Merge pull request #16366 from themechanicalcoder:features2D-tutorial-python

* Add python version of panorama_stitching_rotating_camera and perspective_correction

* Updated code

* added in the docs

* added python code in the docs

* docs change

* Add java tutorial as well

* Add toggle in documentation

* Added the link for Java code

* format code

* Refactored code

doc/tutorials/features2d/homography/homography.markdown

@@ -12,7 +12,9 @@ For detailed explanations about the theory, please refer to a computer vision co
 *   An Invitation to 3-D Vision: From Images to Geometric Models, @cite Ma:2003:IVI
 *   Computer Vision: Algorithms and Applications, @cite RS10
 
-The tutorial code can be found [here](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/features2D/Homography).
+The tutorial code can be found here [C++](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/features2D/Homography),
+[Python](https://github.com/opencv/opencv/tree/3.4/samples/python/tutorial_code/features2D/Homography),
+[Java](https://github.com/opencv/opencv/tree/3.4/samples/java/tutorial_code/features2D/Homography).
 The images used in this tutorial can be found [here](https://github.com/opencv/opencv/tree/3.4/samples/data) (`left*.jpg`).
 
 Basic theory {#tutorial_homography_Basic_theory}
@@ -171,15 +173,45 @@ The following image shows the source image (left) and the chessboard view that w
 
 The first step consists to detect the chessboard corners in the source and desired images:
 
+@add_toggle_cpp
 @snippet perspective_correction.cpp find-corners
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/features2D/Homography/perspective_correction.py find-corners
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/features2D/Homography/PerspectiveCorrection.java find-corners
+@end_toggle
 
 The homography is estimated easily with:
 
+@add_toggle_cpp
 @snippet perspective_correction.cpp estimate-homography
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/features2D/Homography/perspective_correction.py estimate-homography
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/features2D/Homography/PerspectiveCorrection.java estimate-homography
+@end_toggle
 
 To warp the source chessboard view into the desired chessboard view, we use @ref cv::warpPerspective
 
+@add_toggle_cpp
 @snippet perspective_correction.cpp warp-chessboard
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/features2D/Homography/perspective_correction.py warp-chessboard
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/features2D/Homography/PerspectiveCorrection.java warp-chessboard
+@end_toggle
 
 The result image is:
 
@@ -187,7 +219,17 @@ The result image is:
 
 To compute the coordinates of the source corners transformed by the homography:
 
+@add_toggle_cpp
 @snippet perspective_correction.cpp compute-transformed-corners
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/features2D/Homography/perspective_correction.py compute-transformed-corners
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/features2D/Homography/PerspectiveCorrection.java compute-transformed-corners
+@end_toggle
 
 To check the correctness of the calculation, the matching lines are displayed:
 
@@ -499,17 +541,57 @@ The figure below shows the two generated views of the Suzanne model, with only a
 
 With the known associated camera poses and the intrinsic parameters, the relative rotation between the two views can be computed:
 
+@add_toggle_cpp
 @snippet panorama_stitching_rotating_camera.cpp extract-rotation
+@end_toggle
 
+@add_toggle_python
+@snippet samples/python/tutorial_code/features2D/Homography/panorama_stitching_rotating_camera.py extract-rotation
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/features2D/Homography/PanoramaStitchingRotatingCamera.java extract-rotation
+@end_toggle
+
+@add_toggle_cpp
 @snippet panorama_stitching_rotating_camera.cpp compute-rotation-displacement
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/features2D/Homography/panorama_stitching_rotating_camera.py compute-rotation-displacement
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/features2D/Homography/PanoramaStitchingRotatingCamera.java compute-rotation-displacement
+@end_toggle
 
 Here, the second image will be stitched with respect to the first image. The homography can be calculated using the formula above:
 
+@add_toggle_cpp
 @snippet panorama_stitching_rotating_camera.cpp compute-homography
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/features2D/Homography/panorama_stitching_rotating_camera.py compute-homography
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/features2D/Homography/PanoramaStitchingRotatingCamera.java compute-homography
+@end_toggle
 
 The stitching is made simply with:
 
+@add_toggle_cpp
 @snippet panorama_stitching_rotating_camera.cpp stitch
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/features2D/Homography/panorama_stitching_rotating_camera.py stitch
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/features2D/Homography/PanoramaStitchingRotatingCamera.java stitch
+@end_toggle
 
 The resulting image is:
 

samples/java/tutorial_code/features2D/Homography/PanoramaStitchingRotatingCamera.java

