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import torch
import numpy as np
import mmcv
from mmengine.visualization import Visualizer
from third_parts.sam2.build_sam import build_sam2
from third_parts.sam2.sam2_image_predictor import SAM2ImagePredictor
from mmdet.structures.mask import bitmap_to_polygon
IMG_PATH = 'assets/view.jpg'
MODEL_CKPT = "work_dirs/ckpt/sam2_hiera_large.pt"
MODEL_CFG = "sam2_hiera_l.yaml"
def prepare():
torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
if __name__ == '__main__':
prepare()
sam2_model = build_sam2(MODEL_CFG, MODEL_CKPT, device="cuda")
predictor = SAM2ImagePredictor(sam2_model)
image = mmcv.imread(IMG_PATH)
predictor.set_image(image)
input_point = np.array([[500, 475]])
input_label = np.array([1])
masks, scores, logits = predictor.predict(
point_coords=input_point,
point_labels=input_label,
multimask_output=True,
)
sorted_ind = np.argsort(scores)[::-1]
masks = masks[sorted_ind]
scores = scores[sorted_ind]
logits = logits[sorted_ind]
visualizer = Visualizer(image=image)
masks = masks.astype(bool)
masks = masks[0:1]
polygons = []
for i, mask in enumerate(masks):
contours, _ = bitmap_to_polygon(mask)
polygons.extend(contours)
visualizer.draw_polygons(polygons, edge_colors='w', alpha=0.8)
visualizer.draw_binary_masks(masks, alphas=0.8)
visualizer.draw_points(input_point, 'r', marker='*')
result = visualizer.get_image()
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