将极坐标重新投影到笛卡尔网格

Question

我有一个极坐标 (r,theta) 网格（这意味着每个单元格都是一个环面部分），其中包含一些物理量（例如温度）的值，我想重新网格（或重新投影或重新采样）这些值到笛卡尔网格。 是否有任何 Python 包可以做到这一点？

我对将单元格中心的坐标从极坐标转换为笛卡尔坐标不感兴趣 - 这很容易。 相反，我正在寻找一个实际上可以正确重新网格化数据的包。

感谢您的任何建议！

Answer 1

感谢您的回答 - 在对此进行了更多思考后，我想出了以下代码：

import numpy as np

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as mpl

from scipy.interpolate import interp1d
from scipy.ndimage import map_coordinates


def polar2cartesian(r, t, grid, x, y, order=3):

    X, Y = np.meshgrid(x, y)

    new_r = np.sqrt(X*X+Y*Y)
    new_t = np.arctan2(X, Y)

    ir = interp1d(r, np.arange(len(r)), bounds_error=False)
    it = interp1d(t, np.arange(len(t)))

    new_ir = ir(new_r.ravel())
    new_it = it(new_t.ravel())

    new_ir[new_r.ravel() > r.max()] = len(r)-1
    new_ir[new_r.ravel() < r.min()] = 0

    return map_coordinates(grid, np.array([new_ir, new_it]),
                            order=order).reshape(new_r.shape)

# Define original polar grid

nr = 10
nt = 10

r = np.linspace(1, 100, nr)
t = np.linspace(0., np.pi, nt)
z = np.random.random((nr, nt))

# Define new cartesian grid

nx = 100
ny = 200

x = np.linspace(0., 100., nx)
y = np.linspace(-100., 100., ny)

# Interpolate polar grid to cartesian grid (nearest neighbor)

fig = mpl.figure()
ax = fig.add_subplot(111)
ax.imshow(polar2cartesian(r, t, z, x, y, order=0), interpolation='nearest')
fig.savefig('test1.png')

# Interpolate polar grid to cartesian grid (cubic spline)

fig = mpl.figure()
ax = fig.add_subplot(111)
ax.imshow(polar2cartesian(r, t, z, x, y, order=3), interpolation='nearest')
fig.savefig('test2.png')

这不是严格的重新网格化，但可以很好地满足我的需要。 只需发布代码，以防对其他人有用。 随时提出改进建议！

Answer 2

前段时间我在尝试做类似的事情时来到了这篇文章，即将极地数据重新投影到笛卡尔网格中，反之亦然。 这里提出的解决方案工作正常。 但是，执行坐标变换需要一些时间。 我只是想分享另一种可以将处理时间减少多达 50 倍或更多的方法。

该算法使用scipy.ndimage.interpolation.map_coordinates函数。

让我们看一个小例子：

import numpy as np

# Auxiliary function to map polar data to a cartesian plane
def polar_to_cart(polar_data, theta_step, range_step, x, y, order=3):

    from scipy.ndimage.interpolation import map_coordinates as mp

    # "x" and "y" are numpy arrays with the desired cartesian coordinates
    # we make a meshgrid with them
    X, Y = np.meshgrid(x, y)

    # Now that we have the X and Y coordinates of each point in the output plane
    # we can calculate their corresponding theta and range
    Tc = np.degrees(np.arctan2(Y, X)).ravel()
    Rc = (np.sqrt(X**2 + Y**2)).ravel()

    # Negative angles are corrected
    Tc[Tc < 0] = 360 + Tc[Tc < 0]

    # Using the known theta and range steps, the coordinates are mapped to
    # those of the data grid
    Tc = Tc / theta_step
    Rc = Rc / range_step

    # An array of polar coordinates is created stacking the previous arrays
    coords = np.vstack((Ac, Sc))

    # To avoid holes in the 360º - 0º boundary, the last column of the data
    # copied in the begining
    polar_data = np.vstack((polar_data, polar_data[-1,:]))

    # The data is mapped to the new coordinates
    # Values outside range are substituted with nans
    cart_data = mp(polar_data, coords, order=order, mode='constant', cval=np.nan)

    # The data is reshaped and returned
    return(cart_data.reshape(len(y), len(x)).T)

polar_data = ... # Here a 2D array of data is assumed, with shape thetas x ranges

# We create the x and y axes of the output cartesian data
x = y = np.arange(-100000, 100000, 1000)

# We call the mapping function assuming 1 degree of theta step and 500 meters of
# range step. The default order of 3 is used.
cart_data = polar_to_cart(polar_data, 1, 500, x, y)

