如何使用boofcv提取象形文字？
How to extract pictogram using boofcv?
我在将象形图提取为某种可进一步处理的格式时遇到了问题，因为现在我遇到了这样的问题：
当前解决方案的一部分取自 BoofCV ImageTresholding 示例。我对此解决方案的代码如下：
import georegression.metric.UtilAngle;
import java.awt.Color;
import java.awt.event.MouseAdapter;
import java.awt.event.MouseEvent;
import java.awt.image.BufferedImage;
import java.awt.image.WritableRaster;
import java.io.IOException;
import boofcv.alg.color.ColorHsv;
import boofcv.alg.filter.binary.BinaryImageOps;
import boofcv.alg.filter.binary.GThresholdImageOps;
import boofcv.alg.filter.binary.ThresholdImageOps;
import boofcv.gui.ListDisplayPanel;
import boofcv.gui.binary.VisualizeBinaryData;
import boofcv.gui.image.ImagePanel;
import boofcv.gui.image.ShowImages;
import boofcv.io.image.ConvertBufferedImage;
import boofcv.io.image.UtilImageIO;
import boofcv.struct.image.ImageFloat32;
import boofcv.struct.image.ImageUInt8;
import boofcv.struct.image.MultiSpectral;
public class Binaryzation {
static double splitFraction = 0.05;
static double minimumSideFraction = 0.1;
static ListDisplayPanel gui = new ListDisplayPanel();
public static void printClickedColor(final BufferedImage image) {
ImagePanel gui = new ImagePanel(image);
gui.addMouseListener(new MouseAdapter() {
@Override
public void mouseClicked(MouseEvent e) {
float[] color = new float[3];
int rgb = image.getRGB(e.getX(), e.getY());
ColorHsv.rgbToHsv((rgb >> 16) & 0xFF, (rgb >> 8) & 0xFF,
rgb & 0xFF, color);
System.out.println("H = " + color[0] + " S = " + color[1]
+ " V = " + color[2]);
try {
showSelectedColor("Selected", image, color[0], color[1]);
} catch (IOException e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
}
});
ShowImages.showWindow(gui, "Color Selector");
}
public static void showSelectedColor(String name, BufferedImage image,
float hue, float saturation) throws IOException {
ImageUInt8 binary = binaryTreshold(name, image, hue, saturation);
// MAGIC HAPPENDS -removing small objects
ImageUInt8 filtered = BinaryImageOps.erode4(binary, 1, null);
filtered = BinaryImageOps.dilate8(filtered, 1, null);
filtered = BinaryImageOps.removePointNoise(filtered, filtered);
ShowImages.showWindow(filtered, "Binary " + name);
BufferedImage visualFiltered1 = VisualizeBinaryData.renderBinary(
filtered, true, null);
ShowImages.showWindow(visualFiltered1, "Mask");
BufferedImage visualFiltered12 = Fill.fill(visualFiltered1);
ShowImages.showWindow(visualFiltered12, "Filled Mask");
ImageUInt8 mask = ConvertBufferedImage.convertFromSingle(
visualFiltered12, null, ImageUInt8.class);
ImageUInt8 wynik = new ImageUInt8(mask.width, mask.height);
//subtraction of images: wynik=mask-filtered;
int min = 0;
int max = 1;
for (int i = 0; i < mask.height; i++) {
// System.out.println("i=" + i);
for (int j = 0; j < mask.width; j++) {
// System.out.println("j=" + j);
if (filtered.get(j, i) < min)
min = filtered.get(j, i);
if (filtered.get(j, i) > max)
max = filtered.get(j, i);
int filtInt = filtered.get(j, i);
if (filtInt >= 1)
filtInt = 1;
else if (filtInt < 1)
filtInt = 0;
int maskInt = mask.get(j, i);
if (maskInt >= 1)
maskInt = 0;
else if (maskInt < 1)
maskInt = 1;
int diff = maskInt - filtInt;
if (diff == 1) {
diff = 255;
wynik.set(j, i, diff);
} else if (diff == 0) {
diff = 0;
wynik.set(j, i, diff);
} else {
diff = 255;
wynik.set(j, i, diff);
}
}
}
ShowImages.showWindow(wynik, "Wynik=Mask-Filtered");
wynik = BinaryImageOps.erode4(wynik, 1, null);
wynik = BinaryImageOps.dilate8(wynik, 1, null);
wynik = BinaryImageOps.removePointNoise(wynik, wynik);
UtilImageIO.saveImage(wynik, "C:/dev/zdjeciaTestowe/wynik.jpg");
ShowImages.showWindow(wynik, "Wynik=Mask-Filtered After noise remove");
}
private static ImageUInt8 binaryTreshold(String name, BufferedImage image,
float hue, float saturation) throws IOException {
MultiSpectral<ImageFloat32> input = ConvertBufferedImage
.convertFromMulti(image, null, true, ImageFloat32.class);
MultiSpectral<ImageFloat32> hsv = input.createSameShape();
// Convert into HSV
ColorHsv.rgbToHsv_F32(input, hsv);
// Euclidean distance squared threshold for deciding which pixels are
// members of the selected set
float maxDist2 = 0.4f * 0.4f;
// Extract hue and saturation bands which are independent of intensity
ImageFloat32 H = hsv.getBand(0);
ImageFloat32 S = hsv.getBand(1);
// Adjust the relative importance of Hue and Saturation.
