有了openpose数据,下一步如何分析人体姿态
owenm87 发布于2021-04 浏览:4626 回复:3
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小白问:用paddlehub 的openpose_body_estimation得到数据后,下一步如何做可以判断到一个图片里的人有没有类似举手、低头的行为呢?

 

import paddlehub as hub

#image_test = "test1.jpg"
image_test = "test2.jpg"

model = hub.Module(name='openpose_body_estimation')
result = model.predict(image_test,visualization=True)

mylog = open('recode.log', mode = 'a',encoding='utf-8')
print(result, file=mylog)
mylog.close()

 

处理的图片结果图片:

记录的返回信息:

{'candidate': array([[2.56000000e+02, 5.50000000e+01, 9.26660120e-01, 0.00000000e+00],
       [1.37000000e+02, 5.80000000e+01, 9.97184694e-01, 1.00000000e+00],
       [4.09000000e+02, 6.30000000e+01, 9.93659139e-01, 2.00000000e+00],
       [2.49000000e+02, 1.07000000e+02, 9.61833715e-01, 3.00000000e+00],
       [1.22000000e+02, 1.10000000e+02, 9.54079151e-01, 4.00000000e+00],
       [4.15000000e+02, 1.16000000e+02, 9.18871701e-01, 5.00000000e+00],
       [8.00000000e+01, 1.07000000e+02, 8.92051935e-01, 6.00000000e+00],
       [2.01000000e+02, 1.10000000e+02, 8.85539711e-01, 7.00000000e+00],
       [3.77000000e+02, 1.20000000e+02, 9.49104965e-01, 8.00000000e+00],
       [6.40000000e+01, 1.84000000e+02, 9.53616440e-01, 9.00000000e+00],
       [3.76000000e+02, 1.87000000e+02, 8.54201436e-01, 1.00000000e+01],
       [1.80000000e+02, 1.88000000e+02, 9.29694414e-01, 1.10000000e+01],
       [3.81000000e+02, 2.03000000e+02, 8.28347564e-01, 1.20000000e+01],
       [2.14000000e+02, 2.04000000e+02, 8.52818012e-01, 1.30000000e+01],
       [1.06000000e+02, 2.06000000e+02, 4.58440721e-01, 1.40000000e+01],
       [2.95000000e+02, 1.04000000e+02, 9.23401177e-01, 1.50000000e+01],
       [4.54000000e+02, 1.11000000e+02, 8.89868736e-01, 1.60000000e+01],
       [1.65000000e+02, 1.15000000e+02, 8.67759407e-01, 1.70000000e+01],
       [3.22000000e+02, 1.70000000e+02, 9.11746562e-01, 1.80000000e+01],
       [1.64000000e+02, 1.84000000e+02, 6.69153214e-01, 1.90000000e+01],
       [4.60000000e+02, 1.88000000e+02, 8.61127496e-01, 2.00000000e+01],
       [1.49000000e+02, 1.89000000e+02, 6.78965807e-01, 2.10000000e+01],
       [3.07000000e+02, 2.08000000e+02, 8.75384748e-01, 2.20000000e+01],
       [4.33000000e+02, 2.25000000e+02, 8.05526435e-01, 2.30000000e+01],
       [8.10000000e+01, 2.20000000e+02, 7.37039268e-01, 2.40000000e+01],
       [3.95000000e+02, 2.25000000e+02, 7.32784748e-01, 2.50000000e+01],
       [2.35000000e+02, 2.30000000e+02, 7.25677431e-01, 2.60000000e+01],
       [1.12000000e+02, 2.21000000e+02, 8.85800719e-01, 2.70000000e+01],
       [2.01000000e+02, 2.23000000e+02, 9.09173369e-01, 2.80000000e+01],
       [3.74000000e+02, 2.42000000e+02, 7.71196306e-01, 2.90000000e+01],
       [9.70000000e+01, 3.29000000e+02, 6.43031716e-01, 3.00000000e+01],
       [2.44000000e+02, 3.29000000e+02, 6.26661301e-01, 3.10000000e+01],
       [3.77000000e+02, 3.29000000e+02, 1.36328757e-01, 3.20000000e+01],
       [1.37000000e+02, 2.19000000e+02, 6.17978990e-01, 3.30000000e+01],
       [2.90000000e+02, 2.24000000e+02, 7.25328445e-01, 3.40000000e+01],
       [4.44000000e+02, 2.27000000e+02, 7.78226972e-01, 3.50000000e+01],
       [3.38000000e+02, 2.21000000e+02, 9.42319214e-01, 3.60000000e+01],
       [1.52000000e+02, 2.23000000e+02, 8.77975941e-01, 3.70000000e+01],
       [4.07000000e+02, 2.67000000e+02, 9.35000598e-01, 3.80000000e+01],
       [1.57000000e+02, 3.29000000e+02, 7.72364140e-01, 3.90000000e+01],
       [3.07000000e+02, 3.29000000e+02, 4.35196221e-01, 4.00000000e+01],
       [4.25000000e+02, 3.29000000e+02, 1.65057734e-01, 4.10000000e+01],
       [2.48000000e+02, 4.60000000e+01, 9.59142804e-01, 4.20000000e+01],
       [1.30000000e+02, 4.80000000e+01, 9.49559450e-01, 4.30000000e+01],
       [4.01000000e+02, 5.50000000e+01, 9.16016042e-01, 4.40000000e+01],
       [2.65000000e+02, 4.80000000e+01, 9.81730044e-01, 4.50000000e+01],
       [1.47000000e+02, 5.10000000e+01, 9.93008912e-01, 4.60000000e+01],
       [4.19000000e+02, 5.50000000e+01, 9.18187320e-01, 4.70000000e+01],
       [2.28000000e+02, 4.70000000e+01, 9.46349442e-01, 4.80000000e+01],
       [1.14000000e+02, 5.00000000e+01, 8.41706038e-01, 4.90000000e+01],
       [3.91000000e+02, 6.10000000e+01, 9.13940072e-01, 5.00000000e+01],
       [2.73000000e+02, 5.00000000e+01, 3.11306089e-01, 5.10000000e+01],
       [1.55000000e+02, 5.90000000e+01, 6.00095391e-01, 5.20000000e+01],
       [4.30000000e+02, 6.10000000e+01, 8.75765264e-01, 5.30000000e+01]]), 'subset': array([[ 1.        ,  4.        ,  6.        ,  9.        , 14.        ,
        17.        , 19.        , 21.        , 24.        , 27.        ,
        30.        , 33.        , 37.        , 39.        , 43.        ,
        46.        , 49.        , 52.        , 30.03926984, 18.        ],
       [ 0.        ,  3.        ,  7.        , 11.        , 13.        ,
        15.        , 18.        , 22.        , 26.        , 28.        ,
        31.        , 34.        , 36.        , 40.        , 42.        ,
        45.        , 48.        , 51.        , 30.76957117, 18.        ],
       [ 2.        ,  5.        ,  8.        , 10.        , 12.        ,
        16.        , 20.        , 23.        , 25.        , 29.        ,
        32.        , 35.        , 38.        , 41.        , 44.        ,
        47.        , 50.        , 53.        , 30.85516463, 18.        ]]), 'data': array([[[ 37,  51,  33],
        [ 35,  49,  31],
        [ 32,  48,  30],
        ...,
        [ 16,  48,  24],
        [ 16,  48,  24],
        [ 15,  47,  23]],

