1. 参考文献
3D Fully Convolutional Network for Vehicle Detection in Point Cloud
2. 模型实现
'''
Baidu Inc.
Ref:
3D Fully Convolutional Network for Vehicle Detection in Point Cloud
Author: HSW
Date: 2018-05-02
'''
import sys
import numpy as np
import tensorflow as tf
from prepare_data2 import *
from baidu_cnn_3d import *
KITTI_TRAIN_DATA_CNT = 7481
KITTI_TEST_DATA_CNT = 7518
# create 3D-CNN Model
def create_graph(sess, modelType = 0, voxel_shape = (400, 400, 20), activation=tf.nn.relu, is_train = True):
'''
Inputs:
sess: tensorflow Session Object
voxel_shape: voxel shape for network first layer
activation:
phrase_train:
Outputs:
voxel, graph, sess
'''
voxel = tf.placeholder(tf.float32, [None, voxel_shape[0], voxel_shape[1], voxel_shape[2], 1])
phase_train = tf.placeholder(tf.bool, name="phase_train") if is_train else None
with tf.variable_scope("3D_CNN_Model") as scope:
model = Full_CNN_3D_Model()
model.cnn3d_graph(voxel, modelType = modelType, activation=activation, phase_train = is_train)
if is_train:
initialized_var = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="3D_CNN_model")
sess.run(tf.variables_initializer(initialized_var))
return voxel, model, phase_train
# read batch data
def read_batch_data(batch_size, data_set_dir,objectType = "Car", split = "training", resolution=(0.2, 0.2, 0.2), scale=0.25, limitX = (0,80), limitY=(-40,40), limitZ=(-2.5,1.5)):
'''
Inputs:
batch_size:
data_set_dir:
objectType: default is "Car"
split: default is "training"
resolution:
scale: outputSize / inputSize
limitX:
limitY:
limitZ:
Outputs:
'''
kitti_3DVoxel = kitti_3DVoxel_interface(data_set_dir, objectType = objectType, split=split, scale = scale, resolution = resolution, limitX = limitX, limitY = limitY, limitZ = limitZ)
TRAIN_PROCESSED_IDX = 0
TEST_PROCESSED_IDX = 0
if split == "training":
while TRAIN_PROCESSED_IDX < KITTI_TRAIN_DATA_CNT:
batch_voxel = []
batch_g_obj = []
batch_g_cord = []
idx = 0
while idx < batch_size and TRAIN_PROCESSED_IDX < KITTI_TRAIN_DATA_CNT:
print(TRAIN_PROCESSED_IDX)
voxel, g_obj, g_cord = kitti_3DVoxel.read_kitti_data(TRAIN_PROCESSED_IDX)
TRAIN_PROCESSED_IDX += 1
if voxel is None:
continue
idx += 1
# print(voxel.shape)
batch_voxel.append(voxel)
batch_g_obj.append(g_obj)
batch_g_cord.append(g_cord)
yield np.array(batch_voxel, dtype=np.float32)[:, :, :, :, np.newaxis], np.array(batch_g_obj, dtype=np.float32), np.array(batch_g_cord, dtype=np.float32)
elif split == "testing":
while TEST_PROCESSED_IDX < KITTI_TEST_DATA_CNT:
batch_voxel = []
idx = 0
while idx < batch_size and TEST_PROCESSED_IDX < KITTI_TEST_DATA_CNT:
voxel = kitti_3DVoxel.read_kitti_data(iter * batch_size + idx)
TEST_PROCESSED_IDX += 1
if voxel is None:
continue
idx += 1
batch_voxel.append(voxel)
yield np.array(batch_voxel, dtype=np.float32)[:, :, :, :, np.newaxis]
# train 3D-CNN Model
def train(batch_num, data_set_dir, modelType = 0, objectType = "Car", resolution=(0.2,0.2,0.2), scale = 0.25, lr=0.01, limitX=(0,80), limitY=(-40,40), limitZ=(-2.5,1.5), epoch=101):
'''
Inputs:
batch_num:
data_set_dir:
modelType:
objectType:
resolution:
scale:
lr:
limitX, limitY, limitZ:
Outputs:
None
'''
batch_size = batch_num
training_epochs = epoch
sizeX = int(round((limitX[1] - limitX[0]) / resolution[0]))
sizeY = int(round((limitY[1] - limitY[0]) / resolution[0]))
sizeZ = int(round((limitZ[1] - limitZ[0]) / resolution[0]))
voxel_shape = (sizeX, sizeY, sizeZ)
with tf.Session() as sess:
voxel, model, phase_train = create_graph(sess, modelType = modelType, voxel_shape = voxel_shape, activation=tf.nn.relu, is_train = True)
saver = tf.train.Saver()
total_loss, obj_loss, cord_loss, is_obj_loss, non_obj_loss, g_obj, g_cord, y_pred = model.loss_Fun(lossType = 0, cord_loss_weight = 0.02)
optimizer = model.create_optimizer(total_loss, optType = "Adam", learnRate = 0.001)
init = tf.global_variables_initializer()
sess.run(init)
for epoch in range(training_epochs):
batchCnt = 0;
for (batch_voxel, batch_g_obj, batch_g_cord) in read_batch_data(batch_size, data_set_dir, objectType = objectType, split = "training", resolution = resolution, scale = scale, limitX = limitX, limitY = limitY, limitZ = limitZ):
# print("batch_g_obj")
# print(batch_g_obj.shape)
sess.run(optimizer, feed_dict={voxel: batch_voxel, g_obj: batch_g_obj, g_cord: batch_g_cord, phase_train: True})
cord_cost = sess.run(cord_loss, feed_dict={voxel: batch_voxel, g_obj: batch_g_obj, g_cord: batch_g_cord, phase_train: True})
obj_cost = sess.run(is_obj_loss, feed_dict={voxel: batch_voxel, g_obj: batch_g_obj, g_cord: batch_g_cord, phase_train: True})
non_obj_cost = sess.run(non_obj_loss, feed_dict={voxel: batch_voxel, g_obj: batch_g_obj, g_cord: batch_g_cord, phase_train: True})
print("Epoch: ", (epoch + 1), ",", "BatchNum: ", (batchCnt + 1), "," , "cord_cost = ", "{:.9f}".format(cord_cost))
print("Epoch: ", (epoch + 1), ",", "BatchNum: ", (batchCnt + 1), "," , "obj_cost = ", "{:.9f}".format(obj_cost))
print("Epoch: ", (epoch + 1), ",", "BatchNum: ", (batchCnt + 1), "," , "non_obj_cost = ", "{:.9f}".format(non_obj_cost))
batchCnt += 1
if (epoch > 0) and (epoch % 10 == 0):
saver.save(sess, "velodyne_kitti_train_" + str(epoch) + ".ckpt")
print("Training Finishied !")
