导读：waymo提供了两种数据集 motion与perception两种 请注意 本篇为Perception Dataset v1.2与Motion Dataset v1.1版本 其..
waymo提供了两种数据集 motion与perception两种 请注意 本篇为Perception Dataset v1.2与Motion Dataset v1.1版本
其中motion是鸟瞰图 官网中有介绍 主要用于轨迹预测之类的任务
perception主要用于目标检测跟踪之类的任务 是第一视角 有相机和雷达信息 并且在github上有公开的读取数据方法 另外 在读取perception数据时需要安装waymo-open-dataset-tf这个库 安装不上请用清华源 具体请按照官方quick_start教程 另外github有许多已经集成许多功能的代码 搜索waymo就有。
quick_start:
waymo-open-dataset/quick_start.md at master · waymo-research/waymo-open-dataset · GitHub
而motion读取不需要这些 主只需要安装tensorflow以及一些必要的库就行即可
import math
import os
import uuid
import time
from matplotlib import cm
import matplotlib.animation as animation
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import HTML
import itertools
import tensorflow as tf
from google.protobuf import text_format
from waymo_open_dataset.metrics.ops import py_metrics_ops
from waymo_open_dataset.metrics.python import config_util_py as config_util
from waymo_open_dataset.protos import motion_metrics_pb2
# Example field definition
roadgraph_features {
roadgraph_samples/dir :
tf.io.FixedLenFeature([20000, 3], tf.float32, default_value None),
roadgraph_samples/id :
tf.io.FixedLenFeature([20000, 1], tf.int64, default_value None),
roadgraph_samples/type :
tf.io.FixedLenFeature([20000, 1], tf.int64, default_value None),
roadgraph_samples/valid :
tf.io.FixedLenFeature([20000, 1], tf.int64, default_value None),
roadgraph_samples/xyz :
tf.io.FixedLenFeature([20000, 3], tf.float32, default_value None),
# Features of other agents.
state_features {
state/id :
tf.io.FixedLenFeature([128], tf.float32, default_value None),
state/type :
tf.io.FixedLenFeature([128], tf.float32, default_value None),
state/is_sdc :
tf.io.FixedLenFeature([128], tf.int64, default_value None),
state/tracks_to_predict :
tf.io.FixedLenFeature([128], tf.int64, default_value None),
state/current/bbox_yaw :
tf.io.FixedLenFeature([128, 1], tf.float32, default_value None),
state/current/height :
tf.io.FixedLenFeature([128, 1], tf.float32, default_value None),
state/current/length :
tf.io.FixedLenFeature([128, 1], tf.float32, default_value None),
state/current/timestamp_micros :
tf.io.FixedLenFeature([128, 1], tf.int64, default_value None),
state/current/valid :
tf.io.FixedLenFeature([128, 1], tf.int64, default_value None),
state/current/vel_yaw :
tf.io.FixedLenFeature([128, 1], tf.float32, default_value None),
state/current/velocity_x :
tf.io.FixedLenFeature([128, 1], tf.float32, default_value None),
state/current/velocity_y :
tf.io.FixedLenFeature([128, 1], tf.float32, default_value None),
state/current/width :
tf.io.FixedLenFeature([128, 1], tf.float32, default_value None),
state/current/x :
tf.io.FixedLenFeature([128, 1], tf.float32, default_value None),
state/current/y :
tf.io.FixedLenFeature([128, 1], tf.float32, default_value None),
state/current/z :
tf.io.FixedLenFeature([128, 1], tf.float32, default_value None),
state/future/bbox_yaw :
tf.io.FixedLenFeature([128, 80], tf.float32, default_value None),
state/future/height :
tf.io.FixedLenFeature([128, 80], tf.float32, default_value None),
state/future/length :
tf.io.FixedLenFeature([128, 80], tf.float32, default_value None),
state/future/timestamp_micros :
tf.io.FixedLenFeature([128, 80], tf.int64, default_value None),
state/future/valid :
tf.io.FixedLenFeature([128, 80], tf.int64, default_value None),
state/future/vel_yaw :
tf.io.FixedLenFeature([128, 80], tf.float32, default_value None),
state/future/velocity_x :
tf.io.FixedLenFeature([128, 80], tf.float32, default_value None),
state/future/velocity_y :
tf.io.FixedLenFeature([128, 80], tf.float32, default_value None),
state/future/width :
tf.io.FixedLenFeature([128, 80], tf.float32, default_value None),
state/future/x :
tf.io.FixedLenFeature([128, 80], tf.float32, default_value None),
state/future/y :
tf.io.FixedLenFeature([128, 80], tf.float32, default_value None),
state/future/z :
tf.io.FixedLenFeature([128, 80], tf.float32, default_value None),
state/past/bbox_yaw :
tf.io.FixedLenFeature([128, 10], tf.float32, default_value None),
state/past/height :
tf.io.FixedLenFeature([128, 10], tf.float32, default_value None),
