# Use SAM2 with videos
import torch
from sam2.sam2_video_predictor import SAM2VideoPredictor
predictor = SAM2VideoPredictor.from_pretrained(bukuroo/EdgeTAM-ONNX)
with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
state = predictor.init_state(<your_video>)
# add new prompts and instantly get the output on the same frame
frame_idx, object_ids, masks = predictor.add_new_points(state, <your_prompts>):
# propagate the prompts to get masklets throughout the video
for frame_idx, object_ids, masks in predictor.propagate_in_video(state):
...EdgeTAM
ONNX models for inference with EZONNX
Model type:
SegmentationOfficial repository:
EdgeTAMSetup
pip install git+https://github.com/ikeboo/ezonnx.gitUsage
from ezonnx import EdgeTAM, visualize_images tam = EdgeTAM() res = tam.set_image("images/cat.jpg") # add point with positive or not and its label res = tam.set_point((240, 180), True, 1) res = tam.set_point((50,50), True, 2) res = tam.set_point((400,50), True, 2) res = tam.set_point((150, 330), True, 3) visualize_images("Masked Image",res.visualized_img)
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# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(bukuroo/EdgeTAM-ONNX) with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): predictor.set_image(<your_image>) masks, _, _ = predictor.predict(<input_prompts>)