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463 lines
20 KiB
Python
463 lines
20 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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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, i*cell_size))
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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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list_gold_corners = [(cell_size, cell_size), (2*cell_size, cell_size), (2*cell_size, 2*cell_size),
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(cell_size, 2*cell_size)]
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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)
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charucoCorners, charucoIds, corners, ids = detector.detectBoard(img)
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self.assertGreater(len(ids), 0)
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copyChessboardCorners = board.getChessboardCorners()
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copyChessboardCorners -= np.array(board.getRightBottomCorner()) / 2
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projectedCharucoCorners, _ = cv.projectPoints(copyChessboardCorners, rvec, tvec, cameraMatrix, distCoeffs)
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if charucoIds is None:
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self.assertEqual(iteration, 46)
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continue
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for i in range(len(charucoIds)):
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currentId = charucoIds[i]
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self.assertLess(currentId, len(board.getChessboardCorners()))
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reprErr = cv.norm(charucoCorners[i] - projectedCharucoCorners[currentId])
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self.assertLessEqual(reprErr, 5)
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def test_aruco_match_image_points(self):
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aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_50)
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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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aruco_corners = np.array(board.getObjPoints())[:, :, :2]
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aruco_ids = board.getIds()
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obj_points, img_points = board.matchImagePoints(aruco_corners, aruco_ids)
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aruco_corners = aruco_corners.reshape(-1, 2)
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self.assertEqual(aruco_corners.shape[0], obj_points.shape[0])
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self.assertEqual(img_points.shape[0], obj_points.shape[0])
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self.assertEqual(2, img_points.shape[2])
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np.testing.assert_array_equal(aruco_corners, obj_points[:, :, :2].reshape(-1, 2))
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def test_charuco_match_image_points(self):
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aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_50)
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board_size = (3, 4)
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board = cv.aruco.CharucoBoard(board_size, 5.0, 1.0, aruco_dict)
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chessboard_corners = np.array(board.getChessboardCorners())[:, :2]
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chessboard_ids = board.getIds()
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obj_points, img_points = board.matchImagePoints(chessboard_corners, chessboard_ids)
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|
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self.assertEqual(chessboard_corners.shape[0], obj_points.shape[0])
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self.assertEqual(img_points.shape[0], obj_points.shape[0])
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self.assertEqual(2, img_points.shape[2])
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np.testing.assert_array_equal(chessboard_corners, obj_points[:, :, :2].reshape(-1, 2))
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|
|
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def test_draw_detected_markers(self):
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detected_points = [[[10, 10], [50, 10], [50, 50], [10, 50]]]
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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)
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img = cv.aruco.drawDetectedMarkers(img, points1, borderColor=255)
|
|
|
|
# check that the marker borders are painted
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|
contours, _ = cv.findContours(img, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
|
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self.assertEqual(len(contours), 1)
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|
self.assertEqual(img[10, 10], 255)
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self.assertEqual(img[50, 10], 255)
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self.assertEqual(img[50, 50], 255)
|
|
self.assertEqual(img[10, 50], 255)
|
|
|
|
# must throw Exception without extra dimension
|
|
points2 = np.array(detected_points)
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|
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)
|
|
|
|
if __name__ == '__main__':
|
|
NewOpenCVTests.bootstrap()
|