UPDATE Vision 修正box输出

Vision/tool/utils.py

@@ -12,37 +12,6 @@ import numpy as np
 import cv2
 import psutil
 from psutil._common import bytes2human
-from sklearn.decomposition import PCA
-
-def pca(points):
-    # 计算PCA
-    mean = np.mean(points, axis=0)
-    cov = np.cov(points, rowvar=False)
-    eigenvalues, eigenvectors = np.linalg.eigh(cov)
-
-    # 对特征值进行排序并找到对应的特征向量
-    sorted_indices = np.argsort(eigenvalues)[::-1]
-    sorted_eigenvalues = eigenvalues[sorted_indices]
-    sorted_eigenvectors = eigenvectors[:, sorted_indices]
-
-    # # 打印PCA结果
-    # print("PCA Mean:", mean)
-    # print("PCA Eigenvalues:", sorted_eigenvalues)
-    # print("PCA Eigenvectors:\n", sorted_eigenvectors)
-
-    # 通常，最小特征值对应的特征向量是平面的法线
-    normal_vector = sorted_eigenvectors[:, 0] if sorted_eigenvalues[0] < sorted_eigenvalues[
-        1] else sorted_eigenvectors[:, 1]
-    # print("法向量：", normal_vector)
-    return normal_vector
-
-
-def compute_pca_direction(points):
-    # points 是物体点云的坐标
-    pca = PCA(n_components=3)
-    pca.fit(points)
-    primary_direction = pca.components_[0]  # 第一主成分对应的方向向量
-    return primary_direction
 
 def remove_nan(pm, y, x):
     piont_x, piont_y, piont_z = pm[y, x]