0] <= maxCount)
{
stateCount[0]++;
i--;
}
if (stateCount[0] > maxCount)
{
return null;
}
// Now also count down from center
i = startI + 1;
while (i < maxI && image[centerJ, i])
{
stateCount[2]++;
i++;
}
if (i == maxI)
{
return null;
}
while (i < maxI && !image[centerJ, i] && stateCount[3] < maxCount)
{
stateCount[3]++;
i++;
}
if (i == maxI || stateCount[3] >= maxCount)
{
return null;
}
while (i < maxI && image[centerJ, i] && stateCount[4] < maxCount)
{
stateCount[4]++;
i++;
}
if (stateCount[4] >= maxCount)
{
return null;
}
// If we found a finder-pattern-like section, but its size is more than 40% different than
// the original, assume it‘s a false positive
int stateCountTotal = stateCount[0] + stateCount[1] + stateCount[2] + stateCount[3] + stateCount[4];
if (5 * Math.Abs(stateCountTotal - originalStateCountTotal) >= 2 * originalStateCountTotal)
{
return null;
}
return foundPatternCross(stateCount) ? centerFromEnd(stateCount, i) : null;
}
/// <summary> <p>Like {@link #crossCheckVertical(int, int, int, int)}, and in fact is basically identical,
/// except it reads horizontally instead of vertically. This is used to cross-cross
/// check a vertical cross check and locate the real center of the alignment pattern.</p>
/// </summary>
private float? crossCheckHorizontal(int startJ, int centerI, int maxCount, int originalStateCountTotal)
{
int maxJ = image.Width;
int[] stateCount = CrossCheckStateCount;
int j = startJ;
while (j >= 0 && image[j, centerI])
{
stateCount[2]++;
j--;
}
if (j < 0)
{
return null;
}
while (j >= 0 && !image[j, centerI] && stateCount[1] <= maxCount)
{
stateCount[1]++;
j--;
}
if (j < 0 || stateCount[1] > maxCount)
{
return null;
}
while (j >= 0 && image[j, centerI] && stateCount[0] <= maxCount)
{
stateCount[0]++;
j--;
}
if (stateCount[0] > maxCount)
{
return null;
}
j = startJ + 1;
while (j < maxJ && image[j, centerI])
{
stateCount[2]++;
j++;
}
if (j == maxJ)
{
return null;
}
while (j < maxJ && !image[j, centerI] && stateCount[3] < maxCount)
{
stateCount[3]++;
j++;
}
if (j == maxJ || stateCount[3] >= maxCount)
{
return null;
}
while (j < maxJ && image[j, centerI] && stateCount[4] < maxCount)
{
stateCount[4]++;
j++;
}
if (stateCount[4] >= maxCount)
{
return null;
}
// If we found a finder-pattern-like section, but its size is significantly different than
// the original, assume it‘s a false positive
int stateCountTotal = stateCount[0] + stateCount[1] + stateCount[2] + stateCount[3] + stateCount[4];
if (5 * Math.Abs(stateCountTotal - originalStateCountTotal) >= originalStateCountTotal)
{
return null;
}
return foundPatternCross(stateCount) ? centerFromEnd(stateCount, j) : null;
}
/// <summary>
/// <p>This is called when a horizontal scan finds a possible alignment pattern. It will
/// cross check with a vertical scan, and if successful, will, ah, cross-cross-check
/// with another horizontal scan. This is needed primarily to locate the real horizontal
/// center of the pattern in cases of extreme skew.
/// And then we cross-cross-cross check with another diagonal scan.</p>
/// If that succeeds the finder pattern location is added to a list that tracks
/// the number of times each location has been nearly-matched as a finder pattern.
/// Each additional find is more evidence that the location is in fact a finder
/// pattern center
/// </summary>
/// <param name="stateCount">reading state module counts from horizontal scan</param>
/// <param name="i">row where finder pattern may be found</param>
/// <param name="j">end of possible finder pattern in row</param>
/// <param name="pureBarcode">if set to <c>true</c> [pure barcode].</param>
/// <returns>
/// true if a finder pattern candidate was found this time
/// </returns>
protected bool handlePossibleCenter(int[] stateCount, int i, int j, bool pureBarcode)
{
int stateCountTotal = stateCount[0] + stateCount[1] + stateCount[2] + stateCount[3] + stateCount[4];
float? centerJ = centerFromEnd(stateCount, j);
if (centerJ == null)
return false;
float? centerI = crossCheckVertical(i, (int)centerJ.Value, stateCount[2], stateCountTotal); // Cross Check Vertical
if (centerI != null)
{
// Re-cross check
centerJ = crossCheckHorizontal((int)centerJ.Value, (int)centerI.Value, stateCount[2], stateCountTotal); // Cross Check Horizontal
if (centerJ != null &&
(!pureBarcode || crossCheckDiagonal((int) centerI, (int) centerJ, stateCount[2], stateCountTotal))) // Cross Check Diagonal
{
float estimatedModuleSize = stateCountTotal / 7.0f;
bool found = false;
for (int index = 0; index < possibleCenters.Count; index++)
{
var center = possibleCenters[index];
// Look for about the same center and module size:
if (center.aboutEquals(estimatedModuleSize, centerI.Value, centerJ.Value))
{
possibleCenters.RemoveAt(index);
possibleCenters.Insert(index, center.combineEstimate(centerI.Value, centerJ.Value, estimatedModuleSize));
found = true;
break;
}
}
if (!found)
{
var point = new FinderPattern(centerJ.Value, centerI.Value, estimatedModuleSize);
possibleCenters.Add(point);
if (resultPointCallback != null)
{
resultPointCallback(point);
}
}
return true;
}
}
return false;
}
/// <returns> number of rows we could safely skip during scanning, based on the first
/// two finder patterns that have been located. In some cases their position will
/// allow us to infer that the third pattern must lie below a certain point farther
/// down in the image.
