#!/usr/bin/env python
# Python 2/3 compatibility
from __future__ import print_function
import os, tempfile, numpy as np
from math import pi
import cv2 as cv
from tests_common import NewOpenCVTests
def getSyntheticRT(yaw, pitch, distance):
rvec = np.zeros((3, 1), np.float64)
tvec = np.zeros((3, 1), np.float64)
rotPitch = np.array([[-pitch], [0], [0]])
rotYaw = np.array([[0], [yaw], [0]])
rvec, tvec = cv.composeRT(rotPitch, np.zeros((3, 1), np.float64),
rotYaw, np.zeros((3, 1), np.float64))[:2]
tvec = np.array([[0], [0], [distance]])
return rvec, tvec
# see test_aruco_utils.cpp
def projectMarker(img, board, markerIndex, cameraMatrix, rvec, tvec, markerBorder):
markerSizePixels = 100
markerImg = cv.aruco.generateImageMarker(board.getDictionary(), board.getIds()[markerIndex], markerSizePixels, borderBits=markerBorder)
distCoeffs = np.zeros((5, 1), np.float64)
maxCoord = board.getRightBottomCorner()
objPoints = board.getObjPoints()[markerIndex]
for i in range(len(objPoints)):
objPoints[i][0] -= maxCoord[0] / 2
objPoints[i][1] -= maxCoord[1] / 2
objPoints[i][2] -= maxCoord[2] / 2
corners, _ = cv.projectPoints(objPoints, rvec, tvec, cameraMatrix, distCoeffs)
originalCorners = np.array([
[0, 0],
[markerSizePixels, 0],
[markerSizePixels, markerSizePixels],
[0, markerSizePixels],
], np.float32)
transformation = cv.getPerspectiveTransform(originalCorners, corners)
borderValue = 127
aux = cv.warpPerspective(markerImg, transformation, img.shape, None, cv.INTER_NEAREST, cv.BORDER_CONSTANT, borderValue)
assert(img.shape == aux.shape)
mask = (aux == borderValue).astype(np.uint8)
img = img * mask + aux * (1 - mask)
return img
def projectChessboard(squaresX, squaresY, squareSize, imageSize, cameraMatrix, rvec, tvec):
img = np.ones(imageSize, np.uint8) * 255
distCoeffs = np.zeros((5, 1), np.float64)
for y in range(squaresY):
startY = y * squareSize
for x in range(squaresX):
if (y % 2 != x % 2):
continue
startX = x * squareSize
squareCorners = np.array([[startX - squaresX*squareSize/2,
startY - squaresY*squareSize/2,
0]], np.float32)
squareCorners = np.stack((squareCorners[0],
squareCorners[0] + [squareSize, 0, 0],
squareCorners[0] + [squareSize, squareSize, 0],
squareCorners[0] + [0, squareSize, 0]))
projectedCorners, _ = cv.projectPoints(squareCorners, rvec, tvec, cameraMatrix, distCoeffs)
projectedCorners = projectedCorners.astype(np.int64)
projectedCorners = projectedCorners.reshape(1, 4, 2)
img = cv.fillPoly(img, [projectedCorners], 0)
return img
def projectCharucoBoard(board, cameraMatrix, yaw, pitch, distance, imageSize, markerBorder):
rvec, tvec = getSyntheticRT(yaw, pitch, distance)
img = np.ones(imageSize, np.uint8) * 255
for indexMarker in range(len(board.getIds())):
img = projectMarker(img, board, indexMarker, cameraMatrix, rvec, tvec, markerBorder)
chessboard = projectChessboard(board.getChessboardSize()[0], board.getChessboardSize()[1],
board.getSquareLength(), imageSize, cameraMatrix, rvec, tvec)
chessboard = (chessboard != 0).astype(np.uint8)
img = img * chessboard
return img, rvec, tvec
class aruco_objdetect_test(NewOpenCVTests):
def test_board(self):
p1 = np.array([[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0]], dtype=np.float32)
p2 = np.array([[1, 0, 0], [1, 1, 0], [2, 1, 0], [2, 0, 0]], dtype=np.float32)
objPoints = np.array([p1, p2])
