Update help code
Browse files- README.md +2 -4
- helpercode.ipynb +4 -188
README.md
CHANGED
@@ -33,7 +33,7 @@ dataset_info:
|
|
33 |
|
34 |
- **Homepage:** https://www.frodobots.com/
|
35 |
- **Hours of tele-operation:** ~1,000 Hrs
|
36 |
-
- **Dataset Size:**
|
37 |
- **Point of Contact:** michael.cho@frodobots.com
|
38 |
|
39 |
|
@@ -150,8 +150,6 @@ The team behind FrodoBots is focused on building an open-world video gaming expe
|
|
150 |
By sharing this dataset with the research community, we hope to see new innovations that can be tested (via our SDK) directly on our fleet of FrodoBots, and then ultimately deployed back into our production environment in order to create a more fun and safer game.
|
151 |
|
152 |
## **Download**
|
153 |
-
> **_We are releasing the dataset progressively as we complete the video redaction tasks in the coming weeks. We expect the entire dataset to be made fully available by Oct 2023._**
|
154 |
-
|
155 |
Download FrodoBots dataset using the link in this [csv file](https://frodobots-1k.s3.ap-southeast-1.amazonaws.com/frodobots-1k_dataset.csv).
|
156 |
|
157 |
## **Helper code**
|
@@ -165,4 +163,4 @@ BlurIt is a leading solution for data anonymization. A state-of-the-art AI solut
|
|
165 |
|
166 |
## **Contributions**
|
167 |
|
168 |
-
The team at FrodoBots Lab created this dataset, including [Michael Cho](https://twitter.com/micoolcho), Sam Cho, Aaron Tung, Niresh Dravin & Jiamin Ho.
|
|
|
33 |
|
34 |
- **Homepage:** https://www.frodobots.com/
|
35 |
- **Hours of tele-operation:** ~1,000 Hrs
|
36 |
+
- **Dataset Size:** 700+ GB
|
37 |
- **Point of Contact:** michael.cho@frodobots.com
|
38 |
|
39 |
|
|
|
150 |
By sharing this dataset with the research community, we hope to see new innovations that can be tested (via our SDK) directly on our fleet of FrodoBots, and then ultimately deployed back into our production environment in order to create a more fun and safer game.
|
151 |
|
152 |
## **Download**
|
|
|
|
|
153 |
Download FrodoBots dataset using the link in this [csv file](https://frodobots-1k.s3.ap-southeast-1.amazonaws.com/frodobots-1k_dataset.csv).
|
154 |
|
155 |
## **Helper code**
|
|
|
163 |
|
164 |
## **Contributions**
|
165 |
|
166 |
+
The team at FrodoBots Lab created this dataset, including [Michael Cho](https://twitter.com/micoolcho), Sam Cho, Aaron Tung, Niresh Dravin & Jiamin Ho.
|
helpercode.ipynb
CHANGED
@@ -11,7 +11,9 @@
|
|
11 |
{
|
12 |
"cell_type": "markdown",
|
13 |
"id": "8241ff80-87a2-4326-938f-94c7e072420e",
|
14 |
-
"metadata": {
|
|
|
|
|
15 |
"source": [
|
16 |
"## Introduction\n",
|
17 |
"FrodoBots dataset classifies data according to the robot id and session id. Whenever the driver drives a robot, the system will generate a session id to record the data. The data content is as follows\n",
|
@@ -30,10 +32,8 @@
|
|
30 |
"- rear_`session_id`.csv\n",
|
31 |
"6. **Microhone**\n",
|
32 |
"- mic_`session_id`.mp4\n",
|
33 |
-
"- mic_`session_id`.csv\n",
|
34 |
"7. **Speaker**\n",
|
35 |
"- spk_`session_id`_`timestamp`.mp4\n",
|
36 |
-
"- spk_`session_id`_`timestamp`.csv\n",
|
37 |
"\n",
|
38 |
"Before executing this program, you can download the [sample](https://frodobots-1k.s3.ap-southeast-1.amazonaws.com/frodobots-1k_20230907_getting-started.zip) first. And specify the robot id and session id to be processed."
