我想在Python上使用undistortion模块来实现自定义OpenCV函数，就像在numpy中一样。

从documentation可以知道undistort函数只是initUndistortRectifyMap()和remap()的组合。

由于remap()是非常简单的操作，所以主要问题是为remap()实现映射。

我写了一个代码来构造地图，但是在我看来它的运行速度很慢。

代码由三个主要部分组成：

1. 将原始图像重塑为一个形状良好的阵列，以将其与相机矩阵的逆相乘并相乘。
2. z = 1平面中的扭曲点。
3. 再次重塑点以执行另一次乘法以返回到图像点。

我拍了一张（4032 x 3024）大小的图像。

一次矩阵乘法在我的PC上工作大约1秒钟。失真功能的工作时间约为2.4秒。

我尝试将相同形状的矩阵与OpenCV上的C++ Mats相乘，并花费了0.0002秒。

问题是如何加快计算速度，因为在我看来，由于如此大的差异，我做错了事。

I found here an advice to make all arrays contiguous,but this did not help

代码：

import numpy
import time


def _distort_z_1(x,y,k1,k2,k3,k4,k5,k6,p1,p2):
    x2 = x * x
    y2 = y * y
    xy = x * y

    r2 = x2 + y2
    r4 = r2 * r2
    r6 = r4 * r2

    radial = \
        (1 + k1 * r2 + k2 * r4 + k3 * r6) / \
        (1 + k4 * r2 + k5 * r4 + k6 * r6)

    tangential_x = 2 * p1 * xy + p2 * (r2 + 2 * x2)
    tangential_y = p1 * (r2 + 2 * y2) + 2 * p2 * xy

    x_distorted = x * radial + tangential_x
    y_distorted = y * radial + tangential_y

    return x_distorted,y_distorted


# Change dimension from [2 x H x W] to [H x W x 3 x 1] to correctly multiply with [3 x 3] matrix
def _homogeneous_reshape(points_x,points_y):
    points_homogeneous_reshaped = (
        # Add extra axis to change from [H x W x 3] to [H x W x 3 x 1]
        numpy.expand_dims(
            # Change from [3 x H x W] to [H x W x 3]
            numpy.transpose(
                # Change from [2 x H x W] to [3 x H x W] (homogeneous coordinates)
                numpy.stack(
                    numpy.broadcast_arrays(points_x,points_y,1)),(1,2,0)),-1))

    return points_homogeneous_reshaped


def _homogeneous_reshape_back(points_homogeneous_reshaped):
    points_homogeneous = (
        # Get back from [H x W x 3] to [3 x H x W]
        numpy.transpose(
            # Remove extra axis: [H x W x 3 x 1] to [H x W x 3]
            numpy.squeeze(
                points_homogeneous_reshaped),(2,1)))

    # Get back from homogeneous coordinates
    points_x,_ = points_homogeneous

    return points_x,points_y


def _get_undistort_rectify_maps(distortion_coefficients,camera_matrix,image_width,image_height):
    image_points = numpy.meshgrid(range(image_width),range(image_height))

    # print("BEGIN: _homogeneous_reshape")
    start = time.time()
    image_points_homogeneous_reshaped = _homogeneous_reshape(*image_points)
    end = time.time()
    print("END: _homogeneous_reshape",end - start)

    camera_matrix_inv = numpy.linalg.inv(camera_matrix)

    # print("BEGIN: camera_matrix_inv @ image_points_homogeneous_reshaped")
    start = time.time()
    image_points_homogeneous_z_1_reshaped = camera_matrix_inv @ image_points_homogeneous_reshaped
    end = time.time()
    print("END: camera_matrix_inv @ image_points_homogeneous_reshaped",end - start)

    # print("BEGIN: _homogeneous_reshape_back")
    start = time.time()
    image_points_z_1 = _homogeneous_reshape_back(image_points_homogeneous_z_1_reshaped)
    end = time.time()
    print("END: _homogeneous_reshape_back",end - start)

    # print("BEGIN: _distort_z_1")
    start = time.time()
    x_distorted,y_distorted = _distort_z_1(
        *image_points_z_1,**distortion_coefficients)
    end = time.time()
    print("END: _distort_z_1",end - start)

    # print("BEGIN: _homogeneous_reshape")
    start = time.time()
    points_homogeneous_z_1_distorted_reshaped = _homogeneous_reshape(x_distorted,y_distorted)
    end = time.time()
    print("END: _homogeneous_reshape",end - start)

    # print("BEGIN: _homogeneous_reshape")
    start = time.time()
    points_homogeneous_distorted_reshaped = camera_matrix @ points_homogeneous_z_1_distorted_reshaped
    end = time.time()
    print("END: camera_matrix @ points_homogeneous_z_1_distorted_reshaped",end - start)

    # print("BEGIN: _homogeneous_reshape_back")
    start = time.time()
    points_homogeneous_distorted = _homogeneous_reshape_back(points_homogeneous_distorted_reshaped)
    end = time.time()
    print("END: _homogeneous_reshape_back",end - start)

    return (map.astype(numpy.float32) for map in points_homogeneous_distorted)


if __name__ == "__main__":
    image_width = 4032
    image_height = 3024

    distortion_coefficients = {
        "k1": 0,"k2": 0,"k3": 0,"k4": 0,"k5": 0,"k6": 0,"p1": 0,"p2": 0}

    camera_matrix = numpy.array([
        [1000,2016],[0,1000,1512],1]])

    map_x,map_y = _get_undistort_rectify_maps(
        distortion_coefficients,image_height)