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czgdp1807alalek
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Merge pull request #2341 from czgdp1807:issue-2277
Added constructors and destructors for RgbdPlane * declared constructors and destructors of RgbdPlane * definitions written * tests for python bindings added
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modules/rgbd/include/opencv2/rgbd.hpp

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@@ -379,6 +379,25 @@ namespace rgbd
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{
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}
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/** Constructor
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* @param block_size The size of the blocks to look at for a stable MSE
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* @param min_size The minimum size of a cluster to be considered a plane
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* @param threshold The maximum distance of a point from a plane to belong to it (in meters)
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* @param sensor_error_a coefficient of the sensor error. 0 by default, 0.0075 for a Kinect
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* @param sensor_error_b coefficient of the sensor error. 0 by default
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* @param sensor_error_c coefficient of the sensor error. 0 by default
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* @param method The method to use to compute the planes.
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*/
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RgbdPlane(int method, int block_size,
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int min_size, double threshold, double sensor_error_a = 0,
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double sensor_error_b = 0, double sensor_error_c = 0);
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~RgbdPlane();
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CV_WRAP static Ptr<RgbdPlane> create(int method, int block_size, int min_size, double threshold,
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double sensor_error_a = 0, double sensor_error_b = 0,
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double sensor_error_c = 0);
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/** Find The planes in a depth image
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* @param points3d the 3d points organized like the depth image: rows x cols with 3 channels
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* @param normals the normals for every point in the depth image
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#!/usr/bin/env python
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# Python 2/3 compatibility
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from __future__ import print_function
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import os, numpy
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import cv2 as cv
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from tests_common import NewOpenCVTests
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class rgbd_test(NewOpenCVTests):
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def test_computeRgbdPlane(self):
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depth_image = self.get_sample('/cv/rgbd/depth.png', cv.IMREAD_ANYDEPTH)
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if depth_image is None:
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raise unittest.SkipTest("Missing files with test data")
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K = numpy.array([[525, 0, 320.5], [0, 525, 240.5], [0, 0, 1]])
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points3d = cv.rgbd.depthTo3d(depth_image, K)
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normals_computer = normals_computer = cv.rgbd.RgbdNormals_create(480, 640, 5, K)
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normals = normals_computer.apply(points3d)
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rgbd_plane = cv.rgbd.RgbdPlane_create(cv.rgbd.RgbdPlane_RGBD_PLANE_METHOD_DEFAULT, 40, 1600, 0.01, 0, 0, 0)
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_, planes_coeff = rgbd_plane.apply(points3d, normals)
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planes_coeff_expected = \
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numpy.asarray([[[-0.02447728, -0.8678335 , -0.49625182, 4.02800846]],
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[[-0.05055107, -0.86144137, -0.50533485, 3.95456314]],
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[[-0.03294908, -0.86964548, -0.49257591, 3.97052431]],
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[[-0.02886586, -0.87153459, -0.48948362, 7.77550507]],
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[[-0.04455929, -0.87659335, -0.47916424, 3.93200684]],
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[[-0.21514639, 0.18835169, -0.95824611, 7.59479475]],
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[[-0.01006953, -0.86679155, -0.49856904, 4.01355648]],
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[[-0.00876531, -0.87571168, -0.48275498, 3.96768975]],
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[[-0.06395926, -0.86951321, -0.48975089, 4.08618736]],
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[[-0.01403128, -0.87593341, -0.48222789, 7.74559402]],
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[[-0.01143177, -0.87495202, -0.4840748 , 7.75355816]]],
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dtype=numpy.float32)
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eps = 0.05
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self.assertLessEqual(cv.norm(planes_coeff, planes_coeff_expected, cv.NORM_L2), eps)
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if __name__ == '__main__':
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NewOpenCVTests.bootstrap()

modules/rgbd/src/odometry.cpp

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@@ -1009,6 +1009,28 @@ Ptr<DepthCleaner> DepthCleaner::create(int depth_in, int window_size_in, int met
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return makePtr<DepthCleaner>(depth_in, window_size_in, method_in);
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}
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RgbdPlane::RgbdPlane(int method, int block_size,
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int min_size, double threshold, double sensor_error_a,
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double sensor_error_b, double sensor_error_c) :
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method_(method),
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block_size_(block_size),
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min_size_(min_size),
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threshold_(threshold),
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sensor_error_a_(sensor_error_a),
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sensor_error_b_(sensor_error_b),
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sensor_error_c_(sensor_error_c)
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{}
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Ptr<RgbdPlane> RgbdPlane::create(int method, int block_size, int min_size, double threshold,
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double sensor_error_a, double sensor_error_b,
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double sensor_error_c ) {
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return makePtr<RgbdPlane>(method, block_size, min_size, threshold,
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sensor_error_a, sensor_error_b, sensor_error_c);
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}
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RgbdPlane::~RgbdPlane()
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{}
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RgbdFrame::RgbdFrame() : ID(-1)
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{}
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