# test 3D-CNN Model
def test(batch_num, data_set_dir, modelType = 0, objectType = "Car", resolution=(0.2, 0.2, 0.2), scale = 0.25, limitX = (0, 80), limitY = (-40, 40), limitZ=(-2.5, 1.5)):
'''
Inputs:
batch_num:
data_set_dir:
resolution:
scale:
limitX, limitY, limitZ:
Outputs:
None
'''
sizeX = int(round((limitX[1] - limitX[0]) / resolution[0]))
sizeY = int(round((limitY[1] - limitY[0]) / resolution[0]))
sizeZ = int(round((limitZ[1] - limitZ[0]) / resolution[0]))
voxel_shape = (sizeX, sizeY, sizeZ)
batch_size = batch_num;
batch_voxel = read_batch_data(batch_num, data_set_dir, objectType = objectType, split="Testing", resolution=resolution, scale=scale, limitX=limitX, limitY=limitY, limitZ=limitZ)
batch_voxel_x = batch_voxel.reshape(1, batch_voxel.shape[0], batch_voxel.shape[1], batch_voxel.shape[2], 1)
with tf.Session() as sess:
is_train = False
voxel, model, phase_train = create_graph(sess, modelType = modelType, voxel_shape = voxel_shape, activation=tf.nn.relu, is_train = False)
new_saver = tf.train.import_meta_graph("velodyne_kitti_train_40.ckpt.meta")
last_model = "./velodyne_kitti_train_40.ckpt"
saver.restore(sess, last_model)
objectness = model.objectness
cordinate = model.cordinate
y_pred = model.y
objectness = sess.run(objectness, feed_dict={voxel: batch_voxel_x})[0, :, :, :, 0]
cordinate = sess.run(cordinate, feed_dict={voxel:batch_voxel_x})[0]
y_pred = sess.run(y_pred, feed_dict={voxel: batch_voxel_x})[0, :, :, :, 0]
idx = np.where(y_pred >= 0.995)
spheres = np.vstack((index[0], np.vstack((index[1], index[2])))).transpose()
centers = spheres_to_centers(spheres, scale = scale, resolution=resolution, limitX = limitX, limitY = limitY, limitZ = limitZ)
corners = cordinate[idx].reshape[-1, 8, 3] + centers[:, np.newaxis]
print(centers)
print(corners)
if __name__ == "__main__":
batch_num = 3
data_set_dir = "/home/hsw/桌面/PCL_API_Doc/frustum-pointnets-master/dataset"
modelType = 1
objectType = "Car"
resolution = (0.2, 0.2, 0.2)
scale = 0.25
lr = 0.001
limitX = (0, 80)
limitY = (-40, 40)
limitZ = (-2.5, 1.5)
epoch = 101
train(batch_num, data_set_dir = data_set_dir, modelType = modelType, objectType = objectType, resolution=resolution, scale=scale, lr =lr, limitX = limitX, limitY = limitY, limitZ = limitZ)
saver = tf.train.Saver()
2.1 网络模型
'''
Baidu Inc.
Ref:
3D Fully Convolutional Network for Vehicle Detection in Point Cloud
Author: HSW
Date: 2018-05-02
'''
import numpy as np
import tensorflow as tf
class Full_CNN_3D_Model(object):
'''
Define Full CNN Model
'''
def __init__(self):
pass;
def cnn3d_graph(self, voxel, modelType = 0, activation = tf.nn.relu, phase_train = True):
if modelType == 0:
# Modefied 3D-CNN, 该网络结构不可使用,因为降采样太严重(降采样1/8)导致在预测时会出现较大误差
self.layer1 = self.conv3d_layer(voxel , 1, 16, 5, 5, 5, [1, 2, 2, 2, 1], name="layer1", activation=activation, phase_train=phase_train)
self.layer2 = self.conv3d_layer(self.layer1, 16, 32, 5, 5, 5, [1, 2, 2, 2, 1], name="layer2", activation=activation, phase_train=phase_train)
self.layer3 = self.conv3d_layer(self.layer2, 32, 64, 3, 3, 3, [1, 2, 2, 2, 1], name="layer3", activation=activation, phase_train=phase_train)
self.layer4 = self.conv3d_layer(self.layer3, 64, 64, 3, 3, 3, [1, 1, 1, 1, 1], name="layer4", activation=activation, phase_train=phase_train)
self.objectness = self.conv3D_to_output(self.layer4, 64, 2, 3, 3, 3, [1, 1, 1, 1, 1], name="objectness", activation=None)
self.cordinate = self.conv3D_to_output(self.layer4, 64, 24, 3, 3, 3, [1, 1, 1, 1, 1], name="cordinate", activation=None)
self.y = tf.nn.softmax(self.objectness, dim=-1)
elif modelType == 1:
# 3D-CNN(论文网络结构： 降采样1/4，即InputSize / OutputSize = 0.25)
self.layer1 = self.conv3d_layer(voxel , 1, 10, 5, 5, 5, [1, 2, 2, 2, 1], name="layer1", activation=activation, phase_train=phase_train)
self.layer2 = self.conv3d_layer(self.layer1, 10, 20, 5, 5, 5, [1, 2, 2, 2, 1], name="layer2", activation=activation, phase_train=phase_train)
self.layer3 = self.conv3d_layer(self.layer2, 20, 30, 3, 3, 3, [1, 2, 2, 2, 1], name="layer3", activation=activation, phase_train=phase_train)
base_shape = self.layer2.get_shape().as_list()
obj_output_shape = [tf.shape(self.layer3)[0], base_shape[1], base_shape[2], base_shape[3], 2]
cord_output_shape = [tf.shape(self.layer3)[0], base_shape[1], base_shape[2], base_shape[3], 24]
self.objectness = self.deconv3D_to_output(self.layer3, 30, 2, 3, 3, 3, [1, 2, 2, 2, 1], obj_output_shape, name="objectness", activation=None)
self.cordinate = self.deconv3D_to_output(self.layer3, 30, 24, 3, 3, 3, [1, 2, 2, 2, 1], cord_output_shape, name="cordinate", activation=None)
