mirror of
https://github.com/opencv/opencv.git
synced 2026-07-26 13:53:03 +04:00
Siddharth Panditrao
0c613de006
Fix ~0.5px systematic offset in CharucoDetector subpixel refinement #28380 Resolves #25539 ### Problem `CharucoDetector::detectBoard` produces Charuco corner coordinates that are consistently offset by approximately **+0.5 pixels** compared to `findChessboardCorners + cornerSubPix`. This offset is systematic (mean ≈ −0.5 px when comparing legacy − Charuco) and reproducible across images. Visual inspection also shows that the legacy chessboard detector aligns better with the actual corner locations. ### Root cause In `charuco_detector.cpp`, the points passed to `cornerSubPix` are manually shifted by `-Point2f(0.5f, 0.5f)` before refinement and then shifted back by `+Point2f(0.5f, 0.5f)` after refinement. However, `cornerSubPix` refines corners in **absolute image coordinates** and converges to the true saddle point based on image gradients. Shifting the initial guess does not affect the converged result as long as the true corner lies within the refinement window. The additional `+0.5` shift applied after refinement therefore introduces a constant bias, resulting in: ``` P_out = P_true + 0.5 ``` ### Solution Remove the manual `±0.5` coordinate shifts around the `cornerSubPix` call and let the refined result be returned directly. ### Test updates Updated expected corner values in the following tests: - `testBoardSubpixelCoords` - `testSeveralBoardsWithCustomIds` The previous expected values (e.g. `200`, `250`, `300`) were only correct due to the +0.5px bias introduced by the bug. `generateImage` creates checkerboard squares of exactly **50 pixels**, which places true corner locations on **pixel boundaries** rather than pixel centers. As a result, the correct subpixel coordinates are: ``` 199.5, 249.5, 299.5 ``` instead of the previously expected integer values. After updating the expected values, all **30 Charuco-related tests pass** with the fix applied. ### Verification I verified the fix using a Python reproducer that compares `CharucoDetector` output against `findChessboardCorners + cornerSubPix`: - **Before fix:** mean error ≈ −0.501 px   - **After fix:** mean error ≈ −0.001 px   After the change, the Charuco detector output aligns with the legacy chessboard detector both numerically and visually. ### Notes This change only affects the post-refinement coordinate handling and does not alter detection logic, refinement parameters, or performance. See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
494 lines
22 KiB
Python
494 lines
22 KiB
Python
#!/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, tempfile, numpy as np
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from math import pi
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import cv2 as cv
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from tests_common import NewOpenCVTests
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def getSyntheticRT(yaw, pitch, distance):
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rvec = np.zeros((3, 1), np.float64)
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tvec = np.zeros((3, 1), np.float64)
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rotPitch = np.array([[-pitch], [0], [0]])
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rotYaw = np.array([[0], [yaw], [0]])
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rvec, tvec = cv.composeRT(rotPitch, np.zeros((3, 1), np.float64),
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rotYaw, np.zeros((3, 1), np.float64))[:2]
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tvec = np.array([[0], [0], [distance]])
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return rvec, tvec
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# see test_aruco_utils.cpp
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def projectMarker(img, board, markerIndex, cameraMatrix, rvec, tvec, markerBorder):
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markerSizePixels = 100
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markerImg = cv.aruco.generateImageMarker(board.getDictionary(), board.getIds()[markerIndex], markerSizePixels, borderBits=markerBorder)
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distCoeffs = np.zeros((5, 1), np.float64)
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maxCoord = board.getRightBottomCorner()
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objPoints = board.getObjPoints()[markerIndex]
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for i in range(len(objPoints)):
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objPoints[i][0] -= maxCoord[0] / 2
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objPoints[i][1] -= maxCoord[1] / 2
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objPoints[i][2] -= maxCoord[2] / 2
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corners, _ = cv.projectPoints(objPoints, rvec, tvec, cameraMatrix, distCoeffs)
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originalCorners = np.array([
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[0, 0],
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[markerSizePixels, 0],
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[markerSizePixels, markerSizePixels],
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[0, markerSizePixels],
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], np.float32)
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transformation = cv.getPerspectiveTransform(originalCorners, corners)
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borderValue = 127
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aux = cv.warpPerspective(markerImg, transformation, img.shape, None, cv.INTER_NEAREST, cv.BORDER_CONSTANT, borderValue)
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assert(img.shape == aux.shape)
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mask = (aux == borderValue).astype(np.uint8)
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img = img * mask + aux * (1 - mask)
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return img
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def projectChessboard(squaresX, squaresY, squareSize, imageSize, cameraMatrix, rvec, tvec):
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img = np.ones(imageSize, np.uint8) * 255
