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Merge branch 'master' of https://nfeybesse@github.com/genericsystem/g…
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@nfeybesse
nfeybesse committed Jun 29, 2017
2 parents cdfd5ea + 9a5e9a3 commit 759d4b6
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Showing 6 changed files with 149 additions and 116 deletions.
104 changes: 56 additions & 48 deletions gs-cv/src/main/java/org/genericsystem/cv/Img.java
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Original file line number Diff line number Diff line change
Expand Up @@ -363,7 +363,7 @@ public Img multiply(Scalar scalar) {
Core.multiply(src, scalar, result);
return new Img(result);
}

public Img mser() {
Img gray = bgr2Gray();
MatOfKeyPoint keypoint = new MatOfKeyPoint();
Expand Down Expand Up @@ -391,7 +391,7 @@ public Img mser() {
}
return new Img(result).morphologyEx(Imgproc.MORPH_CLOSE, Imgproc.MORPH_RECT, new Size(17, 3));
}

public Img grad() {
Img gray = bgr2Gray();
Img grad = gray.morphologyEx(Imgproc.MORPH_GRADIENT, Imgproc.MORPH_ELLIPSE, new Size(3, 3));
Expand All @@ -407,59 +407,54 @@ public Img grad() {
// return threshold.morphologyEx(Imgproc.MORPH_CLOSE, new
// StructuringElement(Imgproc.MORPH_RECT, new Size(17, 3)));
// }

public Img sobel() {
Img gray = bgr2Gray();
Img sobel = gray.sobel(CvType.CV_8UC1, 1, 0, 3, 1, 0, Core.BORDER_DEFAULT);
Img threshold = sobel.thresHold(0, 255, Imgproc.THRESH_BINARY + Imgproc.THRESH_OTSU);
return threshold.morphologyEx(Imgproc.MORPH_CLOSE, Imgproc.MORPH_RECT, new Size(17, 3));
}

public Img bernsen() {
return bernsen(31, 15);
}

public Img otsu() {
return bgr2Gray().thresHold(0, 255, Imgproc.THRESH_BINARY + Imgproc.THRESH_OTSU);
}

public Img otsuAfterGaussianBlur() {
return otsuAfterGaussianBlur(new Size(5,5));
return otsuAfterGaussianBlur(new Size(5, 5));
}

public Img otsuInv() {
return bgr2Gray().thresHold(0, 255, Imgproc.THRESH_BINARY_INV + Imgproc.THRESH_OTSU);
}

public Img equalizeHisto() {
Mat result = new Mat();
Imgproc.equalizeHist(bgr2Gray().getSrc(), result);
// Imgproc.cvtColor(src, result, Imgproc.COLOR_BGR2YCrCb);
// List<Mat> channels = new ArrayList<>();
// Core.split(result, channels);
// Imgproc.equalizeHist(channels.get(0), channels.get(0));
// Imgproc.equalizeHist(channels.get(1), channels.get(1));
// Imgproc.equalizeHist(channels.get(2), channels.get(2));
// Core.merge(channels, result);
// Imgproc.cvtColor(result, result, Imgproc.COLOR_YCrCb2BGR);
return new Img(result);
}

// Equalize histograms using a Contrast Limited Adaptive Histogram
// Equalization algorithm
public Img equalizeHistoAdaptative() {
public Img equalizeHisto() {
Mat result = new Mat();
Mat channelL = new Mat();
CLAHE clahe = Imgproc.createCLAHE(2.0, new Size(8, 8));
Imgproc.cvtColor(src, result, Imgproc.COLOR_BGR2Lab);
// Extract the luminance (L) channel and apply filter
Mat channelL = new Mat();
Core.extractChannel(result, channelL, 0);
clahe.apply(channelL, channelL);
Imgproc.equalizeHist(channelL, channelL);
Core.insertChannel(channelL, result, 0);
Imgproc.cvtColor(result, result, Imgproc.COLOR_Lab2BGR);
// Imgproc.equalizeHist(bgr2Gray().getSrc(), result);
// Imgproc.cvtColor(src, result, Imgproc.COLOR_BGR2YCrCb);
// List<Mat> channels = new ArrayList<>();
// Core.split(result, channels);
// Imgproc.equalizeHist(channels.get(0), channels.get(0));
// Imgproc.equalizeHist(channels.get(1), channels.get(1));
// Imgproc.equalizeHist(channels.get(2), channels.get(2));
// Core.merge(channels, result);
// Imgproc.cvtColor(result, result, Imgproc.COLOR_YCrCb2BGR);
return new Img(result);
}


