Can we count detected objects in Tensorflow 2 object detection API class wise?
as i am new to this so having hard time in manipulating the output of object detection model according to my use case described below
Say you have two classes tomato and potato in a super market shelf stock, I would like to count each object class wise
for example; potato_count:5 tomato_count:3.
for reference the following output i have is like this, just to give an idea:-
{'raw_detection_scores': array([[3.8237274e-03, 3.1729043e-03, 5.1983595e-03, ..., 1.0126382e-02,4.1468740e-03, 3.5721064e-03],
[3.7932396e-03, 1.9723773e-03, 2.3661852e-03, ..., 3.4036636e-03,
9.3266368e-04, 4.3996871e-03],
[3.2063425e-03, 2.9956400e-03, 3.9784312e-03, ..., 5.5939257e-03,
2.3936033e-03, 2.7040839e-03],
...,
[4.1239262e-03, 3.9246678e-04, 4.5044391e-05, ..., 1.2922287e-04,
2.8958917e-04, 2.2355914e-03],
[1.4656782e-03, 4.8859119e-03, 1.4899671e-03, ..., 2.8479993e-03,
2.8250813e-03, 2.1298528e-03],
[1.8135607e-03, 2.2478402e-03, 1.1820495e-03, ..., 9.5197558e-04,
1.3802052e-03, 2.2761822e-03]], dtype=float32), 'detection_anchor_indices': array([44692., 44710., 44728., 44818., 39652., 44674., 39670., 40036.,
40018., 44800., 39634., 39371., 44830., 38090., 44728., 44731.,
44728., 44710., 10078., 39796., 27838., 37604., 16933., 24833.,
39778., 44659., 45058., 38084., 44791., 44710., 44692., 30244.,
5284., 38090., 37604., 38204., 33593., 38192., 37982., 39796.,
44692., 6635., 33118., 24389., 37604., 44910., 33112., 39601.,
16133., 3845., 39918., 48370., 19204., 44740., 39778., 16792.,
6629., 25763., 38150., 48187., 15839., 38180., 23524., 44914.,
40036., 1438., 25763., 38078., 35992., 38012., 39888., 38084.,
44578., 40018., 51075., 38204., 15833., 37976., 40258., 37604.,
48751., 39906., 31684., 16453., 38054., 5140., 42568., 36484.,
38012., 38202., 37946., 14024., 2404., 40002., 5764., 39870.,
48823., 26878., 38198., 39430.], dtype=float32), 'detection_multiclass_scores': array([[1.6726255e-03, 3.2217724e-07, 2.8865278e-02, ..., 2.6032329e-04,
2.0583175e-05, 4.5886636e-04],
[1.0796189e-03, 5.8811463e-07, 3.6984652e-02, ..., 6.6033006e-04,
3.2279208e-05, 4.3705106e-04],
[6.1860681e-04, 2.4921978e-06, 6.0835034e-02, ..., 2.0631850e-03,
5.2474130e-05, 3.7664175e-04],
...,
[2.0163953e-03, 1.0121465e-03, 1.6601086e-03, ..., 5.1327944e-03,
1.6998947e-03, 9.6607208e-04],
[1.2855232e-03, 5.4006279e-03, 1.0573506e-02, ..., 1.3051391e-02,
1.0753423e-02, 1.3659596e-03],
[8.1962347e-04, 9.5169604e-01, 1.5044212e-03, ..., 5.1358938e-03,
8.4767938e-03, 3.2877922e-04]], dtype=float32),
'detection_classes': array([4, 4, 4, 4, 1, 4, 1, 1, 1, 4, 1, 1, 4, 6, 6, 4, 2, 6, 7, 7, 4, 6,
1, 6, 7, 4, 7, 9, 4, 2, 6, 7, 1, 3, 9, 6, 1, 6, 6, 3, 2, 5, 1, 2,
3, 6, 1, 1, 8, 1, 1, 1, 7, 4, 3, 1, 9, 6, 6, 8, 9, 6, 8, 2, 8, 7,
2, 6, 1, 6, 9, 5, 2, 9, 9, 3, 1, 9, 8, 5, 9, 9, 3, 5, 6, 1, 6, 6,
9, 6, 6, 1, 2, 1, 2, 9, 9, 4, 4, 7]), 'detection_boxes': array([[5.92004418e-01, 1.69490814e-01, 7.45701075e-01, 2.46565759e-01],
[5.89631081e-01, 2.46157080e-01, 7.39599228e-01, 3.18454713e-01],
[5.87109149e-01, 3.14503819e-01, 7.36972034e-01, 3.85336846e-01],
[5.87837219e-01, 7.05797434e-01, 7.28340387e-01, 7.74214983e-01],
[6.35630414e-02, 1.69735521e-01, 2.04962432e-01, 2.47269154e-01],
