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kitti_object.py
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kitti_object.py
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''' Helper class and functions for loading KITTI objects
Author: Charles R. Qi
Date: September 2017
'''
from __future__ import print_function
import os
import sys
import numpy as np
import cv2
import psutil
from PIL import Image
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(BASE_DIR)
sys.path.append(os.path.join(ROOT_DIR, 'mayavi'))
import kitti_util as utils
import argparse
try:
raw_input # Python 2
except NameError:
raw_input = input # Python 3
cbox = np.array([[0,70.4], [-40,40], [-3,1]])
class kitti_object(object):
'''Load and parse object data into a usable format.'''
def __init__(self, root_dir, split='training', args=None):
'''root_dir contains training and testing folders'''
self.root_dir = root_dir
self.split = split
print(root_dir, split)
self.split_dir = os.path.join(root_dir, split)
if split == 'training':
self.num_samples = 7481
elif split == 'testing':
self.num_samples = 7518
else:
print('Unknown split: %s' % (split))
exit(-1)
lidar_dir = 'velodyne'
depth_dir = 'depth'
pred_dir = 'pred'
if args is not None:
lidar_dir = args.lidar
depth_dir = args.depthdir
pred_dir = args.preddir
self.image_dir = os.path.join(self.split_dir, 'image_2')
self.label_dir = os.path.join(self.split_dir, 'label_2')
self.calib_dir = os.path.join(self.split_dir, 'calib')
self.depthpc_dir = os.path.join(self.split_dir, 'depth_pc')
self.lidar_dir = os.path.join(self.split_dir, lidar_dir)
self.depth_dir = os.path.join(self.split_dir, depth_dir)
self.pred_dir = os.path.join(self.split_dir, pred_dir)
def __len__(self):
return self.num_samples
def get_image(self, idx):
assert(idx<self.num_samples)
img_filename = os.path.join(self.image_dir, '%06d.png'%(idx))
return utils.load_image(img_filename)
def get_lidar(self, idx, dtype=np.float64, n_vec=4):
assert(idx<self.num_samples)
lidar_filename = os.path.join(self.lidar_dir, '%06d.bin'%(idx))
print(lidar_filename)
return utils.load_velo_scan(lidar_filename, dtype, n_vec)
def get_calibration(self, idx):
assert(idx<self.num_samples)
calib_filename = os.path.join(self.calib_dir, '%06d.txt'%(idx))
return utils.Calibration(calib_filename)
def get_label_objects(self, idx):
assert(idx<self.num_samples and self.split=='training')
label_filename = os.path.join(self.label_dir, '%06d.txt'%(idx))
return utils.read_label(label_filename)
def get_pred_objects(self, idx):
assert(idx<self.num_samples and self.split=='training')
pred_filename = os.path.join(self.pred_dir, '%06d.txt'%(idx))
is_exist = os.path.exists(pred_filename)
if is_exist:
return utils.read_label(pred_filename)
else:
return None
def get_depth(self, idx):
assert(idx<self.num_samples)
img_filename = os.path.join(self.depth_dir, '%06d.png'%(idx))
return utils.load_depth(img_filename)
def get_depth_image(self, idx):
assert(idx<self.num_samples)
img_filename = os.path.join(self.depth_dir, '%06d.png'%(idx))
return utils.load_depth(img_filename)
def get_depth_pc(self, idx):
assert(idx<self.num_samples)
lidar_filename = os.path.join(self.depthpc_dir, '%06d.bin'%(idx))
