In [1]:
import pandas as pd
In [17]:
labels_df = pd.read_csv("D:\\p_eaglesense\\eaglesense\\data\\topviewkinect\\18\\labels.csv")
In [18]:
labels_df
Out[18]:
frame_id
skeleton_id
activity
orientation
orientation_accurate
0
173
0
-1
0.0
-1
1
174
0
-1
0.0
-1
2
175
0
-1
0.0
-1
3
176
0
-1
0.0
-1
4
177
0
-1
0.0
-1
5
178
0
-1
0.0
-1
6
179
0
-1
0.0
-1
7
180
0
-1
0.0
-1
8
181
0
-1
0.0
-1
9
182
0
-1
0.0
-1
10
183
0
-1
0.0
-1
11
184
0
-1
0.0
-1
12
185
0
-1
0.0
-1
13
186
0
-1
0.0
-1
14
187
0
-1
0.0
-1
15
188
0
-1
0.0
-1
16
189
0
-1
0.0
-1
17
190
0
-1
0.0
-1
18
191
0
-1
68.0
-1
19
192
0
-1
74.0
-1
20
193
0
-1
75.0
-1
21
194
0
-1
120.0
-1
22
195
0
-1
75.0
-1
23
196
0
-1
120.0
-1
24
197
0
-1
110.0
-1
25
198
0
-1
97.0
-1
26
199
0
-1
94.0
-1
27
200
0
-1
83.0
-1
28
201
0
-1
89.0
-1
29
202
0
-1
85.0
-1
...
...
...
...
...
...
2164
2105
0
-1
340.0
-1
2165
2106
0
-1
340.0
-1
2166
2107
0
-1
330.0
-1
2167
2108
0
-1
330.0
-1
2168
2109
0
-1
54.0
-1
2169
2110
0
-1
340.0
-1
2170
2111
0
-1
34.0
-1
2171
2112
0
-1
120.0
-1
2172
2113
0
-1
61.0
-1
2173
2114
0
-1
64.0
-1
2174
2115
0
-1
71.0
-1
2175
2116
0
-1
76.0
-1
2176
2117
0
-1
76.0
-1
2177
2118
0
-1
88.0
-1
2178
2119
0
-1
95.0
-1
2179
2120
0
-1
93.0
-1
2180
2121
0
-1
89.0
-1
2181
2122
0
-1
90.0
-1
2182
2123
0
-1
84.0
-1
2183
2124
0
-1
94.0
-1
2184
2125
0
-1
88.0
-1
2185
2126
0
-1
81.0
-1
2186
2127
0
-1
73.0
-1
2187
2128
0
-1
71.0
-1
2188
2129
0
-1
62.0
-1
2189
2130
0
-1
63.0
-1
2190
2131
0
-1
60.0
-1
2191
2132
0
-1
58.0
-1
2192
2133
0
-1
66.0
-1
2193
2134
0
-1
65.0
-1
2194 rows × 5 columns
In [19]:
labels_df.drop_duplicates(subset="frame_id", keep=False, inplace=True)
In [20]:
labels_df
Out[20]:
frame_id
skeleton_id
activity
orientation
orientation_accurate
0
173
0
-1
0.0
-1
1
174
0
-1
0.0
-1
2
175
0
-1
0.0
-1
3
176
0
-1
0.0
-1
4
177
0
-1
0.0
-1
5
178
0
-1
0.0
-1
6
179
0
-1
0.0
-1
7
180
0
-1
0.0
-1
8
181
0
-1
0.0
-1
9
182
0
-1
0.0
-1
10
183
0
-1
0.0
-1
11
184
0
-1
0.0
-1
12
185
0
-1
0.0
-1
13
186
0
-1
0.0
-1
14
187
0
-1
0.0
-1
15
188
0
-1
0.0
-1
16
189
0
-1
0.0
-1
17
190
0
-1
0.0
-1
18
191
0
-1
68.0
-1
19
192
0
-1
74.0
-1
20
193
0
-1
75.0
-1
21
194
0
-1
120.0
-1
22
195
0
-1
75.0
-1
23
196
0
-1
120.0
-1
24
197
0
-1
110.0
-1
25
198
0
-1
97.0
-1
26
199
0
-1
94.0
-1
27
200
0
-1
83.0
-1
28
201
0
-1
89.0
-1
29
202
0
-1
85.0
-1
...
...
...
...
...
...
