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import os |
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import numpy as np |
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import torch |
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from PIL import Image |
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import time |
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from segment_anything import sam_model_registry, SamPredictor |
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def sam_init(device_id=0): |
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sam_checkpoint = os.path.join(os.path.dirname(__file__), "sam_vit_h_4b8939.pth") |
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model_type = "vit_h" |
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device = "cuda:{}".format(device_id) if torch.cuda.is_available() else "cpu" |
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sam = sam_model_registry[model_type](checkpoint=sam_checkpoint).to(device=device) |
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predictor = SamPredictor(sam) |
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return predictor |
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def sam_out_nosave(predictor, input_image, *bbox_sliders): |
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bbox = np.array(bbox_sliders) |
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image = np.asarray(input_image) |
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start_time = time.time() |
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predictor.set_image(image) |
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masks_bbox, scores_bbox, logits_bbox = predictor.predict( |
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box=bbox, |
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multimask_output=True |
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) |
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print(f"SAM Time: {time.time() - start_time:.3f}s") |
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out_image = np.zeros((image.shape[0], image.shape[1], 4), dtype=np.uint8) |
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out_image[:, :, :3] = image |
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out_image_bbox = out_image.copy() |
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out_image_bbox[:, :, 3] = masks_bbox[-1].astype(np.uint8) * 255 |
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torch.cuda.empty_cache() |
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return Image.fromarray(out_image_bbox, mode='RGBA') |
