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Running
on
Zero
File size: 2,089 Bytes
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# mostly from: https://github.com/cvg/glue-factory/blob/main/gluefactory/geometry/epipolar.py
import numpy as np
import torch
def angle_error_mat(R1, R2):
cos = (torch.trace(torch.einsum("...ij, ...jk -> ...ik", R1.T, R2)) - 1) / 2
cos = torch.clip(cos, -1.0, 1.0) # numerical errors can make it out of bounds
return torch.rad2deg(torch.abs(torch.arccos(cos)))
def angle_error_vec(v1, v2, eps=1e-10):
n = torch.clip(v1.norm(dim=-1) * v2.norm(dim=-1), min=eps)
v1v2 = (v1 * v2).sum(dim=-1) # dot product in the last dimension
return torch.rad2deg(torch.arccos(torch.clip(v1v2 / n, -1.0, 1.0)))
def relative_pose_error(R_gt, t_gt, R, t, ignore_gt_t_thr=0.0, eps=1e-10):
# angle error between 2 vectors
t_err = angle_error_vec(t, t_gt, eps)
t_err = torch.minimum(t_err, 180 - t_err) # handle E ambiguity
if t_gt.norm() < ignore_gt_t_thr: # pure rotation is challenging
t_err = torch.zeros_like(t_err)
# angle error between 2 rotation matrices
r_err = angle_error_mat(R, R_gt)
return t_err, r_err
def cal_error_auc(errors, thresholds):
sort_idx = np.argsort(errors)
errors = np.array(errors.copy())[sort_idx]
recall = (np.arange(len(errors)) + 1) / len(errors)
errors = np.r_[0.0, errors]
recall = np.r_[0.0, recall]
aucs = []
for t in thresholds:
last_index = np.searchsorted(errors, t)
r = np.r_[recall[:last_index], recall[last_index - 1]]
e = np.r_[errors[:last_index], t]
aucs.append(np.round((np.trapz(r, x=e) / t), 4))
return aucs
class AUCMetric:
def __init__(self, thresholds, elements=None):
self._elements = elements
self.thresholds = thresholds
if not isinstance(thresholds, list):
self.thresholds = [thresholds]
def update(self, tensor):
assert tensor.dim() == 1
self._elements += tensor.cpu().numpy().tolist()
def compute(self):
if len(self._elements) == 0:
return np.nan
else:
return cal_error_auc(self._elements, self.thresholds)
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