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我正在尝试运行 Harry R. 建议的代码（问题的第一个解决方案），但我收到一条错误消息，说cv2没有transform方法：

Traceback (most recent call last):
  File "/Users/kkk/PycharmProjects/TestICPPython3.7/ICPTest.py", line 64, in <module>
    M2 = icp(a, b, [0.1, 0.33, np.pi / 2.2], 30)
  File "/Users/kkk/PycharmProjects/TestICPPython3.7/ICPTest.py", line 32, in icp
    src = cv2.transform(src, Tr[0:2])
AttributeError: module 'cv2' has no attribute 'transform'

有人得到这个吗？有什么问题？谢谢

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1 回答 1

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好的，我终于让它在 Python 3.0（我认为原始版本是 Python 2.7）而不是Python 3.7 中工作。请注意，我们有cv2.estimateRigidTransform两个cv2.estimateAffinePartial2D返回变量，而不是原始代码中的一个，因为cv2.estimateRigidTransform现在被消除了。

import cv2
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import NearestNeighbors

def icp(a, b, init_pose=(0, 0, 0), no_iterations=13):

    """
    The Iterative Closest Point estimator.
    Takes two cloudpoints a[x,y], b[x,y], an initial estimation of
    their relative pose and the number of iterations
    Returns the affine transform that transforms
    the cloudpoint a to the cloudpoint b.
    Note:
        (1) This method works for cloudpoints with minor
        transformations. Thus, the result depents greatly on
        the initial pose estimation.
        (2) A large number of iterations does not necessarily
        ensure convergence. Contrarily, most of the time it
        produces worse results.
    """

    src = np.array([a.T], copy=True).astype(np.float32)
    dst = np.array([b.T], copy=True).astype(np.float32)

    # Initialise with the initial pose estimation
    Tr = np.array([[np.cos(init_pose[2]), -np.sin(init_pose[2]), init_pose[0]],
                   [np.sin(init_pose[2]), np.cos(init_pose[2]), init_pose[1]],
                   [0, 0, 1]])

    src = cv2.transform(src, Tr[0:2])

    for i in range(no_iterations):

        # Find the nearest neighbours between the current source and the
        # destination cloudpoint

        nbrs = NearestNeighbors(n_neighbors=1, algorithm='auto').fit(dst[0])
        distances, indices = nbrs.kneighbors(src[0])

        # Compute the transformation between the current source
        # and destination cloudpoint
        T, tmp = cv2.estimateAffinePartial2D(src, dst[0, indices.T], False)
        # Transform the previous source and update the
        # current source cloudpoint
        src = cv2.transform(src, T)
        # Save the transformation from the actual source cloudpoint
        # to the destination
        Tr = np.dot(Tr, np.vstack((T, [0, 0, 1])))

    return Tr[0:2]


# Create the datasets
ang = np.linspace(-np.pi / 2, np.pi / 2, 320)
a = np.array([ang, np.sin(ang)])
th = np.pi / 2
rot = np.array([[np.cos(th), -np.sin(th)], [np.sin(th), np.cos(th)]])
b = np.dot(rot, a) + np.array([[0.2], [0.3]])

# Run the icp
M2 = icp(a, b, [0.1, 0.33, np.pi / 2.2], 30)
#
# # Plot the result
src = np.array([a.T]).astype(np.float32)
res = cv2.transform(src, M2)
plt.figure()
plt.plot(b[0], b[1])
plt.plot(res[0].T[0], res[0].T[1], 'r.')
plt.plot(a[0], a[1])
plt.show()
于 2020-05-30T18:09:41.950 回答