Develop 3D Object Detection for (delivery) boxes

[shero1111]

Hello,

I need to develop an solution to detect and track some kind of boxes like delivery boxes etc.

How to start here? I read that there is already data in the dataset, but How to build a solution up on that to 3D detect Boxes?

Example:

I really appreciate your help.

Thank you.

[ahmadyan]

To start, you'll need annotated data. There are annotated cereal boxes in the dataset, but you'll need to collect your own data and annotate it, then you can train the models for this purpose.

[shero1111]

Have I to develop my own model for that or can I use an existing model for this?

On which site in the documentation or the MediaPipe sited have I to start from to develop an model?

When I for example look at this site: https://google.github.io/mediapipe/solutions/objectron.html
I see 4 different Objectrons...for shoes, cameras etc...I want to develop an objectron for boxes. How to create a model (where to start to do this if nessesary)?

Thank you very mich in advanced!

[ahmadyan]

We haven't released the training code for the models yet, so you have to implement your own model.

[shero1111]

I understand,

Could you tell me from where to start to create a model?

Thank you very much in advanced.

[ahmadyan]

A good starting point would be Tensorflow tutorials, next you can look at the source code of relevant models on Github. https://paperswithcode.com/task/6d-pose-estimation

[jianingwei]

You can also refer to Sec 5.2 of our paper https://arxiv.org/pdf/2012.09988.pdf for our models.

…

On Wed, Jun 2, 2021 at 2:44 PM Adel Ahmadyan ***@***.***> wrote: A good starting point would be Tensorflow tutorials <https://www.tensorflow.org/tutorials>, next you can look at the source code of relevant models on Github. https://paperswithcode.com/task/6d-pose-estimation — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#43 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/APYUXUB2EC66L2AJMSFRX6TTQ2Q33ANCNFSM453LMKLQ> .

[shero1111]

To start, you'll need annotated data. There are annotated cereal boxes in the dataset, but you'll need to collect your own data and annotate it, then you can train the models for this purpose.

How to annotate the data (videos) for the objectron? Unfortunately I read that the annotation tool is not released so that we could use it...

Any idea?

[FPerezHernandez92]

I have the same question. I would like to train a model with a new object but I don't know how to proceed, what should I do? Thank you very much for everything.

[xiezhangxiang]

hello, I have the same question, did you solve it? I use the following code and get weird results, I don't know how to get the 2D keypoints,
`image = cv2.imread(img_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (480, 640))
image = image / 255.
images = [_normalize_image(image)]

images = np.asarray(images)
model=load_model(model_filename, custom_objects={'loss': loss})
preds = model.predict(images)

print(preds) [array([[[[9.6562573e-05],
[1.3071414e-05],
[4.1917715e-06],
...,
[9.8206790e-12],
[9.0398376e-11],
[7.6584143e-07]],

[[2.6079666e-05],
 [3.9501720e-06],
 [1.8212555e-10],
 ...,
 [2.8078556e-15],
 [1.5476401e-19],
 [8.4229308e-09]],

[[2.4633331e-05],
 [9.5524400e-10],
 [1.3348876e-11],
 ...,
 [1.4640141e-18],
 [1.6366661e-21],
 [3.6391362e-11]],

...,

[[1.3883292e-06],
 [1.1347703e-08],
 [6.0462207e-10],
 ...,
 [2.4876311e-08],
 [3.1831805e-11],
 [9.7017306e-08]],

[[4.0202780e-05],
 [5.1800769e-10],
 [1.1579547e-09],
 ...,
 [9.0847864e-13],
 [9.1428205e-11],
 [3.4593342e-10]],

[[1.0546885e-04],
 [1.2635512e-05],
 [1.5344558e-06],
 ...,
 [8.4130400e-09],
 [2.6183332e-11],
 [2.0492683e-09]]]], dtype=float32), array([[[[-0.10607169,  0.36145103, -0.11862978, ...,  0.03101592,
   0.30299583,  0.00596629],
 [-0.18905693,  0.46546325, -0.26854637, ..., -0.06751684,
   0.42021263, -0.18807213],
 [-0.21941239,  0.41301575, -0.26544824, ...,  0.04859204,
   0.40403038, -0.09107076],
 ...,
 [-0.17547359,  0.3736801 , -0.04492063, ...,  0.06182917,
  -0.21378447, -0.03202537],
 [-0.26361176,  0.36289865, -0.18332383, ...,  0.16499005,
  -0.09499758, -0.12895563],
 [-0.24102461,  0.25801325, -0.17738084, ...,  0.11746432,
  -0.16958712,  0.13721858]],

