但是，正如我們所看到的圖像都具有相同的視覺元素（）。
因此，我們應該比較不是整個圖像，而是「相同的視覺元素」。如果您不比較「模板」和「相機」圖像本身，但是以相同方式處理（例如轉換爲二進制黑色/白色）「模板」和「相機」圖像，則可以提高Match的值。例如，嘗試在兩個（「模板」和「相機」）圖像上找到藍色（模板徽標的背景）方塊，並比較該方塊（感興趣區域）。代碼可以是類似的東西：
Bitmap bmImageTemplate = <get your template image Bitmap>;
Bitmap bmTemplate = findLogo(bmImageTemplate); // process template image
Bitmap bmImage = <get your camera image Bitmap>;
Bitmap bmLogo = findLogo(bmImage); // process camera image same way
compareBitmaps(bmTemplate, bmLogo);
其中
private Bitmap findLogo(Bitmap sourceBitmap) {
Bitmap roiBitmap = null;
Mat sourceMat = new Mat(sourceBitmap.getWidth(), sourceBitmap.getHeight(), CvType.CV_8UC3);
Utils.bitmapToMat(sourceBitmap, sourceMat);
Mat roiTmp = sourceMat.clone();
final Mat hsvMat = new Mat();
sourceMat.copyTo(hsvMat);
// convert mat to HSV format for Core.inRange()
Imgproc.cvtColor(hsvMat, hsvMat, Imgproc.COLOR_RGB2HSV);
Scalar lowerb = new Scalar(85, 50, 40); // lower color border for BLUE
Scalar upperb = new Scalar(135, 255, 255); // upper color border for BLUE
Core.inRange(hsvMat, lowerb, upperb, roiTmp); // select only blue pixels
// find contours
List<MatOfPoint> contours = new ArrayList<>();
List<Rect> squares = new ArrayList<>();
Imgproc.findContours(roiTmp, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
// find appropriate bounding rectangles
for (MatOfPoint contour : contours) {
MatOfPoint2f areaPoints = new MatOfPoint2f(contour.toArray());
RotatedRect boundingRect = Imgproc.minAreaRect(areaPoints);
double rectangleArea = boundingRect.size.area();
// test min ROI area in pixels
if (rectangleArea > 400) {
Point rotated_rect_points[] = new Point[4];
boundingRect.points(rotated_rect_points);
Rect rect = Imgproc.boundingRect(new MatOfPoint(rotated_rect_points));
double aspectRatio = rect.width > rect.height ?
(double) rect.height/(double) rect.width : (double) rect.width/(double) rect.height;
if (aspectRatio >= 0.9) {
squares.add(rect);
}
}
}
Mat logoMat = extractSquareMat(roiTmp, getBiggestSquare(squares));
roiBitmap = Bitmap.createBitmap(logoMat.cols(), logoMat.rows(), Bitmap.Config.ARGB_8888);
Utils.matToBitmap(logoMat, roiBitmap);
return roiBitmap;
}
方法extractSquareMat()只是提取整幅圖像
public static Mat extractSquareMat(Mat sourceMat, Rect rect) {
Mat squareMat = null;
int padding = 50;
if (rect != null) {
Rect truncatedRect = new Rect((int) rect.tl().x + padding, (int) rect.tl().y + padding,
rect.width - 2 * padding, rect.height - 2 * padding);
squareMat = new Mat(sourceMat, truncatedRect);
}
return squareMat ;
}
和compareBitmaps()利息（標誌）的區域只是包裝器代碼：
private void compareBitmaps(Bitmap bitmap1, Bitmap bitmap2) {
Mat mat1 = new Mat(bitmap1.getWidth(), bitmap1.getHeight(), CvType.CV_8UC3);
Utils.bitmapToMat(bitmap1, mat1);
Mat mat2 = new Mat(bitmap2.getWidth(), bitmap2.getHeight(), CvType.CV_8UC3);
Utils.bitmapToMat(bitmap2, mat2);
compareMats(mat1, mat2);
}
你的代碼的方法：
private void compareMats(Mat img1, Mat img2) {
FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.BRIEF);
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);
Mat descriptors1 = new Mat();
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
detector.detect(img1, keypoints1);
extractor.compute(img1, keypoints1, descriptors1);
//second image
// Mat img2 = Imgcodecs.imread(path2);
Mat descriptors2 = new Mat();
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
detector.detect(img2, keypoints2);
extractor.compute(img2, keypoints2, descriptors2);
//matcher image descriptors
MatOfDMatch matches = new MatOfDMatch();
matcher.match(descriptors1,descriptors2,matches);
// Filter matches by distance
MatOfDMatch filtered = filterMatchesByDistance(matches);
int total = (int) matches.size().height;
int Match= (int) filtered.size().height;
Log.d("LOG", "total:" + total + " Match:" + Match);
}
static MatOfDMatch filterMatchesByDistance(MatOfDMatch matches){
List<DMatch> matches_original = matches.toList();
List<DMatch> matches_filtered = new ArrayList<DMatch>();
int DIST_LIMIT = 30;
// Check all the matches distance and if it passes add to list of filtered matches
Log.d("DISTFILTER", "ORG SIZE:" + matches_original.size() + "");
for (int i = 0; i < matches_original.size(); i++) {
DMatch d = matches_original.get(i);
if (Math.abs(d.distance) <= DIST_LIMIT) {
matches_filtered.add(d);
}
}
Log.d("DISTFILTER", "FIL SIZE:" + matches_filtered.size() + "");
MatOfDMatch mat = new MatOfDMatch();
mat.fromList(matches_filtered);
return mat;
}
至於結果進行調整大小（比例爲50％），從你的問題的結果保存的圖像是：
D/DISTFILTER: ORG SIZE:237
D/DISTFILTER: FIL SIZE:230
D/LOG: total:237 Match:230
NB！這是一個快速而骯髒的例子，僅用於演示給定模板的方法。
這感覺就像一個尋找OpenCV和/或圖像識別教程的問題。我鏈接到一個不同的SE組，閱讀帖子如：https://dsp.stackexchange.com/questions/17846/template-matching-or-object-recognition可能爲進一步研究提供適當的上下文。 –
爲什麼不更改閾值DIST_LIMIT？獲得更多比賽的目的是什麼，如果30足夠了？ –
30是不夠的。 –