[5.92036664e-01, 9.53008384e-02, 7.46890843e-01, 1.73706189e-01],
[6.85142130e-02, 2.45773658e-01, 2.10277155e-01, 3.21099281e-01],
[9.23785418e-02, 7.77337551e-01, 2.25108251e-01, 8.45668435e-01],
[9.24619362e-02, 7.06092656e-01, 2.26126671e-01, 7.77547657e-01],
[5.85118353e-01, 6.37673438e-01, 7.26098835e-01, 7.05848277e-01],
[5.94619289e-02, 9.38070714e-02, 2.03622460e-01, 1.72308475e-01],
[3.44502553e-02, 7.54546002e-03, 2.08990484e-01, 9.39401984e-02],
[5.87913811e-01, 7.74250209e-01, 7.28712976e-01, 8.34733903e-01],
[9.84132528e-01, 3.18221241e-01, 9.96858120e-01, 3.95583898e-01],
[5.87109149e-01, 3.14503819e-01, 7.36972034e-01, 3.85336846e-01],
[5.89539468e-01, 3.49524260e-01, 7.35065162e-01, 4.21008408e-01],
[5.87109149e-01, 3.14503819e-01, 7.36972034e-01, 3.85336846e-01],
[5.89631081e-01, 2.46157080e-01, 7.39599228e-01, 3.18454713e-01],
[1.87163889e-01, 9.88169909e-01, 3.71130943e-01, 9.98176932e-01],
[9.36717317e-02, 7.77330160e-01, 2.24804163e-01, 8.45728278e-01],
[6.63008153e-01, 9.89469707e-01, 8.10642183e-01, 1.00000000e+00],
[9.70665693e-01, 3.16653520e-01, 9.95440483e-01, 3.85887355e-01],
[3.70503038e-01, 2.54840344e-01, 4.76123840e-01, 3.14984292e-01],
[5.87433934e-01, 7.05650687e-01, 7.27492571e-01, 7.73511648e-01],
[9.28397924e-02, 7.06507027e-01, 2.26004675e-01, 7.77664006e-01],
[5.82323313e-01, 1.54982358e-02, 7.39678025e-01, 1.03125945e-01],
[5.87077260e-01, 7.05565095e-01, 7.27602482e-01, 7.74259925e-01],
[9.83516991e-01, 3.11883837e-01, 9.97174442e-01, 3.89778942e-01],
[5.88727355e-01, 6.20116591e-01, 7.30183959e-01, 6.90428734e-01],
[5.89631081e-01, 2.46157080e-01, 7.39599228e-01, 3.18454713e-01],
[5.92004418e-01, 1.69490814e-01, 7.45701075e-01, 2.46565759e-01],
[7.42125034e-01, 0.00000000e+00, 8.81110668e-01, 8.84985179e-03],
[5.44907227e-02, 0.00000000e+00, 2.06223458e-01, 1.28744319e-02],
[9.84132528e-01, 3.18221241e-01, 9.96858120e-01, 3.95583898e-01],
[9.70665693e-01, 3.16653520e-01, 9.95440483e-01, 3.85887355e-01],
[9.84484971e-01, 5.55115521e-01, 9.97788608e-01, 6.32665694e-01],
[7.99783111e-01, 9.75158930e-01, 9.83929038e-01, 9.97444630e-01],
[9.85278428e-01, 5.28306305e-01, 9.97080624e-01, 6.08543932e-01],
[9.88123000e-01, 9.33963358e-02, 9.99226749e-01, 1.72955215e-01],
[9.36717317e-02, 7.77330160e-01, 2.24804163e-01, 8.45728278e-01],
[5.92004418e-01, 1.69490814e-01, 7.45701075e-01, 2.46565759e-01],
[9.08265784e-02, 7.78585851e-01, 2.25109786e-01, 8.43916476e-01],
[7.94785440e-01, 9.86553550e-01, 9.70936120e-01, 9.98435020e-01],
[5.84929466e-01, 7.75964439e-01, 7.24675894e-01, 8.34971726e-01],
[9.70665693e-01, 3.16653520e-01, 9.95440483e-01, 3.85887355e-01], 3.21474910e-01]],
dtype=float32), 'raw_detection_boxes': array([[-0.0132168 , -0.00798112, 0.03437265, 0.02366759],
[-0.01795438, -0.01333077, 0.04313567, 0.03091241],
[-0.00845873, -0.01297706, 0.02555573, 0.02979016],
[-0.01206583, -0.01901898, 0.03632494, 0.04061931],
[-0.01634497, -0.00570066, 0.04027664, 0.01987169],
[-0.02299639, -0.01094626, 0.05078602, 0.02601441],
[-0.01034649, -0.00047059, 0.03106559, 0.04336115],
[-0.01548673, -0.00679935, 0.03944379, 0.05214766],
[-0.00469762, -0.00637354, 0.02257038, 0.05068764],
[-0.00889431, -0.01532986, 0.03383063, 0.06445184],