+import java.util.ArrayList;
+import java.util.List;
+
+import org.opencv.core.*;
+import org.opencv.core.Range;
+import org.opencv.highgui.HighGui;
+import org.opencv.imgcodecs.Imgcodecs;
+import org.opencv.imgproc.Imgproc;
+
+
+class PanoramaStitchingRotatingCameraRun {
+    void basicPanoramaStitching (String[] args) {
+        String img1path = args[0], img2path = args[1];
+        Mat img1 = new Mat(), img2 = new Mat();
+        img1 = Imgcodecs.imread(img1path);
+        img2 = Imgcodecs.imread(img2path);
+
+        //! [camera-pose-from-Blender-at-location-1]
+        Mat c1Mo = new Mat( 4, 4, CvType.CV_64FC1 );
+        c1Mo.put(0 ,0 ,0.9659258723258972, 0.2588190734386444, 0.0, 1.5529145002365112,
+                 0.08852133899927139, -0.3303661346435547, -0.9396926164627075, -0.10281121730804443,
+                 -0.24321036040782928, 0.9076734185218811, -0.342020183801651, 6.130080699920654,
+                 0, 0, 0, 1 );
+        //! [camera-pose-from-Blender-at-location-1]
+
+        //! [camera-pose-from-Blender-at-location-2]
+        Mat c2Mo = new Mat( 4, 4, CvType.CV_64FC1 );
+        c2Mo.put(0, 0, 0.9659258723258972, -0.2588190734386444, 0.0, -1.5529145002365112,
+                 -0.08852133899927139, -0.3303661346435547, -0.9396926164627075, -0.10281121730804443,
+                 0.24321036040782928, 0.9076734185218811, -0.342020183801651, 6.130080699920654,
+                 0, 0, 0, 1);
+        //! [camera-pose-from-Blender-at-location-2]
+
+        //! [camera-intrinsics-from-Blender]
+        Mat cameraMatrix = new Mat(3, 3, CvType.CV_64FC1);
+        cameraMatrix.put(0, 0, 700.0, 0.0, 320.0, 0.0, 700.0, 240.0, 0, 0, 1 );
+        //! [camera-intrinsics-from-Blender]
+
+        //! [extract-rotation]
+        Range rowRange = new Range(0,3);
+        Range colRange = new Range(0,3);
+        //! [extract-rotation]
+
+        //! [compute-rotation-displacement]
+        //c1Mo * oMc2
+        Mat R1 = new  Mat(c1Mo, rowRange, colRange);
+        Mat R2 = new Mat(c2Mo, rowRange, colRange);
+        Mat R_2to1 = new Mat();
+        Core.gemm(R1, R2.t(), 1, new Mat(), 0, R_2to1 );
+        //! [compute-rotation-displacement]
+
+        //! [compute-homography]
+        Mat tmp = new Mat(), H = new Mat();
+        Core.gemm(cameraMatrix, R_2to1, 1, new Mat(), 0, tmp);
+        Core.gemm(tmp, cameraMatrix.inv(), 1, new Mat(), 0, H);
+        Scalar s = new Scalar(H.get(2, 2)[0]);
+        Core.divide(H, s, H);
+        System.out.println(H.dump());
+        //! [compute-homography]
+
+        //! [stitch]
+        Mat img_stitch = new Mat();
+        Imgproc.warpPerspective(img2, img_stitch, H, new Size(img2.cols()*2, img2.rows()) );
+        Mat half = new Mat();
+        half =  new Mat(img_stitch, new Rect(0, 0, img1.cols(), img1.rows()));
+        img1.copyTo(half);
+        //! [stitch]
+
+        Mat img_compare = new Mat();
+        Mat img_space = Mat.zeros(new Size(50, img1.rows()), CvType.CV_8UC3);
+        List<Mat>list = new ArrayList<>();
+        list.add(img1);
+        list.add(img_space);
+        list.add(img2);
+        Core.hconcat(list, img_compare);
+
+        HighGui.imshow("Compare Images", img_compare);
+        HighGui.imshow("Panorama Stitching", img_stitch);
+        HighGui.waitKey(0);
+        System.exit(0);
+    }
+}
+
+public class PanoramaStitchingRotatingCamera {
+    public static void main(String[] args) {
+        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
+        new PanoramaStitchingRotatingCameraRun().basicPanoramaStitching(args);
+    }
+}

