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Semi-Automatic Segmentation with Deep Extreme Cut

About the application

The application allows to use deep learning models for semi-automatic semantic and instance segmentation. You can get a segmentation polygon from four (or more) extreme points of an object. This application uses the pre-trained DEXTR model which has been converted to Inference Engine format.

We are grateful to K.K. Maninis, S. Caelles, J. Pont-Tuset, and L. Van Gool who permitted using their models in our tool

Build docker image

# OpenVINO component is also needed
docker-compose -f docker-compose.yml -f components/openvino/docker-compose.openvino.yml -f cvat/apps/dextr_segmentation/docker-compose.dextr.yml build

Run docker container

docker-compose -f docker-compose.yml -f components/openvino/docker-compose.openvino.yml -f cvat/apps/dextr_segmentation/docker-compose.dextr.yml up -d

Using

  1. Open a job
  2. Select "Auto Segmentation" in the list of shapes
  3. Run the draw mode as usually (by press the "Create Shape" button or by "N" shortcut)
  4. Click four-six (or more if it's need) extreme points of an object
  5. Close the draw mode as usually (by shortcut or pressing the button "Stop Creation")
  6. Wait a moment and you will get a class agnostic annotation polygon
  7. You can close an annotation request if it is too long (in case if it is queued to rq worker and all workers are busy)