我希望这可以帮助与我处于相同情况的人。

Answer 3

您可以使用scipy.ndimage.geometric_transform更紧凑地完成此scipy.ndimage.geometric_transform 。 下面是一些示例代码：

import numpy as N
import scipy as S
import scipy.ndimage

temperature = <whatever> 
# This is the data in your polar grid.
# The 0th and 1st axes correspond to r and θ, respectively.
# For the sake of simplicity, θ goes from 0 to 2π, 
# and r's units are just its indices.

def polar2cartesian(outcoords, inputshape, origin):
    """Coordinate transform for converting a polar array to Cartesian coordinates. 
    inputshape is a tuple containing the shape of the polar array. origin is a
    tuple containing the x and y indices of where the origin should be in the
    output array."""

    xindex, yindex = outcoords
    x0, y0 = origin
    x = xindex - x0
    y = yindex - y0

    r = N.sqrt(x**2 + y**2)
    theta = N.arctan2(y, x)
    theta_index = N.round((theta + N.pi) * inputshape[1] / (2 * N.pi))

    return (r,theta_index)

temperature_cartesian = S.ndimage.geometric_transform(temperature, polar2cartesian, 
    order=0,
    output_shape = (temperature.shape[0] * 2, temperature.shape[0] * 2),
    extra_keywords = {'inputshape':temperature.shape,
        'center':(temperature.shape[0], temperature.shape[0])})

您可以根据需要更改order=0以获得更好的插值。 此处的输出数组temperature_cartesian是 2r x 2r，但您可以指定任何您喜欢的大小和原点。

Answer 4

是否有任何 Python 包可以做到这一点？

是的！ 现在至少有一个 Python 包具有将矩阵从笛卡尔坐标重新映射到极坐标的功能： abel.tools.polar.reproject_image_into_polar() ，它是PyAbel 包的一部分。

（Iñigo Hernáez Corres 是正确的， scipy.ndimage.interpolation.map_coordinates是迄今为止我们发现的从笛卡尔坐标重新投影到极坐标的最快方法。）

PyAbel 可以通过在命令行中输入以下内容从PyPi安装：

pip install pyabel

然后，在python中，您可以使用以下代码将图像重新投影到极坐标中：

import abel
abel.tools.polar.reproject_image_into_polar(MyImage)

[根据应用程序，您可能会考虑传递jacobian=True参数，它重新调整矩阵的强度以考虑从笛卡尔变换时发生的网格拉伸（改变“bin 大小”）到极坐标。]

这是一个完整的例子：

import numpy as np
import matplotlib.pyplot as plt
import abel

CartImage = abel.tools.analytical.sample_image(501)[201:-200, 201:-200]

PolarImage, r_grid, theta_grid = abel.tools.polar.reproject_image_into_polar(CartImage)

fig, axs = plt.subplots(1,2, figsize=(7,3.5))
axs[0].imshow(CartImage , aspect='auto', origin='lower')
axs[1].imshow(PolarImage, aspect='auto', origin='lower', 
              extent=(np.min(theta_grid), np.max(theta_grid), np.min(r_grid), np.max(r_grid)))

axs[0].set_title('Cartesian')
axs[0].set_xlabel('x')
axs[0].set_ylabel('y')

axs[1].set_title('Polar')
axs[1].set_xlabel('Theta')
axs[1].set_ylabel('r')

plt.tight_layout()
plt.show()

注意：还有另一个很好的讨论（关于将彩色图像重新映射到极坐标）关于 SO： 沿着极坐标系的图像信息

Answer 5

OpenCV 3.4 现在可以使用warpPolar()很容易地做到这一点

调用非常简单：

import numpy as np
import cv2
from matplotlib import pyplot as plt

# Read in our image from disk
image = cv2.imread('washington_quarter.png',0)
plt.imshow(image),plt.show()

margin = 0.9 # Cut off the outer 10% of the image
# Do the polar rotation along 1024 angular steps with a radius of 256 pixels.
polar_img = cv2.warpPolar(image, (256, 1024), (image.shape[0]/2,image.shape[1]/2), image.shape[1]*margin*0.5, cv2.WARP_POLAR_LINEAR)
# Rotate it sideways to be more visually pleasing
polar_img = cv2.rotate(polar_img, cv2.ROTATE_90_COUNTERCLOCKWISE)
plt.imshow(polar_img),plt.show()