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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，以便在进入更困难的现实世界之前仔细检查我的结果。

我在我的固定相机的视野中渲染了十张棋盘在不同位置和旋转的照片。使用 OpenCV 的 Python，我曾经cv2.calibrateCamera从十幅图像中检测到的棋盘角找到内在矩阵，然后用它cv2.solvePnP来找到外在参数（平移和旋转）。

然而，虽然估计的参数接近实际参数，但还是有一些可疑的地方。我最初对翻译的估计是(-0.11205481,-0.0490256,8.13892491). 实际位置是(0,0,8.07105)。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，估计的平移变得更远了。估计：(-0.15933154,0.13367286,9.34058867)。实际：(-1.7918,-1.51073,9.76597)。Z 值接近，但 X 和 Y 不接近。

我完全糊涂了。如果有人能帮我解决这个问题，我将不胜感激。这是代码（它基于 OpenCV 提供的 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, 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
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1 回答 1

27

我想你可能会想到tvecs_new相机的位置。有点令人困惑，事实并非如此！事实上，它是世界原点在相机坐标中的位置。要在对象/世界坐标中获得相机姿势，我相信你需要

-np.matrix(rotation_matrix).T * np.matrix(tvecs_new)

cv2.decomposeProjectionMatrix(P)[-1]您可以使用where Pis the [r|t]3 by 4 外在矩阵获得欧拉角。

我发现这是一篇关于内在和外在的非常好的文章......

于 2013-01-27T00:51:03.450 回答