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Open Source Computer Vision Library
https://opencv.org/
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462 lines
20 KiB
462 lines
20 KiB
#!/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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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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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) |
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# add extra dimension in Python to create Nx4 Mat with 2 channels |
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points1 = np.array(detected_points).reshape(-1, 4, 1, 2) |
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img = cv.aruco.drawDetectedMarkers(img, points1, borderColor=255) |
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# 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) |
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self.assertEqual(img[10, 50], 255) |
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# must throw Exception without extra dimension |
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points2 = np.array(detected_points) |
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with self.assertRaises(Exception): |
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img = cv.aruco.drawDetectedMarkers(img, points2, borderColor=255) |
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def test_draw_detected_charuco(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) |
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# add extra dimension in Python to create Nx1 Mat with 2 channels |
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points = np.array(detected_points).reshape(-1, 1, 2) |
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img = cv.aruco.drawDetectedCornersCharuco(img, points, cornerColor=255) |
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# check that the 4 charuco corners 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), 4) |
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for contour in contours: |
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center_x = round(np.average(contour[:, 0, 0])) |
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center_y = round(np.average(contour[:, 0, 1])) |
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center = [center_x, center_y] |
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self.assertTrue(center in detected_points[0]) |
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# must throw Exception without extra dimension |
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points2 = np.array(detected_points) |
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with self.assertRaises(Exception): |
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img = cv.aruco.drawDetectedCornersCharuco(img, points2, borderColor=255) |
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def test_draw_detected_diamonds(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) |
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# add extra dimension in Python to create Nx4 Mat with 2 channels |
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points = np.array(detected_points).reshape(-1, 4, 1, 2) |
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img = cv.aruco.drawDetectedDiamonds(img, points, borderColor=255) |
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# check that the diamonds 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) |
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self.assertEqual(img[10, 50], 255) |
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# must throw Exception without extra dimension |
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points2 = np.array(detected_points) |
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with self.assertRaises(Exception): |
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img = cv.aruco.drawDetectedDiamonds(img, points2, borderColor=255) |
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if __name__ == '__main__': |
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NewOpenCVTests.bootstrap()
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