import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread('C:\Users\not my user name\Desktop\20140505_124500_4096_HMIIC.jpg', 0)
norm_image = cv2.normalize(img, dst=None, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)
plt.imshow(norm_image, cmap='afmhot', interpolation='bicubic')
plt.xticks([]), plt.yticks([])
plt.show()
The solar disc I'm using:
I'm wondering if there is an easy way to convert the image from cartesian to polar?
Like this example:
Or like this example:
For some reason, I've found many examples in MATLAB but I've yet to find one in Python. I've been looking at this from opencv but I'm not entirely sure it's what I want, as I want to keep the original image/array size. I know converting to polar will 'screw' up the image but that is fine, the main thing I'm wanting to do is measure the intensity of the solar disk from the center out to the edge, plotting a function of intensity vs radius so I can measure limb darkening.
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