samples/java/tutorial_code/features2D/Homography/PerspectiveCorrection.java

+import java.util.ArrayList;
+import java.util.List;
+import java.util.Random;
+
+import org.opencv.core.*;
+import org.opencv.calib3d.Calib3d;
+import org.opencv.highgui.HighGui;
+import org.opencv.imgcodecs.Imgcodecs;
+import org.opencv.imgproc.Imgproc;
+
+
+class PerspectiveCorrectionRun {
+    void perspectiveCorrection (String[] args) {
+        String img1Path = args[0], img2Path = args[1];
+        Mat img1 = Imgcodecs.imread(img1Path);
+        Mat img2 = Imgcodecs.imread(img2Path);
+
+        //! [find-corners]
+        MatOfPoint2f corners1 = new MatOfPoint2f(), corners2 = new MatOfPoint2f();
+        boolean found1 = Calib3d.findChessboardCorners(img1, new Size(9, 6), corners1 );
+        boolean found2 = Calib3d.findChessboardCorners(img2, new Size(9, 6), corners2 );
+        //! [find-corners]
+
+        if (!found1 || !found2) {
+            System.out.println("Error, cannot find the chessboard corners in both images.");
+            System.exit(-1);
+        }
+
+        //! [estimate-homography]
+        Mat H = new Mat();
+        H = Calib3d.findHomography(corners1, corners2);
+        System.out.println(H.dump());
+        //! [estimate-homography]
+
+        //! [warp-chessboard]
+        Mat img1_warp = new Mat();
+        Imgproc.warpPerspective(img1, img1_warp, H, img1.size());
+        //! [warp-chessboard]
+
+        Mat img_draw_warp = new Mat();
+        List<Mat> list1 = new ArrayList<>(), list2 = new ArrayList<>() ;
+        list1.add(img2);
+        list1.add(img1_warp);
+        Core.hconcat(list1, img_draw_warp);
+        HighGui.imshow("Desired chessboard view / Warped source chessboard view", img_draw_warp);
+
+        //! [compute-transformed-corners]
+        Mat img_draw_matches = new Mat();
+        list2.add(img1);
+        list2.add(img2);
+        Core.hconcat(list2, img_draw_matches);
+        Point []corners1Arr = corners1.toArray();
+
+        for (int i = 0 ; i < corners1Arr.length; i++) {
+            Mat pt1 = new Mat(3, 1, CvType.CV_64FC1), pt2 = new Mat();
+            pt1.put(0, 0, corners1Arr[i].x, corners1Arr[i].y, 1 );
+
+            Core.gemm(H, pt1, 1, new Mat(), 0, pt2);
+            double[] data = pt2.get(2, 0);
+            Core.divide(pt2, new Scalar(data[0]), pt2);
+
+            double[] data1 =pt2.get(0, 0);
+            double[] data2 = pt2.get(1, 0);
+            Point end = new Point((int)(img1.cols()+ data1[0]), (int)data2[0]);
+            Imgproc.line(img_draw_matches, corners1Arr[i], end, RandomColor(), 2);
+        }
+
+        HighGui.imshow("Draw matches", img_draw_matches);
+        HighGui.waitKey(0);
+        //! [compute-transformed-corners]
+
+        System.exit(0);
+    }
+
+    Scalar RandomColor () {
+        Random rng = new Random();
+        int r = rng.nextInt(256);
+        int g = rng.nextInt(256);
+        int b = rng.nextInt(256);
+        return new Scalar(r, g, b);
+    }
+}
+
+public class PerspectiveCorrection {
+    public static void main (String[] args) {
+        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
+        new PerspectiveCorrectionRun().perspectiveCorrection(args);
+    }
+}

