mirror of https://github.com/opencv/opencv.git
Alexander Alekhin
5 years ago
commit
bf2f7b0f8b
13 changed files with 593 additions and 82 deletions
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import java.util.ArrayList; |
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import java.util.List; |
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import org.opencv.core.*; |
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import org.opencv.core.Range; |
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import org.opencv.highgui.HighGui; |
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import org.opencv.imgcodecs.Imgcodecs; |
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import org.opencv.imgproc.Imgproc; |
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class PanoramaStitchingRotatingCameraRun { |
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void basicPanoramaStitching (String[] args) { |
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String img1path = args[0], img2path = args[1]; |
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Mat img1 = new Mat(), img2 = new Mat(); |
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img1 = Imgcodecs.imread(img1path); |
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img2 = Imgcodecs.imread(img2path); |
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//! [camera-pose-from-Blender-at-location-1]
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Mat c1Mo = new Mat( 4, 4, CvType.CV_64FC1 ); |
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c1Mo.put(0 ,0 ,0.9659258723258972, 0.2588190734386444, 0.0, 1.5529145002365112, |
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0.08852133899927139, -0.3303661346435547, -0.9396926164627075, -0.10281121730804443, |
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-0.24321036040782928, 0.9076734185218811, -0.342020183801651, 6.130080699920654, |
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0, 0, 0, 1 ); |
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//! [camera-pose-from-Blender-at-location-1]
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//! [camera-pose-from-Blender-at-location-2]
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Mat c2Mo = new Mat( 4, 4, CvType.CV_64FC1 ); |
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c2Mo.put(0, 0, 0.9659258723258972, -0.2588190734386444, 0.0, -1.5529145002365112, |
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-0.08852133899927139, -0.3303661346435547, -0.9396926164627075, -0.10281121730804443, |
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0.24321036040782928, 0.9076734185218811, -0.342020183801651, 6.130080699920654, |
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0, 0, 0, 1); |
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//! [camera-pose-from-Blender-at-location-2]
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//! [camera-intrinsics-from-Blender]
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Mat cameraMatrix = new Mat(3, 3, CvType.CV_64FC1); |
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cameraMatrix.put(0, 0, 700.0, 0.0, 320.0, 0.0, 700.0, 240.0, 0, 0, 1 ); |
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//! [camera-intrinsics-from-Blender]
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//! [extract-rotation]
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Range rowRange = new Range(0,3); |
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Range colRange = new Range(0,3); |
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//! [extract-rotation]
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//! [compute-rotation-displacement]
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//c1Mo * oMc2
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Mat R1 = new Mat(c1Mo, rowRange, colRange); |
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Mat R2 = new Mat(c2Mo, rowRange, colRange); |
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Mat R_2to1 = new Mat(); |
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Core.gemm(R1, R2.t(), 1, new Mat(), 0, R_2to1 ); |
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//! [compute-rotation-displacement]
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//! [compute-homography]
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Mat tmp = new Mat(), H = new Mat(); |
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Core.gemm(cameraMatrix, R_2to1, 1, new Mat(), 0, tmp); |
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Core.gemm(tmp, cameraMatrix.inv(), 1, new Mat(), 0, H); |
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Scalar s = new Scalar(H.get(2, 2)[0]); |
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Core.divide(H, s, H); |
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System.out.println(H.dump()); |
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//! [compute-homography]
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//! [stitch]
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Mat img_stitch = new Mat(); |
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Imgproc.warpPerspective(img2, img_stitch, H, new Size(img2.cols()*2, img2.rows()) ); |
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Mat half = new Mat(); |
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half = new Mat(img_stitch, new Rect(0, 0, img1.cols(), img1.rows())); |
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img1.copyTo(half); |
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//! [stitch]
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Mat img_compare = new Mat(); |
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Mat img_space = Mat.zeros(new Size(50, img1.rows()), CvType.CV_8UC3); |
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List<Mat>list = new ArrayList<>(); |
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list.add(img1); |
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list.add(img_space); |
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list.add(img2); |
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Core.hconcat(list, img_compare); |
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HighGui.imshow("Compare Images", img_compare); |
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HighGui.imshow("Panorama Stitching", img_stitch); |
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HighGui.waitKey(0); |
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System.exit(0); |
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} |
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} |
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public class PanoramaStitchingRotatingCamera { |
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public static void main(String[] args) { |
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System.loadLibrary(Core.NATIVE_LIBRARY_NAME); |
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new PanoramaStitchingRotatingCameraRun().basicPanoramaStitching(args); |
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} |
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} |
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import java.util.ArrayList; |
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import java.util.List; |
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import java.util.Random; |
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import org.opencv.core.*; |
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import org.opencv.calib3d.Calib3d; |
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import org.opencv.highgui.HighGui; |
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import org.opencv.imgcodecs.Imgcodecs; |
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import org.opencv.imgproc.Imgproc; |
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class PerspectiveCorrectionRun { |
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void perspectiveCorrection (String[] args) { |
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String img1Path = args[0], img2Path = args[1]; |