public Img equalizeHistoAdaptative() {
return equalizeHistoAdaptative(2.0, new Size(8, 8));
}

public Img adaptativeMeanThreshold() {
return adaptativeMeanThreshold(11, 2);
}
Expand All @@ -473,21 +468,35 @@ public Img niblackThreshold() {
}

public Img sauvolaThreshold() {
return sauvolaThreshold(11, 2);
return sauvolaThreshold(11, 2);
}

public Img nickThreshold() {
return nickThreshold(11, 2);
return nickThreshold(11, 2);
}

public Img wolfThreshold() {
return wolfThreshold(11, 2);
return wolfThreshold(11, 2);
}

// Equalize histograms using a Contrast Limited Adaptive Histogram
// Equalization algorithm
public Img equalizeHistoAdaptative(double clipLimit, Size titleGridSize) {
Mat result = new Mat();
Mat channelL = new Mat();
CLAHE clahe = Imgproc.createCLAHE(clipLimit, titleGridSize);
Imgproc.cvtColor(src, result, Imgproc.COLOR_BGR2Lab);
// Extract the luminance (L) channel and apply filter
Core.extractChannel(result, channelL, 0);
clahe.apply(channelL, channelL);
Core.insertChannel(channelL, result, 0);
Imgproc.cvtColor(result, result, Imgproc.COLOR_Lab2BGR);
return new Img(result);
}

public Img otsuAfterGaussianBlur(Size blurSize) {
// Same as otsu filtering, but a Gaussian blur is applied first
return bgr2Gray().gaussianBlur(blurSize).thresHold(0, 255,
Imgproc.THRESH_BINARY + Imgproc.THRESH_OTSU);
return bgr2Gray().gaussianBlur(blurSize).thresHold(0, 255, Imgproc.THRESH_BINARY + Imgproc.THRESH_OTSU);
}

public Img sobel(int ddepth, int dx, int dy, int ksize, double scale, double delta, int borderType) {
Expand All @@ -507,7 +516,7 @@ public Img thresHold(double thresh, double maxval, int type) {
Imgproc.threshold(src, result, thresh, maxval, type);
return new Img(result);
}

public Img bernsen(int ksize, int contrast_limit) {
Img gray = bgr2Gray();
Mat ret = Mat.zeros(gray.size(), gray.type());
Expand Down Expand Up @@ -568,40 +577,39 @@ public Img dilateBlacks(double valueThreshold, double saturatioThreshold, double
}

public Img adaptativeMeanThreshold(int blockSize, double C) {
return bgr2Gray().adaptativeThresHold(255, Imgproc.ADAPTIVE_THRESH_MEAN_C,
Imgproc.THRESH_BINARY, blockSize, C);
return bgr2Gray().adaptativeThresHold(255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY, blockSize, C);
}

public Img adaptativeGaussianThreshold(int blockSize, double C) {
return bgr2Gray().adaptativeThresHold(255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,
Imgproc.THRESH_BINARY, blockSize, C);
return bgr2Gray().adaptativeThresHold(255, Imgproc.ADAPTIVE_THRESH_GAUSSIAN_C, Imgproc.THRESH_BINARY, blockSize,
C);
}

