问题描述
我想您可能会认为是tvecs_new
相机位置。事实并非如此!实际上,这是相机坐标系世界起源的位置。为了使相机处于物体/世界坐标中,我相信您需要
-np.matrix(rotation_matrix).T * np.matrix(tvecs_new)
你可以使用获得欧拉角cv2.decomposeProjectionMatrix(P)[-1]
那里P
是[r|t]
3乘4外在矩阵。
解决方法
我正在尝试使用单应性在Blender 3d中校准并找到单个虚拟相机的位置和旋转。我正在使用Blender,以便在进入更加困难的现实世界之前可以仔细检查结果。
从固定相机的角度来看,我在不同位置和旋转位置上绘制了十张国际象棋棋盘的图片。使用OpenCV的Python,我曾经cv2.calibrateCamera
从十张图像中从棋盘的检测到的角落中找到本征矩阵,然后将其cv2.solvePnP
用于寻找外部参数(平移和旋转)。
但是,尽管估计的参数与实际参数很接近,但是仍然有些麻烦。我对翻译的初步估计是(-0.11205481,-0.0490256,8.13892491)
。实际位置是(0,8.07105)
。很接近吧?
但是,当我稍微移动和旋转相机并重新渲染图像时,估计的平移距离就更远了。估计:(-0.15933154,0.13367286,9.34058867)
。实际:(-1.7918,-1.51073,9.76597)
。Z值接近,而X和Y则不。
我完全感到困惑。如果有人可以帮助我解决这个问题,我将不胜感激。这是代码(基于OpenCV随附的Python2校准示例):
#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,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