// Hue has a range of 0 to 2*PI and Saturation from 0 to 1.
float adjustUnits = (float) (Math.PI / 2.0);
// step through each pixel and mark how close it is to the selected
// color
BufferedImage output = new BufferedImage(input.width, input.height,
BufferedImage.TYPE_INT_RGB);
for (int y = 0; y < hsv.height; y++) {
for (int x = 0; x < hsv.width; x++) {
// Hue is an angle in radians, so simple subtraction doesn't
// work
float dh = UtilAngle.dist(H.unsafe_get(x, y), hue);
float ds = (S.unsafe_get(x, y) - saturation) * adjustUnits;
// this distance measure is a bit naive, but good enough for to
// demonstrate the concept
float dist2 = dh * dh + ds * ds;
if (dist2 <= maxDist2) {
System.out.println(image.getRGB(x, y));
output.setRGB(x, y, image.getRGB(x, y));
}
}
}
ImageFloat32 output1 = ConvertBufferedImage.convertFromSingle(output,
null, ImageFloat32.class);
ImageUInt8 binary = new ImageUInt8(input.width, input.height);
double threshold = GThresholdImageOps.computeOtsu(output1, 0, 255);
// Apply the threshold to create a binary image
ThresholdImageOps.threshold(output1, binary, (float) threshold, true);
return binary;
}
public static void main(String args[]) throws IOException {
BufferedImage image = UtilImageIO
.loadImage("C:/dev/zdjeciaTestowe/images.jpg");
// Let the user select a color
printClickedColor(image);
// Display pre-selected colors
showSelectedColor("Yellow", image, 1f, 1f);
}
}
import java.awt.image.BufferedImage;
import boofcv.struct.image.ImageUInt8;
public class Fill {
private static final int BLACK = -16777216;
private static final int WHITE = -1;
/**
* @param input Buffered image
* @return image with filled holes
*/
public static BufferedImage fill(BufferedImage input) {
int width = input.getWidth();
int height = input.getHeight();
BufferedImage output=new BufferedImage(width, height, input.getType());
for (int i = 0; i < height; i++) {
// System.out.println("i=" + i);
for (int j = 0; j < width; j++) {
// System.out.println("j=" + j);
if (input.getRGB(j, i) == WHITE) {
output.setRGB(j, i, WHITE);
} else if (isPreviusWhite(j, i, input)
&& isAnotherWhiteInLine(j, i, input)) {
output.setRGB(j, i, WHITE);
}
}
}
return output;
}
private static boolean isPreviusWhite(int i, int i2, BufferedImage input) {
boolean condition = false;
while (1 < i2) {
if (input.getRGB(i, i2) == WHITE)
return true;
i2--;
}
return condition;
}
private static boolean isAnotherWhiteInLine(int j, int i,
BufferedImage input) {
boolean condition = false;
while (j < input.getWidth()) {
if (input.getRGB(j, i) == WHITE)
return true;
j++;
}
return condition;
}
}
我知道如何在标志上提取象形图，我已经通过从填充蒙版中减去蒙版来完成，但是在获得一些可处理的结果时遇到问题，
因为最后我有一个灰度图像而不是二进制图像（或者在 boofCV ImageUInt8 中）。
如何正确地减去 ImageUInt8 格式的两个图像，使结果也为 ImageUInt8？
今天我已经写了那个算法的更多部分，现在我想问的问题更清楚了。这里添加了代码（部分来自 //subtraction of images: wynik=mask-filtered;）和 2 张附加图片作为处理的产物。
问题是去噪后的最后一张图片是纯黑色的，没有任何信息。如何正确转换图像以获得可处理的内容？？
我做错了什么？
我在最后一张图片上找到了我的问题的解决方案 "Wynik=Mask-Filtered After noise Remove" 有一个象形图，但是灰度中的差异 beetwen piksels 太低以至于很难看到所以问题解决者正在添加：
GrayImageOps.stretch(维尼克, 125, 125, 255, 维尼克);
我在将象形图提取为某种可进一步处理的格式时遇到了问题，因为现在我遇到了这样的问题：
当前解决方案的一部分取自 BoofCV ImageTresholding 示例。我对此解决方案的代码如下：
import georegression.metric.UtilAngle;
import java.awt.Color;
import java.awt.event.MouseAdapter;
import java.awt.event.MouseEvent;
import java.awt.image.BufferedImage;
import java.awt.image.WritableRaster;
import java.io.IOException;
import boofcv.alg.color.ColorHsv;
import boofcv.alg.filter.binary.BinaryImageOps;
import boofcv.alg.filter.binary.GThresholdImageOps;
import boofcv.alg.filter.binary.ThresholdImageOps;
import boofcv.gui.ListDisplayPanel;
import boofcv.gui.binary.VisualizeBinaryData;
