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eval_poses_util.py
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import math
import random
from collections import namedtuple
from scipy.spatial.transform import Rotation
import numpy as np
import logging
_logger = logging.getLogger(__name__)
_logger.setLevel(logging.DEBUG)
TestEstimate = namedtuple("TestEstimate", [
"pose_est",
"pose_gt",
"focal_length",
"confidence",
"image_file"
])
def kabsch(pts1, pts2, estimate_scale=False):
c_pts1 = pts1 - pts1.mean(axis=0)
c_pts2 = pts2 - pts2.mean(axis=0)
covariance = np.matmul(c_pts1.T, c_pts2) / c_pts1.shape[0]
U, S, VT = np.linalg.svd(covariance)
d = np.sign(np.linalg.det(np.matmul(VT.T, U.T)))
correction = np.eye(3)
correction[2, 2] = d
if estimate_scale:
pts_var = np.mean(np.linalg.norm(c_pts2, axis=1) ** 2)
scale_factor = pts_var / np.trace(S * correction)
else:
scale_factor = 1.
R = scale_factor * np.matmul(np.matmul(VT.T, correction), U.T)
t = pts2.mean(axis=0) - np.matmul(R, pts1.mean(axis=0))
T = np.eye(4)
T[:3, :3] = R
T[:3, 3] = t
return T, scale_factor
def print_hyp(hypothesis, hyp_name):
h_translation = np.linalg.norm(hypothesis['transformation'][:3, 3])
h_angle = np.linalg.norm(Rotation.from_matrix(hypothesis['transformation'][:3, :3]).as_rotvec()) * 180 / math.pi
_logger.debug(f"{hyp_name}: score={hypothesis['score']}, translation={h_translation:.2f}m, "
f"rotation={h_angle:.1f}deg.")
def get_inliers(h_T, poses_gt, poses_est, inlier_threshold_t, inlier_threshold_r):
# h_T aligns ground truth poses with estimates poses
poses_gt_transformed = h_T @ poses_gt
# calculate differences in position and rotations
translations_delta = poses_gt_transformed[:, :3, 3] - poses_est[:, :3, 3]
rotations_delta = poses_gt_transformed[:, :3, :3] @ poses_est[:, :3, :3].transpose([0, 2, 1])
# translation inliers
inliers_t = np.linalg.norm(translations_delta, axis=1) < inlier_threshold_t
# rotation inliers
inliers_r = Rotation.from_matrix(rotations_delta).magnitude() < (inlier_threshold_r / 180 * math.pi)
# intersection of both
return np.logical_and(inliers_r, inliers_t)
def estimate_alignment(estimates,
confidence_threshold,
min_cofident_estimates=10,
inlier_threshold_t=0.05,
inlier_threshold_r=5,
ransac_iterations=10000,
refinement_max_hyp=12,
refinement_max_it=8,
estimate_scale=False
):
_logger.debug("Estimate transformation between pose estimates and ground truth.")
# Filter estimates using confidence threshold
valid_estimates = [estimate for estimate in estimates if ((not np.any(np.isinf(estimate.pose_gt))) and (not np.any(np.isnan(estimate.pose_gt))))]
confident_estimates = [estimate for estimate in valid_estimates if estimate.confidence > confidence_threshold]
num_confident_estimates = len(confident_estimates)
_logger.debug(f"{num_confident_estimates} estimates considered confident.")
if num_confident_estimates < min_cofident_estimates:
_logger.debug(f"Too few confident estimates. Aborting alignment.")
return None, 1
# gather estimated and ground truth poses
poses_est = np.ndarray((num_confident_estimates, 4, 4))
poses_gt = np.ndarray((num_confident_estimates, 4, 4))
for i, estimate in enumerate(confident_estimates):
poses_est[i] = estimate.pose_est
poses_gt[i] = estimate.pose_gt
# start robust RANSAC loop
ransac_hypotheses = []
for hyp_idx in range(ransac_iterations):
# sample hypothesis
min_sample_size = 3
samples = random.sample(range(num_confident_estimates), min_sample_size)
h_pts1 = poses_gt[samples, :3, 3]
h_pts2 = poses_est[samples, :3, 3]
h_T, h_scale = kabsch(h_pts1, h_pts2, estimate_scale)
# calculate inliers
inliers = get_inliers(h_T, poses_gt, poses_est, inlier_threshold_t, inlier_threshold_r)
if inliers[samples].sum() >= 3:
# only keep hypotheses if minimal sample is all inliers
ransac_hypotheses.append({
"transformation": h_T,
"inliers": inliers,
"score": inliers.sum(),
"scale": h_scale
})
if len(ransac_hypotheses) == 0:
_logger.debug(f"Did not fine a single valid RANSAC hypothesis, abort alignment estimation.")
return None, 1
# sort according to score
ransac_hypotheses = sorted(ransac_hypotheses, key=lambda x: x['score'], reverse=True)
for hyp_idx, hyp in enumerate(ransac_hypotheses):
print_hyp(hyp, f"Hypothesis {hyp_idx}")
# create shortlist of best hypotheses for refinement
_logger.debug(f"Starting refinement of {refinement_max_hyp} best hypotheses.")
ransac_hypotheses = ransac_hypotheses[:refinement_max_hyp]
# refine all hypotheses in the short list
for ref_hyp in ransac_hypotheses:
print_hyp(ref_hyp, "Pre-Refinement")
# refinement loop
for ref_it in range(refinement_max_it):
# re-solve alignment on all inliers
h_pts1 = poses_gt[ref_hyp['inliers'], :3, 3]
h_pts2 = poses_est[ref_hyp['inliers'], :3, 3]
h_T, h_scale = kabsch(h_pts1, h_pts2, estimate_scale)
# calculate new inliers
inliers = get_inliers(h_T, poses_gt, poses_est, inlier_threshold_t, inlier_threshold_r)
# check whether hypothesis score improved
refined_score = inliers.sum()
if refined_score > ref_hyp['score']:
ref_hyp['transformation'] = h_T
ref_hyp['inliers'] = inliers
ref_hyp['score'] = refined_score
ref_hyp['scale'] = h_scale
print_hyp(ref_hyp, f"Refinement interation {ref_it}")
else:
_logger.debug(f"Stopping refinement. Score did not improve: New score={refined_score}, "
f"Old score={ref_hyp['score']}")
break
# re-sort refined hyotheses
ransac_hypotheses = sorted(ransac_hypotheses, key=lambda x: x['score'], reverse=True)
for hyp_idx, hyp in enumerate(ransac_hypotheses):
print_hyp(hyp, f"Hypothesis {hyp_idx}")
return ransac_hypotheses[0]['transformation'], ransac_hypotheses[0]['scale']