dictionary = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_50)
ids = np.array([0, 1])
board = cv.aruco.Board(objPoints, dictionary, ids)
np.testing.assert_array_equal(board.getIds().squeeze(), ids)
np.testing.assert_array_equal(np.ravel(np.array(board.getObjPoints())), np.ravel(np.concatenate([p1, p2])))
def test_idsAccessibility(self):
ids = np.arange(17)
rev_ids = ids[::-1]
aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_5X5_250)
board = cv.aruco.CharucoBoard((7, 5), 1, 0.5, aruco_dict)
np.testing.assert_array_equal(board.getIds().squeeze(), ids)
board = cv.aruco.CharucoBoard((7, 5), 1, 0.5, aruco_dict, rev_ids)
np.testing.assert_array_equal(board.getIds().squeeze(), rev_ids)
board = cv.aruco.CharucoBoard((7, 5), 1, 0.5, aruco_dict, ids)
np.testing.assert_array_equal(board.getIds().squeeze(), ids)
def test_identify(self):
aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_50)
expected_idx = 9
expected_rotation = 2
bit_marker = np.array([[0, 1, 1, 0], [1, 0, 1, 0], [1, 1, 1, 1], [0, 0, 1, 1]], dtype=np.uint8)
check, idx, rotation = aruco_dict.identify(bit_marker, 0)
self.assertTrue(check, True)
self.assertEqual(idx, expected_idx)
self.assertEqual(rotation, expected_rotation)
def test_getDistanceToId(self):
aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_50)
idx = 7
rotation = 3
bit_marker = np.array([[0, 1, 0, 1], [0, 1, 1, 1], [1, 1, 0, 0], [0, 1, 0, 0]], dtype=np.uint8)
dist = aruco_dict.getDistanceToId(bit_marker, idx)
self.assertEqual(dist, 0)
def test_aruco_detector(self):
aruco_params = cv.aruco.DetectorParameters()
aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_250)
aruco_detector = cv.aruco.ArucoDetector(aruco_dict, aruco_params)
id = 2
marker_size = 100
offset = 10
img_marker = cv.aruco.generateImageMarker(aruco_dict, id, marker_size, aruco_params.markerBorderBits)
img_marker = np.pad(img_marker, pad_width=offset, mode='constant', constant_values=255)
gold_corners = np.array([[offset, offset],[marker_size+offset-1.0,offset],
[marker_size+offset-1.0,marker_size+offset-1.0],
[offset, marker_size+offset-1.0]], dtype=np.float32)
corners, ids, rejected = aruco_detector.detectMarkers(img_marker)
self.assertEqual(1, len(ids))
self.assertEqual(id, ids[0])
for i in range(0, len(corners)):
np.testing.assert_array_equal(gold_corners, corners[i].reshape(4, 2))
def test_aruco_detector_refine(self):
aruco_params = cv.aruco.DetectorParameters()
aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_250)
aruco_detector = cv.aruco.ArucoDetector(aruco_dict, aruco_params)
board_size = (3, 4)
board = cv.aruco.GridBoard(board_size, 5.0, 1.0, aruco_dict)
board_image = board.generateImage((board_size[0]*50, board_size[1]*50), marginSize=10)
corners, ids, rejected = aruco_detector.detectMarkers(board_image)
self.assertEqual(board_size[0]*board_size[1], len(ids))
part_corners, part_ids, part_rejected = corners[:-1], ids[:-1], list(rejected)
part_rejected.append(corners[-1])
refine_corners, refine_ids, refine_rejected, recovered_ids = aruco_detector.refineDetectedMarkers(board_image, board, part_corners, part_ids, part_rejected)
self.assertEqual(board_size[0] * board_size[1], len(refine_ids))
self.assertEqual(1, len(recovered_ids))
self.assertEqual(ids[-1], refine_ids[-1])
self.assertEqual((1, 4, 2), refine_corners[0].shape)