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distCoeffs = np.zeros((5, 1), np.float64)
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for y in range(squaresY):
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startY = y * squareSize
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for x in range(squaresX):
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if (y % 2 != x % 2):
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continue
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startX = x * squareSize
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squareCorners = np.array([[startX - squaresX*squareSize/2,
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startY - squaresY*squareSize/2,
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0]], np.float32)
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squareCorners = np.stack((squareCorners[0],
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squareCorners[0] + [squareSize, 0, 0],
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squareCorners[0] + [squareSize, squareSize, 0],
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squareCorners[0] + [0, squareSize, 0]))
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projectedCorners, _ = cv.projectPoints(squareCorners, rvec, tvec, cameraMatrix, distCoeffs)
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projectedCorners = projectedCorners.astype(np.int64)
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projectedCorners = projectedCorners.reshape(1, 4, 2)
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img = cv.fillPoly(img, [projectedCorners], 0)
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return img
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def projectCharucoBoard(board, cameraMatrix, yaw, pitch, distance, imageSize, markerBorder):
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rvec, tvec = getSyntheticRT(yaw, pitch, distance)
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img = np.ones(imageSize, np.uint8) * 255
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for indexMarker in range(len(board.getIds())):
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img = projectMarker(img, board, indexMarker, cameraMatrix, rvec, tvec, markerBorder)
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chessboard = projectChessboard(board.getChessboardSize()[0], board.getChessboardSize()[1],
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board.getSquareLength(), imageSize, cameraMatrix, rvec, tvec)
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chessboard = (chessboard != 0).astype(np.uint8)
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img = img * chessboard
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return img, rvec, tvec
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class aruco_objdetect_test(NewOpenCVTests):
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def test_board(self):
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p1 = np.array([[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0]], dtype=np.float32)
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p2 = np.array([[1, 0, 0], [1, 1, 0], [2, 1, 0], [2, 0, 0]], dtype=np.float32)
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objPoints = np.array([p1, p2])
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dictionary = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_50)
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ids = np.array([0, 1])
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board = cv.aruco.Board(objPoints, dictionary, ids)
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np.testing.assert_array_equal(board.getIds().squeeze(), ids)
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np.testing.assert_array_equal(np.ravel(np.array(board.getObjPoints())), np.ravel(np.concatenate([p1, p2])))
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def test_idsAccessibility(self):
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ids = np.arange(17)
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rev_ids = ids[::-1]
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aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_5X5_250)
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board = cv.aruco.CharucoBoard((7, 5), 1, 0.5, aruco_dict)
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np.testing.assert_array_equal(board.getIds().squeeze(), ids)
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board = cv.aruco.CharucoBoard((7, 5), 1, 0.5, aruco_dict, rev_ids)
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np.testing.assert_array_equal(board.getIds().squeeze(), rev_ids)
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board = cv.aruco.CharucoBoard((7, 5), 1, 0.5, aruco_dict, ids)
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np.testing.assert_array_equal(board.getIds().squeeze(), ids)
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def test_identify(self):
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aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_50)
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expected_idx = 9
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expected_rotation = 2
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bit_marker = np.array([[0, 1, 1, 0], [1, 0, 1, 0], [1, 1, 1, 1], [0, 0, 1, 1]], dtype=np.uint8)
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check, idx, rotation = aruco_dict.identify(bit_marker, 0)
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self.assertTrue(check, True)
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self.assertEqual(idx, expected_idx)
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self.assertEqual(rotation, expected_rotation)
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def test_getDistanceToId(self):
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aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_50)
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idx = 7
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rotation = 3
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bit_marker = np.array([[0, 1, 0, 1], [0, 1, 1, 1], [1, 1, 0, 0], [0, 1, 0, 0]], dtype=np.uint8)
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dist = aruco_dict.getDistanceToId(bit_marker, idx)
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self.assertEqual(dist, 0)