import boofcv.gui.image.ImagePanel;
import boofcv.gui.image.ShowImages;
import boofcv.io.image.ConvertBufferedImage;
import boofcv.io.image.UtilImageIO;
import boofcv.struct.image.ImageFloat32;
import boofcv.struct.image.ImageUInt8;
import boofcv.struct.image.MultiSpectral;
public class Binaryzation {
static double splitFraction = 0.05;
static double minimumSideFraction = 0.1;
static ListDisplayPanel gui = new ListDisplayPanel();
public static void printClickedColor(final BufferedImage image) {
ImagePanel gui = new ImagePanel(image);
gui.addMouseListener(new MouseAdapter() {
@Override
public void mouseClicked(MouseEvent e) {
float[] color = new float[3];
int rgb = image.getRGB(e.getX(), e.getY());
ColorHsv.rgbToHsv((rgb >> 16) & 0xFF, (rgb >> 8) & 0xFF,
rgb & 0xFF, color);
System.out.println("H = " + color[0] + " S = " + color[1]
+ " V = " + color[2]);
try {
showSelectedColor("Selected", image, color[0], color[1]);
} catch (IOException e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
}
});
ShowImages.showWindow(gui, "Color Selector");
}
public static void showSelectedColor(String name, BufferedImage image,
float hue, float saturation) throws IOException {
ImageUInt8 binary = binaryTreshold(name, image, hue, saturation);
// MAGIC HAPPENDS -removing small objects
ImageUInt8 filtered = BinaryImageOps.erode4(binary, 1, null);
filtered = BinaryImageOps.dilate8(filtered, 1, null);
filtered = BinaryImageOps.removePointNoise(filtered, filtered);
ShowImages.showWindow(filtered, "Binary " + name);
BufferedImage visualFiltered1 = VisualizeBinaryData.renderBinary(
filtered, true, null);
ShowImages.showWindow(visualFiltered1, "Mask");
BufferedImage visualFiltered12 = Fill.fill(visualFiltered1);
ShowImages.showWindow(visualFiltered12, "Filled Mask");
ImageUInt8 mask = ConvertBufferedImage.convertFromSingle(
visualFiltered12, null, ImageUInt8.class);
ImageUInt8 wynik = new ImageUInt8(mask.width, mask.height);
//subtraction of images: wynik=mask-filtered;
int min = 0;
int max = 1;
for (int i = 0; i < mask.height; i++) {
// System.out.println("i=" + i);
for (int j = 0; j < mask.width; j++) {
// System.out.println("j=" + j);
if (filtered.get(j, i) < min)
min = filtered.get(j, i);
if (filtered.get(j, i) > max)
max = filtered.get(j, i);
int filtInt = filtered.get(j, i);
if (filtInt >= 1)
filtInt = 1;
else if (filtInt < 1)
filtInt = 0;
int maskInt = mask.get(j, i);
if (maskInt >= 1)
maskInt = 0;
else if (maskInt < 1)
maskInt = 1;
int diff = maskInt - filtInt;
if (diff == 1) {
diff = 255;
wynik.set(j, i, diff);
} else if (diff == 0) {
diff = 0;
wynik.set(j, i, diff);
} else {
diff = 255;
wynik.set(j, i, diff);
}
}
}
ShowImages.showWindow(wynik, "Wynik=Mask-Filtered");
wynik = BinaryImageOps.erode4(wynik, 1, null);
wynik = BinaryImageOps.dilate8(wynik, 1, null);
wynik = BinaryImageOps.removePointNoise(wynik, wynik);
UtilImageIO.saveImage(wynik, "C:/dev/zdjeciaTestowe/wynik.jpg");
ShowImages.showWindow(wynik, "Wynik=Mask-Filtered After noise remove");
}
private static ImageUInt8 binaryTreshold(String name, BufferedImage image,
float hue, float saturation) throws IOException {
MultiSpectral<ImageFloat32> input = ConvertBufferedImage
.convertFromMulti(image, null, true, ImageFloat32.class);
MultiSpectral<ImageFloat32> hsv = input.createSameShape();
// Convert into HSV
ColorHsv.rgbToHsv_F32(input, hsv);
// Euclidean distance squared threshold for deciding which pixels are
// members of the selected set
float maxDist2 = 0.4f * 0.4f;
// Extract hue and saturation bands which are independent of intensity
ImageFloat32 H = hsv.getBand(0);
ImageFloat32 S = hsv.getBand(1);
// Adjust the relative importance of Hue and Saturation.
