CADC#
This topic describes how to manage the “CADC” dataset.
“CADC” is a fusion dataset with 8 sensors including 7 cameras and 1 lidar
, and has Box3D type of labels on the point cloud data.
(Fig. 6).
See this page for more details about this dataset.
Fig. 6 The preview of a point cloud from “CADC” with Box3D labels.#
Create Fusion Dataset#
Then, create a fusion dataset client by passing the fusion dataset name and is_fusion argument to the GAS client.
gas.create_dataset("CADC", is_fusion=True)
List Dataset Names#
To check if you have created “CADC” fusion dataset, you can list all your available datasets. See this page for details.
The datasets listed here include both datasets and fusion datasets.
gas.list_dataset_names()
Organize Fusion Dataset#
Now we describe how to organize the “CADC” fusion dataset by the FusionDataset
instance before uploading it to TensorBay. It takes the following steps to organize “CADC”.
Write the Catalog#
The first step is to write the catalog. Catalog is a json file contains all label information of one dataset. See this page for more details. The only annotation type for “CADC” is Box3D, and there are 10 category types and 9 attributes types.
1{
2 "BOX3D": {
3 "isTracking": true,
4 "categories": [
5 { "name": "Animal" },
6 { "name": "Bicycle" },
7 { "name": "Bus" },
8 { "name": "Car" },
9 { "name": "Garbage_Container_on_Wheels" },
10 { "name": "Pedestrian" },
11 { "name": "Pedestrian_With_Object" },
12 { "name": "Traffic_Guidance_Objects" },
13 { "name": "Truck" },
14 { "name": "Horse and Buggy" }
15 ],
16 "attributes": [
17 {
18 "name": "stationary",
19 "type": "boolean"
20 },
21 {
22 "name": "camera_used",
23 "enum": [0, 1, 2, 3, 4, 5, 6, 7, null]
24 },
25 {
26 "name": "state",
27 "enum": ["Moving", "Parked", "Stopped"],
28 "parentCategories": ["Car", "Truck", "Bus", "Bicycle", "Horse_and_Buggy"]
29 },
30 {
31 "name": "truck_type",
32 "enum": [
33 "Construction_Truck",
34 "Emergency_Truck",
35 "Garbage_Truck",
36 "Pickup_Truck",
37 "Semi_Truck",
38 "Snowplow_Truck"
39 ],
40 "parentCategories": ["Truck"]
41 },
42 {
43 "name": "bus_type",
44 "enum": ["Coach_Bus", "Transit_Bus", "Standard_School_Bus", "Van_School_Bus"],
45 "parentCategories": ["Bus"]
46 },
47 {
48 "name": "age",
49 "enum": ["Adult", "Child"],
50 "parentCategories": ["Pedestrian", "Pedestrian_With_Object"]
51 },
52 {
53 "name": "traffic_guidance_type",
54 "enum": ["Permanent", "Moveable"],
55 "parentCategories": ["Traffic_Guidance_Objects"]
56 },
57 {
58 "name": "rider_state",
59 "enum": ["With_Rider", "Without_Rider"],
60 "parentCategories": ["Bicycle"]
61 },
62 {
63 "name": "points_count",
64 "type": "integer",
65 "minimum": 0
66 }
67 ]
68 }
69}
Note
The annotations for “CADC” have tracking information, hence the value of isTracking should be set as True.
Write the Dataloader#
The second step is to write the dataloader.
The dataloader function of “CADC” is to manage all the files and annotations of “CADC” into a
FusionDataset instance.
The code block below displays the “CADC” dataloader.
1#!/usr/bin/env python3
2#
3# Copyright 2021 Graviti. Licensed under MIT License.
4#
5# pylint: disable=invalid-name
6
7"""Dataloader of CADC dataset."""