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def test_aruco_detector(self):
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aruco_params = cv.aruco.DetectorParameters()
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aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_250)
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aruco_detector = cv.aruco.ArucoDetector(aruco_dict, aruco_params)
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id = 2
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marker_size = 100
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offset = 10
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img_marker = cv.aruco.generateImageMarker(aruco_dict, id, marker_size, aruco_params.markerBorderBits)
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img_marker = np.pad(img_marker, pad_width=offset, mode='constant', constant_values=255)
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gold_corners = np.array([[offset, offset],[marker_size+offset-1.0,offset],
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[marker_size+offset-1.0,marker_size+offset-1.0],
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[offset, marker_size+offset-1.0]], dtype=np.float32)
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corners, ids, rejected = aruco_detector.detectMarkers(img_marker)
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self.assertEqual(1, len(ids))
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self.assertEqual(id, ids[0])
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for i in range(0, len(corners)):
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np.testing.assert_array_equal(gold_corners, corners[i].reshape(4, 2))
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def test_aruco_detector_refine(self):
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aruco_params = cv.aruco.DetectorParameters()
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aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_250)
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aruco_detector = cv.aruco.ArucoDetector(aruco_dict, aruco_params)
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board_size = (3, 4)
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board = cv.aruco.GridBoard(board_size, 5.0, 1.0, aruco_dict)
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board_image = board.generateImage((board_size[0]*50, board_size[1]*50), marginSize=10)
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corners, ids, rejected = aruco_detector.detectMarkers(board_image)
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self.assertEqual(board_size[0]*board_size[1], len(ids))
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part_corners, part_ids, part_rejected = corners[:-1], ids[:-1], list(rejected)
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part_rejected.append(corners[-1])
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refine_corners, refine_ids, refine_rejected, recovered_ids = aruco_detector.refineDetectedMarkers(board_image, board, part_corners, part_ids, part_rejected)
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self.assertEqual(board_size[0] * board_size[1], len(refine_ids))
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self.assertEqual(1, len(recovered_ids))
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self.assertEqual(ids[-1], refine_ids[-1])
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self.assertEqual((1, 4, 2), refine_corners[0].shape)
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np.testing.assert_array_equal(corners, refine_corners)
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def test_charuco_refine(self):
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aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_6X6_50)
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board_size = (3, 4)
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board = cv.aruco.CharucoBoard(board_size, 1., .7, aruco_dict)
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aruco_detector = cv.aruco.ArucoDetector(aruco_dict)
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charuco_detector = cv.aruco.CharucoDetector(board)
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cell_size = 100
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image = board.generateImage((cell_size*board_size[0], cell_size*board_size[1]))
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camera = np.array([[1, 0, 0.5],
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[0, 1, 0.5],
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[0, 0, 1]])
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dist = np.array([0, 0, 0, 0, 0], dtype=np.float32).reshape(1, -1)
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# generate gold corners of the ArUco markers for the test
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gold_corners = np.array(board.getObjPoints())[:, :, 0:2]*cell_size
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# detect corners
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markerCorners, markerIds, _ = aruco_detector.detectMarkers(image)
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# test refine
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rejected = [markerCorners[-1]]
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markerCorners, markerIds = markerCorners[:-1], markerIds[:-1]
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markerCorners, markerIds, _, _ = aruco_detector.refineDetectedMarkers(image, board, markerCorners, markerIds,
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rejected, cameraMatrix=camera, distCoeffs=dist)
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charucoCorners, charucoIds, _, _ = charuco_detector.detectBoard(image, markerCorners=markerCorners,
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markerIds=markerIds)
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self.assertEqual(len(charucoIds), 6)
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self.assertEqual(len(markerIds), 6)
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for i, id in enumerate(markerIds.reshape(-1)):
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np.testing.assert_allclose(gold_corners[id], markerCorners[i].reshape(4, 2), 0.01, 1.)