np.testing.assert_array_equal(corners, refine_corners)
def test_charuco_refine(self):
aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_6X6_50)
board_size = (3, 4)
board = cv.aruco.CharucoBoard(board_size, 1., .7, aruco_dict)
aruco_detector = cv.aruco.ArucoDetector(aruco_dict)
charuco_detector = cv.aruco.CharucoDetector(board)
cell_size = 100
image = board.generateImage((cell_size*board_size[0], cell_size*board_size[1]))
camera = np.array([[1, 0, 0.5],
[0, 1, 0.5],
[0, 0, 1]])
dist = np.array([0, 0, 0, 0, 0], dtype=np.float32).reshape(1, -1)
# generate gold corners of the ArUco markers for the test
gold_corners = np.array(board.getObjPoints())[:, :, 0:2]*cell_size
# detect corners
markerCorners, markerIds, _ = aruco_detector.detectMarkers(image)
# test refine
rejected = [markerCorners[-1]]
markerCorners, markerIds = markerCorners[:-1], markerIds[:-1]
markerCorners, markerIds, _, _ = aruco_detector.refineDetectedMarkers(image, board, markerCorners, markerIds,
rejected, cameraMatrix=camera, distCoeffs=dist)
charucoCorners, charucoIds, _, _ = charuco_detector.detectBoard(image, markerCorners=markerCorners,
markerIds=markerIds)
self.assertEqual(len(charucoIds), 6)
self.assertEqual(len(markerIds), 6)
for i, id in enumerate(markerIds.reshape(-1)):
np.testing.assert_allclose(gold_corners[id], markerCorners[i].reshape(4, 2), 0.01, 1.)
def test_write_read_dictionary(self):
try:
aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_5X5_50)
markers_gold = aruco_dict.bytesList
# write aruco_dict
fd, filename = tempfile.mkstemp(prefix="opencv_python_aruco_dict_", suffix=".yml")
os.close(fd)
fs_write = cv.FileStorage(filename, cv.FileStorage_WRITE)
aruco_dict.writeDictionary(fs_write)
fs_write.release()
# reset aruco_dict
aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_6X6_250)
# read aruco_dict
fs_read = cv.FileStorage(filename, cv.FileStorage_READ)
aruco_dict.readDictionary(fs_read.root())
fs_read.release()
# check equal
self.assertEqual(aruco_dict.markerSize, 5)
self.assertEqual(aruco_dict.maxCorrectionBits, 3)
np.testing.assert_array_equal(aruco_dict.bytesList, markers_gold)
finally:
if os.path.exists(filename):
os.remove(filename)
def test_charuco_detector(self):
aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_250)
board_size = (3, 3)
board = cv.aruco.CharucoBoard(board_size, 1.0, .8, aruco_dict)
charuco_detector = cv.aruco.CharucoDetector(board)
cell_size = 100
image = board.generateImage((cell_size*board_size[0], cell_size*board_size[1]))
list_gold_corners = []
for i in range(1, board_size[0]):
for j in range(1, board_size[1]):
list_gold_corners.append((j*cell_size, i*cell_size))
gold_corners = np.array(list_gold_corners, dtype=np.float32)
charucoCorners, charucoIds, markerCorners, markerIds = charuco_detector.detectBoard(image)
self.assertEqual(len(charucoIds), 4)
for i in range(0, 4):
self.assertEqual(charucoIds[i], i)
np.testing.assert_allclose(gold_corners, charucoCorners.reshape(-1, 2), 0.01, 0.1)
def test_detect_diamonds(self):
aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_6X6_250)
board_size = (3, 3)
board = cv.aruco.CharucoBoard(board_size, 1.0, .8, aruco_dict)
charuco_detector = cv.aruco.CharucoDetector(board)
cell_size = 120
image = board.generateImage((cell_size*board_size[0], cell_size*board_size[1]))
