我正在尝试使用单个虚拟摄像机在Blender 3D中进行单目标定，找到其位置和旋转角度，并使用单应性矩阵。 我正在使用Blender来检查结果，然后再继续到现实世界，因为那里更难。
我在我的静态摄像机的视野中，在不同的位置和旋转角度下渲染了一个棋盘的十张图片。 使用OpenCV的Python版，我使用cv2.calibrateCamera从十张图像中检测出棋盘上的角点来找到内参矩阵，然后将其用于cv2.solvePnP来找到外参参数(平移和旋转)。
然而，尽管估计的参数接近实际值，但有些奇怪的地方。我的平移的初始估计值是(-0.11205481，-0.0490256，8.13892491)。实际位置是(0,0,8.07105)。非常接近，对吧？
但是，当我轻微移动和旋转相机并重新渲染图像时，估计的平移距离更远了。估计：(-0.15933154，0.13367286，9.34058867)。实际：(-1.7918，-1.51073，9.76597)。Z值很接近，但X和Y不接近。
我完全困惑了。如果有人能帮助我解决这个问题，我将非常感激。这是代码(它基于OpenCV提供的Python2 calibrate示例)：
#imports left out
USAGE = '''
USAGE: calib.py [--save <filename>] [--debug <output path>] [--square_size] [<image mask>]
'''
args, img_mask = getopt.getopt(sys.argv[1:], '', ['save=', 'debug=', 'square_size='])
args = dict(args)
try: img_mask = img_mask[0]
except: img_mask = '../cpp/0*.png'
img_names = glob(img_mask)
debug_dir = args.get('--debug')
square_size = float(args.get('--square_size', 1.0))
pattern_size = (5, 8)
pattern_points = np.zeros( (np.prod(pattern_size), 3), np.float32 )
pattern_points[:,:2] = np.indices(pattern_size).T.reshape(-1, 2)
pattern_points *= square_size
obj_points = []
img_points = []
h, w = 0, 0
count = 0
for fn in img_names:
print 'processing %s...' % fn,
img = cv2.imread(fn, 0)
h, w = img.shape[:2]
found, corners = cv2.findChessboardCorners(img, pattern_size)
if found:
if count == 0:
#corners first is a list of the image points for just the first image.
#This is the image I know the object points for and use in solvePnP
corners_first = []
for val in corners:
corners_first.append(val[0])
np_corners_first = np.asarray(corners_first,np.float64)
count+=1
term = ( cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1 )
cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
if debug_dir:
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawChessboardCorners(vis, pattern_size, corners, found)
path, name, ext = splitfn(fn)
cv2.imwrite('%s/%s_chess.bmp' % (debug_dir, name), vis)
if not found:
print 'chessboard not found'
continue
img_points.append(corners.reshape(-1, 2))
obj_points.append(pattern_points)
print 'ok'
rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, (w, h))
print "RMS:", rms
print "camera matrix:\n", camera_matrix
print "distortion coefficients: ", dist_coefs.ravel()
cv2.destroyAllWindows()
np_xyz = np.array(xyz,np.float64).T #xyz list is from file. Not shown here for brevity
camera_matrix2 = np.asarray(camera_matrix,np.float64)
np_dist_coefs = np.asarray(dist_coefs[:,:],np.float64)
found,rvecs_new,tvecs_new = cv2.solvePnP(np_xyz, np_corners_first,camera_matrix2,np_dist_coefs)
np_rodrigues = np.asarray(rvecs_new[:,:],np.float64)
print np_rodrigues.shape
rot_matrix = cv2.Rodrigues(np_rodrigues)[0]
def rot_matrix_to_euler(R):
y_rot = asin(R[2][0])
x_rot = acos(R[2][2]/cos(y_rot))
z_rot = acos(R[0][0]/cos(y_rot))
y_rot_angle = y_rot *(180/pi)
x_rot_angle = x_rot *(180/pi)
z_rot_angle = z_rot *(180/pi)
return x_rot_angle,y_rot_angle,z_rot_angle
print "Euler_rotation = ",rot_matrix_to_euler(rot_matrix)
print "Translation_Matrix = ", tvecs_new