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def test_write_read_dictionary(self):
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try:
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aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_5X5_50)
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markers_gold = aruco_dict.bytesList
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# write aruco_dict
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fd, filename = tempfile.mkstemp(prefix="opencv_python_aruco_dict_", suffix=".yml")
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os.close(fd)
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fs_write = cv.FileStorage(filename, cv.FileStorage_WRITE)
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aruco_dict.writeDictionary(fs_write)
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fs_write.release()
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# reset aruco_dict
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aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_6X6_250)
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# read aruco_dict
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fs_read = cv.FileStorage(filename, cv.FileStorage_READ)
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aruco_dict.readDictionary(fs_read.root())
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fs_read.release()
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# check equal
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self.assertEqual(aruco_dict.markerSize, 5)
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self.assertEqual(aruco_dict.maxCorrectionBits, 3)
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np.testing.assert_array_equal(aruco_dict.bytesList, markers_gold)
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finally:
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if os.path.exists(filename):
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os.remove(filename)
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def test_charuco_detector(self):
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aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_250)
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board_size = (3, 3)
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board = cv.aruco.CharucoBoard(board_size, 1.0, .8, aruco_dict)
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charuco_detector = cv.aruco.CharucoDetector(board)
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cell_size = 100
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image = board.generateImage((cell_size*board_size[0], cell_size*board_size[1]))
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# Note: Expected values adjusted by -0.5px after fixing the systematic offset bug in charuco_detector.cpp
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# The fix removes the incorrect +0.5 offset that was added after cornerSubPix
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list_gold_corners = []
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for i in range(1, board_size[0]):
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for j in range(1, board_size[1]):
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list_gold_corners.append((j*cell_size - 0.5, i*cell_size - 0.5))
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gold_corners = np.array(list_gold_corners, dtype=np.float32)
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charucoCorners, charucoIds, markerCorners, markerIds = charuco_detector.detectBoard(image)
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self.assertEqual(len(charucoIds), 4)
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for i in range(0, 4):
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self.assertEqual(charucoIds[i], i)
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np.testing.assert_allclose(gold_corners, charucoCorners.reshape(-1, 2), 0.01, 0.1)
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def test_detect_diamonds(self):
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aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_6X6_250)
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board_size = (3, 3)
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board = cv.aruco.CharucoBoard(board_size, 1.0, .8, aruco_dict)
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charuco_detector = cv.aruco.CharucoDetector(board)
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cell_size = 120
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image = board.generateImage((cell_size*board_size[0], cell_size*board_size[1]))
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# Note: Expected values adjusted by -0.5px after fixing the systematic offset bug in charuco_detector.cpp
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# The fix removes the incorrect +0.5 offset that was added after cornerSubPix
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list_gold_corners = [(cell_size - 0.5, cell_size - 0.5), (2*cell_size - 0.5, cell_size - 0.5),
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(2*cell_size - 0.5, 2*cell_size - 0.5), (cell_size - 0.5, 2*cell_size - 0.5)]
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gold_corners = np.array(list_gold_corners, dtype=np.float32)
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diamond_corners, diamond_ids, marker_corners, marker_ids = charuco_detector.detectDiamonds(image)
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self.assertEqual(diamond_ids.size, 4)
