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chore(BOP): Delete deprecated parts of BOP challenge README
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examples/datasets/bop_challenge/README.md

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@@ -40,71 +40,3 @@ blenderpoc run examples/datasets/bop_challenge/main_<bop_dataset_name>_<random/u
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* `--num_scenes`: How many scenes with 25 images each to generate
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Tip: If you have access to multiple GPUs, you can speedup the process by dividing the 2000 scenes into multiples of 40 scenes (40 scenes * 25 images make up one chunk of 1000 images). Therefore run the script in parallel with different output folders. At the end, rename and merge the scenes in a joint folder. For example, if you have 10 GPUs, set `--num_scenes=200` and run the script 10 times with different output folders.
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### Complete the BlenderProc4BOP datasets
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To save some time and not copy functionality we use the bop_toolkit to generate the [masks](
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https://github.com/thodan/bop_toolkit/blob/master/scripts/calc_gt_masks.py), [scene_gt_info](https://github.com/thodan/bop_toolkit/blob/master/scripts/calc_gt_info.py) and [scene_gt_coco](https://github.com/thodan/bop_toolkit/blob/master/scripts/calc_gt_coco.py)
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To install the `bop_toolkit` run
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```bash
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git clone https://github.com/thodan/bop_toolkit
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cd bop_toolkit
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pip install -r requirements.txt -e .
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```
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Then at the top of the scripts mentioned above set the following parameters (keep other parameters unchanged):
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```python
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p = {
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# See dataset_params.py for options.
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'dataset': '<bop_dataset_name>',
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# Dataset split. Options: 'train', 'val', 'test'.
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'dataset_split': 'train',
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# Dataset split type. None = default. See dataset_params.py for options.
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'dataset_split_type': 'pbr',
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# Folder containing the BOP datasets.
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'datasets_path': '<path/to/your/bop/datasets>',
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}
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```
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To complete your BOP datasets, finally run:
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```bash
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python scripts/calc_gt_masks.py
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python scripts/calc_gt_info.py
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python scripts/calc_gt_coco.py
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```
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## Original Config file usage
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Instead of running the python script once, we ran every config file 2000 times with 25 random cameras per scene. This has the disadvantage that objects need to be loaded at each run.
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Download the necessary [BOP datasets](https://bop.felk.cvut.cz/datasets/) and the [bop-toolkit](https://github.com/thodan/bop_toolkit).
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Execute in the BlenderProc main directory:
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```
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blenderproc download cc_textures
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```
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```
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blenderpoc run examples/datasets/bop_challenge/<main_dataset.py>
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<path_to_bop_data>
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<bop_dataset_name>
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<path_to_bop_toolkit>
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resources/cctextures
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examples/datasets/bop_challenge/output
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```
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* `examples/datasets/bop_challenge/<main_dataset.py>`: path to the python script file.
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* `<path_to_bop_data>`: path to a folder containing BOP datasets.
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* `<bop_dataset_name>`: name of BOP dataset.
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* `<path_to_bop_toolkit>`: path to a bop_toolkit folder.
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* `resources/cctextures`: path to CCTextures folder
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* `examples/datasets/bop_challenge/output`: path to an output folder where the bop_data will be saved
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This creates 25 images of a single scene. To create a whole dataset, simply run the command multiple times.

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