def transform_folder_to_world(sub_dir):
base_dir = "E:\Datasets\DataHack\Train"
base_res_dir = "E:\Datasets\DataHack\World\Train"
print('Working on sub_dir: {}'.format(sub_dir))
for idx in data_utils.enumerate_frames(os.path.join(base_dir, sub_dir)):
pc_file = os.path.join(
base_res_dir,
data_utils.frame_to_filename(sub_dir, idx, 'pointcloud'))
if os.path.exists(pc_file):
continue
pc, ego, label = data_utils.read_all_data(
os.path.join(base_dir, sub_dir), idx)
ego_pc = transform_frame_to_world(pc, ego)
ego_pc = np.concatenate((ego_pc, pc[:, 3:4]), -1)
df = (pd.DataFrame(ego_pc) * 100).astype(int)
res_dir = os.path.join(base_res_dir, sub_dir)
if not os.path.exists(res_dir):
os.makedirs(res_dir)
file_name = os.path.join(res_dir, str(idx).zfill(7))
df.to_csv(file_name + '_pointcloud.csv', header=None, index=False)
pd.DataFrame([0] * df.shape[0]).to_csv(file_name + '_labels.csv',
header=None,
index=False)
pd.DataFrame([0.] * 6).T.to_csv(file_name + '_egomotion.csv',
header=None,
index=False)
decimate = 1
#elMin = 9999
#azMin = 9999
#elMax = -9999
#azMax = -9999
# agg_point_cloud_list=[]
prev_img=np.zeros((EL_NUM,AZ_NUM)) # init
imgSum = np.zeros((EL_NUM,AZ_NUM))
prev_img_list=[]
idxXY_list = []
label_list = []
fn_list = []
cloud_len_list=[]
for idx, frame in enumerate(data_utils.enumerate_frames(video_dir)):
if idx < min_idx or idx % decimate != 0:
continue
pc, ego, label,label_pred_new,fn = data_utils.read_all_data(video_dir, frame)
# label=label_pred_new
ego_rt = RotationTranslationData(vecs=(ego[:3], ego[3:]))
ego_pc = ego_rt.apply_transform(pc[:, :3])
ego_pc = np.concatenate((ego_pc, pc[:, 3:4]), -1)
# labeled_pc = np.concatenate((ego_pc, label), -1)
# agg_point_cloud_list.append(labeled_pc)
# img = np.zeros((EL_NUM,AZ_NUM))
# # img1 = np.zeros((EL_NUM,AZ_NUM))
# el = np.rad2deg(np.arctan2(pc[:,2],pc[:,0]))
import pandas as pd
import os
import numpy as np
from utilities.math_utils import RotationTranslationData
from visualizations.vis import pcshow
from utilities import data_utils
if __name__ == '__main__':
base_dir = os.path.dirname(os.getcwd())
video_dir = os.path.join(base_dir, 'data_examples', 'test_video')
agg_point_cloud_list = []
max_frames_to_keep = 10
min_idx = 0
decimate = 1
max_dist = 100
for idx in data_utils.enumerate_frames(video_dir):
if idx < min_idx or idx % decimate != 0:
continue
pc_file = data_utils.frame_to_filename(video_dir, idx, 'pointcloud')
pc, ego, label = data_utils.read_all_data(video_dir, idx)
ego_rt = RotationTranslationData(vecs=(ego[:3], ego[3:]))
ego_pc = ego_rt.apply_transform(pc[:, :3])
ego_pc = np.concatenate((ego_pc, pc[:, 3:4]), -1)
labeled_pc = np.concatenate((ego_pc, label), -1)
agg_point_cloud_list.append(labeled_pc)
if len(agg_point_cloud_list) > max_frames_to_keep:
agg_point_cloud_list = agg_point_cloud_list[1:]
agg_point_cloud = np.concatenate(agg_point_cloud_list, 0)
pc2disp = ego_rt.inverse().apply_transform(agg_point_cloud[:, :3])
pc2disp = np.concatenate((pc2disp, agg_point_cloud[:, 3:]), -1)