self.y = tf.nn.softmax(self.objectness, dim=-1)
# batch Normalize
def batch_norm(self, inputs, phase_train = True, decay = 0.9, eps = 1e-5):
'''
Inputs:
inputs: input data for last layer
phase_train: True / False, = True is train, = False is Test
Outputs:
norm data for next layer
'''
gamma = tf.get_variable("gamma", shape=inputs.get_shape()[-1], dtype=tf.float32, initializer=tf.constant_initializer(1.0))
beta = tf.get_variable("beta", shape=inputs.get_shape()[-1], dtype=tf.float32, initializer=tf.constant_initializer(0.0))
pop_mean = tf.get_variable("pop_mean", trainable=False, shape=inputs.get_shape()[-1], dtype=tf.float32, initializer=tf.constant_initializer(0.0))
pop_var = tf.get_variable("pop_var", trainable=False, shape=inputs.get_shape()[-1], dtype=tf.float32, initializer=tf.constant_initializer(1.0))
axes = range(len(inputs.get_shape()) - 1)
if phase_train == True:
batch_mean, batch_var = tf.nn.moments(inputs, axes = [0, 1, 2, 3])
train_mean = tf.assign(pop_mean, pop_mean * decay + batch_mean*(1 - decay))
train_var = tf.assign(pop_var, pop_var * decay + batch_var * (1 - decay))
with tf.control_dependencies([train_mean, train_var]):
return tf.nn.batch_normalization(inputs, batch_mean, batch_var, beta, gamma, eps)
else:
return tf.nn.batch_normalization(inputs, pop_mean, pop_var, beta, gamma, eps)
# 3D Conv Layer
def conv3d_layer(self, inputs, inputs_dims, outputs_dims, height, width, length, stride, activation=tf.nn.relu, padding="SAME", name="", phase_train = True):
'''
Inputs:
inputs: pre-Layer output
inputs_dims: pre-Layer output channels
outputs_dims: cur-Layer output channels
[length, height, width]: cur-Layer conv3d kernel size
stride: conv3d kernel move step in length/height/width axis
activation: default use relu activation function
padding: conv3d 'padding' parameter
Outputs:
3D Conv. Layer outputs
'''
with tf.variable_scope("conv3D" + name):
# conv3d layer kernel
kernel = tf.get_variable("weights", shape=[length, height, width, inputs_dims, outputs_dims], dtype = tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.01))
# conv3d layer bias
bias = tf.get_variable("bias", shape=[outputs_dims], dtype=tf.float32, initializer=tf.constant_initializer(0.0))
# conv3d
conv = tf.nn.conv3d(inputs, kernel, stride, padding=padding)
bias = tf.nn.bias_add(conv, bias)
if activation:
bias = activation(bias, name="activation")
bias = self.batch_norm(bias, phase_train)
return bias
# 3D Conv to Classification Layer
def conv3D_to_output(self, inputs, inputs_dims, outputs_dims, height, width, length, stride, activation=tf.nn.relu, padding="SAME", name="", phase_train = True):
'''
Inputs:
inputs: pre-Layer outputs
inputs_dims: pre-Layer output channels
outputs_dims: cur-Layer output channels
stride: conv3d kernel move step in length/height/width axis
activation: default use relu activation function
padding: conv3d 'padding' parameter
outputs_shape: de-conv outputs shape
Outputs:
conv outputs
'''
with tf.variable_scope("conv3D" + name):
kernel = tf.get_variable("weights", shape=[length, height, width, inputs_dims, outputs_dims], dtype=tf.float32, initializer=tf.constant_initializer(0.01))
conv = tf.nn.conv3d(inputs, kernel, stride, padding=padding)
return conv
# 3D Deconv. to Classification Layer
def deconv3D_to_output(self, inputs, inputs_dims, outputs_dims, height, width, length, stride, output_shape, activation=tf.nn.relu, padding="SAME", name="", phase_train = True):
'''
Inputs:
inputs: pre-Layer outputs
inputs_dims: pre-Layer output channels
outputs_dims: cur-Layer output channels
stride: conv3d kernel move step in length/height/width axis
activation: default use relu activation function
padding: conv3d 'padding' parameter
outputs_shape: de-conv outputs shape
Outputs:
de-conv outputs
'''
with tf.variable_scope("deconv3D"+name):
kernel = tf.get_variable("weights", shape=[length, height, width, outputs_dims, inputs_dims], dtype=tf.float32, initializer=tf.constant_initializer(0.01))
deconv = tf.nn.conv3d_transpose(inputs, kernel, output_shape, stride, padding="SAME")
return deconv
# define loss
def loss_Fun(self, lossType = 0, cord_loss_weight = 0.02):
'''
Inputs:
lossType: = for difference loss Type
cord_loss_weight: 0.02
Outputs:
'''
if lossType == 0:
# print("g_obj")
# print(self.cordinate.get_shape())
g_obj = tf.placeholder(tf.float32, self.cordinate.get_shape().as_list()[:4])
g_cord = tf.placeholder(tf.float32, self.cordinate.get_shape().as_list())
non_g_obj = tf.subtract(tf.ones_like(g_obj, dtype=tf.float32), g_obj )
elosion = 0.00001
y = self.y
is_obj_loss = -tf.reduce_sum(tf.multiply(g_obj , tf.log(y[:,:,:,:,0] + elosion))) # object loss