[-0.01338234, 0.00258018, 0.03299785, 0.03899822],
[-0.02030504, -0.00274394, 0.04193052, 0.04610612],
[-0.0114202 , 0.00825354, 0.0315875 , 0.05609718],
[-0.01720474, 0.00155611, 0.03969076, 0.06473814],
[-0.0055348 , 0.00137738, 0.02347516, 0.06321988],
[-0.0093858 , -0.00954537, 0.03353771, 0.0789085 ],
[-0.01528691, 0.0120711 , 0.03230394, 0.05128276],
[-0.02242971, 0.00611713, 0.04139108, 0.0590462 ],
[-0.01265933, 0.01957938, 0.03226281, 0.06821183],
[-0.0190082 , 0.01264081, 0.04051029, 0.07676097],
[-0.00625486, 0.01262659, 0.02384217, 0.07535952],
[-0.01057751, 0.00036938, 0.03408406, 0.09211845],
[-0.01712188, 0.02387175, 0.03272626, 0.0631646 ],
[-0.02457684, 0.01729448, 0.04191976, 0.07130254],
[-0.01416131, 0.03209703, 0.03322188, 0.08013913],
[-0.02092581, 0.02524993, 0.04159252, 0.08845924],
[-0.00731821, 0.02507119, 0.02447667, 0.08743346],
[-0.01213621, 0.01294496, 0.03459452, 0.10395688],
[-0.01857999, 0.0361888 , 0.03388733, 0.07542843],
[-0.02637036, 0.02969162, 0.04293538, 0.08341235],
[-0.01507254, 0.04520991, 0.03351783, 0.09184141],
[-0.02222046, 0.03861695, 0.04212021, 0.10008947],
[-0.00780608, 0.03797973, 0.02448018, 0.09932629],
[-0.01303079, 0.02687315, 0.03459996, 0.1151351 ],
[-0.0191509 , 0.04890272, 0.03473954, 0.08777986],
[-0.02749499, 0.04277577, 0.04370061, 0.09534387],
[-0.01489433, 0.05867497, 0.03314201, 0.10344677],
[-0.02239214, 0.05207732, 0.04205906, 0.11197228],
[-0.00734611, 0.05139816, 0.02392033, 0.11116292],
[-0.01289164, 0.0412713 , 0.03449183, 0.12679553],
[-0.01872004, 0.06203329, 0.03483813, 0.09988385],
[-0.02761277, 0.05606709, 0.04412681, 0.10715124], 0.2496243 ]],
dtype=float32), 'detection_scores': array([0.9957284 , 0.9954956 , 0.9948391 , 0.9935589 , 0.9928843 ,
0.9922596 , 0.99091065, 0.9904872 , 0.9904753 , 0.9836049 ,
0.97076845, 0.76198786, 0.11483946, 0.08861226, 0.06485316,
0.06403089, 0.06083503, 0.05606595, 0.05304798, 0.05192479,
0.05068725, 0.0497607 , 0.04650801, 0.04170695, 0.04141748,
0.0396772 , 0.03875464, 0.03834933, 0.03700855, 0.03698465,
0.03656569, 0.03464538, 0.03429574, 0.03408125, 0.033981 ,
0.03356522, 0.03337869, 0.03140217, 0.03058183, 0.02957818,
0.02886528, 0.02712101, 0.02674139, 0.02655837, 0.02634463,
0.02611795, 0.02595255, 0.02580112, 0.0251711 , 0.02473494,
0.02423027, 0.02406707, 0.02352765, 0.02347961, 0.02342641,
0.02327773, 0.02312759, 0.0229713 , 0.02272761, 0.02240831,
0.02240023, 0.02203956, 0.02200234, 0.02167007, 0.02112213,
0.0210447 , 0.02079707, 0.02007249, 0.01999336, 0.01993376,
0.01986268, 0.0196887 , 0.01967749, 0.01877454, 0.01874545,
0.01856974, 0.01855248, 0.01853141, 0.01839408, 0.01838818,
0.01830906, 0.01829055, 0.01759666, 0.01758116, 0.01747909,
0.01745978, 0.01728415, 0.01719788, 0.0171611 , 0.01715598,
0.01704106, 0.01684934, 0.01672551, 0.01663077, 0.01645952,
0.01627839, 0.01607156, 0.01592609, 0.01579505, 0.01570672],
dtype=float32), 'num_detections': 100}
Please guyys help me out in this thanks in advance
Check out his link: https://www.tensorflow.org/api_docs/python/tf/data/Dataset
Here, you can find how to iterate over Dataset using ".as_numpy_iterator()", but also how to use different methods to manipulate the input dataset. Hope this will be useful.