Simple static web-based mask drawer, supporting semantic drawing with interactive Segment Anything Model (SAM) and Video Segmentation Propagation with XMem.

• Video Segmentation with XMem

original video	first frame
segmentation	VideoSeg

• Interactive segmentation

Interactive segmentation

Revert during interactive seg.

From top to bottom

• Clear image
• Drawer
• SAM point-segmenter with interactive functionality (Need backend)
• SAM rect-segmenter (Need backend)
• SAM Seg-Everything (Need backend)
• Undo
• Eraser
• Download
• VideoSeg (Need backend)

After Seg-Everything, the downloaded files would include .zip file, which contains all cut-offs.

For Video Segmentation, according to XMem, an initial segmentation map is needed, which can be easily achieved with SAM. You can upload a video just as uploading an image, then draw a segmentation on it, after which you can click the final button of VideoSeg to upload it to the server and wait for the automatic download of video seg result.

Note: you may not want to draw the segmentation map manually with the tool Drawer (Same problem holds for Eraser), which leads to non-single color paints especially on the edge as shown below. This is not good for XMem video segmentation. For more details please refer to the original paper. Using SAM for segmenting is preferable.

For Interactive Segmentation

1. How to start
   • Click magic wand button (the curso becomes cross)
2. How to use
   • Postive prompt by single left click
   • Negative prompt by single right click
   • The behavior of revert button will change, which removes the latest interactive prompt
3. How to end
   • Click the magic wand button once again (the curso becomes normal). Note: it's actually safe to click any other button while in interactive mode.
   • The latest mask will save to the mask collections
   • The behavior of revert button will be turned back

If you don't need SAM for segmentation, just open segDrawer.html and use tools except SAM segmenter.

If you use SAM segmenter, do following steps (CPU can be time-consuming)

• Download models as mentioned in segment-anything and XMem. For example

wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth
wget -P ./XMem/saves/ https://github.com/hkchengrex/XMem/releases/download/v1.0/XMem.pth

• Install the dependencies

pip install -r requirements.txt

• Launch backend

python server.py

• Go to Browser

http://127.0.0.1:7860

For configuring CPU/GPU and model, just change the code in server.py

sam_checkpoint = "sam_vit_l_0b3195.pth" # "sam_vit_l_0b3195.pth" or "sam_vit_h_4b8939.pth"
model_type = "vit_l" # "vit_l" or "vit_h"
device = "cuda" # "cuda" if torch.cuda.is_available() else "cpu"

Follow this Colab example, or run on Colab. Need to register a ngrok account and copy your token to replace "{your_token}".