Segment with 3 points

@glenn-jocher

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I'm working on adding polygon support to Roboflow annotations so folks can benefit from your copy-paste augmentation when training their custom datasets.

I'm looking at the YOLOv5 segmentation dataloader as implemented here and it looks like a polygon annotation requires at least 4 points to be considered a segment vs a box. I also saw @glenn-jocher mention a minimum of 9 columns here.

How should a triangle (7 columns, class x1 y1 x2 y2 x3 y3) be represented? Should I pad it with a 4th point that's a duplicate of the first?

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@yeldarby

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@yeldarby

@yeldarby oh hey good question! Yes, segmentation labels benefit from access to copy-paste augmentation, but also from smarter box cropping during 'normal' (scale, rotate, translate) augmentation, solving the issue below in #2151.

You can see an example of segmentation labels vs box labels here:

About how to handle a triangle, I didn't account for triangle shapes originally as I thought each segment would have at least 4 vertices. The logic implementing this is here, which will assume any row with > 8 columns to be a segment annotation instead of a box annotation. I think this should be reducible to > 6 though, which would then be able to accomodate triangle segments. I'll create a PR to test for any side effects of the change.

yolov5/utils/datasets.py

Lines 907 to 913 in 055e72a

    
           with open(lb_file) as f: 
        
               lb = [x.split() for x in f.read().strip().splitlines() if len(x)] 
        
               if any([len(x) > 8 for x in lb]):  # is segment 
        
                   classes = np.array([x[0] for x in lb], dtype=np.float32) 
        
                   segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb]  # (cls, xy1...) 
        
                   lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh) 
        
               lb = np.array(lb, dtype=np.float32)

Allow 3-point segments

Awesome! I'll allow 3-vertex exports to YOLOv5 format on our end & revisit+revert if it turns out there are side-effects and that PR can't be merged.

Allow 3-point segments (#6938)

@yeldarby

@yeldarby good news 😃! Your original issue may now be fixed ✅ in PR #6938. This PR allows for 3-point segment labels containing 7 columns: (class xy1 xy2 xy3). Tests show no ill effects on normal and segment COCO128 training.

To receive this update:

Git – git pull from within your yolov5/ directory or git clone https://github.com/ultralytics/yolov5 again
PyTorch Hub – Force-reload model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)
Notebooks – View updated notebooks
Docker – sudo docker pull ultralytics/yolov5:latest to update your image

Thank you for spotting this issue and informing us of the problem. Please let us know if this update resolves the issue for you, and feel free to inform us of any other issues you discover or feature requests that come to mind. Happy trainings with YOLOv5 🚀!

Awesome! I'll allow 3-vertex exports to YOLOv5 format on our end & revisit+revert if it turns out there are side-effects and that PR can't be merged.

Hello,I'm using YOLOv5 for a detection and segmentation network, may I ask how you get segment in pbar? I'm in trouble with it,Thanks

@Steinwang

@Steinwang what's pbar?

Update Neuralmagic-YoloV5 integration to v6.1 (#29)

Allow 3-point segments (ultralytics#6938)

Allow 3-point segments (#6938)

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Segment with 3 points #6931

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	with open(lb_file) as f:
	lb = [x.split() for x in f.read().strip().splitlines() if len(x)]
	if any([len(x) > 8 for x in lb]): # is segment
	classes = np.array([x[0] for x in lb], dtype=np.float32)
	segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb] # (cls, xy1...)
	lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh)
	lb = np.array(lb, dtype=np.float32)

Segment with 3 points #6931

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github-actions commented on Mar 10, 2022

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glenn-jocher commented on Mar 10, 2022

yeldarby commented on Mar 10, 2022

glenn-jocher commented on Mar 10, 2022

Steinwang commented on Apr 2, 2022

glenn-jocher commented on Apr 2, 2022

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