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import numpy as np
import cv2
from PIL import Image
class HomographicAlignment:
"""
Apply homographic alignment on background to match with the source image.
"""
def __init__(self):
self.detector = cv2.ORB_create()
self.matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE)
def __call__(self, src, bgr):
src = np.asarray(src)
bgr = np.asarray(bgr)
keypoints_src, descriptors_src = self.detector.detectAndCompute(src, None)
keypoints_bgr, descriptors_bgr = self.detector.detectAndCompute(bgr, None)
matches = self.matcher.match(descriptors_bgr, descriptors_src, None)
matches.sort(key=lambda x: x.distance, reverse=False)
num_good_matches = int(len(matches) * 0.15)
matches = matches[:num_good_matches]
points_src = np.zeros((len(matches), 2), dtype=np.float32)
points_bgr = np.zeros((len(matches), 2), dtype=np.float32)
for i, match in enumerate(matches):
points_src[i, :] = keypoints_src[match.trainIdx].pt
points_bgr[i, :] = keypoints_bgr[match.queryIdx].pt
H, _ = cv2.findHomography(points_bgr, points_src, cv2.RANSAC)
h, w = src.shape[:2]
bgr = cv2.warpPerspective(bgr, H, (w, h))
msk = cv2.warpPerspective(np.ones((h, w)), H, (w, h))
# For areas that is outside of the background,
# We just copy pixels from the source.
bgr[msk != 1] = src[msk != 1]
src = Image.fromarray(src)
bgr = Image.fromarray(bgr)
return src, bgr
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