Commit b3ad2c32 authored by Gagandeep Singh's avatar Gagandeep Singh Committed by Alexander Alekhin

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
parent c5e0fa98
......@@ -379,6 +379,25 @@ namespace rgbd
{
}
/** Constructor
* @param block_size The size of the blocks to look at for a stable MSE
* @param min_size The minimum size of a cluster to be considered a plane
* @param threshold The maximum distance of a point from a plane to belong to it (in meters)
* @param sensor_error_a coefficient of the sensor error. 0 by default, 0.0075 for a Kinect
* @param sensor_error_b coefficient of the sensor error. 0 by default
* @param sensor_error_c coefficient of the sensor error. 0 by default
* @param method The method to use to compute the planes.
*/
RgbdPlane(int method, int block_size,
int min_size, double threshold, double sensor_error_a = 0,
double sensor_error_b = 0, double sensor_error_c = 0);
~RgbdPlane();
CV_WRAP static Ptr<RgbdPlane> create(int method, int block_size, int min_size, double threshold,
double sensor_error_a = 0, double sensor_error_b = 0,
double sensor_error_c = 0);
/** Find The planes in a depth image
* @param points3d the 3d points organized like the depth image: rows x cols with 3 channels
* @param normals the normals for every point in the depth image
......
#!/usr/bin/env python
# Python 2/3 compatibility
from __future__ import print_function
import os, numpy
import cv2 as cv
from tests_common import NewOpenCVTests
class rgbd_test(NewOpenCVTests):
def test_computeRgbdPlane(self):
depth_image = self.get_sample('/cv/rgbd/depth.png', cv.IMREAD_ANYDEPTH)
if depth_image is None:
raise unittest.SkipTest("Missing files with test data")
K = numpy.array([[525, 0, 320.5], [0, 525, 240.5], [0, 0, 1]])
points3d = cv.rgbd.depthTo3d(depth_image, K)
normals_computer = normals_computer = cv.rgbd.RgbdNormals_create(480, 640, 5, K)
normals = normals_computer.apply(points3d)
rgbd_plane = cv.rgbd.RgbdPlane_create(cv.rgbd.RgbdPlane_RGBD_PLANE_METHOD_DEFAULT, 40, 1600, 0.01, 0, 0, 0)
_, planes_coeff = rgbd_plane.apply(points3d, normals)
planes_coeff_expected = \
numpy.asarray([[[-0.02447728, -0.8678335 , -0.49625182, 4.02800846]],
[[-0.05055107, -0.86144137, -0.50533485, 3.95456314]],
[[-0.03294908, -0.86964548, -0.49257591, 3.97052431]],
[[-0.02886586, -0.87153459, -0.48948362, 7.77550507]],
[[-0.04455929, -0.87659335, -0.47916424, 3.93200684]],
[[-0.21514639, 0.18835169, -0.95824611, 7.59479475]],
[[-0.01006953, -0.86679155, -0.49856904, 4.01355648]],
[[-0.00876531, -0.87571168, -0.48275498, 3.96768975]],
[[-0.06395926, -0.86951321, -0.48975089, 4.08618736]],
[[-0.01403128, -0.87593341, -0.48222789, 7.74559402]],
[[-0.01143177, -0.87495202, -0.4840748 , 7.75355816]]],
dtype=numpy.float32)
eps = 0.05
self.assertLessEqual(cv.norm(planes_coeff, planes_coeff_expected, cv.NORM_L2), eps)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
......@@ -1009,6 +1009,28 @@ Ptr<DepthCleaner> DepthCleaner::create(int depth_in, int window_size_in, int met
return makePtr<DepthCleaner>(depth_in, window_size_in, method_in);
}
RgbdPlane::RgbdPlane(int method, int block_size,
int min_size, double threshold, double sensor_error_a,
double sensor_error_b, double sensor_error_c) :
method_(method),
block_size_(block_size),
min_size_(min_size),
threshold_(threshold),
sensor_error_a_(sensor_error_a),
sensor_error_b_(sensor_error_b),
sensor_error_c_(sensor_error_c)
{}
Ptr<RgbdPlane> RgbdPlane::create(int method, int block_size, int min_size, double threshold,
double sensor_error_a, double sensor_error_b,
double sensor_error_c ) {
return makePtr<RgbdPlane>(method, block_size, min_size, threshold,
sensor_error_a, sensor_error_b, sensor_error_c);
}
RgbdPlane::~RgbdPlane()
{}
RgbdFrame::RgbdFrame() : ID(-1)
{}
......
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