is_exist = os.path.exists(lidar_filename)
if is_exist:
return utils.load_velo_scan(lidar_filename), is_exist
else:
return None, is_exist
#print(lidar_filename, is_exist)
#return utils.load_velo_scan(lidar_filename), is_exist
def get_top_down(self, idx):
pass
def isexist_pred_objects(self, idx):
assert(idx<self.num_samples and self.split=='training')
pred_filename = os.path.join(self.pred_dir, '%06d.txt'%(idx))
return os.path.exists(pred_filename)
def isexist_depth(self, idx):
assert(idx<self.num_samples and self.split=='training')
depth_filename = os.path.join(self.depth_dir, '%06d.txt'%(idx))
return os.path.exists(depth_filename)
class kitti_object_video(object):
''' Load data for KITTI videos '''
def __init__(self, img_dir, lidar_dir, calib_dir):
self.calib = utils.Calibration(calib_dir, from_video=True)
self.img_dir = img_dir
self.lidar_dir = lidar_dir
self.img_filenames = sorted([os.path.join(img_dir, filename) \
for filename in os.listdir(img_dir)])
self.lidar_filenames = sorted([os.path.join(lidar_dir, filename) \
for filename in os.listdir(lidar_dir)])
print(len(self.img_filenames))
print(len(self.lidar_filenames))
#assert(len(self.img_filenames) == len(self.lidar_filenames))
self.num_samples = len(self.img_filenames)
def __len__(self):
return self.num_samples
def get_image(self, idx):
assert(idx<self.num_samples)
img_filename = self.img_filenames[idx]
return utils.load_image(img_filename)
def get_lidar(self, idx):
assert(idx<self.num_samples)
lidar_filename = self.lidar_filenames[idx]
return utils.load_velo_scan(lidar_filename)
def get_calibration(self, unused):
return self.calib
def viz_kitti_video():
video_path = os.path.join(ROOT_DIR, 'dataset/2011_09_26/')
dataset = kitti_object_video(\
os.path.join(video_path, '2011_09_26_drive_0023_sync/image_02/data'),
os.path.join(video_path, '2011_09_26_drive_0023_sync/velodyne_points/data'),
video_path)
print(len(dataset))
for i in range(len(dataset)):
img = dataset.get_image(0)
pc = dataset.get_lidar(0)
Image.fromarray(img).show()
draw_lidar(pc)
raw_input()
pc[:,0:3] = dataset.get_calibration().project_velo_to_rect(pc[:,0:3])
draw_lidar(pc)
raw_input()
return
def show_image_with_boxes(img, objects, calib, show3d=True, depth=None):
''' Show image with 2D bounding boxes '''
img1 = np.copy(img) # for 2d bbox
img2 = np.copy(img) # for 3d bbox
img3 = np.copy(img) # for 3d bbox
for obj in objects:
if obj.type=='DontCare': continue
cv2.rectangle(img1, (int(obj.xmin), int(obj.ymin)),
(int(obj.xmax),int(obj.ymax)), (0,255,0), 2)
box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
img2 = utils.draw_projected_box3d(img2, box3d_pts_2d)
# project
#box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
#box3d_pts_32d = utils.box3d_to_rgb_box00(box3d_pts_3d_velo)
#box3d_pts_32d = calib.project_velo_to_image(box3d_pts_3d_velo)
#img3 = utils.draw_projected_box3d(img3, box3d_pts_32d)
#print("img1:", img1.shape)
Image.fromarray(img1).show()
#print("img3:",img3.shape)
#Image.fromarray(img3).show()
show3d=True
if show3d:
#print("img2:",img2.shape)
Image.fromarray(img2).show()
if depth is not None:
Image.fromarray(depth).show()
