| /OK3568_Linux_fs/external/rknpu2/examples/3rdparty/opencv/opencv-linux-aarch64/share/OpenCV/haarcascades/ |
| H A D | haarcascade_frontalface_default.xml | 58 <stageThreshold>-5.0425500869750977e+00</stageThreshold> 62 0 -1 0 -3.1511999666690826e-02</internalNodes> 64 2.0875380039215088e+00 -2.2172100543975830e+00</leafValues></_> 67 0 -1 1 1.2396000325679779e-02</internalNodes> 69 -1.8633940219879150e+00 1.3272049427032471e+00</leafValues></_> 72 0 -1 2 2.1927999332547188e-02</internalNodes> 74 -1.5105249881744385e+00 1.0625729560852051e+00</leafValues></_> 77 0 -1 3 5.7529998011887074e-03</internalNodes> 79 -8.7463897466659546e-01 1.1760339736938477e+00</leafValues></_> 82 0 -1 4 1.5014000236988068e-02</internalNodes> [all …]
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| H A D | haarcascade_fullbody.xml | 77 Check out the demo movie, e.g. using mplayer or any (Windows/Linux-) player 108 between 6Hz to 14 Hz (on 352 x 288 frames per second) depending on the 113 Additional information e.g. on training parameters, detector 114 combination, detecting other types of objects (e.g. cars) etc. is 123 modalities for their detection, e.g. motion information. I recommend 128 patterns occurring in the world (i.e. image "background"). This is not so 152 <stageThreshold>-1.2288980484008789e+00</stageThreshold> 156 0 -1 0 -5.5820569396018982e-02</internalNodes> 158 5.8697921037673950e-01 -6.2811422348022461e-01</leafValues></_> 161 0 -1 1 -3.8861181586980820e-02</internalNodes> [all …]
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| H A D | haarcascade_upperbody.xml | 77 Check out the demo movie, e.g. using mplayer or any (Windows/Linux-) player 108 between 6Hz to 14 Hz (on 352 x 288 frames per second) depending on the 113 Additional information e.g. on training parameters, detector 114 combination, detecting other types of objects (e.g. cars) etc. is 123 modalities for their detection, e.g. motion information. I recommend 128 patterns occurring in the world (i.e. image "background"). This is not so 152 <stageThreshold>-1.1264339685440063e+00</stageThreshold> 156 0 -1 0 -1.3696029782295227e-02</internalNodes> 158 4.5076468586921692e-01 -4.2179030179977417e-01</leafValues></_> 161 0 -1 1 1.2441449798643589e-02</internalNodes> [all …]
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| H A D | haarcascade_frontalface_alt2.xml | 58 <stageThreshold>3.5069230198860168e-01</stageThreshold> 62 0 1 0 4.3272329494357109e-03 -1 -2 1 1.3076160103082657e-02</internalNodes> 64 3.8381900638341904e-02 8.9652568101882935e-01 65 2.6293140649795532e-01</leafValues></_> 68 0 1 2 5.2434601821005344e-04 -1 -2 3 4.4573000632226467e-03</internalNodes> 70 1.0216630250215530e-01 1.2384019792079926e-01 71 6.9103831052780151e-01</leafValues></_> 74 1 0 4 -9.2708261217921972e-04 -1 -2 5 3.3989109215326607e-04</internalNodes> 76 1.9536970555782318e-01 2.1014410257339478e-01 77 8.2586747407913208e-01</leafValues></_></weakClassifiers></_> [all …]
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| H A D | haarcascade_frontalface_alt_tree.xml | 59 <stageThreshold>-1.3442519903182983e+00</stageThreshold> 63 0 -1 0 3.7895569112151861e-03</internalNodes> 65 -9.2945802211761475e-01 6.4119851589202881e-01</leafValues></_> 68 0 -1 1 1.2098110280930996e-02</internalNodes> 70 -7.1810090541839600e-01 4.7141009569168091e-01</leafValues></_> 73 0 -1 2 1.2138449819758534e-03</internalNodes> 75 -7.2831612825393677e-01 3.0330690741539001e-01</leafValues></_></weakClassifiers></_> 78 <stageThreshold>-1.6378560066223145e+00</stageThreshold> 82 0 -1 3 8.7510552257299423e-03</internalNodes> 84 -8.5947072505950928e-01 3.6881381273269653e-01</leafValues></_> [all …]
