/seg-mask-process

Segmentation mask postprocessing

Primary LanguagePython

Post Process for Segmentation Masks

Usage

Fill pixel areas below the specific threshold using Breadth First Search

대체 텍스트 대체 텍스트

Initialize

python -m venv .seg_venv
source .seg_venv/bin/activate
pip install requirements.txt
mkdir masks

Put mask images inside the masks directory

(Optional)Normalize Mask

  • Visualizing mask images
python norm.py

Process

python process.py

(Optional)CLIP Seg

  • Quick Start: Segmentation Mask
from PIL import Image
import requests
from transformers import AutoProcessor, CLIPSegModel

processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined")

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

inputs = processor(
    text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
)

outputs = model(**inputs)
logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities

References