def show_image_with_boxes_3type(img, objects, calib, objects2d, name, objects_pred):
''' Show image with 2D bounding boxes '''
img1 = np.copy(img) # for 2d bbox
type_list = ['Pedestrian', 'Car', 'Cyclist']
# draw Label
color = (0,255,0)
for obj in objects:
if obj.type not in type_list: continue
cv2.rectangle(img1, (int(obj.xmin), int(obj.ymin)),
(int(obj.xmax),int(obj.ymax)), color, 3)
startx = 5
font = cv2.FONT_HERSHEY_SIMPLEX
text_lables = [ obj.type for obj in objects if obj.type in type_list ]
text_lables.insert(0,"Label:")
for n in range(len(text_lables)):
text_pos = (startx, 25*(n+1))
cv2.putText(img1, text_lables[n], text_pos, font, 0.5, color, 0, cv2.LINE_AA)
# draw 2D Pred
color = (0,0,255)
for obj in objects2d:
cv2.rectangle(img1, (int(obj.box2d[0]), int(obj.box2d[1])),
(int(obj.box2d[2]),int(obj.box2d[3])), color, 2)
startx = 85
font = cv2.FONT_HERSHEY_SIMPLEX
text_lables = [ type_list[obj.typeid-1] for obj in objects2d ]
text_lables.insert(0,"2D Pred:")
for n in range(len(text_lables)):
text_pos = (startx, 25*(n+1))
cv2.putText(img1, text_lables[n], text_pos, font, 0.5, color, 0, cv2.LINE_AA)
# draw 3D Pred
if objects_pred is not None:
color = (255,0,0)
for obj in objects_pred:
if obj.type not in type_list: continue
cv2.rectangle(img1, (int(obj.xmin), int(obj.ymin)),
(int(obj.xmax),int(obj.ymax)), color, 1)
startx = 165
font = cv2.FONT_HERSHEY_SIMPLEX
text_lables = [ obj.type for obj in objects_pred if obj.type in type_list ]
text_lables.insert(0,"3D Pred:")
for n in range(len(text_lables)):
text_pos = (startx, 25*(n+1))
cv2.putText(img1, text_lables[n], text_pos, font, 0.5, color, 0, cv2.LINE_AA)
Image.fromarray(img1).show()
cv2.imwrite("imgs/"+str(name)+".png", img1)
def get_lidar_in_image_fov(pc_velo, calib, xmin, ymin, xmax, ymax,
return_more=False, clip_distance=2.0):
''' Filter lidar points, keep those in image FOV '''
pts_2d = calib.project_velo_to_image(pc_velo)
fov_inds = (pts_2d[:,0]<xmax) & (pts_2d[:,0]>=xmin) & \
(pts_2d[:,1]<ymax) & (pts_2d[:,1]>=ymin)
fov_inds = fov_inds & (pc_velo[:,0]>clip_distance)
imgfov_pc_velo = pc_velo[fov_inds,:]
if return_more:
return imgfov_pc_velo, pts_2d, fov_inds
else:
return imgfov_pc_velo
def get_lidar_index_in_image_fov(pc_velo, calib, xmin, ymin, xmax, ymax,
return_more=False, clip_distance=2.0):
''' Filter lidar points, keep those in image FOV '''
pts_2d = calib.project_velo_to_image(pc_velo)
fov_inds = (pts_2d[:,0]<xmax) & (pts_2d[:,0]>=xmin) & \
(pts_2d[:,1]<ymax) & (pts_2d[:,1]>=ymin)
fov_inds = fov_inds & (pc_velo[:,0]>clip_distance)
return fov_inds
def depth_region_pt3d(depth, obj):
b = obj.box2d
#depth_region = depth[b[0]:b[2],b[2]:b[3],0]
pt3d=[]
#import pdb; pdb.set_trace()
for i in range(int(b[0]),int(b[2])):
for j in range(int(b[1]),int(b[3])):
pt3d.append([j,i,depth[j,i]])
return np.array(pt3d)
def get_depth_pt3d(depth):
pt3d=[]
for i in range(depth.shape[0]):
for j in range(depth.shape[1]):
pt3d.append([i, j, depth[i, j]])
return np.array(pt3d)
def show_lidar_with_depth(pc_velo,
objects,
calib,
img_fov=False,
img_width=None,
img_height=None,
objects_pred=None,
depth=None,
cam_img=None,
constraint_box=False,
pc_label=False,
save=False):
''' Show all LiDAR points.
