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import os
import cv2
import numpy as np
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
from sam2.sam2_image_predictor import SAM2ImagePredictor
from sam2.build_sam import build_sam2
def segment_single_image(image_path, text_prompt,
sam2_checkpoint="/mnt/prev_nas/qhy/MagicMotion/trajectory_construction/Grounded_SAM2/checkpoints/sam2_hiera_large.pt",
model_cfg="sam2_hiera_l.yaml"):
"""
对单张图片进行文本引导分割，返回二值掩码。
Args:
image_path (str): 输入图片路径
text_prompt (str): 文本提示（如 "car"）
sam2_checkpoint (str): SAM2 模型路径
model_cfg (str): SAM2 配置文件名
Returns:
mask (np.ndarray): 二值掩码 (H, W), dtype=bool
success (bool): 是否成功检测到物体
"""
# === 1. 加载模型 ===
device = "cuda" if torch.cuda.is_available() else "cpu"
# Grounding DINO
processor = AutoProcessor.from_pretrained("/mnt/prev_nas/qhy/MagicMotion/trajectory_construction/Grounded_SAM2/checkpoints/grounding-dino-tiny")
grounding_model = AutoModelForZeroShotObjectDetection.from_pretrained(
"/mnt/prev_nas/qhy/MagicMotion/trajectory_construction/Grounded_SAM2/checkpoints/grounding-dino-tiny"
).to(device)
# SAM2 Image Predictor
sam2_model = build_sam2(model_cfg, sam2_checkpoint)
predictor = SAM2ImagePredictor(sam2_model)
# === 2. 读取图像 ===
image_pil = Image.open(image_path).convert("RGB")
image_np = np.array(image_pil)
# === 3. 文本预处理（Grounding DINO 要求小写 + 句号）===
text = text_prompt.strip().lower()
if not text.endswith("."):
text += "."
# === 4. Grounding DINO 检测边界框 ===
inputs = processor(images=image_pil, text=text, return_tensors="pt").to(device)
with torch.no_grad():
outputs = grounding_model(**inputs)
results = processor.post_process_grounded_object_detection(
outputs,
inputs.input_ids,
box_threshold=0.25,
text_threshold=0.3,
target_sizes=[image_pil.size[::-1]] # (H, W)
)
boxes = results[0]["boxes"].cpu().numpy()
if len(boxes) == 0:
print(f"❌ 未检测到 '{text_prompt}'")
return None, False
print(f"✅ 检测到 {len(boxes)} 个 '{text_prompt}'")
# === 5. SAM2 生成掩码 ===
predictor.set_image(image_np)
masks, _, _ = predictor.predict(
point_coords=None,
point_labels=None,
box=boxes, # 可一次处理多个框
multimask_output=False
)
# 合并所有检测到的物体（逻辑或）
if masks.ndim == 4:
masks = masks.squeeze(1) # (N, H, W)
final_mask = np.any(masks, axis=0) # (H, W), bool
return final_mask, True
def save_mask(mask, output_path):
"""保存二值掩码为 PNG（白色=前景，黑色=背景）"""
mask_uint8 = (mask * 255).astype(np.uint8)
cv2.imwrite(output_path, mask_uint8)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="单图文本引导分割")
parser.add_argument("--image", required=True, help="输入图片路径")
parser.add_argument("--text", required=True, help="文本提示（如 'car'）")
parser.add_argument("--output", default="mask.png", help="输出掩码路径")
args = parser.parse_args()
mask, success = segment_single_image(args.image, args.text)
if success:
save_mask(mask, args.output)
print(f"✅ 掩码已保存至: {args.output}")
else:
print("❌ 分割失败")
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