public Img niblackThreshold(int blockSize, double k) {
Mat result = new Mat();
Ximgproc.niBlackThreshold(bgr2Gray().getSrc(), result, 255, Imgproc.THRESH_BINARY,
blockSize, k, Ximgproc.BINARIZATION_NIBLACK);
Ximgproc.niBlackThreshold(bgr2Gray().getSrc(), result, 255, Imgproc.THRESH_BINARY, blockSize, k,
Ximgproc.BINARIZATION_NIBLACK);
return new Img(result);
}

public Img sauvolaThreshold(int blockSize, double k) {
Mat result = new Mat();
Ximgproc.niBlackThreshold(bgr2Gray().getSrc(), result, 255, Imgproc.THRESH_BINARY,
blockSize, k, Ximgproc.BINARIZATION_SAUVOLA);
Ximgproc.niBlackThreshold(bgr2Gray().getSrc(), result, 255, Imgproc.THRESH_BINARY, blockSize, k,
Ximgproc.BINARIZATION_SAUVOLA);
return new Img(result);
}

public Img nickThreshold(int blockSize, double k) {
Mat result = new Mat();
Ximgproc.niBlackThreshold(bgr2Gray().getSrc(), result, 255, Imgproc.THRESH_BINARY,
blockSize, k, Ximgproc.BINARIZATION_NICK);
Ximgproc.niBlackThreshold(bgr2Gray().getSrc(), result, 255, Imgproc.THRESH_BINARY, blockSize, k,
Ximgproc.BINARIZATION_NICK);
return new Img(result);
}

public Img wolfThreshold(int blockSize, double k) {
Mat result = new Mat();
Ximgproc.niBlackThreshold(bgr2Gray().getSrc(), result, 255, Imgproc.THRESH_BINARY,
blockSize, k, Ximgproc.BINARIZATION_WOLF);
Ximgproc.niBlackThreshold(bgr2Gray().getSrc(), result, 255, Imgproc.THRESH_BINARY, blockSize, k,
Ximgproc.BINARIZATION_WOLF);
return new Img(result);
}

Expand Down
2 changes: 1 addition & 1 deletion gs-cv/src/main/java/org/genericsystem/cv/comparator/ComputeTrainedScores.java
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,7 @@
public class ComputeTrainedScores {

private final static String docType = "id-fr-front";
private static final String gsPath = System.getenv("HOME") + "/genericsystem/gs-cv_model/";
private static final String gsPath = System.getenv("HOME") + "/genericsystem/gs-cv_model2/";
private final static Engine engine = new Engine(gsPath, Doc.class,
ImgFilter.class, ZoneGeneric.class, ZoneText.class, Score.class, MeanLevenshtein.class);

Expand Down
6 changes: 3 additions & 3 deletions gs-cv/src/main/java/org/genericsystem/cv/comparator/GetDataInModel.java
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Original file line number Diff line number Diff line change
Expand Up @@ -70,7 +70,7 @@ public static void compute() {
DocClassInstance docClassInstance = docClass.addDocClass(docType);

// Set all the filter names
Map<String,Function<Img,Img>> imgFilters = fillAlgoMap();
Map<String,Function<Img,Img>> imgFilters = fillAlgorithmMap();

// Load the accurate zones
final Zones zones = Zones.load(imgClassDirectory);
Expand All @@ -90,7 +90,7 @@ public static void compute() {
// Persist the changes
engine.getCurrentCache().flush();

// Process each file in the subfolder "/ref2/"
// Process each file in folder imgDirectory
Arrays.asList(new File(imgDirectory).listFiles((dir, name) -> name.endsWith(".png"))).stream().forEach(file -> {
engine.getCurrentCache().mount();
log.info("\nProcessing file: {}", file.getName());
Expand Down Expand Up @@ -148,7 +148,7 @@ public static void compute() {
engine.getCurrentCache().flush();
}

private static Map<String, Function<Img, Img>> fillAlgoMap(){
private static Map<String, Function<Img, Img>> fillAlgorithmMap(){
final Map<String,Function<Img,Img>> map = new HashMap<>();
map.put("original", Img::bgr2Gray);
map.put("reality", Img::bgr2Gray);
Expand Down

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