radon https://scikit-image.org/docs/stable/auto_examples/transform/plot_radon_transform.html#:~:text

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

from skimage.data import shepp_logan_phantom
from skimage.transform import radon, rescale
image = cv2.imread('cropped_image3205_942.png', 0)
# image = rescale(image, scale=0.4, mode='reflect', channel_axis=None)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4.5))

ax1.set_title("Original")
ax1.imshow(image, cmap=plt.cm.Greys_r)

theta = np.linspace(0., 180., max(image.shape), endpoint=False)
sinogram = radon(image, theta=theta)
dx, dy = 0.5 * 180.0 / max(image.shape), 0.5 / sinogram.shape[0]
ax2.set_title("Radon transform\n(Sinogram)")
ax2.set_xlabel("Projection angle (deg)")
ax2.set_ylabel("Projection position (pixels)")
ax2.imshow(sinogram, cmap=plt.cm.Greys_r,
           extent=(-dx, 180.0 + dx, -dy, sinogram.shape[0] + dy),
           aspect='auto')

fig.tight_layout()
plt.show()

import matplotlib.pyplot as plt
from skimage.transform.radon_transform import _get_fourier_filter

filters = ['ramp', 'shepp-logan', 'cosine', 'hamming', 'hann']

for ix, f in enumerate(filters):
    response = _get_fourier_filter(2000, f)
    plt.plot(response, label=f)

plt.xlim([0, 1000])
plt.xlabel('frequency')
plt.legend()
plt.show()

from skimage.transform import iradon

reconstruction_fbp = iradon(sinogram, theta=theta, filter_name='ramp')
error = reconstruction_fbp - image
print(f'FBP rms reconstruction error: {np.sqrt(np.mean(error**2)):.3g}')

imkwargs = dict(vmin=-0.2, vmax=0.2)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4.5),
                               sharex=True, sharey=True)
ax1.set_title("Reconstruction\nFiltered back projection")
ax1.imshow(reconstruction_fbp, cmap=plt.cm.Greys_r)
ax2.set_title("Reconstruction error\nFiltered back projection")
ax2.imshow(reconstruction_fbp - image, cmap=plt.cm.Greys_r, **imkwargs)
plt.show()