[[-0.21957912,  0.32535398, -0.23164174, ..., -0.2085964 ,
   0.43684924, -0.27276033],
 [-0.15121302,  0.3573573 , -0.20246796, ..., -0.10501267,
   0.5066237 , -0.11706068],
 [-0.17524916,  0.3559658 , -0.18497112, ..., -0.1335241 ,
   0.53169703, -0.18370274],
 ...,
 [-0.26286513,  0.30809528, -0.1212045 , ..., -0.08777827,
  -0.13896506, -0.17987725],
 [-0.25899106,  0.33262596, -0.08751082, ..., -0.02343384,
  -0.3164396 , -0.18116182],
 [-0.22164974,  0.23702136, -0.20336536, ..., -0.06228844,
  -0.18289375, -0.30683076]],

[[-0.16058055,  0.32249534, -0.17511356, ..., -0.13031082,
   0.4542202 , -0.22487643],
 [-0.15311602,  0.3490243 , -0.17877994, ..., -0.11121193,
   0.50228304, -0.17089653],
 [-0.20514728,  0.3469826 , -0.18969603, ..., -0.11347326,
   0.5460528 , -0.16435972],
 ...,
 [-0.36025456,  0.4073612 , -0.01529002, ...,  0.24054597,
  -0.38046253,  0.14016253],
 [-0.37262747,  0.4091622 , -0.10438414, ...,  0.36949152,
   0.19607303,  0.03621448],
 [-0.28537005,  0.24178793, -0.12843539, ...,  0.11386134,
  -0.38351035,  0.27503756]],

...,

[[-0.08681132, -0.05887846, -0.01539195, ..., -0.36459795,
   0.5349943 , -0.25741568],
 [-0.04578761, -0.05969733, -0.00410217, ..., -0.41354814,
   0.6133671 , -0.2914826 ],
 [-0.06978828, -0.0289972 ,  0.01747608, ..., -0.423895  ,
   0.5479816 , -0.32753658],
 ...,
 [-0.2598699 ,  0.20992802, -0.04680583, ..., -0.43057957,
   0.15357617, -0.53516096],
 [-0.33677104,  0.20362546, -0.09578266, ..., -0.4407214 ,
   0.04547567, -0.5529746 ],
 [-0.4277043 ,  0.19496255, -0.18552476, ..., -0.42837453,
   0.01995449, -0.4375854 ]],

[[-0.03453992, -0.05292309,  0.00213689, ..., -0.50154454,
   0.6197945 , -0.39903948],
 [-0.03441546, -0.08145237, -0.04914407, ..., -0.4739752 ,
   0.5260091 , -0.33690655],
 [-0.04759429, -0.08588249, -0.04430763, ..., -0.46352687,
   0.53554165, -0.31229335],
 ...,
 [-0.3086209 ,  0.15528192, -0.14666194, ..., -0.46730536,
   0.13626733, -0.5117987 ],
 [-0.37810522,  0.17945792, -0.2264315 , ..., -0.44889984,
   0.17014027, -0.4020097 ],
 [-0.48893178,  0.22216477, -0.34320357, ..., -0.57811224,
  -0.18882565, -0.39809525]],

[[-0.07705554, -0.21781273,  0.0330582 , ..., -0.38549614,
   0.6696893 , -0.17962183],
 [-0.04036303, -0.19197614, -0.05262863, ..., -0.43213007,
   0.46479934, -0.32706207],
 [-0.09982854, -0.22474429, -0.06387011, ..., -0.39725167,
   0.3695163 , -0.24147348],
 ...,
 [-0.2948659 ,  0.10649519, -0.16847448, ..., -0.4088996 ,
   0.07583192, -0.3535105 ],
 [-0.33526367,  0.16336042, -0.26918498, ..., -0.6608317 ,
  -0.21164288, -0.4696032 ],
 [-0.5637162 ,  0.04995263, -0.39664903, ..., -0.57493746,
   0.04123268, -0.45364913]]]], dtype=float32)]

(1, 160, 120, 1)
(1, 160, 120, 16)`
how can i get the keypoints?

[HripsimeS]

We haven't released the training code for the models yet, so you have to implement your own model.

@ahmadyan Hello. The training code for the models is released already since June 2021 ?

[XinyueZ]

same question?
where is code for training?

[tranhogdiep]

Sorry, but any news on the training code?