Draw 3d box in LiDAR point cloud (in velo coord system) '''
if 'mlab' not in sys.modules: import mayavi.mlab as mlab
from viz_util import draw_lidar_simple, draw_lidar, draw_gt_boxes3d
print(('All point num: ', pc_velo.shape[0]))
fig = mlab.figure(figure=None, bgcolor=(0,0,0),
fgcolor=None, engine=None, size=(1000, 500))
if img_fov:
pc_velo_index = get_lidar_index_in_image_fov(pc_velo[:,:3], calib, 0, 0,
img_width, img_height)
pc_velo = pc_velo[pc_velo_index,:]
print(('FOV point num: ', pc_velo.shape))
print("pc_velo",pc_velo.shape)
draw_lidar(pc_velo, fig=fig, pc_label=pc_label)
# Draw depth
if depth is not None:
depth_pc_velo = calib.project_depth_to_velo(depth, constraint_box)
indensity = np.ones((depth_pc_velo.shape[0],1)) * 0.5
depth_pc_velo = np.hstack((depth_pc_velo, indensity))
print("depth_pc_velo:",depth_pc_velo.shape)
print("depth_pc_velo:",type(depth_pc_velo))
print(depth_pc_velo[:5])
draw_lidar(depth_pc_velo, fig=fig, pts_color=(1,1,1))
if save:
data_idx=0
vely_dir = "data/obj/training/depth_pc"
save_filename = os.path.join(vely_dir, '%06d.bin'%(data_idx))
print(save_filename)
#np.save(save_filename+".npy", np.array(depth_pc_velo))
depth_pc_velo=depth_pc_velo.astype(np.float32)
depth_pc_velo.tofile(save_filename)
color=(0,1,0)
for obj in objects:
if obj.type=='DontCare':continue
# Draw 3d bounding box
box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
print("box3d_pts_3d_velo:")
print(box3d_pts_3d_velo)
draw_gt_boxes3d([box3d_pts_3d_velo], fig=fig, color=color)
if objects_pred is not None:
color=(1,0,0)
for obj in objects_pred:
if obj.type=='DontCare':continue
# Draw 3d bounding box
box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
print("box3d_pts_3d_velo:")
print(box3d_pts_3d_velo)
draw_gt_boxes3d([box3d_pts_3d_velo], fig=fig, color=color)
# Draw heading arrow
ori3d_pts_2d, ori3d_pts_3d = utils.compute_orientation_3d(obj, calib.P)
ori3d_pts_3d_velo = calib.project_rect_to_velo(ori3d_pts_3d)
x1,y1,z1 = ori3d_pts_3d_velo[0,:]
x2,y2,z2 = ori3d_pts_3d_velo[1,:]
mlab.plot3d([x1, x2], [y1, y2], [z1,z2], color=color,
tube_radius=None, line_width=1, figure=fig)
mlab.show(1)
def save_depth0(data_idx, pc_velo, calib, img_fov, img_width, img_height, \
depth, constraint_box=False):
if img_fov:
pc_velo_index = get_lidar_index_in_image_fov(pc_velo[:,:3], calib, 0, 0,
img_width, img_height)
pc_velo = pc_velo[pc_velo_index,:]
type = np.zeros((pc_velo.shape[0],1))
pc_velo = np.hstack((pc_velo, type))
print(('FOV point num: ', pc_velo.shape))
# Draw depth
if depth is not None:
depth_pc_velo = calib.project_depth_to_velo(depth, constraint_box)
indensity = np.ones((depth_pc_velo.shape[0],1)) * 0.5
depth_pc_velo = np.hstack((depth_pc_velo, indensity))
type = np.ones((depth_pc_velo.shape[0],1))
depth_pc_velo = np.hstack((depth_pc_velo, type))
print("depth_pc_velo:",depth_pc_velo.shape)
depth_pc = np.concatenate((pc_velo, depth_pc_velo),axis=0)
print("depth_pc:",depth_pc.shape)
vely_dir = "data/obj/training/depth_pc"
save_filename = os.path.join(vely_dir, '%06d.bin'%(data_idx))
depth_pc=depth_pc.astype(np.float32)
depth_pc.tofile(save_filename)
def save_depth(data_idx, pc_velo, calib, img_fov, img_width, img_height, \
depth, constraint_box=False):
if depth is not None:
depth_pc_velo = calib.project_depth_to_velo(depth, constraint_box)
indensity = np.ones((depth_pc_velo.shape[0],1)) * 0.5
depth_pc = np.hstack((depth_pc_velo, indensity))
print("depth_pc:",depth_pc.shape)
vely_dir = "data/obj/training/depth_pc"
save_filename = os.path.join(vely_dir, '%06d.bin'%(data_idx))
depth_pc=depth_pc.astype(np.float32)
depth_pc.tofile(save_filename)
def show_lidar_with_boxes(pc_velo, objects, calib, img_fov=False, img_width=None,
img_height=None, objects_pred=None, depth=None, cam_img=None):
''' Show all LiDAR points.
