我对openCV很陌生，目前我正在尝试在android设备上运行对象检测。我基本上做的是在我的应用程序中显示一个相机预览，当我点击它时它会捕获一张图片。然后将这张图片提供给 runFeatureHomography - 方法，该方法首先抓取第二张图片，与拍摄的图片进行比较。然后该方法在两张图片中找到关键点，计算它们并将它们匹配到一个名为 img_matches 的 Mat 中。我猜尽可能基本。
我现在试图在这里检测的对象是某种卡，就像信用卡的格式一样。卡片是蓝色的，上面有很多白色和黄色的文字。我只能发一个链接，这就是为什么我不能显示他们的照片。
我不知道为什么，但是当我最后显示结果/或将结果作为位图保存到我的手机时，它总是看起来像这样：
http://oi44.tinypic.com/oaqel0.jpg <-- 完成所有操作后的结果图像。
这表明我想检测的对象确实被检测到了，但我不知道为什么有黑色背景而不是卡片的图片。为什么它不按原样显示我的两个图像，只显示它们的所有线条？
在我的代码中我使用这三个：
FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.ORB);
DescriptorMatchermatcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE);
这是我的一些代码：
private void runFeatureHomography(Bitmap image)
{
Mat img_object = getObjectImage();
Mat img_scene = newEmptyMat();
Bitmap myimg = image.copy(Bitmap.Config.ARGB_8888, true);
Utils.bitmapToMat(myimg, img_scene);
MatOfKeyPoint keyPoints_object = detectObjectKeyPoints();
MatOfKeyPoint keyPoints_scene = new MatOfKeyPoint();
this.detector.detect(img_scene, keyPoints_scene);
Mat descriptors_object = calculateObjectDescriptor();
Mat descriptors_scene = newEmptyMat();
this.extractor.compute(img_scene, keyPoints_scene, descriptors_scene);
MatOfDMatch matches = new MatOfDMatch();
matcher.match(descriptors_object, descriptors_scene, matches);
double min_dist = Double.MAX_VALUE;
for (int i = -1; ++i < descriptors_object.rows();)
{
double dist = matches.toArray()[i].distance;
if (dist < min_dist)
{
min_dist = dist;
}
}
List<DMatch> good_matches = new ArrayList<DMatch>();
for (int i = -1; ++i < descriptors_object.rows();)
{
if (matches.toArray()[i].distance <= 3 * min_dist)
{
good_matches.add(matches.toArray()[i]);
}
}
System.out.println("4");
Mat img_matches = newEmptyMat();
Features2d.drawMatches(
img_object,
keyPoints_object,
img_scene,
keyPoints_scene,
new MatOfDMatch(good_matches.toArray(new DMatch[good_matches
.size()])), img_matches, Scalar.all(-1),
Scalar.all(-1), new MatOfByte(),
Features2d.NOT_DRAW_SINGLE_POINTS);
List<Point> object = new ArrayList<Point>();
List<Point> scene = new ArrayList<Point>();
for (int i = -1; ++i < good_matches.size();)
{
object.add(keyPoints_object.toArray()[good_matches.get(i).queryIdx].pt);
scene.add(keyPoints_scene.toArray()[good_matches.get(i).trainIdx].pt);
}
Mat H = Calib3d.findHomography(
new MatOfPoint2f(object.toArray(new Point[object.size()])),
new MatOfPoint2f(scene.toArray(new Point[scene.size()])),
Calib3d.RANSAC, 3);
Point[] object_corners = new Point[4];
object_corners[0] = new Point(0, 0);
object_corners[1] = new Point(img_object.cols(), 0);
object_corners[2] = new Point(img_object.cols(), img_object.rows());
object_corners[3] = new Point(0, img_object.rows());
MatOfPoint2f scene_corners2f = new MatOfPoint2f();
Core.perspectiveTransform(new MatOfPoint2f(object_corners),
scene_corners2f, H);
Point[] scene_corners = scene_corners2f.toArray();
Point[] scene_corners_norm = new Point[4];
scene_corners_norm[0] = new Point(scene_corners[0].x