       [[ 33,  47,  29],
        [ 32,  46,  28],
        [ 30,  46,  28],
        ...,
        [ 17,  49,  25],
        [ 17,  49,  25],
        [ 16,  48,  24]],

       [[ 26,  40,  22],
        [ 27,  41,  23],
        [ 26,  42,  24],
        ...,
        [ 19,  51,  27],
        [ 18,  50,  26],
        [ 18,  50,  26]],

       ...,

       [[ 34,  33,  23],
        [ 59,  39,  28],
        [ 76,  56,  39],
        ...,
        [151,  66, 234],
        [166,  85, 242],
        [175, 103, 247]],

       [[ 31,  37,  20],
        [ 56,  39,  20],
        [ 90,  66,  38],
        ...,
        [174,  95, 246],
        [166,  87, 238],
        [162,  83, 234]],

       [[ 38,  44,  27],
        [ 61,  44,  25],
        [ 93,  69,  41],
        ...,
        [174,  95, 246],
        [167,  88, 239],
        [162,  83, 234]]], dtype=uint8)}
{'candidate': array([[ 90.        ,  38.        ,   0.87060368,   0.        ],
       [102.        ,  65.        ,   0.77316552,   1.        ],
       [ 70.        ,  67.        ,   0.71633345,   2.        ],
       [ 26.        ,  87.        ,   0.79980302,   3.        ],
       [ 44.        ,  45.        ,   0.77932775,   4.        ],
       [135.        ,  62.        ,   0.65731257,   5.        ],
       [178.        ,  94.        ,   0.80417168,   6.        ],
       [162.        ,  48.        ,   0.7487663 ,   7.        ],
       [ 82.        ,  32.        ,   0.88247955,   8.        ],
       [ 96.        ,  30.        ,   0.95693362,   9.        ],
       [ 75.        ,  38.        ,   0.76395959,  10.        ],
       [109.        ,  32.        ,   0.89137608,  11.        ]]), 'subset': array([[ 0.        ,  1.        ,  2.        ,  3.        ,  4.        ,
         5.        ,  6.        ,  7.        , -1.        , -1.        ,
        -1.        , -1.        , -1.        , -1.        ,  8.        ,
         9.        , 10.        , 11.        , 19.70410003, 12.        ]]), 'data': array([[[255, 255, 255],
        [255, 255, 255],
        [255, 255, 255],
        ...,
        [209, 218, 205],
        [208, 217, 204],
        [207, 216, 203]],

       [[255, 255, 255],
        [255, 255, 255],
        [255, 255, 255],
        ...,
        [209, 218, 205],
        [208, 217, 204],
        [207, 216, 203]],

       [[255, 255, 255],
        [255, 255, 255],
        [255, 255, 255],
        ...,
        [209, 218, 205],
        [208, 217, 204],
        [207, 216, 203]],

       ...,

       [[240, 242, 236],
        [240, 242, 236],
        [240, 242, 236],
        ...,
        [190, 201, 181],
        [190, 201, 181],
        [189, 200, 180]],

       [[240, 242, 236],
        [240, 242, 236],
        [240, 242, 236],
        ...,
        [189, 200, 180],
        [189, 200, 180],
        [189, 200, 180]],

       [[241, 243, 237],
        [241, 243, 237],
        [241, 243, 237],
        ...,
        [188, 199, 179],
        [188, 199, 179],
        [188, 199, 179]]], dtype=uint8)}

ps:是不是需要找训练集打标签训练,用coco数据集可以吗,一时不知如何下手,Thanks♪(・ω・)ノ

 

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共3条回复 最后由在下段公子回复于2021-07
#20在下段公子回复于2021-07

同学,解决了吗,遇到了一样的问题

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#15用户已被禁言回复于2021-05

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#8用户已被禁言回复于2021-05

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