non_obj_loss = tf.reduce_sum(tf.multiply(non_g_obj, tf.log(y[:, :, :, :, 0] + elosion))) # non-object loss
cross_entropy = tf.add(is_obj_loss, non_obj_loss)
obj_loss = cross_entropy
cord_diff = tf.multiply(g_obj , tf.reduce_sum(tf.square(tf.subtract(self.cordinate, g_cord)), 4)) # cord loss
cord_loss = tf.multiply(tf.reduce_sum(cord_diff), cord_loss_weight)
return tf.add(obj_loss, cord_loss), obj_loss, cord_loss, is_obj_loss, non_obj_loss, g_obj, g_cord, y
# Create Optimizer
def create_optimizer(self, all_loss, optType = "Adam", learnRate = 0.001):
'''
Inputs:
all_loss: graph all_loss
lr: learn rate
Outputs:
optimizer
'''
if optType == "Adam":
opt = tf.train.AdamOptimizer(learnRate)
optimizer = opt.minimize(all_loss)
return optimizer
2.2 数据预处理
'''Prepase KITTI data for 3D Object detection
Ref: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud
Author: Shiwen He
Date: 28 April 2018
'''
import numpy as np
from kitti_object import kitti_object as kittiReader
import kitti_util
# lidar data => 3D Grid Voxel
# filter lidar data by camera FoV
def filter_camera_fov(pc):
'''
Inputs:
pc: n x 3
Outputs:
filter_pc: m x 3, m <= 3
Notices:
FoV: from -45 degree to 45 degree
'''
logic_fov = np.logical_and((pc[:, 1] < pc[:, 0] - 0.27), (-pc[:, 1] < pc[:, 0] - 0.27))
filter_pc = pc[logic_fov]
return filter_pc
# filter lidar data by detection range
def filter_lidar_range(pc, limitX, limitY, limitZ):
'''
Inputs:
pc: n x 3,
limitX, limitY, limitZ: 1 x 2
Outputs:
filter_pc: m x 3, m <= n
'''
logic_x = np.logical_and(pc[:, 0] >= limitX[0], pc[:, 0] < limitX[1])
logic_y = np.logical_and(pc[:, 1] >= limitY[0], pc[:, 1] < limitY[1])
logic_z = np.logical_and(pc[:, 2] >= limitZ[0], pc[:, 2] < limitZ[1])
logic_xyz = np.logical_and(logic_x, np.logical_and(logic_y, logic_z))
filter_pc = pc[:, :3][logic_xyz]
return filter_pc
# filter center + corners
def filter_center_corners(centers, corners, boxsizes, limitX, limitY, limitZ):
'''
Inputs:
centers: n x 3
corners: n x 8 x 3
limitX, limitY, limitZ: 1 x 2
Outputs:
filter_centers: m x 3, m <= n
filter_corners: m x 3, m <= n
'''
logic_x = np.logical_and(centers[:, 0] >= limitX[0], centers[:, 0] < limitX[1])
logic_y = np.logical_and(centers[:, 1] >= limitY[0], centers[:, 1] < limitY[1])
logic_z = np.logical_and(centers[:, 2] >= limitZ[0], centers[:, 2] < limitZ[1])
logic_xyz = np.logical_and(logic_x, np.logical_and(logic_y, logic_z))
filter_centers_1 = centers[logic_xyz, :]
filter_corners_1 = corners[logic_xyz, :, :]
filter_boxsizes_1 = boxsizes[logic_xyz, :]
shape_centers = filter_centers_1.shape;
filter_centers = np.zeros([shape_centers[0], 3])
filter_corners = np.zeros([shape_centers[0], 8, 3]);
filter_boxsizes = np.zeros([shape_centers[0], 3]);
idx = 0
for idx2 in range(shape_centers[0]):
logic_x = np.logical_and(filter_corners_1[idx2, :, 0] >= limitX[0], filter_corners_1[idx2, :, 0] < limitX[1])
logic_y = np.logical_and(filter_corners_1[idx2, :, 1] >= limitY[0], filter_corners_1[idx2, :, 1] < limitY[1])
logic_z = np.logical_and(filter_corners_1[idx2, :, 2] >= limitZ[0], filter_corners_1[idx2, :, 2] < limitZ[1])
logic_xyz = np.logical_and(logic_x, np.logical_and(logic_y, logic_z))
if logic_xyz.all():
filter_centers[idx, :3] = filter_centers_1[idx2, :]
filter_corners[idx, :8, :3] = filter_corners_1[idx2, :, :]
filter_boxsizes[idx, :3] = filter_boxsizes_1[idx2, :]
idx += 1
if idx > 0:
return filter_centers[:idx, :], filter_corners[:idx, :, :], filter_boxsizes[:idx, :]
else:
return None, None, None
def filter_label(object3Ds, objectType = 'Car'):
'''
Inputs:
object3Ds:
objectType:
Outputs:
centers, corners, rotatey
'''
idx = 0
data = np.zeros([50, 7]).astype(np.float32)
for iter in object3Ds:
if iter.type == "DontCare":
continue;
if iter.type == objectType:
# position
data[idx, 0] = iter.t[0]
data[idx, 1] = iter.t[1]
data[idx, 2] = iter.t[2]
# size
data[idx, 3] = iter.h
data[idx, 4] = iter.w
data[idx, 5] = iter.l
# rotate
data[idx, 6] = iter.ry
idx += 1
if idx > 0:
return data[:idx, :3], data[:idx, 3:6], data[:idx, 6]
else:
return None, None, None
def proj_to_velo(calib_data):
"""
Inputs:
calib_data:
Outputs:
project matrix: from camera cordination to velodyne cordination
"""
rect = calib_data.R0; # calib_data["R0_rect"].reshape(3, 3)
velo_to_cam = calib_data.V2C; # calib_data["Tr_velo_to_cam"].reshape(3, 4)
inv_rect = np.linalg.inv(rect)
inv_velo_to_cam = np.linalg.pinv(velo_to_cam[:, :3])
return np.dot(inv_velo_to_cam, inv_rect)
# corners_3d
def compute_3d_corners(centers, sizes, rotates):
'''
Inputs:
centers:
rotates:
sizes:
Outputs:
corners_3d: n x 8 x 3 array in Lidar coord.
