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view_results.py
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view_results.py
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# -- coding: UTF-8 --
''' Helper class and functions for loading KITTI objects
Author: Charles R. Qi
Date: September 2017
modified to plot results by Shuqiao Sun in September 2018
'''
from __future__ import print_function
import os
import sys
import numpy as np
import cv2
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
try:
raw_input # Python 2
except NameError:
raw_input = input # Python 3
class kitti_object(object):
'''Load and parse object data into a usable format.'''
def __init__(self, ROOT_DIR, split='val'):
'''root_dir contains training and testing folders'''
self.root_dir = os.path.join(ROOT_DIR, 'dataset/KITTI/object')
self.split = split
self.split_dir = os.path.join(self.root_dir, 'training')
if split == 'training':
self.num_samples = 7481
elif split == 'testing':
self.num_samples = 7518
elif split == 'val':
self.num_samples = 3769
else:
print('Unknown split: %s' % (split))
exit(-1)
self.image_dir = os.path.join(self.split_dir, 'image_2')
self.calib_dir = os.path.join(self.split_dir, 'calib')
self.lidar_dir = os.path.join(self.split_dir, 'velodyne')
self.label_dir = os.path.join(ROOT_DIR, 'train/detection_results_v1/data')
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):
assert(idx<self.num_samples)
lidar_filename = os.path.join(self.lidar_dir, '%06d.bin'%(idx))
return utils.load_velo_scan(lidar_filename)
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=='val')
label_filename = os.path.join(self.label_dir, '%06d.txt'%(idx))
return utils.read_label(label_filename)
def get_depth_map(self, idx):
pass
def get_top_down(self, idx):
pass
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):
''' Show image with 2D bounding boxes '''
img1 = np.copy(img) # for 2d bbox
img2 = 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)
Image.fromarray(img1).show()
if show3d:
Image.fromarray(img2).show()
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 show_lidar_with_boxes(pc_velo, objects, calib,
img_fov=False, img_width=None, img_height=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, calib, 0, 0,
img_width, img_height)
print(('FOV point num: ', pc_velo.shape[0]))
draw_lidar(pc_velo, fig=fig)
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)
# 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,:]
draw_gt_boxes3d([box3d_pts_3d_velo], fig=fig)
mlab.plot3d([x1, x2], [y1, y2], [z1,z2], color=(0.5,0.5,0.5),
tube_radius=None, line_width=1, figure=fig)
mlab.show(1)
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 dataset_viz():
dataset = kitti_object(ROOT_DIR)
val_idx = [line.rstrip() for line in open(os.path.join(ROOT_DIR, 'kitti/image_sets/val.txt'))]
for i in range(len(val_idx)):
# Load data from dataset
data_idx = int(str(val_idx[i]))
objects = dataset.get_label_objects(data_idx)
# 显示所有目标的信息
for j in range(len(objects)):
objects[j].print_object()
img = dataset.get_image(data_idx)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_height, img_width, img_channel = img.shape
print(('Image shape: ', img.shape))
pc_velo = dataset.get_lidar(data_idx)[:,0:3]
calib = dataset.get_calibration(data_idx)
# Draw 2d and 3d boxes on image
show_image_with_boxes(img, objects, calib, False)
raw_input()
# Show all LiDAR points. Draw 3d box in LiDAR point cloud
show_lidar_with_boxes(pc_velo, objects, calib, True, img_width, img_height)
raw_input()
if __name__=='__main__':
import mayavi.mlab as mlab
from viz_util import draw_lidar_simple, draw_lidar, draw_gt_boxes3d
dataset_viz()