state/past/length :
tf.io.FixedLenFeature([128, 10], tf.float32, default_value None),
state/past/timestamp_micros :
tf.io.FixedLenFeature([128, 10], tf.int64, default_value None),
state/past/valid :
tf.io.FixedLenFeature([128, 10], tf.int64, default_value None),
state/past/vel_yaw :
tf.io.FixedLenFeature([128, 10], tf.float32, default_value None),
state/past/velocity_x :
tf.io.FixedLenFeature([128, 10], tf.float32, default_value None),
state/past/velocity_y :
tf.io.FixedLenFeature([128, 10], tf.float32, default_value None),
state/past/width :
tf.io.FixedLenFeature([128, 10], tf.float32, default_value None),
state/past/x :
tf.io.FixedLenFeature([128, 10], tf.float32, default_value None),
state/past/y :
tf.io.FixedLenFeature([128, 10], tf.float32, default_value None),
state/past/z :
tf.io.FixedLenFeature([128, 10], tf.float32, default_value None),
traffic_light_features {
traffic_light_state/current/state :
tf.io.FixedLenFeature([1, 16], tf.int64, default_value None),
traffic_light_state/current/valid :
tf.io.FixedLenFeature([1, 16], tf.int64, default_value None),
traffic_light_state/current/x :
tf.io.FixedLenFeature([1, 16], tf.float32, default_value None),
traffic_light_state/current/y :
tf.io.FixedLenFeature([1, 16], tf.float32, default_value None),
traffic_light_state/current/z :
tf.io.FixedLenFeature([1, 16], tf.float32, default_value None),
traffic_light_state/past/state :
tf.io.FixedLenFeature([10, 16], tf.int64, default_value None),
traffic_light_state/past/valid :
tf.io.FixedLenFeature([10, 16], tf.int64, default_value None),
traffic_light_state/past/x :
tf.io.FixedLenFeature([10, 16], tf.float32, default_value None),
traffic_light_state/past/y :
tf.io.FixedLenFeature([10, 16], tf.float32, default_value None),
traffic_light_state/past/z :
tf.io.FixedLenFeature([10, 16], tf.float32, default_value None),
dir 文件位置
features_description {}
features_description.update(roadgraph_features)
features_description.update(state_features)
features_description.update(traffic_light_features)
dataset tf.data.TFRecordDataset(dir, compression_type )
data next(dataset.as_numpy_iterator())
parsed tf.io.parse_single_example(data, features_description)
def create_figure_and_axes(size_pixels):
Initializes a unique figure and axes for plotting.
fig, ax plt.subplots(1, 1, num uuid.uuid4())
# Sets output image to pixel resolution.
dpi 100
size_inches size_pixels / dpi
fig.set_size_inches([size_inches, size_inches])
fig.set_dpi(dpi)
fig.set_facecolor( white )
ax.set_facecolor( white )
ax.xaxis.label.set_color( black )
ax.tick_params(axis x , colors black )
ax.yaxis.label.set_color( black )
ax.tick_params(axis y , colors black )
fig.set_tight_layout(True)
ax.grid(False)
return fig, ax
def fig_canvas_image(fig):
Returns a [H, W, 3] uint8 np.array image from fig.canvas.tostring_rgb().
# Just enough margin in the figure to display xticks and yticks.
fig.subplots_adjust(
left 0.08, bottom 0.08, right 0.98, top 0.98, wspace 0.0, hspace 0.0)
fig.canvas.draw()
data np.frombuffer(fig.canvas.tostring_rgb(), dtype np.uint8)
return data.reshape(fig.canvas.get_width_height()[::-1] (3,))
Compute a color map array of shape [num_agents, 4].
colors cm.get_cmap( jet , num_agents)
colors colors(range(num_agents))
np.random.shuffle(colors)
return colors
center_y: float. y coordinate for center of data.
center_x: float. x coordinate for center of data.
width: float. Width of data.
valid_states all_states[all_states_mask]
all_y valid_states[..., 1]
all_x valid_states[..., 0]
center_y (np.max(all_y) np.min(all_y)) / 2
center_x (np.max(all_x) np.min(all_x)) / 2
range_y np.ptp(all_y)
range_x np.ptp(all_x)
width max(range_y, range_x)
return center_y, center_x, width
# Set axes. Should be at least 10m on a side and cover 160% of agents.
size max(10, width * 1.0)
ax.axis([
-size / 2 center_x, size / 2 center_x, -size / 2 center_y,
size / 2 center_y
ax.set_aspect( equal )
image fig_canvas_image(fig)
plt.close(fig)
return image
decoded_example: Dictionary containing agent info about all modeled agents.
size_pixels: The size in pixels of the output image.