samples/python/tutorial_code/features2D/Homography/panorama_stitching_rotating_camera.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import numpy as np
+import cv2 as cv
+
+def basicPanoramaStitching(img1Path, img2Path):
+    img1 = cv.imread(cv.samples.findFile(img1Path))
+    img2 = cv.imread(cv.samples.findFile(img2Path))
+
+    # [camera-pose-from-Blender-at-location-1]
+    c1Mo = np.array([[0.9659258723258972, 0.2588190734386444, 0.0, 1.5529145002365112],
+                     [ 0.08852133899927139, -0.3303661346435547, -0.9396926164627075, -0.10281121730804443],
+                     [-0.24321036040782928, 0.9076734185218811, -0.342020183801651, 6.130080699920654],
+                     [0, 0, 0, 1]],dtype=np.float64)
+    # [camera-pose-from-Blender-at-location-1]
+
+    # [camera-pose-from-Blender-at-location-2]
+    c2Mo = np.array([[0.9659258723258972, -0.2588190734386444, 0.0, -1.5529145002365112],
+                     [-0.08852133899927139, -0.3303661346435547, -0.9396926164627075, -0.10281121730804443],
+                     [0.24321036040782928, 0.9076734185218811, -0.342020183801651, 6.130080699920654],
+                     [0, 0, 0, 1]],dtype=np.float64)
+    # [camera-pose-from-Blender-at-location-2]
+
+    # [camera-intrinsics-from-Blender]
+    cameraMatrix = np.array([[700.0, 0.0, 320.0], [0.0, 700.0, 240.0], [0, 0, 1]], dtype=np.float32)
+    # [camera-intrinsics-from-Blender]
+
+    # [extract-rotation]
+    R1 = c1Mo[0:3, 0:3]
+    R2 = c2Mo[0:3, 0:3]
+    #[extract-rotation]
+
+    # [compute-rotation-displacement]
+    R2 = R2.transpose()
+    R_2to1 = np.dot(R1,R2)
+    # [compute-rotation-displacement]
+
+    # [compute-homography]
+    H = cameraMatrix.dot(R_2to1).dot(np.linalg.inv(cameraMatrix))
+    H = H / H[2][2]
+    # [compute-homography]
+
+    # [stitch]
+    img_stitch = cv.warpPerspective(img2, H, (img2.shape[1]*2, img2.shape[0]))
+    img_stitch[0:img1.shape[0], 0:img1.shape[1]] = img1
+    # [stitch]
+
+    img_space = np.zeros((img1.shape[0],50,3), dtype=np.uint8)
+    img_compare = cv.hconcat([img1,img_space, img2])
+
+    cv.imshow("Final", img_compare)
+    cv.imshow("Panorama", img_stitch)
+    cv.waitKey(0)
+
+def main():
+    import argparse
+    parser = argparse.ArgumentParser(description="Code for homography tutorial. Example 5: basic panorama stitching from a rotating camera.")
+    parser.add_argument("-I1","--image1", help = "path to first image", default="Blender_Suzanne1.jpg")
+    parser.add_argument("-I2","--image2", help = "path to second image", default="Blender_Suzanne2.jpg")
+    args = parser.parse_args()
+    print("Panorama Stitching Started")
+    basicPanoramaStitching(args.image1, args.image2)
+    print("Panorama Stitching Completed Successfully")
+
+
+if __name__ == '__main__':
+    main()

samples/python/tutorial_code/features2D/Homography/perspective_correction.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import numpy as np
+import cv2 as cv
+import sys
+
+
+def randomColor():
+    color = np.random.randint(0, 255,(1, 3))
+    return color[0].tolist()
+
+def  perspectiveCorrection(img1Path, img2Path ,patternSize ):
+    img1 = cv.imread(cv.samples.findFile(img1Path))
+    img2 = cv.imread(cv.samples.findFile(img2Path))
+
+    # [find-corners]
+    ret1, corners1 = cv.findChessboardCorners(img1, patternSize)
+    ret2, corners2 = cv.findChessboardCorners(img2, patternSize)
+    # [find-corners]
+
+    if not ret1 or not ret2:
+        print("Error, cannot find the chessboard corners in both images.")
+        sys.exit(-1)
+
+    # [estimate-homography]
+    H, _ = cv.findHomography(corners1, corners2)
+    print(H)
+    # [estimate-homography]
+
+    # [warp-chessboard]
+    img1_warp = cv.warpPerspective(img1, H, (img1.shape[1], img1.shape[0]))
+    # [warp-chessboard]
+
+    img_draw_warp = cv.hconcat([img2, img1_warp])
+    cv.imshow("Desired chessboard view / Warped source chessboard view", img_draw_warp )
+
+    corners1 = corners1.tolist()
+    corners1 = [a[0] for a in corners1]
+
+    # [compute-transformed-corners]
+    img_draw_matches = cv.hconcat([img1, img2])
+    for i in range(len(corners1)):
+        pt1 = np.array([corners1[i][0], corners1[i][1], 1])
+        pt1 = pt1.reshape(3, 1)
+        pt2 = np.dot(H, pt1)
+        pt2 = pt2/pt2[2]
+        end = (int(img1.shape[1] + pt2[0]), int(pt2[1]))
+        cv.line(img_draw_matches, tuple([int(j) for j in corners1[i]]), end, randomColor(), 2)
+
+    cv.imshow("Draw matches", img_draw_matches)
+    cv.waitKey(0)
+    # [compute-transformed-corners]
+
+def main():
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-I1', "--image1", help="Path to the first image", default="left02.jpg")
+    parser.add_argument('-I2', "--image2", help="Path to the second image", default="left01.jpg")
+    parser.add_argument('-H', "--height", help="Height of pattern size", default=6)
+    parser.add_argument('-W', "--width", help="Width of pattern size", default=9)
+    args = parser.parse_args()
+
+    img1Path = args.image1
+    img2Path = args.image2
+    h = args.height
+    w = args.width
+    perspectiveCorrection(img1Path, img2Path, (w, h))
+
+if __name__ == "__main__":
+    main()