'''
# print(centers)
corners = []
for place, rotate, sz in zip(centers, rotates, sizes):
x, y, z = place
h, w, l = sz
if l > 10:
continue
corner = np.array([
[x - l / 2., y - w / 2., z],
[x + l / 2., y - w / 2., z],
[x - l / 2., y + w / 2., z],
[x - l / 2., y - w / 2., z + h],
[x - l / 2., y + w / 2., z + h],
[x + l / 2., y + w / 2., z],
[x + l / 2., y - w / 2., z + h],
[x + l / 2., y + w / 2., z + h],
])
corner -= np.array([x, y, z])
rotate_matrix = np.array([
[np.cos(rotate), -np.sin(rotate), 0],
[np.sin(rotate), np.cos(rotate), 0],
[0, 0, 1]
])
a = np.dot(corner, rotate_matrix.transpose())
a += np.array([x, y, z])
corners.append(a)
corners_3d = np.array(corners)
return corners_3d
# lidar data to 3D Grid Voxel
def lidar_to_binary_voxel(pc, resolution, limitX, limitY, limitZ):
'''
Inputs:
pc: n x 3,
resolution: 1 x 3,
limitX, limitY, limitZ: 1 x 2
Outputs:
voxel: shape is inputSize
'''
voxel_pc = np.zeros_like(pc).astype(np.int32)
# Compute PointCloud Position in 3D Grid
voxel_pc[:, 0] = ((pc[:, 0] - limitX[0]) / resolution[0]).astype(np.int32)
voxel_pc[:, 1] = ((pc[:, 1] - limitY[0]) / resolution[1]).astype(np.int32)
voxel_pc[:, 2] = ((pc[:, 2] - limitZ[0]) / resolution[2]).astype(np.int32)
# 3D Grid
voxel = np.zeros((int(round(limitX[1] - limitX[0]) / resolution[0]), int(round(limitY[1] - limitY[0]) / resolution[1]), \
int(round((limitZ[1] - limitZ[0]) / resolution[2]))))
# 3D Grid Value
voxel[voxel_pc[:, 0], voxel_pc[:, 1], voxel_pc[:, 2]] = 1
return voxel
# label center to 3D Grid Voxel Center(sphere)
def center_to_sphere(centers, boxsize, scale, resolution, limitX, limitY, limitZ):
'''
Inputs:
center: n x 3
boxsize: n x 3
scale: 1 x 1, = outputSize / inputSize
resolution: 1 x 3
limitX, limitY, limitZ: 1 x 2
Outputs:
spheres: m x 3, m <= n
'''
# from 3D Box's bottom center => 3D center
move_center = centers.copy();
print("centers")
print(centers)
print("boxsize")
print(boxsize)
move_center[:, 2] = centers[:, 2] + boxsize[:, 0] / 2;
# compute Label Center PointCloud Position in 3D Grid
spheres = np.zeros_like(move_center).astype(np.int32)
spheres[:, 0] = ((move_center[:, 0] - limitX[0]) / resolution[0] * scale).astype(np.int32)
spheres[:, 1] = ((move_center[:, 1] - limitY[0]) / resolution[1] * scale).astype(np.int32)
spheres[:, 2] = ((move_center[:, 2] - limitZ[0]) / resolution[2] * scale).astype(np.int32)
print("move_center")
print(move_center)
print("spheres")
print(spheres)
return spheres
# 3D Grid Voxel Center(sphere) to label center
def sphere_to_center(spheres, scale, resolution, limitX, limitY, limitZ):
'''
Inputs:
spheres: n x 3
scale: 1 x 1, = outputSize / inputSize
resolution: 1 x 3
limitX, limitY, limitZ: 1 x 2
Outputs:
centers: m x 3, m <= 3
'''
centers = np.zeros_like(spheres).astype(np.float32);
centers[:, 0] = spheres[:, 0] * resolution[0] / scale + limitX[0]
centers[:, 1] = spheres[:, 1] * resolution[1] / scale + limitY[0]
centers[:, 2] = spheres[:, 2] * resolution[2] / scale + limitZ[0]
return centers
# label corners to 3D Grid Voxel: corners - centers
def corners_to_train(spheres, corners, scale, resolution, limitX, limitY, limitZ):
'''
Inputs:
spheres: n x 3
corners: n x 8 x 3
scale: 1 x 1, = outputSize / inputSize
resolution: 1 x 3
limitX, limitY, limitZ: 1 x 2
Outputs:
train_corners: m x 3, m <= n
'''
# 3D Grid Voxel Center => label center
centers = sphere_to_center(spheres, scale, resolution, limitX, limitY, limitZ)
train_corners = np.zeros_like(corners).astype(np.float32)
# train corners for regression loss
for index, (corner, center) in enumerate(zip(corners, centers)):
train_corners[index] = corner - center
return train_corners
# create center and cordination for train
def create_train_label(centers, corners, boxsize, scale, resolution, limitX, limitY, limitZ):
'''
Inputs:
centers: n x 3
corners: n x 8 x 3
boxsize: n x 3
scale: 1 x 1, outputSize / inputSize
resolution: 1 x 3
limitX. limitY, limitZ: 1 x 2
Outputs:
train_centers: m x 3, m <= n
train_corners: m x 3, m <= n
'''
train_centers = center_to_sphere(centers, boxsize, scale, resolution, limitX, limitY, limitZ)
train_corners = corners_to_train(train_centers, corners, scale, resolution, limitX, limitY, limitZ)
return train_centers, train_corners
def create_obj_map(train_centers, scale, resolution, limitX, limitY, limitZ):
'''
Inputs:
centers: n x 3
scale: 1 x 1, outputSize / inputSize
resolution: 1 x 3
limitX, limitY, limitZ: 1 x 2
Outputs:
obj_map: shape is scale * inputSize
'''
# 3D Grid
sizeX = int(round((limitX[1] - limitX[0]) / resolution[0] * scale))
sizeY = int(round((limitY[1] - limitY[0]) / resolution[1] * scale))
sizeZ = int(round((limitZ[1] - limitZ[0]) / resolution[2] * scale))
obj_map = np.zeros([sizeX, sizeY, sizeZ])
# print("sizeX, sizeY, sizeZ")
# print(sizeX, sizeY, sizeZ)
# objectness map: label center in objectness map where value is 1
obj_map[train_centers[:,0], train_centers[:, 1], train_centers[:, 2]] = 1;
return obj_map
def create_cord_map(train_centers, train_corners, scale, resolution, limitX, limitY, limitZ):
'''
Inputs:
train_centers: n x 3
train_corners: n x 8 x 3
scale: 1 x 1, outputSize / inputSize
resolution: 1 x 3
limitX, limitY, limitZ: 1 x 2
Outputs:
cord_map: shape is inputSize * scale
'''
# reshape train_corners: n x 8 x 3 => n x 24
corners = train_corners.reshape(train_corners.shape[0], -1)
# 3D Grid
sizeX = int(round((limitX[1] - limitX[0]) / resolution[0] * scale))
sizeY = int(round((limitY[1] - limitY[0]) / resolution[1] * scale))
sizeZ = int(round((limitZ[1] - limitZ[0]) / resolution[2] * scale))
sizeD = 24
cord_map = np.zeros([sizeX, sizeY, sizeZ, sizeD])
# print(train_centers)
cord_map[train_centers[:,0], train_centers[:, 1], train_centers[:, 2]] = corners
return cord_map
# kitti data interface:
class kitti_3DVoxel_interface(object):
def __init__(self, root_dir, objectType = 'Car', split='training', scale = 0.25, resolution = (0.2, 0.2, 0.2), limitX = (0, 80), limitY = (-40, 40), limitZ = (-2.5, 1.5)):
'''
Inputs:
case1 root_dir: train or val. data dir, train or val.'s file struct like:
root_dir->training->velodyne
root_dir->training->calib
root_dir->training->label_2