Draw 3d box in LiDAR point cloud (in velo coord system) '''
if 'mlab' not in sys.modules: import mayavi.mlab as mlab
from viz_util import draw_lidar_simple, draw_lidar, draw_gt_boxes3d
print(('All point num: ', pc_velo.shape[0]))
fig = mlab.figure(figure=None, bgcolor=(0,0,0),
fgcolor=None, engine=None, size=(1000, 500))
if img_fov:
pc_velo = get_lidar_in_image_fov(pc_velo[:,0:3], calib, 0, 0,
img_width, img_height)
print(('FOV point num: ', pc_velo.shape[0]))
print("pc_velo",pc_velo.shape)
draw_lidar(pc_velo, fig=fig)
#pc_velo=pc_velo[:,0:3]
color=(0,1,0)
for obj in objects:
if obj.type=='DontCare':continue
# Draw 3d bounding box
box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
print("box3d_pts_3d_velo:")
print(box3d_pts_3d_velo)
draw_gt_boxes3d([box3d_pts_3d_velo], fig=fig, color=color)
# Draw depth
if depth is not None:
#import pdb; pdb.set_trace()
depth_pt3d = depth_region_pt3d(depth, obj)
depth_UVDepth = np.zeros_like(depth_pt3d)
depth_UVDepth[:,0] = depth_pt3d[:,1]
depth_UVDepth[:,1] = depth_pt3d[:,0]
depth_UVDepth[:,2] = depth_pt3d[:,2]
print("depth_pt3d:",depth_UVDepth)
dep_pc_velo = calib.project_image_to_velo(depth_UVDepth)
print("dep_pc_velo:",dep_pc_velo)
draw_lidar(dep_pc_velo, fig=fig, pts_color=(1,1,1))
#
# Draw heading arrow
ori3d_pts_2d, ori3d_pts_3d = utils.compute_orientation_3d(obj, calib.P)
ori3d_pts_3d_velo = calib.project_rect_to_velo(ori3d_pts_3d)
x1,y1,z1 = ori3d_pts_3d_velo[0,:]
x2,y2,z2 = ori3d_pts_3d_velo[1,:]
mlab.plot3d([x1, x2], [y1, y2], [z1,z2], color=color,
tube_radius=None, line_width=1, figure=fig)
if objects_pred is not None:
color=(1,0,0)
for obj in objects_pred:
if obj.type=='DontCare':continue
# Draw 3d bounding box
box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
print("box3d_pts_3d_velo:")
print(box3d_pts_3d_velo)
draw_gt_boxes3d([box3d_pts_3d_velo], fig=fig, color=color)
# Draw heading arrow
ori3d_pts_2d, ori3d_pts_3d = utils.compute_orientation_3d(obj, calib.P)
ori3d_pts_3d_velo = calib.project_rect_to_velo(ori3d_pts_3d)
x1,y1,z1 = ori3d_pts_3d_velo[0,:]
x2,y2,z2 = ori3d_pts_3d_velo[1,:]
mlab.plot3d([x1, x2], [y1, y2], [z1,z2], color=color,
tube_radius=None, line_width=1, figure=fig)
mlab.show(1)
def box_min_max(box3d):
box_min = np.min(box3d, axis=0)
box_max = np.max(box3d, axis=0)
return box_min, box_max
def get_velo_whl(box3d, pc):
bmin, bmax = box_min_max(box3d)
ind = np.where((pc[:,0]>=bmin[0]) & (pc[:,0]<=bmax[0]) \
& (pc[:,1]>=bmin[1]) & (pc[:,1]<=bmax[1]) \
& (pc[:,2]>=bmin[2]) & (pc[:,2]<=bmax[2]))[0]
#print(pc[ind,:])
if len(ind)>0:
vmin, vmax = box_min_max(pc[ind,:])
return vmax - vmin
else:
return 0,0,0,0
def stat_lidar_with_boxes(pc_velo, objects, calib):
''' Show all LiDAR points.