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Mat img1 = Imgcodecs.imread(img1Path); |
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Mat img2 = Imgcodecs.imread(img2Path); |
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//! [find-corners]
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MatOfPoint2f corners1 = new MatOfPoint2f(), corners2 = new MatOfPoint2f(); |
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boolean found1 = Calib3d.findChessboardCorners(img1, new Size(9, 6), corners1 ); |
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boolean found2 = Calib3d.findChessboardCorners(img2, new Size(9, 6), corners2 ); |
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//! [find-corners]
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if (!found1 || !found2) { |
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System.out.println("Error, cannot find the chessboard corners in both images."); |
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System.exit(-1); |
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} |
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//! [estimate-homography]
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Mat H = new Mat(); |
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H = Calib3d.findHomography(corners1, corners2); |
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System.out.println(H.dump()); |
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//! [estimate-homography]
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//! [warp-chessboard]
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Mat img1_warp = new Mat(); |
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Imgproc.warpPerspective(img1, img1_warp, H, img1.size()); |
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//! [warp-chessboard]
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Mat img_draw_warp = new Mat(); |
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List<Mat> list1 = new ArrayList<>(), list2 = new ArrayList<>() ; |
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list1.add(img2); |
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list1.add(img1_warp); |
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Core.hconcat(list1, img_draw_warp); |
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HighGui.imshow("Desired chessboard view / Warped source chessboard view", img_draw_warp); |
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//! [compute-transformed-corners]
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Mat img_draw_matches = new Mat(); |
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list2.add(img1); |
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list2.add(img2); |
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Core.hconcat(list2, img_draw_matches); |
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Point []corners1Arr = corners1.toArray(); |
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for (int i = 0 ; i < corners1Arr.length; i++) { |
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Mat pt1 = new Mat(3, 1, CvType.CV_64FC1), pt2 = new Mat(); |
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pt1.put(0, 0, corners1Arr[i].x, corners1Arr[i].y, 1 ); |
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Core.gemm(H, pt1, 1, new Mat(), 0, pt2); |
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double[] data = pt2.get(2, 0); |
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Core.divide(pt2, new Scalar(data[0]), pt2); |
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double[] data1 =pt2.get(0, 0); |
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double[] data2 = pt2.get(1, 0); |
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Point end = new Point((int)(img1.cols()+ data1[0]), (int)data2[0]); |
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Imgproc.line(img_draw_matches, corners1Arr[i], end, RandomColor(), 2); |
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} |
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HighGui.imshow("Draw matches", img_draw_matches); |
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HighGui.waitKey(0); |
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//! [compute-transformed-corners]
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System.exit(0); |
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} |
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Scalar RandomColor () { |
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Random rng = new Random(); |
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int r = rng.nextInt(256); |
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int g = rng.nextInt(256); |
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int b = rng.nextInt(256); |
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return new Scalar(r, g, b); |
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} |
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} |
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public class PerspectiveCorrection { |
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public static void main (String[] args) { |
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System.loadLibrary(Core.NATIVE_LIBRARY_NAME); |
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new PerspectiveCorrectionRun().perspectiveCorrection(args); |
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} |
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} |
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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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# Python 2/3 compatibility |
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from __future__ import print_function |
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import numpy as np |
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import cv2 as cv |
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def basicPanoramaStitching(img1Path, img2Path): |
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img1 = cv.imread(cv.samples.findFile(img1Path)) |
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img2 = cv.imread(cv.samples.findFile(img2Path)) |
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# [camera-pose-from-Blender-at-location-1] |
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c1Mo = np.array([[0.9659258723258972, 0.2588190734386444, 0.0, 1.5529145002365112], |
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[ 0.08852133899927139, -0.3303661346435547, -0.9396926164627075, -0.10281121730804443], |
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[-0.24321036040782928, 0.9076734185218811, -0.342020183801651, 6.130080699920654], |
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[0, 0, 0, 1]],dtype=np.float64) |
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# [camera-pose-from-Blender-at-location-1] |
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# [camera-pose-from-Blender-at-location-2] |
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c2Mo = np.array([[0.9659258723258972, -0.2588190734386444, 0.0, -1.5529145002365112], |
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[-0.08852133899927139, -0.3303661346435547, -0.9396926164627075, -0.10281121730804443], |
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[0.24321036040782928, 0.9076734185218811, -0.342020183801651, 6.130080699920654], |
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[0, 0, 0, 1]],dtype=np.float64) |
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# [camera-pose-from-Blender-at-location-2] |
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# [camera-intrinsics-from-Blender] |
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cameraMatrix = np.array([[700.0, 0.0, 320.0], [0.0, 700.0, 240.0], [0, 0, 1]], dtype=np.float32) |
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# [camera-intrinsics-from-Blender] |
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# [extract-rotation] |
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R1 = c1Mo[0:3, 0:3] |
