align.py

import numpy as np
import cv2


def align():
	 

	im1 = cv2.imread('before.jpg')
	im2 = cv2.imread('after.jpg')
	


	# Convert images to grayscale
	im1_gray = cv2.cvtColor(im1,cv2.COLOR_BGR2GRAY)
	im2_gray = cv2.cvtColor(im2,cv2.COLOR_BGR2GRAY)
	 
	# Find size of image1
	sz = im1.shape
	 
	# Define the motion model
	warp_mode = cv2.MOTION_TRANSLATION
	 
	# Define 2x3 or 3x3 matrices and initialize the matrix to identity
	if warp_mode == cv2.MOTION_HOMOGRAPHY :
	    warp_matrix = np.eye(3, 3, dtype=np.float32)
	else :
	    warp_matrix = np.eye(2, 3, dtype=np.float32)
	 
	# Specify the number of iterations.
	number_of_iterations = 5000;
	 
	# Specify the threshold of the increment
	# in the correlation coefficient between two iterations
	termination_eps = 1e-10;
	 
	# Define termination criteria
	criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, number_of_iterations,  termination_eps)
	 
	# Run the ECC algorithm. The results are stored in warp_matrix.
	(cc, warp_matrix) = cv2.findTransformECC (im1_gray,im2_gray,warp_matrix, warp_mode, criteria)
	 
	if warp_mode == cv2.MOTION_HOMOGRAPHY :
	    # Use warpPerspective for Homography 
	    im2_aligned = cv2.warpPerspective (im2, warp_matrix, (sz[1],sz[0]), flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP)
	else :
	    # Use warpAffine for Translation, Euclidean and Affine
	    im2_aligned = cv2.warpAffine(im2, warp_matrix, (sz[1],sz[0]), flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP);
	 
	# Show final results

	cv2.imwrite('aligned_A.jpg', im1)
	cv2.imwrite('aligned_B.jpg', im2_aligned)