Question

我正在尝试重建相机捕获的对象的3d位置，使其在2d平面上的位置以及所有相机校准参数。我正在使用Python和OpenCV。

我已经搜索并尝试了多种解决方案，但无法实现所需的转换。我的主要问题是我没有足够的图形背景知识来理解和执行所需的确切步骤。

<?xml version="1.0"?>
<opencv_storage>
<intrinsic type_id="opencv-matrix">
  <rows>3</rows>
  <cols>3</cols>
  <dt>f</dt>
  <data>
    4.04310596e+003 0. 9.15485046e+002
    0. 4.03170264e+003 4.26480865e+002
    0. 0. 1.</data></intrinsic>
<rotation_vector type_id="opencv-matrix">
  <rows>1</rows>
  <cols>3</cols>
  <dt>f</dt>
  <data>
    -4.56216574e-001 1.76409543e+000 2.05966163e+000</data></rotation_vector>
<rotation_matrix type_id="opencv-matrix">
  <rows>3</rows>
  <cols>3</cols>
  <dt>f</dt>
  <data>
    -8.71332586e-001 -4.90659207e-001 5.74691826e-003 8.10814202e-002
    -1.32417098e-001 9.87872243e-001 -4.83947605e-001 8.61231267e-001
    1.55162677e-001</data></rotation_matrix>
<translation type_id="opencv-matrix">
  <rows>1</rows>
  <cols>3</cols>
  <dt>f</dt>
  <data>
    3.16912168e+004 -1.31297791e+003 8.73433125e+004</data></translation>
<distortion type_id="opencv-matrix">
  <rows>1</rows>
  <cols>4</cols>
  <dt>f</dt>
  <data>
    4.86164242e-001 -3.57553625e+000 -1.77373271e-002 -3.11793620e-003</data></distortion>
<points_2d type_id="opencv-matrix">
  <rows>10</rows>
  <cols>1</cols>
  <dt>"2f"</dt>
  <data>
    1454. 223. 463. 375. 742. 461. 1163. 588. 1704. 755. 646. 550. 129.
    497. 567. 690. 196. 738. 546. 935.</data></points_2d>
<points_3d type_id="opencv-matrix">
  <rows>10</rows>
  <cols>3</cols>
  <dt>f</dt>
  <data>
    0. 34000. 0. 36000. 20160. 0. 36000. 7.31248877e+003 0. 36000.
    -7.31248877e+003 0. 36000. -20160. 0. 41500. 0. 0. 47000. 9160. 0.
    47000. -9160. 0. 52500. -9160. 0. 52500. -20160. 0.</data></points_3d>
<reprojection_errors type_id="opencv-matrix">
  <rows>1</rows>
  <cols>20</cols>
  <dt>f</dt>
  <data>
    19. -2. -9. -2. 0. 1. -1. -1. 3. 1. 0. 1. -19. 0. -8. 0. -4. 2. 9.
    1.</data></reprojection_errors>
</opencv_storage>

这是我所拥有的，例如2d和3d点以及所有相机校准参数：固有，失真等。

我应该执行2d到3d转换的顺序是什么？查看数据，我想将（1454.0，223.0）转换为（0.0，34000.0，0.0），依此类推。

Answer 1

在this question的第二部分中，您可以找到一些数学方法来解决问题，以及该解决方案的c ++实现。

无论如何，我已经在Python中实现了类似的解决方案，如下所示：

matrices = [
    "intrinsic",
    "rotation_vector",
    "rotation_matrix",
    "translation",
    "distortion",
    "points_2d",
    "points_3d",
    "reprojection_errors"
]

# Load data from persistent storage
dic = {}
data = cv2.FileStorage(storage_file, cv2.FILE_STORAGE_READ)
for m in matrices:
    dic[m] = data.getNode(m).mat()

# Prepare matrices
rotation_matrix = np.mat(dic["rotation_matrix"])
translation_vector = np.mat(dic["translation"])
intrinsic_matrix = np.mat(dic["intrinsic"])

# Extrinsic Parameters Matrix
translation_vector_transposed = np.transpose(translation_vector)
extrinsic_matrix = np.concatenate((rotation_matrix, translation_vector_transposed), axis=1)

# Projection Matrix
projection_matrix = intrinsic_matrix * extrinsic_matrix

# Homography Matrix
p11 = projection_matrix[0,0]
p12 = projection_matrix[0,1]
p14 = projection_matrix[0,3]
p21 = projection_matrix[1,0]
p22 = projection_matrix[1,1]
p24 = projection_matrix[1,3]
p31 = projection_matrix[2,0]
p32 = projection_matrix[2,1]
p34 = projection_matrix[2,3]
homography_matrix = np.array([[p11,p12,p14], [p21,p22,p24], [p31,p32,p34]], dtype=np.float)
homography_matrix_inverse = inv(homography_matrix)

for i in range(0,10):

    # Prepare points
    np.set_printoptions(suppress=True)
    point_2D = np.append(np.array(dic["points_2d"][i]), np.array([[1]]), axis=1)
    print("\nPoint2D:", end=" ")
    print_point(point_2D)
    point_3d_expected = dic["points_3d"][i]
    print("\nPoint3D Exptected:", end=" ")
    print_point_simple(point_3d_expected)

    # Projection
    point_3D_w = np.mat(homography_matrix_inverse) * np.mat(np.transpose(point_2D))

    # Normalization
    point_3D = np.divide(point_3D_w,point_3D_w[2])
    point_3D[2] = 0

    # Show Result
    print("\nPoint3D:", end=" ")
    print_point(point_3D)
    print('')

希望这会对您有所帮助。

如何在考虑相机校准的情况下执行2d到3d重建

1 个答案: