Yasuhiro Suzuki comments

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                                            Yasuhiro Suzuki

The way `--output_coco_json` works right now is it operates on a dataset (which doesn't need to have ground truth). So you need to give a `--dataset` parameter instead of an `--image` parameter. The dataset you use doesn't need annotations, simply create a COCO dataset with just images and an empty annotation array and follow the dataset definition of `coco2017_testdev_dataset`.

@piseabhijeet Hi. Thanks sharing your tip. Do you know that generating `.json` file from `my_image.png` ? I typed `python eval.py --trained_model=weights/yolact_base_540_20000.pth --score_threshold=0.6 --top_k=50 --dataset=my_custom_dataset --output_coco_json` but show me `NameError: name...

How to get mask?

@kidpaul94 Thanks! Do you know that how to convert numpy of mask_data to segmentation data([x, y, x, y, x, y ...])?

How to get mask?

@kidpaul94 Thanks!!, I will try soon.

Question: Is there a way to generate coco.json for a new directory of images.

@davodogster thanks share the code. but not working... still run? I tried all copies and paste it at `eval.py` explode the indentation error...

Question: Is there a way to generate coco.json for a new directory of images.

@davodogster wow. very flash! Thanks a lot! but show me IndexError: tuple index out of range, line 148, in prep_display path2 = path_split[1] I was wonder if you can fix...

Question: Is there a way to generate coco.json for a new directory of images.

@davodogster wow... so fast... I changed path_split[1] to path_split[0] and nominate output directory (original none type.. ) now, I get instance mask segmentation image file. but no generate coco json...

Question: Is there a way to generate coco.json for a new directory of images.

yes sir! tanks a lot

Added meshing of density field using marching cubes

@amanshenoy thanks a lot! i have created a mesh file. but, how do i apply color to the mesh?