将位图转换为字节数组不会产生预期的结果
我的后端中有以下代码。我需要在Android Bitmap中使用它。
init我尝试了以下方法：
1）使用class CustomMapView: MKMapView {
private func zoom() {
let dortmundLocation = CLLocation(latitude: 51.516667, longitude: 7.466667)
let dortmunRegion = MKCoordinateRegion(center: CLLocationCoordinate2D(latitude: dortmundLocation.coordinate.latitude, longitude: dortmundLocation.coordinate.longitude), span: MKCoordinateSpan(latitudeDelta: 0.01, longitudeDelta: 0.01))
self.setRegion(dortmunRegion, animated: true)
}
}
函数
public byte[] extractBytes (String ImageName) throws IOException {
// open image
File imgPath = new File(ImageName);
BufferedImage bufferedImage = ImageIO.read(imgPath);
// get DataBufferBytes from Raster
WritableRaster raster = bufferedImage .getRaster();
DataBufferByte data = (DataBufferByte) raster.getDataBuffer();
return ( data.getData() );
}
2）使用Bitmap.compress()功能
src.compress(Bitmap.CompressFormat.JPEG, 100, stream);
byte[] byteArray = stream.toByteArray();
这两种方法都给我错误的结果，这与我的后端输出不匹配。
在后端，我有以下代码用于组件标记算法。
BlobDemo.java
Bitmap.copyPixelsToBuffer()BlobFinder.java
int size = src.getRowBytes() * src.getHeight();
ByteBuffer byteBuffer = ByteBuffer.allocate(size);
src.copyPixelsToBuffer(byteBuffer);
byte[] byteArray = byteBuffer.array();
我需要将以上代码转换为Android。我被困在这里。
public class BlobDemo
{
static ListDisplayPanel gui2 = new ListDisplayPanel();
public BlobDemo(String filename)
{
// Load Source image
BufferedImage srcImage = null;
try {
File imgFile = new File(filename);
srcImage = javax.imageio.ImageIO.read(imgFile);
}
catch (IOException ioE) {
System.err.println(ioE);
System.exit(1);
}
int width = srcImage.getWidth();
int height = srcImage.getHeight();
// Get raw image data
Raster raster = srcImage.getRaster();
DataBuffer buffer = raster.getDataBuffer();
int type = buffer.getDataType();
if (type != DataBuffer.TYPE_BYTE)
{
System.err.println("Wrong image data type");
System.exit(1);
}
if (buffer.getNumBanks() != 1)
{
System.err.println("Wrong image data format");
System.exit(1);
}
for(int i=0;i<10;i++){
for(int j=0;j<10;j++){
int pix = srcImage.getRGB(i, j);
Color mycolor = new Color(pix);
int red = mycolor.getRed();
int green = mycolor.getGreen();
int blue = mycolor.getBlue();
String val ="("+red+","+green+","+blue+")";
System.out.print(val);
}
System.out.println();
}
DataBufferByte byteBuffer = (DataBufferByte) buffer;
byte[] srcData =( (DataBufferByte) (buffer)).getData();
// Sanity check image
if (width * height * 3 != srcData.length) {
System.err.println("Unexpected image data size. Should be RGB image");
System.exit(1);
}
// Output Image info
System.out.printf("Loaded image: '%s', width: %d, height: %d, num bytes: %d\n", filename, width, height, srcData.length);
// Create Monochrome version - using basic threshold technique
byte[] monoData = new byte[width * height];
int srcPtr = 0;
int monoPtr = 0;
while (srcPtr < srcData.length)
{
int val = ((srcData[srcPtr]&0xFF) + (srcData[srcPtr+1]&0xFF) + (srcData[srcPtr+2]&0xFF)) / 3;
monoData[monoPtr] = (val > 128) ? 0 : (byte) 0xFF;
srcPtr += 3;
monoPtr += 1;
}
byte[] dstData = new byte[srcData.length];
// Create Blob Finder
BlobFinder finder = new BlobFinder(width, height);
ArrayList<BlobFinder.Blob> blobList = new ArrayList<BlobFinder.Blob>();
finder.detectBlobs(monoData, dstData, 0, -1, (byte)0, blobList);
// List Blobs
System.out.printf("Found %d blobs:\n", blobList.size());
for (BlobFinder.Blob blob : blobList){
System.out.println(blob);
}
}
public static void main(String[] args)
{
System.out.println("Blob Finder Demo");
new BlobDemo("/home/maruthi/Desktop/images.jpg");
}
}
打印这些值时，得到的结果完全不同。
我也尝试了以下方法。
public class BlobFinder
{