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| H A D | haarcascade_frontalface_alt.xml | 58 <stageThreshold>8.2268941402435303e-01</stageThreshold> 62 0 -1 0 4.0141958743333817e-03</internalNodes> 64 3.3794190734624863e-02 8.3781069517135620e-01</leafValues></_> 67 0 -1 1 1.5151339583098888e-02</internalNodes> 69 1.5141320228576660e-01 7.4888122081756592e-01</leafValues></_> 72 0 -1 2 4.2109931819140911e-03</internalNodes> 74 9.0049281716346741e-02 6.3748198747634888e-01</leafValues></_></weakClassifiers></_> 77 <stageThreshold>6.9566087722778320e+00</stageThreshold> 81 0 -1 3 1.6227109590545297e-03</internalNodes> 83 6.9308586418628693e-02 7.1109461784362793e-01</leafValues></_> [all …]
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| H A D | haarcascade_eye.xml | 57 <maxWeakCount>6</maxWeakCount> 58 <stageThreshold>-1.4562760591506958e+00</stageThreshold> 62 0 -1 0 1.2963959574699402e-01</internalNodes> 64 -7.7304208278656006e-01 6.8350148200988770e-01</leafValues></_> 67 0 -1 1 -4.6326808631420135e-02</internalNodes> 69 5.7352751493453979e-01 -4.9097689986228943e-01</leafValues></_> 72 0 -1 2 -1.6173090785741806e-02</internalNodes> 74 6.0254341363906860e-01 -3.1610709428787231e-01</leafValues></_> 77 0 -1 3 -4.5828841626644135e-02</internalNodes> 79 6.4177548885345459e-01 -1.5545040369033813e-01</leafValues></_> [all …]
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| H A D | haarcascade_smile.xml | 60 <stageThreshold>-1.2678639888763428e+00</stageThreshold> 64 0 -1 0 -4.8783610691316426e-04</internalNodes> 66 5.9219348430633545e-01 -4.4163608551025391e-01</leafValues></_> 69 0 -1 1 -4.2209611274302006e-04</internalNodes> 71 3.0318650603294373e-01 -3.2912918925285339e-01</leafValues></_> 74 0 -1 2 -4.9940118333324790e-04</internalNodes> 76 4.8563310503959656e-01 -4.2923060059547424e-01</leafValues></_> 79 0 -1 3 3.7289198487997055e-02</internalNodes> 81 -2.8667300939559937e-01 5.9979999065399170e-01</leafValues></_> 84 0 -1 4 1.4334049774333835e-03</internalNodes> [all …]
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| /OK3568_Linux_fs/external/rknpu2/examples/3rdparty/opencv/opencv-linux-armhf/share/OpenCV/haarcascades/ |
| H A D | haarcascade_frontalface_default.xml | 58 <stageThreshold>-5.0425500869750977e+00</stageThreshold> 62 0 -1 0 -3.1511999666690826e-02</internalNodes> 64 2.0875380039215088e+00 -2.2172100543975830e+00</leafValues></_> 67 0 -1 1 1.2396000325679779e-02</internalNodes> 69 -1.8633940219879150e+00 1.3272049427032471e+00</leafValues></_> 72 0 -1 2 2.1927999332547188e-02</internalNodes> 74 -1.5105249881744385e+00 1.0625729560852051e+00</leafValues></_> 77 0 -1 3 5.7529998011887074e-03</internalNodes> 79 -8.7463897466659546e-01 1.1760339736938477e+00</leafValues></_> 82 0 -1 4 1.5014000236988068e-02</internalNodes> [all …]
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| H A D | haarcascade_fullbody.xml | 77 Check out the demo movie, e.g. using mplayer or any (Windows/Linux-) player 108 between 6Hz to 14 Hz (on 352 x 288 frames per second) depending on the 113 Additional information e.g. on training parameters, detector 114 combination, detecting other types of objects (e.g. cars) etc. is 123 modalities for their detection, e.g. motion information. I recommend 128 patterns occurring in the world (i.e. image "background"). This is not so 152 <stageThreshold>-1.2288980484008789e+00</stageThreshold> 156 0 -1 0 -5.5820569396018982e-02</internalNodes> 158 5.8697921037673950e-01 -6.2811422348022461e-01</leafValues></_> 161 0 -1 1 -3.8861181586980820e-02</internalNodes> [all …]
