mmpose.core.evaluation.mesh_eval 源代码

# ------------------------------------------------------------------------------
# Adapted from https://github.com/akanazawa/hmr
# Original licence: Copyright (c) 2018 akanazawa, under the MIT License.
# ------------------------------------------------------------------------------

import numpy as np


[文档]def compute_similarity_transform(source_points, target_points): """Computes a similarity transform (sR, t) that takes a set of 3D points source_points (N x 3) closest to a set of 3D points target_points, where R is an 3x3 rotation matrix, t 3x1 translation, s scale. And return the transformed 3D points source_points_hat (N x 3). i.e. solves the orthogonal Procrutes problem. Notes: Points number: N Args: source_points (np.ndarray([N, 3])): Source point set. target_points (np.ndarray([N, 3])): Target point set. Returns: source_points_hat (np.ndarray([N, 3])): Transformed source point set. """ assert target_points.shape[0] == source_points.shape[0] assert target_points.shape[1] == 3 and source_points.shape[1] == 3 source_points = source_points.T target_points = target_points.T # 1. Remove mean. mu1 = source_points.mean(axis=1, keepdims=True) mu2 = target_points.mean(axis=1, keepdims=True) X1 = source_points - mu1 X2 = target_points - mu2 # 2. Compute variance of X1 used for scale. var1 = np.sum(X1**2) # 3. The outer product of X1 and X2. K = X1.dot(X2.T) # 4. Solution that Maximizes trace(R'K) is R=U*V', where U, V are # singular vectors of K. U, _, Vh = np.linalg.svd(K) V = Vh.T # Construct Z that fixes the orientation of R to get det(R)=1. Z = np.eye(U.shape[0]) Z[-1, -1] *= np.sign(np.linalg.det(U.dot(V.T))) # Construct R. R = V.dot(Z.dot(U.T)) # 5. Recover scale. scale = np.trace(R.dot(K)) / var1 # 6. Recover translation. t = mu2 - scale * (R.dot(mu1)) # 7. Transform the source points: source_points_hat = scale * R.dot(source_points) + t source_points_hat = source_points_hat.T return source_points_hat