8
9import json
10import os
11from datetime import datetime
12from typing import Any, Dict, List
13
14import quaternion
15
16from tensorbay.dataset import Data, Frame, FusionDataset
17from tensorbay.exception import ModuleImportError
18from tensorbay.label import LabeledBox3D
19from tensorbay.opendataset._utility import glob
20from tensorbay.sensor import Camera, Lidar, Sensors
21
22DATASET_NAME = "CADC"
23
24
25def CADC(path: str) -> FusionDataset:
26 """`CADC <http://cadcd.uwaterloo.ca/index.html>`_ dataset.
27
28 The file structure should be like::
29
30 <path>
31 2018_03_06/
32 0001/
33 3d_ann.json
34 labeled/
35 image_00/
36 data/
37 0000000000.png
38 0000000001.png
39 ...
40 timestamps.txt
41 ...
42 image_07/
43 data/
44 timestamps.txt
45 lidar_points/
46 data/
47 timestamps.txt
48 novatel/
49 data/
50 dataformat.txt
51 timestamps.txt
52 ...
53 0018/
54 calib/
55 00.yaml
56 01.yaml
57 02.yaml
58 03.yaml
59 04.yaml
60 05.yaml
61 06.yaml
62 07.yaml
63 extrinsics.yaml
64 README.txt
65 2018_03_07/
66 2019_02_27/
67
68 Arguments:
69 path: The root directory of the dataset.
70
71 Returns:
72 Loaded `~tensorbay.dataset.dataset.FusionDataset` instance.
73
74 """
75 root_path = os.path.abspath(os.path.expanduser(path))
76
77 dataset = FusionDataset(DATASET_NAME)
78 dataset.notes.is_continuous = True
79 dataset.load_catalog(os.path.join(os.path.dirname(__file__), "catalog.json"))
80
81 for date in os.listdir(root_path):
82 date_path = os.path.join(root_path, date)
83 sensors = _load_sensors(os.path.join(date_path, "calib"))
84 for index in os.listdir(date_path):
85 if index == "calib":
86 continue
87
88 segment = dataset.create_segment(f"{date}-{index}")
89 segment.sensors = sensors
90 segment_path = os.path.join(root_path, date, index)
91 data_path = os.path.join(segment_path, "labeled")
92
93 with open(os.path.join(segment_path, "3d_ann.json"), encoding="utf-8") as fp:
94 # The first line of the json file is the json body.
95 annotations = json.loads(fp.readline())
96 timestamps = _load_timestamps(sensors, data_path)
97 for frame_index, annotation in enumerate(annotations):
98 segment.append(_load_frame(sensors, data_path, frame_index, annotation, timestamps))
99
100 return dataset
101
102
103def _load_timestamps(sensors: Sensors, data_path: str) -> Dict[str, List[str]]:
104 timestamps = {}
105 for sensor_name in sensors.keys():
106 data_folder = f"image_{sensor_name[-2:]}" if sensor_name != "LIDAR" else "lidar_points"
107 timestamp_file = os.path.join(data_path, data_folder, "timestamps.txt")
108 with open(timestamp_file, encoding="utf-8") as fp:
109 timestamps[sensor_name] = fp.readlines()
110
111 return timestamps
112
113
114def _load_frame(
115 sensors: Sensors,
116 data_path: str,
117 frame_index: int,
118 annotation: Dict[str, Any],
119 timestamps: Dict[str, List[str]],
120) -> Frame:
121 frame = Frame()
122 for sensor_name in sensors.keys():
123 # The data file name is a string of length 10 with each digit being a number:
124 # 0000000000.jpg
125 # 0000000001.bin
126 stem = f"{frame_index:010}"
127
128 # Each line of the timestamps file looks like:
129 # 2018-03-06 15:02:33.000000000
130 timestamp = datetime.strptime(
131 timestamps[sensor_name][frame_index][:23], "%Y-%m-%d %H:%M:%S.%f"
132 ).timestamp()
133 if sensor_name != "LIDAR":
134 # The image folder corresponds to different cameras, whose name is likes "CAM00".