|
39 |
]
|
@@ -835,190 +835,6 @@
|
|
835 |
"front_camera_df = pd.read_csv(dataset_dir + '/' + bot_id + '/' + session_id + '/rear_' + session_id + '.csv')\n",
|
836 |
"front_camera_df.head()"
|
837 |
]
|
838 |
-
},
|
839 |
-
{
|
840 |
-
"cell_type": "markdown",
|
841 |
-
"id": "3cde5990-5ad5-42d6-871f-9a0a32686cad",
|
842 |
-
"metadata": {},
|
843 |
-
"source": [
|
844 |
-
"### Microphone audio"
|
845 |
-
]
|
846 |
-
},
|
847 |
-
{
|
848 |
-
"cell_type": "code",
|
849 |
-
"execution_count": 12,
|
850 |
-
"id": "4f0d3b6e-f8ec-4983-a4f1-543d1e837a58",
|
851 |
-
"metadata": {},
|
852 |
-
"outputs": [
|
853 |
-
{
|
854 |
-
"data": {
|
855 |
-
"text/html": [
|
856 |
-
"<div>\n",
|
857 |
-
"<style scoped>\n",
|
858 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
859 |
-
" vertical-align: middle;\n",
|
860 |
-
" }\n",
|
861 |
-
"\n",
|
862 |
-
" .dataframe tbody tr th {\n",
|
863 |
-
" vertical-align: top;\n",
|
864 |
-
" }\n",
|
865 |
-
"\n",
|
866 |
-
" .dataframe thead th {\n",
|
867 |
-
" text-align: right;\n",
|
868 |
-
" }\n",
|
869 |
-
"</style>\n",
|
870 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
871 |
-
" <thead>\n",
|
872 |
-
" <tr style=\"text-align: right;\">\n",
|
873 |
-
" <th></th>\n",
|
874 |
-
" <th>packet_id</th>\n",
|
875 |
-
" <th>timestamp</th>\n",
|
876 |
-
" </tr>\n",
|
877 |
-
" </thead>\n",
|
878 |
-
" <tbody>\n",
|
879 |
-
" <tr>\n",
|
880 |
-
" <th>0</th>\n",
|
881 |
-
" <td>1</td>\n",
|
882 |
-
" <td>1.684762e+09</td>\n",
|
883 |
-
" </tr>\n",
|
884 |
-
" <tr>\n",
|
885 |
-
" <th>1</th>\n",
|
886 |
-
" <td>2</td>\n",
|
887 |
-
" <td>1.684762e+09</td>\n",
|
888 |
-
" </tr>\n",
|
889 |
-
" <tr>\n",
|
890 |
-
" <th>2</th>\n",
|
891 |
-
" <td>3</td>\n",
|
892 |
-
" <td>1.684762e+09</td>\n",
|
893 |
-
" </tr>\n",
|
894 |
-
" <tr>\n",
|
895 |
-
" <th>3</th>\n",
|
896 |
-
" <td>4</td>\n",
|
897 |
-
" <td>1.684762e+09</td>\n",
|
898 |
-
" </tr>\n",
|
899 |
-
" <tr>\n",
|
900 |
-
" <th>4</th>\n",
|
901 |
-
" <td>5</td>\n",
|
902 |
-
" <td>1.684762e+09</td>\n",
|
903 |
-
" </tr>\n",
|
904 |
-
" </tbody>\n",
|
905 |
-
"</table>\n",
|
906 |
-
"</div>"
|
907 |
-
],
|
908 |
-
"text/plain": [
|
909 |
-
" packet_id timestamp\n",
|
910 |
-
"0 1 1.684762e+09\n",
|
911 |
-
"1 2 1.684762e+09\n",
|
912 |
-
"2 3 1.684762e+09\n",
|
913 |
-
"3 4 1.684762e+09\n",
|
914 |
-
"4 5 1.684762e+09"
|
915 |
-
]
|
916 |
-
},
|
917 |
-
"execution_count": 12,
|
918 |
-
"metadata": {},
|
919 |
-
"output_type": "execute_result"
|
920 |
-
}
|
921 |
-
],
|
922 |
-
"source": [
|
923 |
-
"mic_df = pd.read_csv(dataset_dir + '/' + bot_id + '/' + session_id + '/mic_' + session_id + '.csv')\n",
|
924 |
-
"mic_df.head()"
|
925 |
-
]
|
926 |
-
},
|
927 |
-
{
|
928 |
-
"cell_type": "markdown",
|
929 |
-
"id": "79158bba-e2d3-49d6-ba1d-b7c36ec11550",
|
930 |
-
"metadata": {},
|
931 |
-
"source": [
|
932 |
-
"### Speaker audio"
|
933 |
-
]
|
934 |
-
},
|
935 |
-
{
|
936 |
-
"cell_type": "code",
|
937 |
-
"execution_count": 13,
|
938 |
-
"id": "a4213899-c0ed-4a7c-b7c0-42c948611c5e",
|
939 |
-
"metadata": {},
|
940 |
-
"outputs": [
|
941 |
-
{
|
942 |
-
"data": {
|
943 |
-
"text/html": [
|
944 |
-
"<div>\n",
|
945 |
-
"<style scoped>\n",
|
946 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
947 |
-
" vertical-align: middle;\n",
|
948 |
-
" }\n",
|
949 |
-
"\n",
|
950 |
-
" .dataframe tbody tr th {\n",
|
951 |
-
" vertical-align: top;\n",
|
952 |
-
" }\n",
|
953 |
-
"\n",
|
954 |
-
" .dataframe thead th {\n",
|
955 |
-