private byte[][] COLOUR_ARRAY = {{(byte)103, (byte)121, (byte)255},
{(byte)249, (byte)255, (byte)139},
{(byte)140, (byte)255, (byte)127},
{(byte)167, (byte)254, (byte)255},
{(byte)255, (byte)111, (byte)71}};
private int width;
private int height;
private int[] labelBuffer;
private int[] labelTable;
private int[] xMinTable;
private int[] xMaxTable;
private int[] yMinTable;
private int[] yMaxTable;
private int[] massTable;
static class Blob
{
public int xMin;
public int xMax;
public int yMin;
public int yMax;
public int mass;
public Blob(int xMin, int xMax, int yMin, int yMax, int mass)
{
this.xMin = xMin;
this.xMax = xMax;
this.yMin = yMin;
this.yMax = yMax;
this.mass = mass;
}
public String toString()
{
return String.format("X: %4d -> %4d, Y: %4d -> %4d, mass: %6d", xMin, xMax, yMin, yMax, mass);
}
}
public BlobFinder(int width, int height)
{
this.width = width;
this.height = height;
labelBuffer = new int[width * height];
// The maximum number of blobs is given by an image filled with equally spaced single pixel
// blobs. For images with less blobs, memory will be wasted, but this approach is simpler and
// probably quicker than dynamically resizing arrays
int tableSize = width * height / 4;
labelTable = new int[tableSize];
xMinTable = new int[tableSize];
xMaxTable = new int[tableSize];
yMinTable = new int[tableSize];
yMaxTable = new int[tableSize];
massTable = new int[tableSize];
}
public List<Blob> detectBlobs(byte[] srcData, byte[] dstData, int minBlobMass, int maxBlobMass, byte matchVal, List<Blob> blobList)
{
if (dstData != null && dstData.length != srcData.length * 3)
throw new IllegalArgumentException("Bad array lengths: srcData 1 byte/pixel (mono), dstData 3 bytes/pixel (RGB)");
// This is the neighbouring pixel pattern. For position X, A, B, C & D are checked
// A B C
// D X
int srcPtr = 0;
int aPtr = -width - 1;
int bPtr = -width;
int cPtr = -width + 1;
int dPtr = -1;
int label = 1;
// Iterate through pixels looking for connected regions. Assigning labels
for (int y=0 ; y<height ; y++)
{
for (int x=0 ; x<width ; x++)
{
labelBuffer[srcPtr] = 0;
// Check if on foreground pixel
if (srcData[srcPtr] == matchVal)
{
// Find label for neighbours (0 if out of range)
int aLabel = (x > 0 && y > 0) ? labelTable[labelBuffer[aPtr]] : 0;
int bLabel = (y > 0) ? labelTable[labelBuffer[bPtr]] : 0;
int cLabel = (x < width-1 && y > 0) ? labelTable[labelBuffer[cPtr]] : 0;
int dLabel = (x > 0) ? labelTable[labelBuffer[dPtr]] : 0;
// Look for label with least value
int min = Integer.MAX_VALUE;
if (aLabel != 0 && aLabel < min) min = aLabel;
if (bLabel != 0 && bLabel < min) min = bLabel;
if (cLabel != 0 && cLabel < min) min = cLabel;
if (dLabel != 0 && dLabel < min) min = dLabel;
// If no neighbours in foreground
if (min == Integer.MAX_VALUE)
{
labelBuffer[srcPtr] = label;
labelTable[label] = label;
// Initialise min/max x,y for label
yMinTable[label] = y;
yMaxTable[label] = y;
xMinTable[label] = x;
xMaxTable[label] = x;
massTable[label] = 1;
label ++;
}
// Neighbour found
else
{
// Label pixel with lowest label from neighbours
labelBuffer[srcPtr] = min;
// Update min/max x,y for label
yMaxTable[min] = y;
massTable[min]++;
if (x < xMinTable[min]) xMinTable[min] = x;
if (x > xMaxTable[min]) xMaxTable[min] = x;
if (aLabel != 0) labelTable[aLabel] = min;
if (bLabel != 0) labelTable[bLabel] = min;
if (cLabel != 0) labelTable[cLabel] = min;
if (dLabel != 0) labelTable[dLabel] = min;
}
}
srcPtr ++;
aPtr ++;
bPtr ++;
cPtr ++;
dPtr ++;
}
}
// Iterate through labels pushing min/max x,y values towards minimum label
if (blobList == null) blobList = new ArrayList<Blob>();
for (int i=label-1 ; i>0 ; i--)