135 # The image folder looks like "image_00".
136 camera_folder = f"image_{sensor_name[-2:]}"
137 image_file = f"{stem}.png"
138
139 data = Data(
140 os.path.join(data_path, camera_folder, "data", image_file),
141 target_remote_path=f"{camera_folder}-{image_file}",
142 timestamp=timestamp,
143 )
144 else:
145 data = Data(
146 os.path.join(data_path, "lidar_points", "data", f"{stem}.bin"),
147 timestamp=timestamp,
148 )
149 data.label.box3d = _load_labels(annotation["cuboids"])
150
151 frame[sensor_name] = data
152 return frame
153
154
155def _load_labels(boxes: List[Dict[str, Any]]) -> List[LabeledBox3D]:
156 labels = []
157 for box in boxes:
158 dimension = box["dimensions"]
159 position = box["position"]
160
161 attributes = box["attributes"]
162 attributes["stationary"] = box["stationary"]
163 attributes["camera_used"] = box["camera_used"]
164 attributes["points_count"] = box["points_count"]
165
166 label = LabeledBox3D(
167 size=(
168 dimension["y"], # The "y" dimension is the width from front to back.
169 dimension["x"], # The "x" dimension is the width from left to right.
170 dimension["z"],
171 ),
172 translation=(
173 position["x"], # "x" axis points to the forward facing direction of the object.
174 position["y"], # "y" axis points to the left direction of the object.
175 position["z"],
176 ),
177 rotation=quaternion.from_rotation_vector((0, 0, box["yaw"])),
178 category=box["label"],
179 attributes=attributes,
180 instance=box["uuid"],
181 )
182 labels.append(label)
183
184 return labels
185
186
187def _load_sensors(calib_path: str) -> Sensors:
188 try:
189 import yaml # pylint: disable=import-outside-toplevel
190 except ModuleNotFoundError as error:
191 raise ModuleImportError(module_name=error.name, package_name="pyyaml") from error
192
193 sensors = Sensors()
194
195 lidar = Lidar("LIDAR")
196 lidar.set_extrinsics()
197 sensors.add(lidar)
198
199 with open(os.path.join(calib_path, "extrinsics.yaml"), encoding="utf-8") as fp:
200 extrinsics = yaml.load(fp, Loader=yaml.FullLoader)
201
202 for camera_calibration_file in glob(os.path.join(calib_path, "[0-9]*.yaml")):
203 with open(camera_calibration_file, encoding="utf-8") as fp:
204 camera_calibration = yaml.load(fp, Loader=yaml.FullLoader)
205
206 # camera_calibration_file looks like:
207 # /path-to-CADC/2018_03_06/calib/00.yaml
208 camera_name = f"CAM{os.path.splitext(os.path.basename(camera_calibration_file))[0]}"
209 camera = Camera(camera_name)
210 camera.description = camera_calibration["camera_name"]
211
212 camera.set_extrinsics(matrix=extrinsics[f"T_LIDAR_{camera_name}"])
213
214 camera_matrix = camera_calibration["camera_matrix"]["data"]
215 camera.set_camera_matrix(matrix=[camera_matrix[:3], camera_matrix[3:6], camera_matrix[6:9]])
216
217 distortion = camera_calibration["distortion_coefficients"]["data"]
218 camera.set_distortion_coefficients(**dict(zip(("k1", "k2", "p1", "p2", "k3"), distortion)))
219
220 sensors.add(camera)
221 return sensors
create a fusion dataset#
To load a fusion dataset, we first need to create an instance of FusionDataset.(L75)
Note that after creating the Fusion Dataset,
you need to set the is_continuous attribute of notes to True,(L76)
since the frames
in each fusion segment is time-continuous.
load the catalog#
Same as dataset, you also need to load the catalog.(L77) The catalog file “catalog.json” is in the same directory with dataloader file.
create fusion segments#
In this example, we create fusion segments by dataset.create_segment(SEGMENT_NAME).(L86)
We manage the data under the subfolder(L33) of the date folder(L32) into a fusion segment
and combine two folder names to form a segment name,
which is to ensure that frames in each segment are continuous.
add sensors to fusion segments#
After constructing the fusion segment, the sensors corresponding to different data should be added to the fusion segment.(L87)
In “CADC” , there is a need for projection, so we need not only the name for each sensor, but also the calibration parameters.