Draw 3d box in LiDAR point cloud (in velo coord system) '''
#print(('All point num: ', pc_velo.shape[0]))
#draw_lidar(pc_velo, fig=fig)
#color=(0,1,0)
for obj in objects:
if obj.type=='DontCare':continue
box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
v_l, v_w, v_h,_ = get_velo_whl(box3d_pts_3d_velo, pc_velo)
print("%.4f %.4f %.4f %s"%(v_w, v_h, v_l, obj.type))
def show_lidar_on_image(pc_velo, img, calib, img_width, img_height):
''' Project LiDAR points to image '''
imgfov_pc_velo, pts_2d, fov_inds = get_lidar_in_image_fov(pc_velo,
calib, 0, 0, img_width, img_height, True)
imgfov_pts_2d = pts_2d[fov_inds,:]
imgfov_pc_rect = calib.project_velo_to_rect(imgfov_pc_velo)
import matplotlib.pyplot as plt
cmap = plt.cm.get_cmap('hsv', 256)
cmap = np.array([cmap(i) for i in range(256)])[:,:3]*255
for i in range(imgfov_pts_2d.shape[0]):
depth = imgfov_pc_rect[i,2]
color = cmap[int(640.0/depth),:]
cv2.circle(img, (int(np.round(imgfov_pts_2d[i,0])),
int(np.round(imgfov_pts_2d[i,1]))),
2, color=tuple(color), thickness=-1)
Image.fromarray(img).show()
return img
def show_lidar_topview_with_boxes(pc_velo, objects, calib, objects_pred=None):
''' top_view image'''
#print('pc_velo shape: ',pc_velo.shape)
top_view = utils.lidar_to_top(pc_velo)
top_image = utils.draw_top_image(top_view)
print('top_image:', top_image.shape)
# gt
def bbox3d(obj):
box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
return box3d_pts_3d_velo
boxes3d = [bbox3d(obj) for obj in objects if obj.type!='DontCare']
gt = np.array(boxes3d)
#print("box2d BV:",boxes3d)
lines = [ obj.type for obj in objects if obj.type!='DontCare' ]
top_image = utils.draw_box3d_on_top(top_image, gt, text_lables=lines, scores=None, thickness=1, is_gt=True)
# pred
if objects_pred is not None:
boxes3d = [bbox3d(obj) for obj in objects_pred if obj.type!='DontCare']
gt = np.array(boxes3d)
lines = [ obj.type for obj in objects_pred if obj.type!='DontCare' ]
top_image = utils.draw_box3d_on_top(top_image, gt, text_lables=lines, scores=None, thickness=1, is_gt=False)
Image.fromarray(top_image).show()
def dataset_viz(root_dir, args):
dataset = kitti_object(root_dir, args=args)
## load 2d detection results
objects2ds = read_det_file('box2d.list')
for data_idx in range(len(dataset)):
if args.ind>0:
data_idx=args.ind
# Load data from dataset
objects = dataset.get_label_objects(data_idx)
objects2d = objects2ds[data_idx]
objects_pred = None
if args.pred:
#if not dataset.isexist_pred_objects(data_idx):
# continue
objects_pred = dataset.get_pred_objects(data_idx)
if objects_pred == None:
continue
if objects_pred == None:
print("no pred file")
#objects_pred[0].print_object()
n_vec = 4
if args.pc_label:
n_vec = 5
dtype=np.float32
if args.dtype64:
dtype=np.float64
pc_velo = dataset.get_lidar(data_idx, dtype, n_vec)[:,0:n_vec]
calib = dataset.get_calibration(data_idx)
img = dataset.get_image(data_idx)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_height, img_width, img_channel = img.shape
print(data_idx, 'image shape: ', img.shape)
print(data_idx, 'velo shape: ', pc_velo.shape)
if args.depth:
depth, is_exist = dataset.get_depth(data_idx)
print(data_idx, 'depth shape: ', depth.shape)
else:
depth=None
#depth = cv2.cvtColor(depth, cv2.COLOR_BGR2RGB)
#depth_height, depth_width, depth_channel = img.shape
#print(('Image shape: ', img.shape))
if args.stat:
stat_lidar_with_boxes(pc_velo, objects, calib)
continue
print("======== Objects in Ground Truth ========")
n_obj = 0
for obj in objects:
if obj.type != 'DontCare':
print("=== {} object ===".format(n_obj+1))
obj.print_object()
n_obj += 1
# Draw 3d box in LiDAR point cloud
if args.show_lidar_topview_with_boxes:
# Draw lidar top view
show_lidar_topview_with_boxes(pc_velo, objects, calib, objects_pred)
#show_image_with_boxes_3type(img, objects, calib, objects2d, data_idx, objects_pred)
if args.show_image_with_boxes:
# Draw 2d and 3d boxes on image
show_image_with_boxes(img, objects, calib, True, depth)
if args.show_lidar_with_depth:
# Draw 3d box in LiDAR point cloud
show_lidar_with_depth(pc_velo, objects, calib, args.img_fov, img_width, img_height, \
objects_pred, depth, img, constraint_box=args.const_box, save=args.save_depth, pc_label=args.pc_label)
#show_lidar_with_boxes(pc_velo, objects, calib, True, img_width, img_height, \
# objects_pred, depth, img)
if args.show_lidar_on_image:
