标签：eye frame cv2 标定 hand points params 3d 手眼
目录
一、使用标定板类型
使用的是非对称圆分布的标定板如下图
二、标定步骤及实现
opencv实现
2.1 相机外参标定
相机外参标定结果点如下图，红色点为标定结果还原的点，绿色点为原始点
相机外参标定实现
def calibrate():
#相机内参
cameraMatrix =np.array([[2753.8901912306533,0,949.2910576185922],
[0,2753.5946107288023,593.6244109240248],
[0,0,1]])
distCoeffs =np.array([
-0.06501463142556438,
0.1712297534594541,
5.265462242831182e-05,
0.00032355313360590214,
0
])
#标定板行列数
pattern_shape=(4,5)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
#标定板上圆心的物理坐标定义（根据标定板的规格：间距为35mm）
example_3d = np.array([[0,0,0],[70,0,0], [140,0,0],[210,0,0],
[35,35,0],[105,35,0],[175,35,0],[245,35,0],
[0,70,0],[70,70,0],[140,70,0],[210,70,0],
[35,105,0],[105,105,0],[175,105,0],[245,105,0],
[0,140,0],[70,140,0],[140,140,0],[210,140,0],
],dtype="float32")
example_3d = example_3d*0.001#单位是米
points_3d = []
points_2d = []
images = glob.glob('G:/calib_images/EyeInHand/*.jpg') # 拍摄的十几张标定板图片所在目录
for fname in images:
# ret, frame = camera.cap.read()
print(fname)
frame = cv2.imread(fname)
# assert ret
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
ret, bin = cv2.threshold(gray_frame, 171, 255, cv2.THRESH_BINARY)
k = np.ones((5, 5), np.uint8)
bin_CLOSE = cv2.morphologyEx(bin, cv2.MORPH_CLOSE, k)
# cv2.imshow("bin", bin_CLOSE)
# cv2.waitKey(0)
params = cv2.SimpleBlobDetector_Params()
params.filterByArea = True
params.maxArea = 24034
params.minArea = 961
params.filterByColor = True
params.filterByConvexity = False
params.blobColor = 255
params.minThreshold = 171
params.maxThreshold = 251
params.minDistBetweenBlobs = 50
params.filterByInertia = True
params.minInertiaRatio = 0.01
blobDetector = cv2.SimpleBlobDetector_create(params)
ret, corners = cv2.findCirclesGrid(
bin_CLOSE, pattern_shape, flags=cv2.CALIB_CB_ASYMMETRIC_GRID ,blobDetector =blobDetector
)
if ret:
corners2 = cv2.cornerSubPix(bin_CLOSE, corners, (11, 11), (-1, -1), criteria)
points_3d.append(example_3d.copy())
points_2d.append(corners2)
print("Found calibration %i " % (len(points_3d)))
drawn_frame = cv2.drawChessboardCorners(frame, pattern_shape, corners2, corners is not None)
cv2.imwrite('drawn_frame.jpg',drawn_frame)
# cv2.imshow("calib", drawn_frame)
ret, camera_matrix, distortion_coefficients, rvecs, tvecs = cv2.calibrateCamera(points_3d, points_2d,
gray_frame.shape[::-1],
cameraMatrix, distCoeffs) #cameraMatrix, distCoeffs
# print(camera_matrix)
# print(distortion_coefficients)
# Ret, rvec, tvec = cv2.solvePnP(example_3d, corners2, cameraMatrix, distCoeffs)
#将物理坐标还原回像素坐标查看标定精度
imgpts, jac = cv2.projectPoints(points_3d[-1], rvecs[-1], tvecs[-1], camera_matrix, distortion_coefficients)
img = draw(frame, corners2, imgpts)#画图显示像素点和标定还原点
cv2.imshow('',img)
cv2.waitKey(0)
# plt.show()
# cv2.waitKey(10)
# break
ret, camera_matrix, distortion_coefficients, rvecs, tvecs = cv2.calibrateCamera(points_3d, points_2d, gray_frame.shape[::-1], cameraMatrix, distCoeffs)#
print('camera_matrix:\n',camera_matrix)
assert ret
return rvecs, tvecs2.2 手眼标定
手眼标定也使用opencv中的方法实现，代码如下
Rcf,Tcf = cv2.calibrateHandEye(Rfbs,Tfbs,Rtcs,Ttcs,method=cv2.CALIB_HAND_EYE_ANDREFF)标签：eye,frame,cv2,标定,hand,points,params,3d,手眼 来源： https://blog.csdn.net/Bolly_He/article/details/118306721
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