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computePointIds.py
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computePointIds.py
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# Author: Eduardo Ruiz
# Date:
import sys
import glob
from io_utils import load_pcd_data_binary
from pointcloud_utils import *
import os
import pypcd
DATA = '/home/eduardo/Documents/deep-interaction-tensor/data/'
dtypes_ids = {'names': ('id'), 'formats': ('f4')}
current = os.path.dirname(os.path.abspath(__file__))
if __name__ == '__main__':
if len(sys.argv)<2:
print('Need scene name')
else:
scene_name = sys.argv[1]
if '.pcd' in scene_name or '.ply' in scene_name:
real_scene=DATA+scene_name
#remove extension for search
scene_name=scene_name.split('.')[0]
else:
real_scene=DATA+scene_name+'.pcd'
if not os.path.exists(real_scene):
real_scene=DATA+scene_name+'_d.pcd'
base_name = DATA+'*_'+scene_name+'_3D_*.pcd'
#find results
all_mains = sorted(glob.glob(base_name))
# read input scene
input_cloud, _, _ = load_pcd_data_binary(real_scene)
print('From {} \nFound {} files'.format(current, len(all_mains)))
for fname in all_mains:
file_id = fname.split('/')[-1].split('_')[-1].split('.')[0]
#sampled points
sample_file = DATA+file_id+'_samplePoints.pcd'
sampled_cloud, _, _ = load_pcd_data_binary(sample_file)
# get ids
sampled_ids = compute_subset_ids(input_cloud, sampled_cloud)
assert sampled_cloud.shape[0]==sampled_ids.shape[0], 'Wrong sizes'
#good points
good_file = DATA + file_id + '_goodPoints.pcd'
good_cloud, _, _ = load_pcd_data_binary(good_file)
# goods ids as subset of sampled
good_ids = compute_subset_ids(sampled_cloud, good_cloud)
assert good_cloud.shape[0] == good_ids.shape[0], 'Wrong sizes'
# print(sampled_cloud.shape, sampled_ids.shape)
# print(good_cloud.shape,good_ids.shape)
name = DATA+file_id+'_goodPointsIds.pcd'
actual_data_array = np.zeros(good_ids.shape[0], dtype=[('id', 'f4')])
actual_data_array['id'] = good_ids[:, 0]
new_cloud = pypcd.PointCloud.from_array(actual_data_array)
new_cloud.save_pcd(name, compression='ascii')
print('Saved {} data points in {}'.format(good_ids.shape, name))
name = DATA + file_id + '_samplePointsIds.pcd'
actual_data_array = np.zeros(sampled_ids.shape[0], dtype=[('id', 'f4')])
actual_data_array['id'] = sampled_ids[:, 0]
new_cloud = pypcd.PointCloud.from_array(actual_data_array)
new_cloud.save_pcd(name, compression='ascii')
print('Saved {} data points in {}'.format(sampled_ids.shape, name))