case2 root_dir: test data dir, test's file struct like:
root_dir->testing->velodyne
root_dir->testing->calib
Outputs:
-None
'''
self.root_dir = root_dir
self.split = split
self.object = kittiReader(self.root_dir, self.split)
self.objectType = objectType
self.scale = scale
self.resolution = resolution
self.limitX = limitX
self.limitY = limitY
self.limitZ = limitZ
def read_kitti_data(self, idx = 0):
'''
Inputs:
idx: training or testing sample index
Outputs:
voxel : inputSize
obj_map : scale * inputSize
cord_map : scale * inputSize
'''
kitti_Object3Ds = None
kitti_Lidar = None
kitti_Calib = None
if self.split == 'training':
# read Lidar data + Lidar Label + Calib data
kitti_Object3Ds = self.object.get_label_objects(idx);
kitti_Lidar = self.object.get_lidar(idx);
kitti_Calib = self.object.get_calibration(idx);
# lidar data filter
filter_fov = filter_camera_fov(kitti_Lidar)
filter_range = filter_lidar_range(filter_fov, self.limitX, self.limitY, self.limitZ)
# label filter
centers, boxsizes, rotates = filter_label(kitti_Object3Ds, self.objectType)
if centers is None:
return None, None, None
# label center: Notice from camera Coordination to velo. Coordination
if not(kitti_Calib is None):
proj_velo = proj_to_velo(kitti_Calib)[:, :3]
centers = np.dot(centers, proj_velo.transpose())[:, :3]
# label corners:
corners = compute_3d_corners(centers, boxsizes, rotates)
# print(corners)
# print(corners.shape)
# filter centers + corners
filter_centers, filter_corners, boxsizes = filter_center_corners(centers, corners, boxsizes, self.limitX, self.limitY, self.limitZ)
# print(filter_centers)
# print(filter_corners)
if not(filter_centers is None):
# training center
train_centers, train_corners = create_train_label(filter_centers, filter_corners, boxsizes, self.scale, self.resolution, self.limitX, self.limitY, self.limitZ)
# print("filter_centers")
# print(filter_centers)
# print("train_centers")
# print(train_centers)
# obj_map / cord_map / voxel
obj_map = create_obj_map(train_centers, self.scale, self.resolution, self.limitX, self.limitY, self.limitZ)
cord_map = create_cord_map(train_centers, train_corners, self.scale, self.resolution, self.limitX, self.limitY, self.limitZ)
voxel = lidar_to_binary_voxel(filter_range, self.resolution, self.limitX, self.limitY, self.limitZ)
return voxel, obj_map, cord_map
else:
return None, None, None
elif self.split == 'testing':
# read Lidar Data + Calib + Data
kitti_Lidar = self.object.get_lidar(idx);
kitti_Calib = self.object.get_calibration(idx);
# lidar data filter
filter_fov = filter_camera_fov(kitti_Lidar)
filter_range = filter_lidar_range(filter_fov, self.limitX, self.limitY, self.limitZ)
voxel = lidar_to_binary_voxel(filter_range, self.resolution, self.limitX, self.limitY, self.limitZ)
return voxel
if __name__ == '__main__':
data_dir = "/home/hsw/桌面/PCL_API_Doc/frustum-pointnets-master/dataset"
kitti_3DVoxel = kitti_3DVoxel_interface(data_dir, objectType = 'Car', split='training', scale = 0.25, resolution = (0.2, 0.2, 0.2), limitX = (0, 80), limitY = (-40, 40), limitZ = (-2.5, 1.5))
sampleIdx = 195;
voxel, obj_map, cord_map = kitti_3DVoxel.read_kitti_data(sampleIdx)
if not(voxel is None):
print(voxel.shape)
print(obj_map.shape)
print(cord_map.shape)
2.3 KITTI数据读取相关
''' 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
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
# 3D static data
class kitti_object(object):
'''Load and parse object data into a usable format.'''
def __init__(self, root_dir, split='training'):
'''root_dir contains training and testing folders'''
self.root_dir = root_dir
self.split = 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)
# data dir
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(self.split_dir, 'label_2')
def __len__(self):
return self.num_samples
# read image: return image
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)
# read lidar: return n x 4
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)
# read calib file:
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)
# read label
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)
# read depth map
def get_depth_map(self, idx):
pass
# read top_down image
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(os.path.join(ROOT_DIR, 'dataset/KITTI/object'))
for data_idx in range(len(dataset)):
# Load data from dataset
objects = dataset.get_label_objects(data_idx)
objects[0].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()
""" Helper methods for loading and parsing KITTI data.
Author: Charles R. Qi
Date: September 2017
"""
from __future__ import print_function
import numpy as np
import cv2
import os
class Object3d(object):
''' 3d object label '''
def __init__(self, label_file_line):
data = label_file_line.split(' ')
data[1:] = [float(x) for x in data[1:]]
# extract label, truncation, occlusion
self.type = data[0] # 'Car', 'Pedestrian', ...
self.truncation = data[1] # truncated pixel ratio [0..1]
self.occlusion = int(data[2]) # 0=visible, 1=partly occluded, 2=fully occluded, 3=unknown
self.alpha = data[3] # object observation angle [-pi..pi]
# extract 2d bounding box in 0-based coordinates
self.xmin = data[4] # left
self.ymin = data[5] # top
self.xmax = data[6] # right
self.ymax = data[7] # bottom
self.box2d = np.array([self.xmin,self.ymin,self.xmax,self.ymax])
# extract 3d bounding box information
self.h = data[8] # box height
self.w = data[9] # box width
self.l = data[10] # box length (in meters)
self.t = (data[11],data[12],data[13]) # location (x,y,z) in camera coord.
self.ry = data[14] # yaw angle (around Y-axis in camera coordinates) [-pi..pi]
def print_object(self):
print('Type, truncation, occlusion, alpha: %s, %d, %d, %f' % \
(self.type, self.truncation, self.occlusion, self.alpha))
print('2d bbox (x0,y0,x1,y1): %f, %f, %f, %f' % \
(self.xmin, self.ymin, self.xmax, self.ymax))
print('3d bbox h,w,l: %f, %f, %f' % \
(self.h, self.w, self.l))
print('3d bbox location, ry: (%f, %f, %f), %f' % \
(self.t[0],self.t[1],self.t[2],self.ry))
class Calibration(object):
''' Calibration matrices and utils
3d XYZ in <label>.txt are in rect camera coord.