And to manage all the Sensors (L81, L183) corresponding to different data,
the parameters from calibration files are extracted.
Lidar sensor only has extrinsics,
here we regard the lidar as the origin of the point cloud 3D coordinate system, and set the extrinsics as defaults(L189).
To keep the projection relationship between sensors,
we set the transform from the camera 3D coordinate system to the lidar 3D coordinate system
as Camera extrinsics(L205).
Besides extrinsics(),
Camera sensor also has intrinsics(),
which are used to project 3D points to 2D pixels.
The intrinsics consist of two parts,
CameraMatrix and DistortionCoefficients.(L208-L211)
add frames to segment#
After adding the sensors to the fusion segments, the frames should be added into the continuous segment in order(L96).
Each frame contains the data corresponding to each sensor, and each data should be added to the frame under the key of sensor name(L147).
In fusion datasets, it is common that not all data have labels. In “CADC”, only point cloud files(Lidar data) have Box3D type of labels(L145). See this page for more details about Box3D annotation details.
Note
The CADC dataloader above uses relative import(L16-L19). However, when you write your own dataloader you should use regular import. And when you want to contribute your own dataloader, remember to use relative import.
Visualize Dataset#
Optionally, the organized dataset can be visualized by Pharos, which is a TensorBay SDK plug-in. This step can help users to check whether the dataset is correctly organized. Please see Visualization for more details.
Upload Fusion Dataset#
After you finish the dataloader and organize the “CADC” into a
FusionDataset instance, you can upload it
to TensorBay for sharing, reuse, etc.
# fusion_dataset is the one you initialized in "Organize Fusion Dataset" section
fusion_dataset_client = gas.upload_dataset(fusion_dataset, jobs=8)
fusion_dataset_client.commit("initial commit")
Remember to execute the commit step after uploading. If needed, you can re-upload and commit again. Please see this page for more details about version control.
Note
Commit operation can also be done on our GAS Platform.
Read Fusion Dataset#
Now you can read “CADC” dataset from TensorBay.
fusion_dataset = FusionDataset("CADC", gas)
In dataset “CADC”, there are lots of
FusionSegments: 2018_03_06/0001, 2018_03_07/0001, …
You can get the segment names by list them all.
fusion_dataset.keys()
You can get a segment by passing the required segment name or the segment index.
fusion_segment = fusion_dataset["2018_03_06/0001"]
fusion_segment = fusion_dataset[0]
Note
If the segment or fusion segment is created without given name, then its name will be “”.
In the 2018_03_06/0001 fusion segment,
there are several sensors.
You can get all the sensors by accessing the sensors
of the FusionSegment.
sensors = fusion_segment.sensors
In each fusion segment, there are a sequence of frames. You can get one by index.
frame = fusion_segment[0]
In each frame, there are several data corresponding to different sensors. You can get each data by the corresponding sensor name.
for sensor_name in sensors.keys():
data = frame[sensor_name]
In “CADC”, only data
under Lidar
has a sequence of Box3D annotations.
You can get one by index.
lidar_data = frame["LIDAR"]
label_box3d = lidar_data.label.box3d[0]
category = label_box3d.category
attributes = label_box3d.attributes
There is only one label type in “CADC” dataset, which is box3d.
The information stored in category is
one of the category names in “categories” list of catalog.json.
The information stored in attributes
is some of the attributes in “attributes” list of catalog.json.
See this page for more details about the structure of Box3D.
Delete Fusion Dataset#
To delete “CADC”, run the following code:
gas.delete_dataset("CADC")