import cv2
import numpy as np
def ecc_align(im1, im2, number_of_iterations=2000):
"""
Find the translation motion matrix that align im2 to im1.
The Enhanced Correlation Coefficient image alignment algorithm is based on a 2008 paper titled
Parametric Image Alignment using Enhanced Correlation Coefficient Maximization
by Georgios D. Evangelidis and Emmanouil Z. Psarakis.
They propose using a new similarity measure called Enhanced Correlation Coefficient (ECC)
for estimating the parameters of the motion model.
Parameters:
im1: image 1
im2: image 2
Returns:
the cv2.MOTION_TRANSLATION motion model 2x3 matrix that maps im2 to im1.
"""
# 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 matrice and initialize the matrix to identity
warp_matrix = np.eye(2, 3, dtype=np.float32)
# 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)
# 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.imshow("Image 1", im1)
cv2.imshow("Image 2", im2)
cv2.imshow("Aligned Image 2", im2_aligned)
cv2.waitKey(0)
return cc, warp_matrix
