point cloud voxel style split

voxel_split_pointcloud.py

import numpy as np
from itertools import product

# voxel range
range_size = 11

x_lin = np.linspace(-1.5, 1.5, range_size)
y_lin = np.linspace(-1.5, 1.5, range_size)
z_lin = np.linspace(-1.5, 1.5, range_size)

xyz_range = list(product(list(range(range_size-1)),
                         list(range(range_size-1)),
                         list(range(range_size-1))))


def count_voxel(file_path):
    # load test data
    test_data = np.load(file_path)
    
    # check min max of point cloud
    print(np.max(test_data))
    print(np.min(test_data))
    
    for test in test_data:
        ss = 0
        cnt_arr = []
      
        # count how much point in x y z range
        for x_idx, y_idx, z_idx in xyz_range:
            t = test[(x_lin[x_idx] <= test[:, 0]) & (test[:, 0] < x_lin[x_idx+1])]
            t = t[(y_lin[y_idx] <= t[:, 1]) & (t[:, 1] < y_lin[y_idx+1])]
            t = t[(z_lin[z_idx] <= t[:, 2]) & (t[:, 2] < z_lin[z_idx+1])]

            cnt_arr.append(t.shape[0])

        print(np.sum(cnt_arr) == 2048)
    
    return cnt_arr


count_voxel('Results/train_point_class_part3/images/shape16_test_encoder3d.npy')
count_voxel('Results/train_point_class_part3/images/shape16_test_data.npy')