simplify2
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@@ -28,10 +28,20 @@ class SAM2StreamingProcessor:
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self.device = torch.device(config.get('hardware', {}).get('device', 'cuda'))
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self.device = torch.device(config.get('hardware', {}).get('device', 'cuda'))
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# SAM2 model configuration
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# SAM2 model configuration
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model_cfg = config.get('matting', {}).get('sam2_model_cfg', 'sam2.1_hiera_l')
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model_cfg_name = config.get('matting', {}).get('sam2_model_cfg', 'sam2.1_hiera_l')
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checkpoint = config.get('matting', {}).get('sam2_checkpoint',
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checkpoint = config.get('matting', {}).get('sam2_checkpoint',
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'segment-anything-2/checkpoints/sam2.1_hiera_large.pt')
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'segment-anything-2/checkpoints/sam2.1_hiera_large.pt')
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# Map config name to full path
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config_mapping = {
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'sam2.1_hiera_t': 'segment-anything-2/sam2/configs/sam2.1/sam2.1_hiera_t.yaml',
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'sam2.1_hiera_s': 'segment-anything-2/sam2/configs/sam2.1/sam2.1_hiera_s.yaml',
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'sam2.1_hiera_b+': 'segment-anything-2/sam2/configs/sam2.1/sam2.1_hiera_b+.yaml',
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'sam2.1_hiera_l': 'segment-anything-2/sam2/configs/sam2.1/sam2.1_hiera_l.yaml',
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}
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model_cfg = config_mapping.get(model_cfg_name, model_cfg_name)
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# Build predictor (simple, clean approach)
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# Build predictor (simple, clean approach)
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self.predictor = build_sam2_video_predictor(
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self.predictor = build_sam2_video_predictor(
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model_cfg,
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model_cfg,
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