OpenCV affine transformation won't perform

Question

I'm trying to perform basic affine transformation using pivot points.

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

img = cv2.imread('earth.png')
img_pivots = cv2.imread('earth_keys.png')
map_img = cv2.imread('earth2.png')
map_pivots = cv2.imread('earth2_keys.png')

pts_img_R = np.transpose(np.where(img_pivots[:, :, 2] > 0 ))
pts_img_G = np.transpose(np.where(img_pivots[:, :, 1] > 0 ))
pts_img_B = np.transpose(np.where(img_pivots[:, :, 0] > 0 ))
pts_img = np.vstack([pts_img_R, pts_img_G, pts_img_B])
pts_map_R = np.transpose(np.where(map_pivots[:, :, 2] > 0 ))
pts_map_G = np.transpose(np.where(map_pivots[:, :, 1] > 0 ))
pts_map_B = np.transpose(np.where(map_pivots[:, :, 0] > 0 ))
pts_map = np.vstack([pts_map_R, pts_map_G, pts_map_B])

M = cv2.estimateRigidTransform(pts_map.astype(np.float32), pts_img.astype(np.float32), True)

dst = cv2.warpAffine(map_img,M,(img.shape[1], img.shape[0]))

plt.subplot(121),plt.imshow(img),plt.title('earth.png')
plt.subplot(122),plt.imshow(dst),plt.title('earth2.png transrofmed')
plt.show()

On both images I made 3 points (R, G & B) and saved them in separate images ('earth_keys.png' for 'earth.png' and 'earth2_keys.png' for 'earth2.png'). All I want is to match pivot points on 'earth2.png' with pivot points on 'earth.png'.

Still, all I get after transformation is this

I'm assuming that I misplaced some arguments or something like this, but I tried all combinations and got all types of wrong results, but still can't spot it.

Example images (with pivots)

Edit: Changed pivots number to 6

Still wrong transformation

M is now equal to

 array([[  4.33809524e+00,   8.28571429e-01,  -5.85633333e+02],
   [ -6.22380952e+00,  -1.69285714e+00,   1.03468333e+03]])

Example with 6 pivots

Answer 1

How confident are you in your pivot points ?

If I plot them on your images, I obtain this:

Which gives, after manual superposition, something that looks like your result:

If I define points manually for 3 correspondences, I get this:

pts_img = np.vstack([[68,33],   [22,84],  [113,87]] )
pts_map = np.vstack([[115,101], [30,199], [143,198]])

It's still not perfect, but it may be closer to what you want to achieve.

To conclude, I'd recommend you to check how you compute your keypoints , and, in case of doubt, to do a manual superposition .