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| H A D | haarcascade_upperbody.xml | 77 Check out the demo movie, e.g. using mplayer or any (Windows/Linux-) player 108 between 6Hz to 14 Hz (on 352 x 288 frames per second) depending on the 113 Additional information e.g. on training parameters, detector 114 combination, detecting other types of objects (e.g. cars) etc. is 123 modalities for their detection, e.g. motion information. I recommend 128 patterns occurring in the world (i.e. image "background"). This is not so 152 <stageThreshold>-1.1264339685440063e+00</stageThreshold> 156 0 -1 0 -1.3696029782295227e-02</internalNodes> 158 4.5076468586921692e-01 -4.2179030179977417e-01</leafValues></_> 161 0 -1 1 1.2441449798643589e-02</internalNodes> [all …]
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| H A D | haarcascade_frontalface_alt2.xml | 58 <stageThreshold>3.5069230198860168e-01</stageThreshold> 62 0 1 0 4.3272329494357109e-03 -1 -2 1 1.3076160103082657e-02</internalNodes> 64 3.8381900638341904e-02 8.9652568101882935e-01 65 2.6293140649795532e-01</leafValues></_> 68 0 1 2 5.2434601821005344e-04 -1 -2 3 4.4573000632226467e-03</internalNodes> 70 1.0216630250215530e-01 1.2384019792079926e-01 71 6.9103831052780151e-01</leafValues></_> 74 1 0 4 -9.2708261217921972e-04 -1 -2 5 3.3989109215326607e-04</internalNodes> 76 1.9536970555782318e-01 2.1014410257339478e-01 77 8.2586747407913208e-01</leafValues></_></weakClassifiers></_> [all …]
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| H A D | haarcascade_frontalface_alt_tree.xml | 59 <stageThreshold>-1.3442519903182983e+00</stageThreshold> 63 0 -1 0 3.7895569112151861e-03</internalNodes> 65 -9.2945802211761475e-01 6.4119851589202881e-01</leafValues></_> 68 0 -1 1 1.2098110280930996e-02</internalNodes> 70 -7.1810090541839600e-01 4.7141009569168091e-01</leafValues></_> 73 0 -1 2 1.2138449819758534e-03</internalNodes> 75 -7.2831612825393677e-01 3.0330690741539001e-01</leafValues></_></weakClassifiers></_> 78 <stageThreshold>-1.6378560066223145e+00</stageThreshold> 82 0 -1 3 8.7510552257299423e-03</internalNodes> 84 -8.5947072505950928e-01 3.6881381273269653e-01</leafValues></_> [all …]
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| H A D | haarcascade_frontalface_alt.xml | 58 <stageThreshold>8.2268941402435303e-01</stageThreshold> 62 0 -1 0 4.0141958743333817e-03</internalNodes> 64 3.3794190734624863e-02 8.3781069517135620e-01</leafValues></_> 67 0 -1 1 1.5151339583098888e-02</internalNodes> 69 1.5141320228576660e-01 7.4888122081756592e-01</leafValues></_> 72 0 -1 2 4.2109931819140911e-03</internalNodes> 74 9.0049281716346741e-02 6.3748198747634888e-01</leafValues></_></weakClassifiers></_> 77 <stageThreshold>6.9566087722778320e+00</stageThreshold> 81 0 -1 3 1.6227109590545297e-03</internalNodes> 83 6.9308586418628693e-02 7.1109461784362793e-01</leafValues></_> [all …]
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| H A D | haarcascade_eye.xml | 57 <maxWeakCount>6</maxWeakCount> 58 <stageThreshold>-1.4562760591506958e+00</stageThreshold> 62 0 -1 0 1.2963959574699402e-01</internalNodes> 64 -7.7304208278656006e-01 6.8350148200988770e-01</leafValues></_> 67 0 -1 1 -4.6326808631420135e-02</internalNodes> 69 5.7352751493453979e-01 -4.9097689986228943e-01</leafValues></_> 72 0 -1 2 -1.6173090785741806e-02</internalNodes> 74 6.0254341363906860e-01 -3.1610709428787231e-01</leafValues></_> 77 0 -1 3 -4.5828841626644135e-02</internalNodes> 79 6.4177548885345459e-01 -1.5545040369033813e-01</leafValues></_> [all …]