+ img_object.cols(), scene_corners[0].y);
scene_corners_norm[1] = new Point(scene_corners[1].x
+ img_object.cols(), scene_corners[1].y);
scene_corners_norm[2] = new Point(scene_corners[2].x
+ img_object.cols(), scene_corners[2].y);
scene_corners_norm[3] = new Point(scene_corners[3].x
+ img_object.cols(), scene_corners[3].y);
Core.line(img_matches, scene_corners_norm[0], scene_corners_norm[1],
new Scalar(0, 255, 0), 4);
Core.line(img_matches, scene_corners_norm[1], scene_corners_norm[2],
new Scalar(0, 255, 0), 4);
Core.line(img_matches, scene_corners_norm[2], scene_corners_norm[3],
new Scalar(0, 255, 0), 4);
Core.line(img_matches, scene_corners_norm[3], scene_corners_norm[0],
new Scalar(0, 255, 0), 4);
bmp = Bitmap.createBitmap(img_matches.cols(), img_matches.rows(),
Bitmap.Config.ARGB_8888);
Intent resultIntent = new Intent("com.example.capturetest.Result");
startActivity(resultIntent);
}
private volatile Mat cachedObjectDescriptor = null;
private volatile MatOfKeyPoint cachedObjectKeyPoints = null;
private volatile Mat cachedObjectImage = null;
private Mat calculateObjectDescriptor()
{
Mat objectDescriptor = this.cachedObjectDescriptor;
if (objectDescriptor == null)
{
Mat objectImage = getObjectImage();
MatOfKeyPoint objectKeyPoints = detectObjectKeyPoints();
objectDescriptor = newEmptyMat();
this.extractor.compute(objectImage, objectKeyPoints,
objectDescriptor);
this.cachedObjectDescriptor = objectDescriptor;
}
return objectDescriptor;
}
private MatOfKeyPoint detectObjectKeyPoints()
{
MatOfKeyPoint objectKeyPoints = this.cachedObjectKeyPoints;
if (objectKeyPoints == null)
{
Mat objectImage = getObjectImage();
objectKeyPoints = new MatOfKeyPoint();
this.detector.detect(objectImage, objectKeyPoints);
this.cachedObjectKeyPoints = objectKeyPoints;
}
return objectKeyPoints;
}
private Mat getObjectImage()
{
Mat objectImage = this.cachedObjectImage;
if (objectImage == null)
{
objectImage = newEmptyMat();
Bitmap bitmap = ((BitmapDrawable) iv.getDrawable()).getBitmap();
Bitmap img = bitmap.copy(Bitmap.Config.ARGB_8888, false);
Utils.bitmapToMat(img, objectImage);
this.cachedObjectImage = objectImage;
}
return objectImage;
}
private Mat newEmptyMat()
{
return new Mat();
}
在这一行之后 matcher.match(descriptors_object, descriptors_scene, matches); 我尝试将三个 Mat img_object、img_scene 和匹配转换为位图并将它们保存到我的 android 设备中以供检查。他们看起来都像他们应该的样子，所以直到这一点一切都很好。
但是在这部分之后...
Mat img_matches = newEmptyMat();
Features2d.drawMatches(
img_object,
keyPoints_object,
img_scene,
keyPoints_scene,
new MatOfDMatch(good_matches.toArray(new DMatch[good_matches
.size()])), img_matches, Scalar.all(-1),
Scalar.all(-1), new MatOfByte(),
Features2d.NOT_DRAW_SINGLE_POINTS);
...我尝试将 Mat img_matches （如果我做对了，它应该具有两个输入图片的所有信息）转换为位图并将其保存在我的 android 设备上，但图片看起来像图片上面的链接（带线条的黑色图片而不是带线条的卡片图片）。
你们有谁知道我在这里做错了什么？此刻我似乎陷入了困境。
提前谢谢各位。
编辑：
只是想让您知道，我在桌面上运行和工作的代码与普通的 java 程序相同。图片是从那里的网络摄像头拍摄的。结果图像在桌面程序中显示绝对正确，用卡片和线条代替黑色和线条；）