Comments (3)
Dear author, I want to predict the depth from many images captured by different cameras. it will be spend long time if we put a single image to the model every time. So I want to put a batch of images to the model one time, but there is a problem, the camera has different intrinsics, and cannot use a normal cameral model. I have tried to the inference function, but miss in "get_func('mono.model.model_pipelines.' + model_type)(cfg)"
I hope this issue would help. By the way, do not forget to set the launcher to make the codes execute in DDP mode.
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Dear author, I want to predict the depth from many images captured by different cameras. it will be spend long time if we put a single image to the model every time. So I want to put a batch of images to the model one time, but there is a problem, the camera has different intrinsics, and cannot use a normal cameral model. I have tried to the inference function, but miss in "get_func('mono.model.model_pipelines.' + model_type)(cfg)"
I hope this issue would help. By the way, do not forget to set the launcher to make the codes execute in DDP mode.
Dear author, #20 (comment) seems not the answer to this issue. I think use the same test method as NYU, generate json file but without depth gt setting.
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Dear author, I want to predict the depth from many images captured by different cameras. it will be spend long time if we put a single image to the model every time. So I want to put a batch of images to the model one time, but there is a problem, the camera has different intrinsics, and cannot use a normal cameral model. I have tried to the inference function, but miss in "get_func('mono.model.model_pipelines.' + model_type)(cfg)"
The code supports different camera settings in a batch. You only need to enclose the original camera information in JSON file.
Please see this: https://github.com/YvanYin/Metric3D/blob/main/mono/utils/custom_data.py
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Related Issues (20)
- Pixel represented focal length or real world scale focal length(mm) HOT 2
- Some problems in Training
- Supporting old GPUs? HOT 3
- metric_scale in nyu.py HOT 1
- Speed Up Inference HOT 2
- NYU dataset and json HOT 1
- Inference Speed data
- normals not normal HOT 2
- Unable to adjust scale of depth correctly in the wild-mode HOT 1
- How to convert the DINO2reg-ViT model to an ONNX model HOT 1
- torch.hub.load error HOT 4
- Failed to find function: mono.model.backbones.convnext_large HOT 1
- Fine tune on custom dataset HOT 4
- Sparse GT depth from LiDAR for supervision? HOT 1
- Question regarding losses HOT 1
- Depth scale vs Metric scale HOT 5
- What does the pkl file contain in training with Matterport3D?
- generate only a depth matrix without generating a 3D point cloud HOT 2
- Is there any reference code to generate kitti dataset annotation?
- Camera parameters of taskonomy HOT 2
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