// Hue has a range of 0 to 2*PI and Saturation from 0 to 1.
float adjustUnits = (float) (Math.PI / 2.0);
// step through each pixel and mark how close it is to the selected
// color
BufferedImage output = new BufferedImage(input.width, input.height,
BufferedImage.TYPE_INT_RGB);
for (int y = 0; y < hsv.height; y++) {
for (int x = 0; x < hsv.width; x++) {
// Hue is an angle in radians, so simple subtraction doesn't
// work
float dh = UtilAngle.dist(H.unsafe_get(x, y), hue);
float ds = (S.unsafe_get(x, y) - saturation) * adjustUnits;
// this distance measure is a bit naive, but good enough for to
// demonstrate the concept
float dist2 = dh * dh + ds * ds;
if (dist2 <= maxDist2) {
System.out.println(image.getRGB(x, y));
output.setRGB(x, y, image.getRGB(x, y));
}
}
}
ImageFloat32 output1 = ConvertBufferedImage.convertFromSingle(output,
null, ImageFloat32.class);
ImageUInt8 binary = new ImageUInt8(input.width, input.height);
double threshold = GThresholdImageOps.computeOtsu(output1, 0, 255);
// Apply the threshold to create a binary image
ThresholdImageOps.threshold(output1, binary, (float) threshold, true);
return binary;
}
public static void main(String args[]) throws IOException {
BufferedImage image = UtilImageIO
.loadImage("C:/dev/zdjeciaTestowe/images.jpg");
// Let the user select a color
printClickedColor(image);
// Display pre-selected colors
showSelectedColor("Yellow", image, 1f, 1f);
}
}
import java.awt.image.BufferedImage;
import boofcv.struct.image.ImageUInt8;
public class Fill {
private static final int BLACK = -16777216;
private static final int WHITE = -1;
/**
* @param input Buffered image
* @return image with filled holes
*/
public static BufferedImage fill(BufferedImage input) {
int width = input.getWidth();
int height = input.getHeight();
BufferedImage output=new BufferedImage(width, height, input.getType());
for (int i = 0; i < height; i++) {
// System.out.println("i=" + i);
for (int j = 0; j < width; j++) {
// System.out.println("j=" + j);
if (input.getRGB(j, i) == WHITE) {
output.setRGB(j, i, WHITE);
} else if (isPreviusWhite(j, i, input)
&& isAnotherWhiteInLine(j, i, input)) {
output.setRGB(j, i, WHITE);
}
}
}
return output;
}
private static boolean isPreviusWhite(int i, int i2, BufferedImage input) {
boolean condition = false;
while (1 < i2) {
if (input.getRGB(i, i2) == WHITE)
return true;
i2--;
}
return condition;
}
private static boolean isAnotherWhiteInLine(int j, int i,
BufferedImage input) {
boolean condition = false;
while (j < input.getWidth()) {
if (input.getRGB(j, i) == WHITE)
return true;
j++;
}
return condition;
}
}
我知道如何在标志上提取象形图，我已经通过从填充蒙版中减去蒙版来完成，但是在获得一些可处理的结果时遇到问题，
因为最后我有一个灰度图像而不是二进制图像（或者在 boofCV ImageUInt8 中）。
如何正确地减去 ImageUInt8 格式的两个图像，使结果也为 ImageUInt8？
今天我已经写了那个算法的更多部分，现在我想问的问题更清楚了。这里添加了代码（部分来自 //subtraction of images: wynik=mask-filtered;）和 2 张附加图片作为处理的产物。
问题是去噪后的最后一张图片是纯黑色的，没有任何信息。如何正确转换图像以获得可处理的内容？？ 我做错了什么？
我在最后一张图片上找到了我的问题的解决方案 "Wynik=Mask-Filtered After noise Remove" 有一个象形图，但是灰度中的差异 beetwen piksels 太低以至于很难看到所以问题解决者正在添加： GrayImageOps.stretch(维尼克, 125, 125, 255, 维尼克);