Converting An Image From Cartesian To Polar - Limb Darkening

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.no

Solution 1:

OpenCV has functions to convert images from Cartesian form to Polar and vice-versa. Since you require to convert the image to polar form the following can be adopted:

Code:

import cv2
import numpy as np

source = cv2.imread('C:/Users/selwyn77/Desktop/sun.jpg', 1)

#--- ensure image is of the type float ---
img = source.astype(np.float32)

#--- the following holds the square root of the sum of squares of the image dimensions ---#--- this is done so that the entire width/height of the original image is used to express the complete circular range of the resulting polar image ---
value = np.sqrt(((img.shape[0]/2.0)**2.0)+((img.shape[1]/2.0)**2.0))

polar_image = cv2.linearPolar(img,(img.shape[0]/2, img.shape[1]/2), value, cv2.WARP_FILL_OUTLIERS)

polar_image = polar_image.astype(np.uint8)
cv2.imshow("Polar Image", polar_image)

cv2.waitKey(0)
cv2.destroyAllWindows()

Result:

Solution 2:

You can do polar-cartesian distortion just on the command line with ImageMagick in the Terminal - it is installed on most Linux distros and is available for macOS and Windows:

convert sun.jpg +distort DePolar 0 result.jpg

There are some excellent hints and tips from Anthony Thyssen here.

Solution 3:

scikit-image also offers a transformation along these lines. See skimage.transform.warp_polar.

Note, this does introduce an interpolation of pixel intensities.

See also polar demo for usage examples.