2d box xy are in image2 coord
Points in <lidar>.bin are in Velodyne coord.
y_image2 = P^2_rect * x_rect
y_image2 = P^2_rect * R0_rect * Tr_velo_to_cam * x_velo
x_ref = Tr_velo_to_cam * x_velo
x_rect = R0_rect * x_ref
P^2_rect = [f^2_u, 0, c^2_u, -f^2_u b^2_x;
0, f^2_v, c^2_v, -f^2_v b^2_y;
0, 0, 1, 0]
= K * [1|t]
image2 coord:
----> x-axis (u)
|
|
v y-axis (v)
velodyne coord:
front x, left y, up z
rect/ref camera coord:
right x, down y, front z
Ref (KITTI paper): http://www.cvlibs.net/publications/Geiger2013IJRR.pdf
TODO(rqi): do matrix multiplication only once for each projection.
'''
def __init__(self, calib_filepath, from_video=False):
if from_video:
calibs = self.read_calib_from_video(calib_filepath)
else:
calibs = self.read_calib_file(calib_filepath)
# Projection matrix from rect camera coord to image2 coord
self.P = calibs['P2']
self.P = np.reshape(self.P, [3,4])
# Rigid transform from Velodyne coord to reference camera coord
self.V2C = calibs['Tr_velo_to_cam']
self.V2C = np.reshape(self.V2C, [3,4])
self.C2V = inverse_rigid_trans(self.V2C)
# Rotation from reference camera coord to rect camera coord
self.R0 = calibs['R0_rect']
self.R0 = np.reshape(self.R0,[3,3])
# Camera intrinsics and extrinsics
self.c_u = self.P[0,2]
self.c_v = self.P[1,2]
self.f_u = self.P[0,0]
self.f_v = self.P[1,1]
self.b_x = self.P[0,3]/(-self.f_u) # relative
self.b_y = self.P[1,3]/(-self.f_v)
def read_calib_file(self, filepath):
''' Read in a calibration file and parse into a dictionary.
Ref: https://github.com/utiasSTARS/pykitti/blob/master/pykitti/utils.py
'''
data = {}
with open(filepath, 'r') as f:
for line in f.readlines():
line = line.rstrip()
if len(line)==0: continue
key, value = line.split(':', 1)
# The only non-float values in these files are dates, which
# we don't care about anyway
try:
data[key] = np.array([float(x) for x in value.split()])
except ValueError:
pass
return data
def read_calib_from_video(self, calib_root_dir):
''' Read calibration for camera 2 from video calib files.
there are calib_cam_to_cam and calib_velo_to_cam under the calib_root_dir
'''
data = {}
cam2cam = self.read_calib_file(os.path.join(calib_root_dir, 'calib_cam_to_cam.txt'))
velo2cam = self.read_calib_file(os.path.join(calib_root_dir, 'calib_velo_to_cam.txt'))
Tr_velo_to_cam = np.zeros((3,4))
Tr_velo_to_cam[0:3,0:3] = np.reshape(velo2cam['R'], [3,3])
Tr_velo_to_cam[:,3] = velo2cam['T']
data['Tr_velo_to_cam'] = np.reshape(Tr_velo_to_cam, [12])
data['R0_rect'] = cam2cam['R_rect_00']
data['P2'] = cam2cam['P_rect_02']
return data
def cart2hom(self, pts_3d):
''' Input: nx3 points in Cartesian
Oupput: nx4 points in Homogeneous by pending 1
'''
n = pts_3d.shape[0]
pts_3d_hom = np.hstack((pts_3d, np.ones((n,1))))
return pts_3d_hom
# ===========================
# ------- 3d to 3d ----------
# ===========================
def project_velo_to_ref(self, pts_3d_velo):
pts_3d_velo = self.cart2hom(pts_3d_velo) # nx4
return np.dot(pts_3d_velo, np.transpose(self.V2C))
def project_ref_to_velo(self, pts_3d_ref):
pts_3d_ref = self.cart2hom(pts_3d_ref) # nx4
return np.dot(pts_3d_ref, np.transpose(self.C2V))
def project_rect_to_ref(self, pts_3d_rect):
''' Input and Output are nx3 points '''
return np.transpose(np.dot(np.linalg.inv(self.R0), np.transpose(pts_3d_rect)))
def project_ref_to_rect(self, pts_3d_ref):
''' Input and Output are nx3 points '''
return np.transpose(np.dot(self.R0, np.transpose(pts_3d_ref)))
def project_rect_to_velo(self, pts_3d_rect):
''' Input: nx3 points in rect camera coord.
Output: nx3 points in velodyne coord.
'''
pts_3d_ref = self.project_rect_to_ref(pts_3d_rect)
return self.project_ref_to_velo(pts_3d_ref)
def project_velo_to_rect(self, pts_3d_velo):
pts_3d_ref = self.project_velo_to_ref(pts_3d_velo)
return self.project_ref_to_rect(pts_3d_ref)
# ===========================
# ------- 3d to 2d ----------
# ===========================
def project_rect_to_image(self, pts_3d_rect):
''' Input: nx3 points in rect camera coord.
Output: nx2 points in image2 coord.
'''
pts_3d_rect = self.cart2hom(pts_3d_rect)
pts_2d = np.dot(pts_3d_rect, np.transpose(self.P)) # nx3
pts_2d[:,0] /= pts_2d[:,2]
pts_2d[:,1] /= pts_2d[:,2]
return pts_2d[:,0:2]
def project_velo_to_image(self, pts_3d_velo):
''' Input: nx3 points in velodyne coord.
Output: nx2 points in image2 coord.
'''
pts_3d_rect = self.project_velo_to_rect(pts_3d_velo)
return self.project_rect_to_image(pts_3d_rect)
# ===========================
# ------- 2d to 3d ----------
# ===========================
def project_image_to_rect(self, uv_depth):
''' Input: nx3 first two channels are uv, 3rd channel
is depth in rect camera coord.
Output: nx3 points in rect camera coord.