/// </returns>
private int findRowSkip()
{
int max = possibleCenters.Count;
if (max <= 1)
{
return 0;
}
ResultPoint firstConfirmedCenter = null;
foreach (var center in possibleCenters)
{
if (center.Count >= CENTER_QUORUM)
{
if (firstConfirmedCenter == null)
{
firstConfirmedCenter = center;
}
else
{
// We have two confirmed centers
// How far down can we skip before resuming looking for the next
// pattern? In the worst case, only the difference between the
// difference in the x / y coordinates of the two centers.
// This is the case where you find top left last.
hasSkipped = true;
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index=‘!DefaultContextWindowIndex‘&keyword=‘jlca1042‘"
return (int)(Math.Abs(firstConfirmedCenter.X - center.X) - Math.Abs(firstConfirmedCenter.Y - center.Y)) / 2;
}
}
}
return 0;
}
/// <returns> true if we have found at least 3 finder patterns that have been detected
/// at least {@link #CENTER_QUORUM} times each, and, the estimated module size of the
/// candidates is "pretty similar"
/// </returns>
private bool haveMultiplyConfirmedCenters()
{
int confirmedCount = 0;
float totalModuleSize = 0.0f;
int max = possibleCenters.Count;
foreach (var pattern in possibleCenters)
{
if (pattern.Count >= CENTER_QUORUM)
{
confirmedCount++;
totalModuleSize += pattern.EstimatedModuleSize;
}
}
if (confirmedCount < 3)
{
return false;
}
// OK, we have at least 3 confirmed centers, but, it‘s possible that one is a "false positive"
// and that we need to keep looking. We detect this by asking if the estimated module sizes
// vary too much. We arbitrarily say that when the total deviation from average exceeds
// 5% of the total module size estimates, it‘s too much.
float average = totalModuleSize / max;
float totalDeviation = 0.0f;
for (int i = 0; i < max; i++)
{
var pattern = possibleCenters[i];
totalDeviation += Math.Abs(pattern.EstimatedModuleSize - average);
}
return totalDeviation <= 0.05f * totalModuleSize;
}
/// <returns> the 3 best {@link FinderPattern}s from our list of candidates. The "best" are
/// those that have been detected at least {@link #CENTER_QUORUM} times, and whose module
/// size differs from the average among those patterns the least
/// </returns>
private FinderPattern[] selectBestPatterns()
{
int startSize = possibleCenters.Count;
if (startSize < 3)
{
// Couldn‘t find enough finder patterns
return null;
}
// Filter outlier possibilities whose module size is too different
if (startSize > 3)
{
// But we can only afford to do so if we have at least 4 possibilities to choose from
float totalModuleSize = 0.0f;
float square = 0.0f;
foreach (var center in possibleCenters)
{
float size = center.EstimatedModuleSize;
totalModuleSize += size;
square += size * size;
}
float average = totalModuleSize / startSize;
float stdDev = (float)Math.Sqrt(square / startSize - average * average);
possibleCenters.Sort(new FurthestFromAverageComparator(average));
float limit = Math.Max(0.2f * average, stdDev);
for (int i = 0; i < possibleCenters.Count && possibleCenters.Count > 3; i++)
{
FinderPattern pattern = possibleCenters[i];
if (Math.Abs(pattern.EstimatedModuleSize - average) > limit)
{
possibleCenters.RemoveAt(i);
i--;
}
}
}
if (possibleCenters.Count > 3)
{
// Throw away all but those first size candidate points we found.
