我正在寻找一种方法来比较几乎相同（95% 以上相似度）但可以围绕中心轴旋转的图像。
我是整个计算机图形/视觉领域的新手，我不确定是否有现成的工具或系统，或者即使这是正确的堆栈交换要求。
目前，我正在研究使用 C# 生成位图直方图，然后通过 Panda 运行它进行分析，并根据直方图的相似性对图像进行理想分组，尽管我听说 OpenCv + tesseract 可能是一个可行的替代方案。
【问题讨论】：
标签： image-processing ocr
您需要计算两个图像之间的仿射变换矩阵，以便获取有关缩放、平移和旋转的信息。
这个矩阵看起来如何以及如何获得旋转差异？
我使用了以下 Java 代码（使用 OpenCV 3.2）计算 scaling、translation 和 rotation 两个 Mat 图像之间的差异。我希望你会发现它有用。
static void calculateDifferences(Mat img1, Mat img2){
// Initialization
FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor descriptor = DescriptorExtractor.create(DescriptorExtractor.ORB);
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);
// First image objects
Mat img1_descriptors = new Mat();
MatOfKeyPoint img1_keypoints_mat = new MatOfKeyPoint();
// Detect KeyPoints for first image
detector.detect(img1, img1_keypoints_mat);
descriptor.compute(img1, img1_keypoints_mat, img1_descriptors);
// Second image objects
Mat img2_descriptors = new Mat();
MatOfKeyPoint img2_keypoints_mat = new MatOfKeyPoint();
// Detect KeyPoints for second image
detector.detect(img2, img2_keypoints_mat);
descriptor.compute(img2, img2_keypoints_mat, img2_descriptors);
// Match KeyPoints
MatOfDMatch matOfDMatch = new MatOfDMatch();
matcher.match(img1_descriptors, img2_descriptors, matOfDMatch);
// Filtering the matches
List<DMatch> dMatchList = matOfDMatch.toList();
Double max_dist = 0.0;
Double min_dist = 100.0;
for(int i = 0; i < img1_descriptors.rows(); i++){
Double dist = (double) dMatchList.get(i).distance;
if(dist < min_dist) min_dist = dist;
if(dist > max_dist) max_dist = dist;
}
LinkedList<DMatch> good_matches = new LinkedList<>();
for(int i = 0; i < img1_descriptors.rows(); i++){
if(dMatchList.get(i).distance < 3*min_dist){
good_matches.addLast(dMatchList.get(i));
}
}
// Converting to MatOfPoint2f format
LinkedList<Point> img1_points_list = new LinkedList<>();
LinkedList<Point> img2_points_list = new LinkedList<>();
List<KeyPoint> img1_keyPoints_list = img1_keypoints_mat.toList();
List<KeyPoint> img2_keyPoints_list = img2_keypoints_mat.toList();
int limit = good_matches.size();
for(int i = 0; i < limit; i++){
img1_points_list.addLast(img1_keyPoints_list.get(good_matches.get(i).queryIdx).pt);
img2_points_list.addLast(img2_keyPoints_list.get(good_matches.get(i).trainIdx).pt);
}
MatOfPoint2f img1_point2f_mat = new MatOfPoint2f();
img1_point2f_mat.fromList(img1_points_list);
MatOfPoint2f img2_point2f_mat = new MatOfPoint2f();
img2_point2f_mat.fromList(img2_points_list);
// Computing the affine transform matrix
Mat result = Video.estimateRigidTransform(img1_point2f_mat, img2_point2f_mat, true);
printMat(result); // Printing the optimal affine transformation 2x3 array
// The following variables correspond to the estimateRigidTransform result as shown here: https://stackoverflow.com/a/29511091/5165833
double a = result.get(0,0)[0];
double b = result.get(0,1)[0];
double d = result.get(1,1)[0];
double c = result.get(1,0)[0];
double tx = result.get(0,2)[0];
double ty = result.get(1,2)[0];
// Solving for scale,translation and rotation as shown in the link above
double scale_x = Math.signum(a) * Math.sqrt( (a*a) + (b*b) ); // Axis x scale difference
double scale_y = Math.signum(d) * Math.sqrt( (c*c) + (d*d) ); // Axis y scale difference
double translation = ty; // The translation difference
double rotation_angle = Math.atan2(c,d); // Rotation difference
// Printing results
println("Scale_x diff: " + scale_x);
println("Scale_y diff: " + scale_y);
println("Translation diff: " + translation);
println("Rotation diff: " + rotation_angle);
}
static void printMat(Mat m)
{
for (int x=0; x < m.height(); x++) {
for (int y=0; y < m.width(); y++) {
System.out.printf("%f",m.get(x,y)[0]);
System.out.printf("%s"," ");
}
System.out.println();
}
}
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