list_gold_corners = [(cell_size, cell_size), (2*cell_size, cell_size), (2*cell_size, 2*cell_size),
(cell_size, 2*cell_size)]
gold_corners = np.array(list_gold_corners, dtype=np.float32)
diamond_corners, diamond_ids, marker_corners, marker_ids = charuco_detector.detectDiamonds(image)
self.assertEqual(diamond_ids.size, 4)
self.assertEqual(marker_ids.size, 4)
for i in range(0, 4):
self.assertEqual(diamond_ids[0][i], i)
np.testing.assert_allclose(gold_corners, np.array(diamond_corners, dtype=np.float32).reshape(-1, 2), 0.01, 0.1)
# check no segfault when cameraMatrix or distCoeffs are not initialized
def test_charuco_no_segfault_params(self):
dictionary = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_1000)
board = cv.aruco.CharucoBoard((10, 10), 0.019, 0.015, dictionary)
charuco_parameters = cv.aruco.CharucoParameters()
detector = cv.aruco.CharucoDetector(board)
detector.setCharucoParameters(charuco_parameters)
self.assertIsNone(detector.getCharucoParameters().cameraMatrix)
self.assertIsNone(detector.getCharucoParameters().distCoeffs)
def test_charuco_no_segfault_params_constructor(self):
dictionary = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_1000)
board = cv.aruco.CharucoBoard((10, 10), 0.019, 0.015, dictionary)
charuco_parameters = cv.aruco.CharucoParameters()
detector = cv.aruco.CharucoDetector(board, charucoParams=charuco_parameters)
self.assertIsNone(detector.getCharucoParameters().cameraMatrix)
self.assertIsNone(detector.getCharucoParameters().distCoeffs)
# similar to C++ test CV_CharucoDetection.accuracy
def test_charuco_detector_accuracy(self):
iteration = 0
cameraMatrix = np.eye(3, 3, dtype=np.float64)
imgSize = (500, 500)
params = cv.aruco.DetectorParameters()
params.minDistanceToBorder = 3
board = cv.aruco.CharucoBoard((4, 4), 0.03, 0.015, cv.aruco.getPredefinedDictionary(cv.aruco.DICT_6X6_250))
detector = cv.aruco.CharucoDetector(board, detectorParams=params)
cameraMatrix[0, 0] = cameraMatrix[1, 1] = 600
cameraMatrix[0, 2] = imgSize[0] / 2
cameraMatrix[1, 2] = imgSize[1] / 2
# for different perspectives
distCoeffs = np.zeros((5, 1), dtype=np.float64)
for distance in [0.2, 0.4]:
for yaw in range(-55, 51, 25):
for pitch in range(-55, 51, 25):
markerBorder = iteration % 2 + 1
iteration += 1
# create synthetic image
img, rvec, tvec = projectCharucoBoard(board, cameraMatrix, yaw * pi / 180, pitch * pi / 180, distance, imgSize, markerBorder)
params.markerBorderBits = markerBorder
detector.setDetectorParameters(params)
if (iteration % 2 != 0):
charucoParameters = cv.aruco.CharucoParameters()
charucoParameters.cameraMatrix = cameraMatrix
charucoParameters.distCoeffs = distCoeffs
detector.setCharucoParameters(charucoParameters)
charucoCorners, charucoIds, corners, ids = detector.detectBoard(img)
self.assertGreater(len(ids), 0)
copyChessboardCorners = board.getChessboardCorners()
copyChessboardCorners -= np.array(board.getRightBottomCorner()) / 2
projectedCharucoCorners, _ = cv.projectPoints(copyChessboardCorners, rvec, tvec, cameraMatrix, distCoeffs)
if charucoIds is None:
self.assertEqual(iteration, 46)
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[1])
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[1])
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)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()