proj = project.ProjectMgr(args.project)
proj.load_images_info()
proj.load_features()
proj.undistort_keypoints()
source = 'matches_direct'
print("Loading matches:", source)
matches_orig = pickle.load(open(os.path.join(args.project, source), "rb"))
print('Number of original features:', len(matches_orig))
print("Loading optimized matches: matches_opt")
matches_opt = pickle.load(open(os.path.join(args.project, "matches_opt"),
"rb"))
print('Number of optimized features:', len(matches_opt))
# load the group connections within the image set
group_list = groups.load(args.project)
print('Main group size:', len(group_list[0]))
# compute the depth of each feature for each image
def compute_feature_depths(image_list, group, matches):
print("Computing depths for all match points...")
# init structures
for image in image_list:
image.z_list = []
# make a list of distances for each feature of each image
for match in matches:
feat_ned = match[0]
count = 0
import transformations
r2d = 180.0 / math.pi
d2r = math.pi / 180.0
parser = argparse.ArgumentParser(
description='Set the aircraft poses from flight data.')
parser.add_argument('--project', required=True, help='project directory')
args = parser.parse_args()
proj = project.ProjectMgr(args.project)
print("Loading image info...")
proj.load_images_info()
group_list = groups.load(proj.analysis_dir)
# compute an average transform between original camera attitude estimate
# and optimized camera attitude estimate
quats = []
for i, image in enumerate(proj.image_list):
if image.name in group_list[0]:
print(image.name)
ned, ypr, q0 = image.get_camera_pose(opt=False)
ned, ypr, q1 = image.get_camera_pose(opt=True)
# rx = q1 * conj(q0)
conj_q0 = transformations.quaternion_conjugate(q0)
rx = transformations.quaternion_multiply(q1, conj_q0)
rx /= transformations.vector_norm(rx)
print(' ', rx)
(yaw_rad, pitch_rad,