Comments (17)
I cannot understand what you asked
sorry,maybe I need to understand how to transform dataset format again. I feel so sorry about disturbing you. Happy Christmas Eve!
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You can see this code. Once you convert your dataset to MSCOCO format, you can easily adapt that code to use your dataset.
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from posefix_release.
Hello, I also meet a problem that my own dataset how to get annotations? or this file is not needed?Thanks.
from posefix_release.
I cannot understand what you asked
from posefix_release.
Hello, I want to ask a question that my own data also need annotations file like COCO's person_keypoints_val2017.json?
from posefix_release.
Right. You should convert annotation of your own dataset to MSCOCO format.
from posefix_release.
Maybe these codes would be helpful.
https://github.com/mks0601/TF-SimpleHumanPose/tree/master/tool
These codes convert mpii or posetrack datasets to coco format.
from posefix_release.
Thanks a lot, I will try!
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Hello, I used coco2017val dataset to get results from TF-simple pose, then take it as input_pose.json, but also appear "FileNotFoundError: [Errno 2] No such file or directory: 'tmp_result_0.pkl" ,I don't know how to deal with it. Please give me some advices, thank you very much!
from posefix_release.
The tmp_result_0.pkl
is a just temporal file, which is automatically generated during testing. You should set proper dataset
and testset
in main/config.py
and proper path in data/COCO/dataset.py
.
from posefix_release.
The
tmp_result_0.pkl
is a just temporal file, which is automatically generated during testing. You should set properdataset
andtestset
inmain/config.py
and proper path indata/COCO/dataset.py
.
Thank you, my problem has solved.
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@mks0601 Hi, Could you please tell me how to generate /test_on_trainset/result.json
in my own dataset.
Could I produce it by TF-SimpleHumanPose
?, It is indispensable? and what's it role?
from posefix_release.
Most of the images do not have all keypoint coordinate annotations because of truncations or severe occlusions. I used test_on_trainset
to fill the not annotated keypoint coordinates. The format of the .json
file exactly follows MSCOCO format. You can use any human pose estimation method to get it.
from posefix_release.
@mks0601 Thanks your kind respond, Do you mean that original coordinate annotations may not include all keypoints, So the function of test_on_trainset
is to mitigate the problem ?
from posefix_release.
Yes. For example, the annotation file of COCO dataset contains keypoints
like the below figure.
Among x1, y1, v1, ...
, v1
provides the below information.
Each keypoint has a 0-indexed location x,y and a visibility flag v defined as v=0: not labeled (in which case x=y=0), v=1: labeled but not visible, and v=2: labeled and visible.
If v==0, the coordinates are not annotated, which requires test_on_trainset
.
from posefix_release.
So kind of you, thanks!
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Related Issues (20)
- calculate miss_prob , jitter_prob and inv_prob on other dataset HOT 1
- Statistics on latency and throughput HOT 1
- demo for a single image HOT 4
- About Fig.5 in your paper HOT 2
- Can we directly implement Posefix on Inference phase or should we have to do it from training itself? HOT 3
- Combining all the three datasets HOT 3
- I can't use my GPU? HOT 1
- Find data HOT 6
- converting model to ONNX for C++ inference with TensorRT HOT 3
- Is there any plan to provide pre-trained weights available? HOT 2
- inefficient heavy-CPU training HOT 7
- input image normalization HOT 4
- the reason behind the usage of render_onehot_heatmap for training target instead of render_gaussian_heatmap HOT 3
- the usage of the softmaxed heatmaps as the keypoint confidence scores
- Format of result.json HOT 1
- Missing modules in requirements
- FileNotFoundError: [Errno 2] No such file or directory: 'tmp_result_0.pkl' and IndexError: list index out of range HOT 3
- tensorflow 2
- I appreciate your work!
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