'cv2' 没有属性'transform'答案

【问题标题】：'cv2' has no attribute 'transform''cv2' 没有属性'transform'
【发布时间】：2020-09-18 01:16:03
【问题描述】：

我正在尝试运行 Harry R. 建议的代码 here（问题的第一个解决方案），我收到一条错误消息，提示 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'

有人收到了吗？有什么问题？谢谢

【问题讨论】：

我认为您将转换称为不是一种方法。你输入了 transform() 吗？
是的，我做到了。它说NameError: name 'transform' is not defined。其实是通过cv2定义的——docs.opencv.org/2.4/modules/core/doc/operations_on_arrays.html
您是否有自己的名为cv2.py 的脚本被导入而不是实际模块？
不，实际上它似乎不适用于 Python 3.7。它适用于 Python 3.0。

标签： python opencv cv2 image-registration

【解决方案1】：

好的，我终于让它在 Python 3.0（我认为原始版本是 Python 2.7）中工作，而在 Python 3.7 中不是。请注意，cv2.estimateRigidTransform 被更改为 cv2.estimateAffinePartial2D 并且我们有两个返回变量而不是原始代码中的一个，因为现在消除了 cv2.estimateRigidTransform。

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()

【讨论】：