2164
2105
0
-1
340.0
-1
2165
2106
0
-1
340.0
-1
2166
2107
0
-1
330.0
-1
2167
2108
0
-1
330.0
-1
2168
2109
0
-1
54.0
-1
2169
2110
0
-1
340.0
-1
2170
2111
0
-1
34.0
-1
2171
2112
0
-1
120.0
-1
2172
2113
0
-1
61.0
-1
2173
2114
0
-1
64.0
-1
2174
2115
0
-1
71.0
-1
2175
2116
0
-1
76.0
-1
2176
2117
0
-1
76.0
-1
2177
2118
0
-1
88.0
-1
2178
2119
0
-1
95.0
-1
2179
2120
0
-1
93.0
-1
2180
2121
0
-1
89.0
-1
2181
2122
0
-1
90.0
-1
2182
2123
0
-1
84.0
-1
2183
2124
0
-1
94.0
-1
2184
2125
0
-1
88.0
-1
2185
2126
0
-1
81.0
-1
2186
2127
0
-1
73.0
-1
2187
2128
0
-1
71.0
-1
2188
2129
0
-1
62.0
-1
2189
2130
0
-1
63.0
-1
2190
2131
0
-1
60.0
-1
2191
2132
0
-1
58.0
-1
2192
2133
0
-1
66.0
-1
2193
2134
0
-1
65.0
-1
1662 rows × 5 columns
In [24]:
labels_df.loc[labels_df['frame_id'] == 2134]
Out[24]:
frame_id
skeleton_id
activity
orientation
orientation_accurate
2193
2134
0
-1
65.0
-1
In [ ]:
In [25]:
labels_df.set_index("frame_id", inplace=True)
In [29]:
labels_df.loc[2134]
Out[29]:
skeleton_id 0.0
activity -1.0
orientation 65.0
orientation_accurate -1.0
Name: 2134, dtype: float64
In [ ]:
In [ ]:
In [5]:
labels_df
Out[5]:
skeleton_id
activity
orientation
orientation_accurate
frame_id
173
0
-1
0.0
-1
174
0
-1
0.0
-1
175
0
-1
0.0
-1
176
0
-1
0.0
-1
177
0
-1
0.0
-1
178
0
-1
0.0
-1
179
0
-1
0.0
-1
180
0
-1
0.0
-1
181
0
-1
0.0
-1
182
0
-1
0.0
-1
183
0
-1
0.0
-1
184
0
-1
0.0
-1
185
0
-1
0.0
-1
186
0
-1
0.0
-1
187
0
-1
0.0
-1
188
0
-1
0.0
-1
189
0
-1
0.0
-1
190
0
-1
0.0
-1
191
0
-1
68.0
-1
192
0
-1
74.0
-1
193
0
-1
75.0
-1
194
0
-1
120.0
-1
195
0
-1
75.0
-1
196
0
-1
120.0
-1
197
0
-1
110.0
-1
198
0
-1
97.0
-1
199
0
-1
94.0
-1
200
0
-1
83.0
-1
201
0
-1
89.0
-1
202
0
-1
85.0
-1
...
...
...
...
...
2105
0
-1
340.0
-1
2106
0
-1
340.0
-1
2107
0
-1
330.0
-1
2108
0
-1
330.0
-1
2109
0
-1
54.0
-1
2110
0
-1
340.0
-1
2111
0
-1
34.0
-1
2112
0
-1
120.0
-1
2113
0
-1
61.0
-1
2114
0
-1
64.0
-1
2115
0
-1
71.0
-1
2116
0
-1
76.0
-1
2117
0
-1
76.0
-1
2118
0
-1
88.0
-1
2119
0
-1
95.0
-1
2120
0
-1
93.0
-1
2121
0
-1
89.0
-1
2122
0
-1
90.0
-1
2123
0
-1
84.0
-1
2124
0
-1
94.0
-1
2125
0
-1
88.0
-1
2126
0
-1
81.0
-1
2127
0
-1
73.0
-1
2128
0
-1
71.0
-1
2129
0
-1
62.0
-1
2130
0
-1
63.0
-1
2131
0
-1
60.0
-1
2132
0
-1
58.0
-1
2133
0
-1
66.0
-1
2134
0
-1
65.0
-1
2194 rows × 4 columns
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [6]:
labels_series = labels_df.loc[:, "activity"]
In [10]:
labels_series
Out[10]:
frame_id
173 -1
174 -1
175 -1
176 -1
177 -1
178 -1
179 -1
180 -1
181 -1
182 -1
183 -1
184 -1
185 -1
186 -1
187 -1
188 -1
189 -1
190 -1
191 -1
192 -1
193 -1
194 -1
195 -1
196 -1
197 -1
198 -1
199 -1
200 -1
201 -1
202 -1
..
2105 -1
2106 -1
2107 -1
2108 -1
2109 -1
2110 -1
2111 -1
2112 -1
2113 -1
2114 -1
2115 -1
2116 -1
2117 -1
2118 -1
2119 -1
2120 -1
2121 -1
2122 -1
2123 -1
2124 -1
2125 -1
2126 -1
2127 -1
2128 -1
2129 -1
2130 -1
2131 -1
2132 -1
2133 -1
2134 -1
Name: activity, Length: 2194, dtype: int64
In [16]:
labels_series.iloc[:1000]
Out[16]:
frame_id
173 -1
174 -1
175 -1
176 -1
177 -1
178 -1
179 -1
180 -1
181 -1
182 -1
183 -1
184 -1
185 -1
186 -1
187 -1
188 -1
189 -1
190 -1
191 -1
192 -1
193 -1
194 -1
195 -1
196 -1
197 -1
198 -1
199 -1
200 -1
201 -1
202 -1
..
913 -1
914 -1
915 -1
916 -1
917 -1
918 -1
919 -1
920 -1
921 -1
922 -1
923 -1
924 -1
925 -1
926 -1
927 -1
928 -1
929 -1
930 -1
931 -1
932 -1
933 -1
934 -1
935 -1
936 -1
937 -1
938 -1
939 -1
940 -1
941 -1
942 -1
Name: activity, Length: 1000, dtype: int64
In [ ]:
In [ ]:
In [ ]:
In [ ]:
Content source: cjw-charleswu/eaglesense
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