Returns:
T of [H, W, 3] uint8 np.arrays of the drawn matplotlib s figure canvas.
# [num_agents, num_past_steps, 2] float32.
past_states tf.stack(
[decoded_example[ state/past/x ], decoded_example[ state/past/y ]],
-1).numpy()
past_states_mask decoded_example[ state/past/valid ].numpy() 0.0
# [num_agents, 1, 2] float32.
current_states tf.stack(
[decoded_example[ state/current/x ], decoded_example[ state/current/y ]],
-1).numpy()
current_states_mask decoded_example[ state/current/valid ].numpy() 0.0
# [num_agents, num_future_steps, 2] float32.
future_states tf.stack(
[decoded_example[ state/future/x ], decoded_example[ state/future/y ]],
-1).numpy()
future_states_mask decoded_example[ state/future/valid ].numpy() 0.0
# [num_points, 3] float32.
roadgraph_xyz decoded_example[ roadgraph_samples/xyz ].numpy()
num_agents, num_past_steps, _ past_states.shape
num_future_steps future_states.shape[1]
color_map get_colormap(num_agents)
# [num_agens, num_past_steps 1 num_future_steps, depth] float32.
all_states np.concatenate([past_states, current_states, future_states], 1)
# [num_agens, num_past_steps 1 num_future_steps] float32.
all_states_mask np.concatenate(
[past_states_mask, current_states_mask, future_states_mask], 1)
center_y, center_x, width get_viewport(all_states, all_states_mask)
images []
# Generate images from past time steps.
for i, (s, m) in enumerate(
zip(
np.split(past_states, num_past_steps, 1),
np.split(past_states_mask, num_past_steps, 1))):
im visualize_one_step(s[:, 0], m[:, 0], roadgraph_xyz,
past: %d % (num_past_steps - i), center_y,
center_x, width, color_map, size_pixels)
images.append(im)
# Generate one image for the current time step.
s current_states
m current_states_mask
im visualize_one_step(s[:, 0], m[:, 0], roadgraph_xyz, current , center_y,
center_x, width, color_map, size_pixels)
images.append(im)
# Generate images from future time steps.
for i, (s, m) in enumerate(
zip(
np.split(future_states, num_future_steps, 1),
np.split(future_states_mask, num_future_steps, 1))):
im visualize_one_step(s[:, 0], m[:, 0], roadgraph_xyz,
future: %d % (i 1), center_y, center_x, width,
color_map, size_pixels)
images.append(im)
return images
anim animation.FuncAnimation(
fig, animate_func, frames len(images) // 2, interval 100)
plt.close(fig)
return anim
HTML(anim.to_html5_video())
官方给的教程 生成的是一个动画 当然 这些动画没什么用 只需要里面的数据。上面代码主要的读取数据就是这一句 它包含了一个文件的信息 可以debug看一下 包含了许多属性,具体参见此处https://waymo.com/open/data/motion/tfexample 数据中有许多标注的为-1 这些数据没什么用
parsed tf.io.parse_single_example(data, features_description)
完整版数据集下载请前往官网下载https://waymo.com/open/download/"> https://waymo.com/open/download/
此处只提供小部分用于学习 如有侵权 请及时联系删除
百度云链接
perception v1.2 里面只提供了train的第一个文件
链接 https://pan.baidu.com/s/1PfPnVsWs7H47fi015vKL-g
提取码 1lzk
motion v1.1 提供train valid test里面的第一个文件
链接 https://pan.baidu.com/s/1RX4ISe23rkO-7OXM3imFpg
提取码 frb9
waymo提供了两种数据集，motion与perception两种其中motion在是鸟瞰图，官网中有介绍，主要用于轨迹预测之类的任务perception主要用于目标检测跟踪之类的任务，是第一视角，有相机和雷达信息，并且在github上有公开的读取数据方法，另外，在读取perception数据时需要安装waymo-open-dataset-tf这个库，安装不上请用清华源，具体请按照官方quick_start教程，另外github有许多已经集成许多功能的代码，搜索waymo就有 复制链接
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