{
if (labelTable[i] != i)
{
if (xMaxTable[i] > xMaxTable[labelTable[i]]) xMaxTable[labelTable[i]] = xMaxTable[i];
if (xMinTable[i] < xMinTable[labelTable[i]]) xMinTable[labelTable[i]] = xMinTable[i];
if (yMaxTable[i] > yMaxTable[labelTable[i]]) yMaxTable[labelTable[i]] = yMaxTable[i];
if (yMinTable[i] < yMinTable[labelTable[i]]) yMinTable[labelTable[i]] = yMinTable[i];
massTable[labelTable[i]] += massTable[i];
int l = i;
while (l != labelTable[l]) l = labelTable[l];
labelTable[i] = l;
}
else
{
// Ignore blobs that butt against corners
if (i == labelBuffer[0]) continue; // Top Left
if (i == labelBuffer[width]) continue; // Top Right
if (i == labelBuffer[(width*height) - width + 1]) continue; // Bottom Left
if (i == labelBuffer[(width*height) - 1]) continue; // Bottom Right
if (massTable[i] >= minBlobMass && (massTable[i] <= maxBlobMass || maxBlobMass == -1))
{
Blob blob = new Blob(xMinTable[i], xMaxTable[i], yMinTable[i], yMaxTable[i], massTable[i]);
blobList.add(blob);
}
}
}
// If dst buffer provided, fill with coloured blobs
if (dstData != null)
{
for (int i=label-1 ; i>0 ; i--)
{
if (labelTable[i] != i)
{
int l = i;
while (l != labelTable[l]) l = labelTable[l];
labelTable[i] = l;
}
}
// Renumber lables into sequential numbers, starting with 0
int newLabel = 0;
for (int i=1 ; i<label ; i++)
{
if (labelTable[i] == i) labelTable[i] = newLabel++;
else labelTable[i] = labelTable[labelTable[i]];
}
srcPtr = 0;
int dstPtr = 0;
while (srcPtr < srcData.length)
{
if (srcData[srcPtr] == matchVal)
{
int c = labelTable[labelBuffer[srcPtr]] % COLOUR_ARRAY.length;
dstData[dstPtr] = COLOUR_ARRAY[c][0];
dstData[dstPtr+1] = COLOUR_ARRAY[c][1];
dstData[dstPtr+2] = COLOUR_ARRAY[c][2];
}
else
{
dstData[dstPtr] = 0;
dstData[dstPtr+1] = 0;
dstData[dstPtr+2] = 0;
}
srcPtr ++;
dstPtr += 3;
}
}
return blobList;
}
}
示例：
后端代码：
for(int i=0;i<10;i++) {
for(int j=0;j<10;j++) {
int pix= src.getPixel(i,j);
int red = Color.red(pix);
int green= Color.green(pix);
int blue = Color.blue(pix);
int alpha = Color.alpha(pix);
String val ="("+red+","+green+","+blue+")";
System.out.print(val);
}
System.out.println();
}
后端输出：
int[] pix1=new int[width*height];
src.getPixels(pix1, 0, width, 0, 0, width , height );
byte[] pixels= new byte[pix1.length*3];
for (int i = 0; i < pix1.length; i++) {
int td = pix1[i];
int tind = i * 3;
pixels[tind++] = (byte) ((td >> 0) & 0xFF);
pixels[tind++] = (byte) ((td >> 8) & 0xFF);
pixels[tind++] = (byte) ((td >> 16) & 0xFF);
}
Android代码：
for(int i=0;i<10;i++){
for(int j=0;j<10;j++){
int pix = srcImage.getRGB(i, j);
Color mycolor = new Color(pix);
int red = mycolor.getRed();
int green = mycolor.getGreen();
int blue = mycolor.getBlue();
String val ="("+red+","+green+","+blue+")";
System.out.print(val);
}
System.out.println();
}
Android输出：
(253,253,253)(254,254,254)(254,254,254)(254,254,254)(254,254,254)(254,254,254)(254,254,254)(254,254,254)(254,254,254)(254,254,254)
(254,254,254)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)
(254,254,254)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)
(254,254,254)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)
(254,254,254)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)
(254,254,254)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)
(254,254,254)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)
(254,254,254)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)
(254,254,254)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)
(254,254,254)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)(255,255,255)
如何在Android中获得与原始功能相同的字节数组？
0 个答案:
没有答案
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