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| H A D | haarcascade_smile.xml | 60 <stageThreshold>-1.2678639888763428e+00</stageThreshold> 64 0 -1 0 -4.8783610691316426e-04</internalNodes> 66 5.9219348430633545e-01 -4.4163608551025391e-01</leafValues></_> 69 0 -1 1 -4.2209611274302006e-04</internalNodes> 71 3.0318650603294373e-01 -3.2912918925285339e-01</leafValues></_> 74 0 -1 2 -4.9940118333324790e-04</internalNodes> 76 4.8563310503959656e-01 -4.2923060059547424e-01</leafValues></_> 79 0 -1 3 3.7289198487997055e-02</internalNodes> 81 -2.8667300939559937e-01 5.9979999065399170e-01</leafValues></_> 84 0 -1 4 1.4334049774333835e-03</internalNodes> [all …]
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| /OK3568_Linux_fs/external/rknpu2/examples/3rdparty/opencv/OpenCV-android-sdk/sdk/etc/haarcascades/ |
| H A D | haarcascade_frontalface_default.xml | 58 <stageThreshold>-5.0425500869750977e+00</stageThreshold> 62 0 -1 0 -3.1511999666690826e-02</internalNodes> 64 2.0875380039215088e+00 -2.2172100543975830e+00</leafValues></_> 67 0 -1 1 1.2396000325679779e-02</internalNodes> 69 -1.8633940219879150e+00 1.3272049427032471e+00</leafValues></_> 72 0 -1 2 2.1927999332547188e-02</internalNodes> 74 -1.5105249881744385e+00 1.0625729560852051e+00</leafValues></_> 77 0 -1 3 5.7529998011887074e-03</internalNodes> 79 -8.7463897466659546e-01 1.1760339736938477e+00</leafValues></_> 82 0 -1 4 1.5014000236988068e-02</internalNodes> [all …]
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| H A D | haarcascade_fullbody.xml | 77 Check out the demo movie, e.g. using mplayer or any (Windows/Linux-) player 108 between 6Hz to 14 Hz (on 352 x 288 frames per second) depending on the 113 Additional information e.g. on training parameters, detector 114 combination, detecting other types of objects (e.g. cars) etc. is 123 modalities for their detection, e.g. motion information. I recommend 128 patterns occurring in the world (i.e. image "background"). This is not so 152 <stageThreshold>-1.2288980484008789e+00</stageThreshold> 156 0 -1 0 -5.5820569396018982e-02</internalNodes> 158 5.8697921037673950e-01 -6.2811422348022461e-01</leafValues></_> 161 0 -1 1 -3.8861181586980820e-02</internalNodes> [all …]
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| H A D | haarcascade_upperbody.xml | 77 Check out the demo movie, e.g. using mplayer or any (Windows/Linux-) player 108 between 6Hz to 14 Hz (on 352 x 288 frames per second) depending on the 113 Additional information e.g. on training parameters, detector 114 combination, detecting other types of objects (e.g. cars) etc. is 123 modalities for their detection, e.g. motion information. I recommend 128 patterns occurring in the world (i.e. image "background"). This is not so 152 <stageThreshold>-1.1264339685440063e+00</stageThreshold> 156 0 -1 0 -1.3696029782295227e-02</internalNodes> 158 4.5076468586921692e-01 -4.2179030179977417e-01</leafValues></_> 161 0 -1 1 1.2441449798643589e-02</internalNodes> [all …]
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| H A D | haarcascade_frontalface_alt2.xml | 58 <stageThreshold>3.5069230198860168e-01</stageThreshold> 62 0 1 0 4.3272329494357109e-03 -1 -2 1 1.3076160103082657e-02</internalNodes> 64 3.8381900638341904e-02 8.9652568101882935e-01 65 2.6293140649795532e-01</leafValues></_> 68 0 1 2 5.2434601821005344e-04 -1 -2 3 4.4573000632226467e-03</internalNodes> 70 1.0216630250215530e-01 1.2384019792079926e-01 71 6.9103831052780151e-01</leafValues></_> 74 1 0 4 -9.2708261217921972e-04 -1 -2 5 3.3989109215326607e-04</internalNodes> 76 1.9536970555782318e-01 2.1014410257339478e-01 77 8.2586747407913208e-01</leafValues></_></weakClassifiers></_> [all …]