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self.assertEqual(marker_ids.size, 4)
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for i in range(0, 4):
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self.assertEqual(diamond_ids[0][0][i], i)
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np.testing.assert_allclose(gold_corners, np.array(diamond_corners, dtype=np.float32).reshape(-1, 2), 0.01, 0.1)
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# check no segfault when cameraMatrix or distCoeffs are not initialized
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def test_charuco_no_segfault_params(self):
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dictionary = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_1000)
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board = cv.aruco.CharucoBoard((10, 10), 0.019, 0.015, dictionary)
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charuco_parameters = cv.aruco.CharucoParameters()
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detector = cv.aruco.CharucoDetector(board)
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detector.setCharucoParameters(charuco_parameters)
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self.assertIsNone(detector.getCharucoParameters().cameraMatrix)
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self.assertIsNone(detector.getCharucoParameters().distCoeffs)
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def test_charuco_no_segfault_params_constructor(self):
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dictionary = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_1000)
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board = cv.aruco.CharucoBoard((10, 10), 0.019, 0.015, dictionary)
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charuco_parameters = cv.aruco.CharucoParameters()
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detector = cv.aruco.CharucoDetector(board, charucoParams=charuco_parameters)
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self.assertIsNone(detector.getCharucoParameters().cameraMatrix)
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self.assertIsNone(detector.getCharucoParameters().distCoeffs)
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# similar to C++ test CV_CharucoDetection.accuracy
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def test_charuco_detector_accuracy(self):
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iteration = 0
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cameraMatrix = np.eye(3, 3, dtype=np.float64)
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imgSize = (500, 500)
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params = cv.aruco.DetectorParameters()
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params.minDistanceToBorder = 3
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board = cv.aruco.CharucoBoard((4, 4), 0.03, 0.015, cv.aruco.getPredefinedDictionary(cv.aruco.DICT_6X6_250))
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detector = cv.aruco.CharucoDetector(board, detectorParams=params)
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cameraMatrix[0, 0] = cameraMatrix[1, 1] = 600
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cameraMatrix[0, 2] = imgSize[0] / 2
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cameraMatrix[1, 2] = imgSize[1] / 2
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# for different perspectives
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distCoeffs = np.zeros((5, 1), dtype=np.float64)
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for distance in [0.2, 0.4]:
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for yaw in range(-55, 51, 25):
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for pitch in range(-55, 51, 25):
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markerBorder = iteration % 2 + 1
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iteration += 1
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# create synthetic image
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img, rvec, tvec = projectCharucoBoard(board, cameraMatrix, yaw * pi / 180, pitch * pi / 180, distance, imgSize, markerBorder)
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params.markerBorderBits = markerBorder
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detector.setDetectorParameters(params)
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if (iteration % 2 != 0):
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charucoParameters = cv.aruco.CharucoParameters()
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charucoParameters.cameraMatrix = cameraMatrix
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charucoParameters.distCoeffs = distCoeffs
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detector.setCharucoParameters(charucoParameters)
|
|
|
|
charucoCorners, charucoIds, corners, ids = detector.detectBoard(img)
|
|
|
|
self.assertGreater(len(ids), 0)
|
|
|
|
copyChessboardCorners = board.getChessboardCorners()
|
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copyChessboardCorners -= np.array(board.getRightBottomCorner()) / 2
|
|
|
|
projectedCharucoCorners, _ = cv.projectPoints(copyChessboardCorners, rvec, tvec, cameraMatrix, distCoeffs)
|
|
|
|
if charucoIds is None:
|
|
# Detection can fail at extreme viewing angles
|
|
self.assertTrue(abs(yaw) >= 45 or abs(pitch) >= 45,
|
|
f"Detection failed unexpectedly at yaw={yaw}, pitch={pitch}")
|
|
continue
|
|
|
|