float totalModuleSize = 0.0f;
foreach (var possibleCenter in possibleCenters)
{
totalModuleSize += possibleCenter.EstimatedModuleSize;
}
float average = totalModuleSize / possibleCenters.Count;
possibleCenters.Sort(new CenterComparator(average));
//possibleCenters.subList(3, possibleCenters.Count).clear();
possibleCenters = possibleCenters.GetRange(0, 3);
}
return new[]
{
possibleCenters[0],
possibleCenters[1],
possibleCenters[2]
};
}
/// <summary>
/// Orders by furthest from average
/// </summary>
private sealed class FurthestFromAverageComparator : IComparer<FinderPattern>
{
private readonly float average;
public FurthestFromAverageComparator(float f)
{
average = f;
}
public int Compare(FinderPattern x, FinderPattern y)
{
float dA = Math.Abs(y.EstimatedModuleSize - average);
float dB = Math.Abs(x.EstimatedModuleSize - average);
return dA < dB ? -1 : dA == dB ? 0 : 1;
}
}
/// <summary> <p>Orders by {@link FinderPattern#getCount()}, descending.</p></summary>
private sealed class CenterComparator : IComparer<FinderPattern>
{
private readonly float average;
public CenterComparator(float f)
{
average = f;
}
public int Compare(FinderPattern x, FinderPattern y)
{
if (y.Count == x.Count)
{
float dA = Math.Abs(y.EstimatedModuleSize - average);
float dB = Math.Abs(x.EstimatedModuleSize - average);
return dA < dB ? 1 : dA == dB ? 0 : -1;
}
return y.Count - x.Count;
}
}
}
}
寻找PatternFinder流程为：先按照1:1:3:1:1的比例逐行扫描，寻找Qrcode的定位点并校验点位点(横向，竖向，对角线斜向)，找到最初两个定位点以后，
通过findRowSkip()更新隔行检测参数提高检测效率，继续寻找定位点直至定位点全部找到，然后通过selectBestPatterns()选择最优的定位点，然后将最优
定位点相关信息处理后返回供上层调用。
qrcode->decoder目录下的Decoder（密封）类：
2.源码架构
BarcodeReader类继承了BarcodeReaderGeneric类，实现了接口IBarcodeReader, IMultipleBarcodeReader：
public class BarcodeReader : BarcodeReaderGeneric<Bitmap>, IBarcodeReader, IMultipleBarcodeReader
BarcodeReaderGeneric类实现了接口IBarcodeReaderGeneric<T>, IMultipleBarcodeReaderGeneric<T>。其中Decode虚拟方法为：
/// <summary>
/// Tries to decode a barcode within an image which is given by a luminance source.
/// That method gives a chance to prepare a luminance source completely before calling
/// the time consuming decoding method. On the other hand there is a chance to create
/// a luminance source which is independent from external resources (like Bitmap objects)
/// and the decoding call can be made in a background thread.
/// </summary>
/// <param name="luminanceSource">The luminance source.</param>
/// <returns></returns>
virtual public Result Decode(LuminanceSource luminanceSource)
{
var result = default(Result);
var binarizer = CreateBinarizer(luminanceSource);
var binaryBitmap = new BinaryBitmap(binarizer);
var multiformatReader = Reader as MultiFormatReader;
var rotationCount = 0;
var rotationMaxCount = 1;
if (AutoRotate)
{
Options.Hints[DecodeHintType.TRY_HARDER_WITHOUT_ROTATION] = true;
rotationMaxCount = 4;
}
else
{
if (Options.Hints.ContainsKey(DecodeHintType.TRY_HARDER_WITHOUT_ROTATION))
Options.Hints.Remove(DecodeHintType.TRY_HARDER_WITHOUT_ROTATION);
}
for (; rotationCount < rotationMaxCount; rotationCount++)
{
if (usePreviousState && multiformatReader != null)
{
result = multiformatReader.decodeWithState(binaryBitmap);
}
else
{
result = Reader.decode(binaryBitmap, Options.Hints);
usePreviousState = true;
}
if (result == null)
{
if (TryInverted && luminanceSource.InversionSupported)
{
binaryBitmap = new BinaryBitmap(CreateBinarizer(luminanceSource.invert()));
if (usePreviousState && multiformatReader != null)
{
result = multiformatReader.decodeWithState(binaryBitmap);
}
else
{
result = Reader.decode(binaryBitmap, Options.Hints);
usePreviousState = true;
}
}
}
if (result != null ||
!luminanceSource.RotateSupported ||
!AutoRotate)
break;
binaryBitmap = new BinaryBitmap(CreateBinarizer(luminanceSource.rotateCounterClockwise()));
}
if (result != null)
{
if (result.ResultMetadata == null)
{
result.putMetadata(ResultMetadataType.ORIENTATION, rotationCount * 90);
}
else if (!result.ResultMetadata.ContainsKey(ResultMetadataType.ORIENTATION))
{
result.ResultMetadata[ResultMetadataType.ORIENTATION] = rotationCount * 90;
}
else
{
// perhaps the core decoder rotates the image already (can happen if TryHarder is specified)
result.ResultMetadata[ResultMetadataType.ORIENTATION] = ((int)(result.ResultMetadata[ResultMetadataType.ORIENTATION]) + rotationCount * 90) % 360;
}
OnResultFound(result);
}
return result;
}
2.编码（待续）
时间： 2024-10-29 19:08:06