def registrate(drone_img_ori, pcl_img_ori, mask_image, args):
common_args = {
'pcl_mask': args.pcl_mask,
'drone_mask': args.drone_mask,
'save_masked_pcl': args.save_masked_pcl,
'save_masked_drone': args.save_masked_drone,
'save_keypoints': args.save_keypoints,
'save_csv': args.save_csv,
'save_matching': args.save_matching
}
result = {}
# Preprocess Images
img_preprocessor = Preprocessor(drone_img_ori, pcl_img_ori, mask_image)
img_preprocessor.preprocessing()
imgs = img_preprocessor.get_processed_imgs()
processed_drone_img = imgs['processed_drone_img']
processed_pcl_img = imgs['processed_pcl_img']
processed_drone_mask = imgs['processed_drone_mask']
processed_pcl_mask = imgs['processed_pcl_mask']
masked_drone_img = imgs['masked_drone_img']
masked_pcl_img = imgs['masked_pcl_img']
if common_args['save_masked_pcl'] is True:
result.update({'masked_pcl': masked_pcl_img})
if common_args['save_masked_drone'] is True:
result.update({'masked_drone': masked_drone_img})
# Extract Features
drone_feature_extractor = FeatureExtractor(processed_drone_img, "SIFT",
args)
pcl_feature_extractor = FeatureExtractor(processed_pcl_img, "SIFT", args)
if common_args['pcl_mask'] is True:
print("pcl_mask: True")
pcl_feature_extractor.compute(mask=processed_pcl_mask)
else:
print("No pcl_mask")
pcl_feature_extractor.compute(mask=None)
if common_args['drone_mask'] is True:
print("drone_mask: True")
drone_feature_extractor.compute(mask=processed_drone_mask)
else:
print('No drone_mask')
drone_feature_extractor.compute(mask=None)
drone_features, drone_descs = drone_feature_extractor.get_features_and_descriptors(
)
pcl_features, pcl_descs = pcl_feature_extractor.get_features_and_descriptors(
)
if common_args['save_keypoints'] is True:
keypoints_lidar = cv2.drawKeypoints(
pcl_img_ori,
pcl_features,
outImage=np.array([]),
color=(0, 0, 255),
flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
keypoints_drone = cv2.drawKeypoints(
drone_img_ori,
drone_features,
outImage=np.array([]),
color=(0, 0, 255),
flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
result.update({'keypoints_lidar_image': keypoints_lidar})
result.update({'keypoints_drone_image': keypoints_drone})
# Find Matching
matcher = Matcher(drone_features, drone_descs, pcl_features, pcl_descs,
args)
matcher.extract_match()
raw_matchs = matcher.get_matchs()
good_matchs = matcher.get_good_matchs()
# Find Homography
homography, status = find_homography(drone_features, pcl_features,
good_matchs, args)
if common_args['save_csv'] is True:
result.update({'drone_total_keypoints': len(drone_features)})
result.update({'pcl_total_keypoints': len(pcl_features)})
result.update({'num_inliers': (status.ravel().astype(int) == 1).sum()})
result.update({'num_raw_matches': len(raw_matchs)})
result.update({'num_good_matches': len(good_matchs)})
result.update({'homography': homography})
if common_args['save_matching'] is True:
matching1 = matcher.draw_matches(processed_drone_img,
processed_pcl_img, status, homography)
matching2 = matcher.draw_matches(processed_drone_img,
processed_pcl_img, None, homography)
matching3 = matcher.draw_matches(processed_drone_img,
processed_pcl_img)
result.update({'matching1': matching1})
result.update({'matching2': matching2})
result.update({'matching3': matching3})
registated_image = cv2.warpPerspective(
drone_img_ori, homography,
(processed_pcl_img.shape[1], processed_pcl_img.shape[0]))
ret_image = cv2.add(registated_image,
cv2.cvtColor(processed_pcl_img, cv2.COLOR_GRAY2BGR))
result.update({'image': ret_image})
return result