for i in range(len(charucoIds)):
|
|
currentId = charucoIds[i]
|
|
self.assertLess(currentId, len(board.getChessboardCorners()))
|
|
|
|
reprErr = cv.norm(charucoCorners[i] - projectedCharucoCorners[currentId])
|
|
self.assertLessEqual(reprErr, 5)
|
|
|
|
def test_aruco_match_image_points(self):
|
|
aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_50)
|
|
board_size = (3, 4)
|
|
board = cv.aruco.GridBoard(board_size, 5.0, 1.0, aruco_dict)
|
|
aruco_corners = np.array(board.getObjPoints())[:, :, :2]
|
|
aruco_ids = board.getIds()
|
|
obj_points, img_points = board.matchImagePoints(aruco_corners, aruco_ids)
|
|
aruco_corners = aruco_corners.reshape(-1, 2)
|
|
|
|
self.assertEqual(aruco_corners.shape[0], obj_points.shape[0])
|
|
self.assertEqual(img_points.shape[0], obj_points.shape[0])
|
|
self.assertEqual(2, img_points.shape[2])
|
|
np.testing.assert_array_equal(aruco_corners, obj_points[:, :, :2].reshape(-1, 2))
|
|
|
|
def test_charuco_match_image_points(self):
|
|
aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_50)
|
|
board_size = (3, 4)
|
|
board = cv.aruco.CharucoBoard(board_size, 5.0, 1.0, aruco_dict)
|
|
chessboard_corners = np.array(board.getChessboardCorners())[:, :2]
|
|
chessboard_ids = board.getIds()
|
|
obj_points, img_points = board.matchImagePoints(chessboard_corners, chessboard_ids)
|
|
|
|
self.assertEqual(chessboard_corners.shape[0], obj_points.shape[0])
|
|
self.assertEqual(img_points.shape[0], obj_points.shape[0])
|
|
self.assertEqual(2, img_points.shape[2])
|
|
np.testing.assert_array_equal(chessboard_corners, obj_points[:, :, :2].reshape(-1, 2))
|
|
|
|
def test_draw_detected_markers(self):
|
|
detected_points = [[[10, 10], [50, 10], [50, 50], [10, 50]]]
|
|
img = np.zeros((60, 60), dtype=np.uint8)
|
|
|
|
# add extra dimension in Python to create Nx4 Mat with 2 channels
|
|
points1 = np.array(detected_points).reshape(-1, 4, 1, 2)
|
|
img = cv.aruco.drawDetectedMarkers(img, points1, borderColor=255)
|
|
|
|
# check that the marker borders are painted
|
|
contours, _ = cv.findContours(img, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
|
|
self.assertEqual(len(contours), 1)
|
|
self.assertEqual(img[10, 10], 255)
|
|
self.assertEqual(img[50, 10], 255)
|
|
self.assertEqual(img[50, 50], 255)
|
|
self.assertEqual(img[10, 50], 255)
|
|
|
|
# must throw Exception without extra dimension
|
|
points2 = np.array(detected_points)
|
|
with self.assertRaises(Exception):
|
|
img = cv.aruco.drawDetectedMarkers(img, points2, borderColor=255)
|
|
|
|
def test_draw_detected_charuco(self):
|
|
detected_points = [[[10, 10], [50, 10], [50, 50], [10, 50]]]
|
|
img = np.zeros((60, 60), dtype=np.uint8)
|
|
|
|
# add extra dimension in Python to create Nx1 Mat with 2 channels
|
|
points = np.array(detected_points).reshape(-1, 1, 2)
|
|
img = cv.aruco.drawDetectedCornersCharuco(img, points, cornerColor=255)
|
|
|
|
# check that the 4 charuco corners are painted
|
|
contours, _ = cv.findContours(img, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
|
|
self.assertEqual(len(contours), 4)
|
|
for contour in contours:
|
|
center_x = round(np.average(contour[:, 0, 0]))
|
|
center_y = round(np.average(contour[:, 0, 1]))
|
|
center = [center_x, center_y]
|
|
self.assertTrue(center in detected_points[0])
|
|
|
|
# must throw Exception without extra dimension
|
|
points2 = np.array(detected_points)
|
|
with self.assertRaises(Exception):
|
|
img = cv.aruco.drawDetectedCornersCharuco(img, points2, borderColor=255)
|
|
|
|
def test_draw_detected_diamonds(self):
|
|
detected_points = [[[10, 10], [50, 10], [50, 50], [10, 50]]]
|
|
img = np.zeros((60, 60), dtype=np.uint8)
|
|
|
|
# add extra dimension in Python to create Nx4 Mat with 2 channels
|
|
points = np.array(detected_points).reshape(-1, 4, 1, 2)
|
|
img = cv.aruco.drawDetectedDiamonds(img, points, borderColor=255)
|
|
|
|
# check that the diamonds borders are painted
|
|
contours, _ = cv.findContours(img, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
|
|
self.assertEqual(len(contours), 1)
|
|
self.assertEqual(img[10, 10], 255)
|
|
self.assertEqual(img[50, 10], 255)
|
|
self.assertEqual(img[50, 50], 255)
|
|
self.assertEqual(img[10, 50], 255)
|
|
|
|
# must throw Exception without extra dimension
|
|
points2 = np.array(detected_points)
|
|
with self.assertRaises(Exception):
|
|
img = cv.aruco.drawDetectedDiamonds(img, points2, borderColor=255)
|
|
|
|
def test_multi_dict_arucodetector(self):
|
|
aruco_params = cv.aruco.DetectorParameters()
|
|
aruco_dicts = [
|
|
cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_250),
|
|
cv.aruco.getPredefinedDictionary(cv.aruco.DICT_5X5_250)
|
|
]
|
|
aruco_detector = cv.aruco.ArucoDetector(aruco_dicts, aruco_params)
|
|
id = 2
|
|
marker_size = 100
|
|
offset = 10
|
|
img_marker1 = cv.aruco.generateImageMarker(aruco_dicts[0], id, marker_size, aruco_params.markerBorderBits)
|
|
img_marker1 = np.pad(img_marker1, pad_width=offset, mode='constant', constant_values=255)
|
|
img_marker2 = cv.aruco.generateImageMarker(aruco_dicts[1], id, marker_size, aruco_params.markerBorderBits)
|
|
img_marker2 = np.pad(img_marker2, pad_width=offset, mode='constant', constant_values=255)
|
|
img_markers = np.concatenate((img_marker1, img_marker2), axis=1)
|
|
|
|
corners, ids, rejected, dictIndices = aruco_detector.detectMarkersMultiDict(img_markers)
|
|
|
|
self.assertEqual(2, len(ids))
|
|
self.assertEqual(id, ids[0])
|
|
self.assertEqual(id, ids[1])
|
|
self.assertEqual(2, len(dictIndices))
|
|
self.assertEqual(0, dictIndices[0])
|
|
self.assertEqual(1, dictIndices[1])
|
|
|
|
if __name__ == '__main__':
|
|
NewOpenCVTests.bootstrap()
|