" text-align: right;\n",
|
956 |
-
" }\n",
|
957 |
-
"</style>\n",
|
958 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
959 |
-
" <thead>\n",
|
960 |
-
" <tr style=\"text-align: right;\">\n",
|
961 |
-
" <th></th>\n",
|
962 |
-
" <th>packet_id</th>\n",
|
963 |
-
" <th>timestamp</th>\n",
|
964 |
-
" </tr>\n",
|
965 |
-
" </thead>\n",
|
966 |
-
" <tbody>\n",
|
967 |
-
" <tr>\n",
|
968 |
-
" <th>0</th>\n",
|
969 |
-
" <td>1</td>\n",
|
970 |
-
" <td>1.684762e+09</td>\n",
|
971 |
-
" </tr>\n",
|
972 |
-
" <tr>\n",
|
973 |
-
" <th>1</th>\n",
|
974 |
-
" <td>2</td>\n",
|
975 |
-
" <td>1.684762e+09</td>\n",
|
976 |
-
" </tr>\n",
|
977 |
-
" <tr>\n",
|
978 |
-
" <th>2</th>\n",
|
979 |
-
" <td>3</td>\n",
|
980 |
-
" <td>1.684762e+09</td>\n",
|
981 |
-
" </tr>\n",
|
982 |
-
" <tr>\n",
|
983 |
-
" <th>3</th>\n",
|
984 |
-
" <td>4</td>\n",
|
985 |
-
" <td>1.684762e+09</td>\n",
|
986 |
-
" </tr>\n",
|
987 |
-
" <tr>\n",
|
988 |
-
" <th>4</th>\n",
|
989 |
-
" <td>5</td>\n",
|
990 |
-
" <td>1.684762e+09</td>\n",
|
991 |
-
" </tr>\n",
|
992 |
-
" </tbody>\n",
|
993 |
-
"</table>\n",
|
994 |
-
"</div>"
|
995 |
-
],
|
996 |
-
"text/plain": [
|
997 |
-
" packet_id timestamp\n",
|
998 |
-
"0 1 1.684762e+09\n",
|
999 |
-
"1 2 1.684762e+09\n",
|
1000 |
-
"2 3 1.684762e+09\n",
|
1001 |
-
"3 4 1.684762e+09\n",
|
1002 |
-
"4 5 1.684762e+09"
|
1003 |
-
]
|
1004 |
-
},
|
1005 |
-
"execution_count": 13,
|
1006 |
-
"metadata": {},
|
1007 |
-
"output_type": "execute_result"
|
1008 |
-
}
|
1009 |
-
],
|
1010 |
-
"source": [
|
1011 |
-
"spk_df = pd.read_csv(dataset_dir + '/' + bot_id + '/' + session_id + '/spk_' + session_id + '_1684761920.csv')\n",
|
1012 |
-
"spk_df.head()"
|
1013 |
-
]
|
1014 |
-
},
|
1015 |
-
{
|
1016 |
-
"cell_type": "code",
|
1017 |
-
"execution_count": null,
|
1018 |
-
"id": "26389e72-c97b-4fad-bd0f-8362f6f5ed3d",
|
1019 |
-
"metadata": {},
|
1020 |
-
"outputs": [],
|
1021 |
-
"source": []
|
1022 |
}
|
1023 |
],
|
1024 |
"metadata": {
|
@@ -1037,7 +853,7 @@
|
|
1037 |
"name": "python",
|
1038 |
"nbconvert_exporter": "python",
|
1039 |
"pygments_lexer": "ipython3",
|
1040 |
-
"version": "3.
|
1041 |
}
|
1042 |
},
|
1043 |
"nbformat": 4,
|
|
|
11 |
{
|
12 |
"cell_type": "markdown",
|
13 |
"id": "8241ff80-87a2-4326-938f-94c7e072420e",
|
14 |
+
"metadata": {
|
15 |
+
"jp-MarkdownHeadingCollapsed": true
|
16 |
+
},
|
17 |
"source": [
|
18 |
"## Introduction\n",
|
19 |
"FrodoBots dataset classifies data according to the robot id and session id. Whenever the driver drives a robot, the system will generate a session id to record the data. The data content is as follows\n",
|
|
|
32 |
"- rear_`session_id`.csv\n",
|
33 |
"6. **Microhone**\n",
|
34 |
"- mic_`session_id`.mp4\n",
|
|
|
35 |
"7. **Speaker**\n",
|
36 |
"- spk_`session_id`_`timestamp`.mp4\n",
|
|
|
37 |
"\n",
|
38 |
"Before executing this program, you can download the [sample](https://frodobots-1k.s3.ap-southeast-1.amazonaws.com/frodobots-1k_20230907_getting-started.zip) first. And specify the robot id and session id to be processed."
|
39 |
]
|
|
|
835 |
"front_camera_df = pd.read_csv(dataset_dir + '/' + bot_id + '/' + session_id + '/rear_' + session_id + '.csv')\n",
|
836 |
"front_camera_df.head()"
|
837 |
]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
838 |
}
|
839 |
],
|
840 |
"metadata": {
|
|
|
853 |
"name": "python",
|
854 |
"nbconvert_exporter": "python",
|
855 |
"pygments_lexer": "ipython3",
|
856 |
+
"version": "3.11.2"
|
857 |
}
|
858 |
},
|
859 |
"nbformat": 4,
|