# Show LiDAR points on image.
show_lidar_on_image(pc_velo[:,0:3], img, calib, img_width, img_height)
input_str=raw_input()
mlab.close(all=True)
for proc in psutil.process_iter():
if proc.name() == "display":
proc.kill()
if input_str == "killall":
break
def depth_to_lidar_format(root_dir, args):
dataset = kitti_object(root_dir, args=args)
for data_idx in range(len(dataset)):
# Load data from dataset
pc_velo = dataset.get_lidar(data_idx)[:,0:4]
calib = dataset.get_calibration(data_idx)
depth, is_exist = dataset.get_depth(data_idx)
img = dataset.get_image(data_idx)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_height, img_width, img_channel = img.shape
print(data_idx, 'image shape: ', img.shape)
print(data_idx, 'velo shape: ', pc_velo.shape)
print(data_idx, 'depth shape: ', depth.shape)
#depth = cv2.cvtColor(depth, cv2.COLOR_BGR2RGB)
#depth_height, depth_width, depth_channel = img.shape
#print(('Image shape: ', img.shape))
save_depth(data_idx, pc_velo, calib, args.img_fov, img_width, img_height, \
depth, constraint_box=args.const_box)
input_str=raw_input()
def read_det_file(det_filename):
''' Parse lines in 2D detection output files '''
det_id2str = {1:'Pedestrian', 2:'Car', 3:'Cyclist'}
objects={}
with open(det_filename, 'r') as f:
for line in f.readlines():
obj = utils.Object2d(line.rstrip())
if obj.img_name not in objects.keys():
objects[obj.img_name]=[]
objects[obj.img_name].append(obj)
#objects = [utils.Object2d(line.rstrip()) for line in f.readlines()]
return objects
if __name__=='__main__':
import mayavi.mlab as mlab
from viz_util import draw_lidar_simple, draw_lidar, draw_gt_boxes3d
parser = argparse.ArgumentParser(description='PyTorch Training RPN')
parser.add_argument('-d', '--dir', type=str, default="data/obj", metavar='N',
help='input (default: data/object)')
parser.add_argument('-i', '--ind', type=int, default=0, metavar='N',
help='input (default: data/object)')
parser.add_argument('-p','--pred', action='store_true', help='show predict results')
parser.add_argument('-s','--stat', action='store_true', help=' stat the w/h/l of point cloud in gt bbox')
parser.add_argument('-l','--lidar', type=str, default="velodyne", metavar='N',
help='velodyne dir (default: velodyne)')
parser.add_argument('-e','--depthdir', type=str, default="depth", metavar='N',
help='depth dir (default: depth)')
parser.add_argument('-r','--preddir', type=str, default="pred", metavar='N',
help='predicted boxes (default: pred)')
parser.add_argument('--gen_depth', action='store_true', help='generate depth')
parser.add_argument('--vis', action='store_true', help='show images')
parser.add_argument('--depth', action='store_true', help='load depth')
parser.add_argument('--img_fov', action='store_true', help='front view mapping')
parser.add_argument('--const_box', action='store_true', help='constraint box')
parser.add_argument('--save_depth', action='store_true', help='save depth into file')
parser.add_argument('--pc_label', action='store_true', help='5-verctor lidar, pc with label')
parser.add_argument('--dtype64', action='store_true', help='for float64 datatype, default float64')
parser.add_argument('--show_lidar_on_image', action='store_true', help='project lidar on image')
parser.add_argument('--show_lidar_with_depth', action='store_true', help='--show_lidar, depth is supported')
parser.add_argument('--show_image_with_boxes', action='store_true', help='show lidar')
parser.add_argument('--show_lidar_topview_with_boxes', action='store_true', help='show lidar topview')
args = parser.parse_args()
if args.pred:
assert os.path.exists(args.dir+"/training/pred")
if args.vis:
dataset_viz(args.dir, args)
if args.gen_depth:
depth_to_lidar_format(args.dir, args)