geopavlakos
commented
1 month ago We don't support osmesa, but if I remember correctly, osmesa doesn't return an alpha channel as the fourth channel. So, to get the overlay, you need to do something like this (haven't been able to test this, so you might want to adapt it, but I hope you can follow the logic):
alpha_channel = np.max(cam_view[:,:,:3]>0, axis=2, keepdims=True)
input_img_overlay = input_img[:,:,:3] * (1-alpha_channel) + cam_view[:,:,:3] * alpha_channelAlternatively, you can lookup online how to resolve the problem with egl.
Dear Author Team: I successfully downloaded your code, but there were some issues while running your demo.as follows: `python demo.py --img_folder example_data --out_folder demo_out --batch_size=48 --side_view --save_mesh --full_frame apex is not installed apex is not installed apex is not installed /opt/conda/lib/python3.10/site-packages/mmcv/cnn/bricks/transformer.py:27: UserWarning: Fail to import
MultiScaleDeformableAttentionfrommmcv.ops.multi_scale_deform_attn, You should installmmcv-fullif you need this module. warnings.warn('Fail to importMultiScaleDeformableAttentionfrom ' WARNING: You are using a MANO model, with only 10 shape coefficients. Use load_from_local loader The model and loaded state dict do not match exactlyunexpected key in source state_dict: backbone.blocks.0.mlp.experts.0.weight, backbone.blocks.0.mlp.experts.0.bias, backbone.blocks.0.mlp.experts.1.weight, backbone.blocks.0.mlp.experts.1.bias, backbone.blocks.0.mlp.experts.2.weight, backbone.blocks.0.mlp.experts.2.bias, backbone.blocks.0.mlp.experts.3.weight, backbone.blocks.0.mlp.experts.3.bias, backbone.blocks.0.mlp.experts.4.weight, backbone.blocks.0.mlp.experts.4.bias, backbone.blocks.0.mlp.experts.5.weight, backbone.blocks.0.mlp.experts.5.bias, backbone.blocks.1.mlp.experts.0.weight, backbone.blocks.1.mlp.experts.0.bias, backbone.blocks.1.mlp.experts.1.weight, backbone.blocks.1.mlp.experts.1.bias, backbone.blocks.1.mlp.experts.2.weight, backbone.blocks.1.mlp.experts.2.bias, backbone.blocks.1.mlp.experts.3.weight, backbone.blocks.1.mlp.experts.3.bias, backbone.blocks.1.mlp.experts.4.weight, backbone.blocks.1.mlp.experts.4.bias, 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/opt/conda/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1682343967769/work/aten/src/ATen/native/TensorShape.cpp:3483.) return _VF.meshgrid(tensors, *kwargs) # type: ignore[attr-defined] downsampling_factor=4.098026275634766 downsampling_factor=4.663270950317383 Traceback (most recent call last): File "/workspace/humplus2/hamer/demo.py", line 207, in
main()
File "/workspace/humplus2/hamer/demo.py", line 202, in main
input_img_overlay = input_img[:,:,:3] (1-cam_view[:,:,3:]) + cam_view[:,:,:3] * cam_view[:,:,3:]
ValueError: operands could not be broadcast together with shapes (1600,1182,3) (1600,1182,0) `
The only change I made to your code was to add "os.environ["PYOPENGL_PLATFORM"] = "osmesa"" at line 35 of the "demo.py" to address the issue of pyrender not being able to use GPU. if i didn't add "os.environ["PYOPENGL_PLATFORM"] = "osmesa"", it shows "ValueError: Invalid device ID (0)!".I check my pytorch-gpu, it's ok! If you could give me some advice on these two issues, I would be extremely grateful！！ best wishes !