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import os
import json
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
VALID_DIR = {"left", "right", "front", "back"}
def load_dir_map(jsonl_path: str):
mp = {}
with open(jsonl_path, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if not line:
continue
try:
obj = json.loads(line)
except Exception:
continue
vid = obj.get("video_id", None)
direc = obj.get("direction", None)
if isinstance(direc, str):
direc = direc.strip().lower()
if direc not in VALID_DIR:
direc = None
else:
direc = None
if vid is not None:
mp[str(vid)] = direc
return mp
def save_mask(mask_bool: np.ndarray, out_path: str):
os.makedirs(os.path.dirname(out_path), exist_ok=True)
mask_uint8 = (mask_bool.astype(np.uint8) * 255)
cv2.imwrite(out_path, mask_uint8)
def build_models(
device,
dino_dir="/mnt/prev_nas/qhy/MagicMotion/trajectory_construction/Grounded_SAM2/checkpoints/grounding-dino-tiny",
sam2_checkpoint="/mnt/prev_nas/qhy/MagicMotion/trajectory_construction/Grounded_SAM2/checkpoints/sam2_hiera_large.pt",
model_cfg="sam2_hiera_l.yaml",
):
processor = AutoProcessor.from_pretrained(dino_dir)
grounding_model = AutoModelForZeroShotObjectDetection.from_pretrained(dino_dir).to(device)
sam2_model = build_sam2(model_cfg, sam2_checkpoint)
predictor = SAM2ImagePredictor(sam2_model)
return processor, grounding_model, predictor
@torch.no_grad()
def segment_single_image(image_path, text_prompt, processor, grounding_model, predictor, device,
box_threshold=0.25, text_threshold=0.3):
image_pil = Image.open(image_path).convert("RGB")
image_np = np.array(image_pil)
text = text_prompt.strip().lower()
if not text.endswith("."):
text += "."
inputs = processor(images=image_pil, text=text, return_tensors="pt").to(device)
outputs = grounding_model(**inputs)
results = processor.post_process_grounded_object_detection(
outputs,
inputs.input_ids,
box_threshold=box_threshold,
text_threshold=text_threshold,
target_sizes=[image_pil.size[::-1]] # (H, W)
)
boxes = results[0]["boxes"].detach().cpu().numpy()
if len(boxes) == 0:
return None, False
predictor.set_image(image_np)
masks, _, _ = predictor.predict(
point_coords=None,
point_labels=None,
box=boxes,
multimask_output=False
)
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 main():
import argparse
p = argparse.ArgumentParser()
p.add_argument("--prompts_json", default="/mnt/prev_nas/qhy_1/datasets/unedit_image_prompts/genspace_prompts_vlm.json")
p.add_argument("--dir_jsonl", default="/mnt/5T_nas/cwl/wan/OmniGen2/data_configs/train/example/edit/qwen_direction_merged.jsonl")
# 图片用 video_id 定位：{img_root}/{video_id}.png 或 {img_root}/{video_id}
p.add_argument("--img_root", default="/mnt/prev_nas/qhy_1/datasets/flux_gen_images_edit")
p.add_argument("--img_ext", default=".png", help="when image file is {video_id}{img_ext}")
p.add_argument("--out_dir", default="/mnt/prev_nas/qhy_1/datasets/flux_gen_images_masks")
p.add_argument("--overwrite", action="store_true")
p.add_argument("--start", type=int, default=0)
p.add_argument("--end", type=int, default=-1)
p.add_argument("--dino_dir", default="/mnt/prev_nas/qhy/MagicMotion/trajectory_construction/Grounded_SAM2/checkpoints/grounding-dino-tiny")
p.add_argument("--sam2_ckpt", default="/mnt/prev_nas/qhy/MagicMotion/trajectory_construction/Grounded_SAM2/checkpoints/sam2_hiera_large.pt")
p.add_argument("--sam2_cfg", default="sam2_hiera_l.yaml")
args = p.parse_args()
dir_map = load_dir_map(args.dir_jsonl)
with open(args.prompts_json, "r", encoding="utf-8") as f:
data = json.load(f)
samples = data["samples"]
start = max(0, args.start)
end = len(samples) if args.end < 0 else min(len(samples), args.end)
samples = samples[start:end]
print(f"samples: [{start}:{end}) -> {len(samples)}")
device = "cuda" if torch.cuda.is_available() else "cpu"
processor, grounding_model, predictor = build_models(
device=device,
dino_dir=args.dino_dir,
sam2_checkpoint=args.sam2_ckpt,
model_cfg=args.sam2_cfg,
)
for s in samples:
video_id = s.get("sample_id")
obj_class = s.get("object_class")
if not video_id or not obj_class:
continue
# qwen_direction_merged.jsonl 里可能是 allocentric_000.png 这种
direction = dir_map.get(video_id, None)
if direction is None:
direction = dir_map.get(video_id + ".png", None)
# 如果 jsonl 里有 .png，而 video_id 本身已经带扩展名，这里也不会影响
if direction is None:
continue
# 找图片：优先 {img_root}/{video_id}，不存在再试 {video_id}{img_ext}
img_path = os.path.join(args.img_root, video_id)
if not os.path.exists(img_path):
img_path2 = os.path.join(args.img_root, video_id + args.img_ext)
if os.path.exists(img_path2):
img_path = img_path2
else:
print("missing image:", img_path, "or", img_path2)
continue
# 保存同名 mask（统一用 png）
out_name = video_id
if not out_name.lower().endswith(".png"):
out_name = out_name + ".png"
out_path = os.path.join(args.out_dir, out_name)
if (not args.overwrite) and os.path.exists(out_path):
continue
mask, ok = segment_single_image(
img_path, obj_class, processor, grounding_model, predictor, device
)
if not ok or mask is None:
print(f"no detection: {video_id} text={obj_class}")
continue
save_mask(mask, out_path)
print("saved:", out_path)
if __name__ == "__main__":
main()
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