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| H A D | haarcascade_frontalface_alt_tree.xml | 59 <stageThreshold>-1.3442519903182983e+00</stageThreshold> 63 0 -1 0 3.7895569112151861e-03</internalNodes> 65 -9.2945802211761475e-01 6.4119851589202881e-01</leafValues></_> 68 0 -1 1 1.2098110280930996e-02</internalNodes> 70 -7.1810090541839600e-01 4.7141009569168091e-01</leafValues></_> 73 0 -1 2 1.2138449819758534e-03</internalNodes> 75 -7.2831612825393677e-01 3.0330690741539001e-01</leafValues></_></weakClassifiers></_> 78 <stageThreshold>-1.6378560066223145e+00</stageThreshold> 82 0 -1 3 8.7510552257299423e-03</internalNodes> 84 -8.5947072505950928e-01 3.6881381273269653e-01</leafValues></_> [all …]
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| H A D | haarcascade_frontalface_alt.xml | 58 <stageThreshold>8.2268941402435303e-01</stageThreshold> 62 0 -1 0 4.0141958743333817e-03</internalNodes> 64 3.3794190734624863e-02 8.3781069517135620e-01</leafValues></_> 67 0 -1 1 1.5151339583098888e-02</internalNodes> 69 1.5141320228576660e-01 7.4888122081756592e-01</leafValues></_> 72 0 -1 2 4.2109931819140911e-03</internalNodes> 74 9.0049281716346741e-02 6.3748198747634888e-01</leafValues></_></weakClassifiers></_> 77 <stageThreshold>6.9566087722778320e+00</stageThreshold> 81 0 -1 3 1.6227109590545297e-03</internalNodes> 83 6.9308586418628693e-02 7.1109461784362793e-01</leafValues></_> [all …]
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| H A D | haarcascade_eye.xml | 57 <maxWeakCount>6</maxWeakCount> 58 <stageThreshold>-1.4562760591506958e+00</stageThreshold> 62 0 -1 0 1.2963959574699402e-01</internalNodes> 64 -7.7304208278656006e-01 6.8350148200988770e-01</leafValues></_> 67 0 -1 1 -4.6326808631420135e-02</internalNodes> 69 5.7352751493453979e-01 -4.9097689986228943e-01</leafValues></_> 72 0 -1 2 -1.6173090785741806e-02</internalNodes> 74 6.0254341363906860e-01 -3.1610709428787231e-01</leafValues></_> 77 0 -1 3 -4.5828841626644135e-02</internalNodes> 79 6.4177548885345459e-01 -1.5545040369033813e-01</leafValues></_> [all …]
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| H A D | haarcascade_smile.xml | 60 <stageThreshold>-1.2678639888763428e+00</stageThreshold> 64 0 -1 0 -4.8783610691316426e-04</internalNodes> 66 5.9219348430633545e-01 -4.4163608551025391e-01</leafValues></_> 69 0 -1 1 -4.2209611274302006e-04</internalNodes> 71 3.0318650603294373e-01 -3.2912918925285339e-01</leafValues></_> 74 0 -1 2 -4.9940118333324790e-04</internalNodes> 76 4.8563310503959656e-01 -4.2923060059547424e-01</leafValues></_> 79 0 -1 3 3.7289198487997055e-02</internalNodes> 81 -2.8667300939559937e-01 5.9979999065399170e-01</leafValues></_> 84 0 -1 4 1.4334049774333835e-03</internalNodes> [all …]
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| H A D | haarcascade_lowerbody.xml | 77 Check out the demo movie, e.g. using mplayer or any (Windows/Linux-) player 108 between 6Hz to 14 Hz (on 352 x 288 frames per second) depending on the 113 Additional information e.g. on training parameters, detector 114 combination, detecting other types of objects (e.g. cars) etc. is 123 modalities for their detection, e.g. motion information. I recommend 128 patterns occurring in the world (i.e. image "background"). This is not so 152 <stageThreshold>-1.4308550357818604e+00</stageThreshold> 156 0 -1 0 -1.6869869083166122e-02</internalNodes> 158 5.4657417535781860e-01 -6.3678038120269775e-01</leafValues></_> 161 0 -1 1 2.5349899660795927e-03</internalNodes> [all …]
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