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https://github.com/SikongJueluo/Mini-Nav.git
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69 lines
1.9 KiB
Python
69 lines
1.9 KiB
Python
from typing import Any
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import numpy as np
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from PIL import Image
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def segment_image(
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mask_generator: Any,
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image: Image.Image,
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min_area: int = 32 * 32,
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max_masks: int = 5,
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points_per_batch=64,
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) -> list[dict[str, Any]]:
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"""Segment image using SAM to extract object masks.
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Args:
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mask_generator: SAM2 mask generator.
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image: PIL Image to segment.
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min_area: Minimum mask area threshold in pixels.
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max_masks: Maximum number of masks to return.
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points_per_batch: Number of prompt points to process in each batch.
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Returns:
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List of mask dictionaries with keys:
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- segment: Binary mask (numpy array)
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- area: Mask area in pixels
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- bbox: Bounding box [x, y, width, height]
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- predicted_iou: Model's confidence in the mask
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- stability_score: Stability score for the mask
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"""
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# Convert PIL Image to numpy array
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image_np = np.array(image.convert("RGB"))
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# Generate masks
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masks = mask_generator(image_np, points_per_batch=points_per_batch)["masks"]
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if not masks:
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return []
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# Filter by minimum area
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filtered_masks = [m for m in masks if m["area"] >= min_area]
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if not filtered_masks:
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return []
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# Sort by area (largest first) and limit to max_masks
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sorted_masks = sorted(filtered_masks, key=lambda x: x["area"], reverse=True)
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return sorted_masks[:max_masks]
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def extract_masked_region(
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image: Image.Image,
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mask: np.ndarray,
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) -> Image.Image:
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"""Extract masked region from image.
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Args:
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image: Original PIL Image.
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mask: Binary mask as numpy array (True = keep).
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Returns:
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PIL Image with only the masked region visible.
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"""
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image_np = np.array(image.convert("RGB"))
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# Apply mask
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masked_np = image_np * mask[:, :, np.newaxis]
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return Image.fromarray(masked_np.astype(np.uint8))
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