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gen2channel.py
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gen2channel.py
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import os
import sys
import cv2
import torch
import skimage.transform
import numpy as np
import torch.nn.functional as F
from tqdm.auto import tqdm
from kitti_utils import generate_depth_map
from multiprocessing import Process, Queue, Pool, cpu_count
regenerate = False
test_only = False
demo = False
if sys.argv[1] == 'regen':
regenerate = True
print("regenerating, will clean previous files")
if sys.argv[2] == 'r200':
input_folder = 'random200'
output_folder = 'r200_2cha'
print("for random 200 points sample")
if sys.argv[2] == 'r100':
input_folder = 'random100'
output_folder = 'r100_2cha'
print("for random 200 points sample")
elif sys.argv[2] == '4beam':
input_folder = '4beam'
output_folder = '2channel'
print("for 4-beams sample")
elif sys.argv[2] == '1beam' or sys.argv[2] == '2beam' or sys.argv[2] == '3beam' or sys.argv[2] == '16beam':
input_folder = sys.argv[2]
output_folder = '2channel{}'.format(sys.argv[2])
print("for {} sample".format(sys.argv[2]))
if len(sys.argv) > 3:
if sys.argv[3] == 'test_only':
test_only = True
elif sys.argv[3] == 'demo':
demo = True
def get_4beam(folder, frame_index, side, do_flip):
side_map = {"2": 2, "3": 3, "l": 2, "r": 3}
calib_path = os.path.join(folder.split("/")[0]+'/'+folder.split("/")[1])
velo_filename = os.path.join(
folder,
"{}/{:010d}.bin".format(input_folder, int(frame_index)))
depth_gt = generate_depth_map(calib_path, velo_filename, side_map[side], shape=[384, 1280])
depth_gt = F.max_pool2d(torch.tensor(depth_gt).unsqueeze(0),
2, ceil_mode=True).squeeze().numpy()
if do_flip:
depth_gt = np.fliplr(depth_gt)
return depth_gt
def get_4beam_2channel(fourbeam, height=192, width=640, expand=2):
expanded_depth = torch.zeros([height, width], dtype=torch.float32)
confidence_map = torch.zeros([height, width], dtype=torch.float32)
accumulate = torch.zeros([height, width], dtype=torch.float32)
for i in range(76, 190):
for j in range(2, 638):
if fourbeam[i][j] != 0:
expanded_depth[i][j] = fourbeam[i][j]
confidence_map[i][j] = 1
accumulate[i][j] = 1
for dis in range(1, expand+1):
confidence = 1/(dis+1)
for horizontal in range(1, dis+1):
x = horizontal
y = dis - horizontal
if accumulate[i+x][j+y] == 0 or confidence_map[i+x][j+y] < confidence:
expanded_depth[i+x][j+y] = fourbeam[i][j]
confidence_map[i+x][j+y] = confidence
accumulate[i+x][j+y] = 1
elif confidence_map[i+x][j+y] == confidence:
expanded_depth[i + x][j + y] += fourbeam[i][j]
accumulate[i + x][j + y] += 1
if x != 0:
x = -horizontal
y = dis - horizontal
if accumulate[i + x][j + y] == 0 or confidence_map[i + x][j + y] < confidence:
expanded_depth[i + x][j + y] = fourbeam[i][j]
confidence_map[i + x][j + y] = confidence
accumulate[i + x][j + y] = 1
elif confidence_map[i + x][j + y] == confidence:
expanded_depth[i + x][j + y] += fourbeam[i][j]
accumulate[i + x][j + y] += 1
if y != 0:
x = horizontal
y = horizontal - dis
if accumulate[i + x][j + y] == 0 or confidence_map[i + x][j + y] < confidence:
expanded_depth[i + x][j + y] = fourbeam[i][j]
confidence_map[i + x][j + y] = confidence
accumulate[i + x][j + y] = 1
elif confidence_map[i + x][j + y] == confidence:
expanded_depth[i + x][j + y] += fourbeam[i][j]
accumulate[i + x][j + y] += 1
if x != 0 and y != 0:
x = -horizontal
y = horizontal - dis
if accumulate[i + x][j + y] == 0 or confidence_map[i + x][j + y] < confidence:
expanded_depth[i + x][j + y] = fourbeam[i][j]
confidence_map[i + x][j + y] = confidence
accumulate[i + x][j + y] = 1
elif confidence_map[i + x][j + y] == confidence:
expanded_depth[i + x][j + y] += fourbeam[i][j]