'''
n = uv_depth.shape[0]
x = ((uv_depth[:,0]-self.c_u)*uv_depth[:,2])/self.f_u + self.b_x
y = ((uv_depth[:,1]-self.c_v)*uv_depth[:,2])/self.f_v + self.b_y
pts_3d_rect = np.zeros((n,3))
pts_3d_rect[:,0] = x
pts_3d_rect[:,1] = y
pts_3d_rect[:,2] = uv_depth[:,2]
return pts_3d_rect
def project_image_to_velo(self, uv_depth):
pts_3d_rect = self.project_image_to_rect(uv_depth)
return self.project_rect_to_velo(pts_3d_rect)
def rotx(t):
''' 3D Rotation about the x-axis. '''
c = np.cos(t)
s = np.sin(t)
return np.array([[1, 0, 0],
[0, c, -s],
[0, s, c]])
def roty(t):
''' Rotation about the y-axis. '''
c = np.cos(t)
s = np.sin(t)
return np.array([[c, 0, s],
[0, 1, 0],
[-s, 0, c]])
def rotz(t):
''' Rotation about the z-axis. '''
c = np.cos(t)
s = np.sin(t)
return np.array([[c, -s, 0],
[s, c, 0],
[0, 0, 1]])
def transform_from_rot_trans(R, t):
''' Transforation matrix from rotation matrix and translation vector. '''
R = R.reshape(3, 3)
t = t.reshape(3, 1)
return np.vstack((np.hstack([R, t]), [0, 0, 0, 1]))
def inverse_rigid_trans(Tr):
''' Inverse a rigid body transform matrix (3x4 as [R|t])
[R'|-R't; 0|1]
'''
inv_Tr = np.zeros_like(Tr) # 3x4
inv_Tr[0:3,0:3] = np.transpose(Tr[0:3,0:3])
inv_Tr[0:3,3] = np.dot(-np.transpose(Tr[0:3,0:3]), Tr[0:3,3])
return inv_Tr
def read_label(label_filename):
lines = [line.rstrip() for line in open(label_filename)]
objects = [Object3d(line) for line in lines]
return objects
def load_image(img_filename):
return cv2.imread(img_filename)
def load_velo_scan(velo_filename):
scan = np.fromfile(velo_filename, dtype=np.float32)
scan = scan.reshape((-1, 4))
return scan
def project_to_image(pts_3d, P):
''' Project 3d points to image plane.
Usage: pts_2d = projectToImage(pts_3d, P)
input: pts_3d: nx3 matrix
P: 3x4 projection matrix
output: pts_2d: nx2 matrix
P(3x4) dot pts_3d_extended(4xn) = projected_pts_2d(3xn)
=> normalize projected_pts_2d(2xn)
<=> pts_3d_extended(nx4) dot P'(4x3) = projected_pts_2d(nx3)
=> normalize projected_pts_2d(nx2)
'''
n = pts_3d.shape[0]
pts_3d_extend = np.hstack((pts_3d, np.ones((n,1))))
print(('pts_3d_extend shape: ', pts_3d_extend.shape))
pts_2d = np.dot(pts_3d_extend, np.transpose(P)) # nx3
pts_2d[:,0] /= pts_2d[:,2]
pts_2d[:,1] /= pts_2d[:,2]
return pts_2d[:,0:2]
# corners_2d + corners_3d
def compute_box_3d(obj, P):
''' Takes an object and a projection matrix (P) and projects the 3d
bounding box into the image plane.
Returns:
corners_2d: (8,2) array in left image coord.
corners_3d: (8,3) array in in rect camera coord.
'''
# compute rotational matrix around yaw axis
R = roty(obj.ry)
# 3d bounding box dimensions
l = obj.l;
w = obj.w;
h = obj.h;
# 3d bounding box corners
x_corners = [l/2,l/2,-l/2,-l/2,l/2,l/2,-l/2,-l/2];
y_corners = [0,0,0,0,-h,-h,-h,-h];
z_corners = [w/2,-w/2,-w/2,w/2,w/2,-w/2,-w/2,w/2];
# rotate and translate 3d bounding box
corners_3d = np.dot(R, np.vstack([x_corners,y_corners,z_corners]))
#print corners_3d.shape
corners_3d[0,:] = corners_3d[0,:] + obj.t[0];
corners_3d[1,:] = corners_3d[1,:] + obj.t[1];
corners_3d[2,:] = corners_3d[2,:] + obj.t[2];
#print 'cornsers_3d: ', corners_3d
# only draw 3d bounding box for objs in front of the camera
if np.any(corners_3d[2,:]<0.1):
corners_2d = None
return corners_2d, np.transpose(corners_3d)
# project the 3d bounding box into the image plane
corners_2d = project_to_image(np.transpose(corners_3d), P);
#print 'corners_2d: ', corners_2d
return corners_2d, np.transpose(corners_3d)
def compute_orientation_3d(obj, P):
''' Takes an object and a projection matrix (P) and projects the 3d
object orientation vector into the image plane.
Returns:
orientation_2d: (2,2) array in left image coord.
orientation_3d: (2,3) array in in rect camera coord.
'''
# compute rotational matrix around yaw axis
R = roty(obj.ry)
# orientation in object coordinate system
orientation_3d = np.array([[0.0, obj.l],[0,0],[0,0]])
# rotate and translate in camera coordinate system, project in image
orientation_3d = np.dot(R, orientation_3d)
orientation_3d[0,:] = orientation_3d[0,:] + obj.t[0]
orientation_3d[1,:] = orientation_3d[1,:] + obj.t[1]
orientation_3d[2,:] = orientation_3d[2,:] + obj.t[2]
# vector behind image plane?
if np.any(orientation_3d[2,:]<0.1):
orientation_2d = None
return orientation_2d, np.transpose(orientation_3d)
# project orientation into the image plane
orientation_2d = project_to_image(np.transpose(orientation_3d), P);
return orientation_2d, np.transpose(orientation_3d)
def draw_projected_box3d(image, qs, color=(255,255,255), thickness=2):
''' Draw 3d bounding box in image
qs: (8,3) array of vertices for the 3d box in following order:
1 -------- 0
/| /|
2 -------- 3 .
| | | |
. 5 -------- 4
|/ |/
6 -------- 7
'''
qs = qs.astype(np.int32)
for k in range(0,4):
# Ref: http://docs.enthought.com/mayavi/mayavi/auto/mlab_helper_functions.html
i,j=k,(k+1)%4
# use LINE_AA for opencv3
cv2.line(image, (qs[i,0],qs[i,1]), (qs[j,0],qs[j,1]), color, thickness, cv2.CV_AA)
i,j=k+4,(k+1)%4 + 4
cv2.line(image, (qs[i,0],qs[i,1]), (qs[j,0],qs[j,1]), color, thickness, cv2.CV_AA)
i,j=k,k+4
cv2.line(image, (qs[i,0],qs[i,1]), (qs[j,0],qs[j,1]), color, thickness, cv2.CV_AA)
return image
3. 通过测试还在训练，但是我的硬件设备较差，所以，训练速度比较慢