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R2 = c2Mo[0:3, 0:3] |
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#[extract-rotation] |
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# [compute-rotation-displacement] |
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R2 = R2.transpose() |
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R_2to1 = np.dot(R1,R2) |
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# [compute-rotation-displacement] |
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# [compute-homography] |
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H = cameraMatrix.dot(R_2to1).dot(np.linalg.inv(cameraMatrix)) |
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H = H / H[2][2] |
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# [compute-homography] |
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# [stitch] |
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img_stitch = cv.warpPerspective(img2, H, (img2.shape[1]*2, img2.shape[0])) |
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img_stitch[0:img1.shape[0], 0:img1.shape[1]] = img1 |
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# [stitch] |
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img_space = np.zeros((img1.shape[0],50,3), dtype=np.uint8) |
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img_compare = cv.hconcat([img1,img_space, img2]) |
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cv.imshow("Final", img_compare) |
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cv.imshow("Panorama", img_stitch) |
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cv.waitKey(0) |
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def main(): |
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import argparse |
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parser = argparse.ArgumentParser(description="Code for homography tutorial. Example 5: basic panorama stitching from a rotating camera.") |
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parser.add_argument("-I1","--image1", help = "path to first image", default="Blender_Suzanne1.jpg") |
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parser.add_argument("-I2","--image2", help = "path to second image", default="Blender_Suzanne2.jpg") |
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args = parser.parse_args() |
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print("Panorama Stitching Started") |
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basicPanoramaStitching(args.image1, args.image2) |
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print("Panorama Stitching Completed Successfully") |
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if __name__ == '__main__': |
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main() |
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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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# Python 2/3 compatibility |
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from __future__ import print_function |
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import numpy as np |
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import cv2 as cv |
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import sys |
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def randomColor(): |
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color = np.random.randint(0, 255,(1, 3)) |
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return color[0].tolist() |
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def perspectiveCorrection(img1Path, img2Path ,patternSize ): |
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img1 = cv.imread(cv.samples.findFile(img1Path)) |
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img2 = cv.imread(cv.samples.findFile(img2Path)) |
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# [find-corners] |
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ret1, corners1 = cv.findChessboardCorners(img1, patternSize) |
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ret2, corners2 = cv.findChessboardCorners(img2, patternSize) |
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# [find-corners] |
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if not ret1 or not ret2: |
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print("Error, cannot find the chessboard corners in both images.") |
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sys.exit(-1) |
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# [estimate-homography] |
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H, _ = cv.findHomography(corners1, corners2) |
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print(H) |
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# [estimate-homography] |
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# [warp-chessboard] |
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img1_warp = cv.warpPerspective(img1, H, (img1.shape[1], img1.shape[0])) |
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# [warp-chessboard] |
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img_draw_warp = cv.hconcat([img2, img1_warp]) |
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cv.imshow("Desired chessboard view / Warped source chessboard view", img_draw_warp ) |
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corners1 = corners1.tolist() |
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corners1 = [a[0] for a in corners1] |
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# [compute-transformed-corners] |
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img_draw_matches = cv.hconcat([img1, img2]) |
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for i in range(len(corners1)): |
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pt1 = np.array([corners1[i][0], corners1[i][1], 1]) |
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pt1 = pt1.reshape(3, 1) |
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pt2 = np.dot(H, pt1) |
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pt2 = pt2/pt2[2] |
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end = (int(img1.shape[1] + pt2[0]), int(pt2[1])) |
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cv.line(img_draw_matches, tuple([int(j) for j in corners1[i]]), end, randomColor(), 2) |
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cv.imshow("Draw matches", img_draw_matches) |
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cv.waitKey(0) |
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# [compute-transformed-corners] |
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def main(): |
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import argparse |
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parser = argparse.ArgumentParser() |
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parser.add_argument('-I1', "--image1", help="Path to the first image", default="left02.jpg") |
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parser.add_argument('-I2', "--image2", help="Path to the second image", default="left01.jpg") |
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parser.add_argument('-H', "--height", help="Height of pattern size", default=6) |
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parser.add_argument('-W', "--width", help="Width of pattern size", default=9) |
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args = parser.parse_args() |
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img1Path = args.image1 |
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img2Path = args.image2 |
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h = args.height |
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w = args.width |
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perspectiveCorrection(img1Path, img2Path, (w, h)) |
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if __name__ == "__main__": |
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main() |
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