accumulate[i + x][j + y] += 1
accumulate[accumulate == 0] = 1
expanded_depth = torch.div(expanded_depth, accumulate)
return expanded_depth, confidence_map
data_path = 'kitti_data/'
def gen2channel(line):
folder = data_path + line[:-1].split()[0]
idx = int(line[:-1].split()[1])
side = line[:-1].split()[2]
out_path = folder+'/{}'.format(output_folder)
if not os.path.exists(out_path):
os.mkdir(out_path)
if not regenerate:
if os.path.isfile(out_path + '/{}_{}_{}.npy'.format(idx, side, False)):
if os.path.isfile(out_path + '/{}_{}_{}.npy'.format(idx, side, True)):
return
four_beam = get_4beam(folder, idx, side, False)
flip_four_beam = get_4beam(folder, idx, side, True)
four_beam = torch.from_numpy(four_beam.astype(np.float32))/100.0
flip_four_beam = torch.from_numpy(flip_four_beam.astype(np.float32))/100.0
expanded_depth, confidence_map = get_4beam_2channel(four_beam)
two_channel = torch.stack([expanded_depth, confidence_map]).numpy()
expanded_depth, confidence_map = get_4beam_2channel(flip_four_beam)
flip_two_channel = torch.stack([expanded_depth, confidence_map]).numpy()
'''
mask = (four_beam > 0)
crop_mask = torch.zeros_like(mask)
crop_mask[78:190, 23:617] = 1 # 375 1242
mask = mask * crop_mask
print(two_channel[0, :, :][mask] / four_beam[mask])
print(mask)
for i in range(192):
for j in range(640):
if two_channel[0,i,j] != four_beam[i,j] and mask[i,j]:
print(i, j, two_channel[0,i,j], four_beam[i,j])
if two_channel[1,i,j] != 1 and mask[i,j]:
print("not one ",i, j, two_channel[1,i,j])
cv2.imwrite('4beam.jpg', flip_four_beam.numpy()*255)
cv2.imwrite('expanded.jpg', expanded_depth.numpy()*255)
cv2.imwrite('confidence.jpg', confidence_map.numpy()*255)
yy, xx = torch.meshgrid([torch.arange(0, 192, dtype=torch.int32),
torch.arange(0, 640, dtype=torch.int32)])
indices = expanded_depth != 0
depth_feat = torch.stack((yy[indices], xx[indices], expanded_depth[indices]*255), -1).view([-1, 3])
indices = flip_four_beam != 0
beam_feat = torch.stack([yy[indices], xx[indices], flip_four_beam[indices]*255], -1).view([-1, 3])
import open3d
pcd_depth = open3d.geometry.PointCloud()
pcd_depth.points = open3d.utility.Vector3dVector(depth_feat)
pcd_beam = open3d.geometry.PointCloud()
pcd_beam.points = open3d.utility.Vector3dVector(beam_feat)
pcd_beam.paint_uniform_color([0, 0, 0])
open3d.visualization.draw_geometries([pcd_depth, pcd_beam])
import pdb
pdb.set_trace()
'''
np.save(out_path+'/{}_{}_{}.npy'.format(idx, side, False), two_channel)
np.save(out_path + '/{}_{}_{}.npy'.format(idx, side, True), flip_two_channel)
#print(out_path+'/{}_{}'.format(idx, side))
def update(*a):
pbar.update()
test_file_path = 'splits/eigen/test_files.txt'
test_file = open(test_file_path, 'r')
lines = test_file.readlines()
test_file.close()
if not test_only:
train_file_path = 'splits/eigen_zhou/train_files.txt'
train_file = open(train_file_path, 'r')
val_file_path = 'splits/eigen_zhou/val_files.txt'
val_file = open(val_file_path, 'r')
lines += train_file.readlines() + val_file.readlines()
train_file_path = 'splits/eigen_full/train_files.txt'
train_file = open(train_file_path, 'r')
val_file_path = 'splits/eigen_full/val_files.txt'
val_file = open(val_file_path, 'r')
lines += train_file.readlines() + val_file.readlines()
lines = list(set(lines))
train_file.close()
val_file.close()
if demo:
demo_file_path = 'splits/demo/demo.txt'
demo_file = open(demo_file_path, 'r')
lines = demo_file.readlines()
demo_file.close()
print("using {} cpu cores".format(cpu_count()))
pool = Pool(cpu_count())
pbar = tqdm(total=len(lines))
for line in lines:
#gen2channel(line)
pool.apply_async(gen2channel, args=(line,), callback=update)
pool.close()
pool.join()
pbar.clear(nolock=False)
pbar.close()