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import os |
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import json |
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import cv2 |
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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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from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection |
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from sam2.sam2_image_predictor import SAM2ImagePredictor |
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from sam2.build_sam import build_sam2 |
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VALID_DIR = {"left", "right", "front", "back"} |
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def load_dir_map(jsonl_path: str): |
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mp = {} |
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with open(jsonl_path, "r", encoding="utf-8") as f: |
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for line in f: |
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line = line.strip() |
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if not line: |
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continue |
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try: |
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obj = json.loads(line) |
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except Exception: |
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continue |
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vid = obj.get("video_id", None) |
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direc = obj.get("direction", None) |
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if isinstance(direc, str): |
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direc = direc.strip().lower() |
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if direc not in VALID_DIR: |
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direc = None |
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else: |
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direc = None |
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if vid is not None: |
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mp[str(vid)] = direc |
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return mp |
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def save_mask(mask_bool: np.ndarray, out_path: str): |
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os.makedirs(os.path.dirname(out_path), exist_ok=True) |
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mask_uint8 = (mask_bool.astype(np.uint8) * 255) |
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cv2.imwrite(out_path, mask_uint8) |
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def build_models( |
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device, |
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dino_dir="/mnt/prev_nas/qhy/MagicMotion/trajectory_construction/Grounded_SAM2/checkpoints/grounding-dino-tiny", |
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sam2_checkpoint="/mnt/prev_nas/qhy/MagicMotion/trajectory_construction/Grounded_SAM2/checkpoints/sam2_hiera_large.pt", |
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model_cfg="sam2_hiera_l.yaml", |
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): |
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processor = AutoProcessor.from_pretrained(dino_dir) |
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grounding_model = AutoModelForZeroShotObjectDetection.from_pretrained(dino_dir).to(device) |
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sam2_model = build_sam2(model_cfg, sam2_checkpoint) |
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predictor = SAM2ImagePredictor(sam2_model) |
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return processor, grounding_model, predictor |
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@torch.no_grad() |
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def segment_single_image(image_path, text_prompt, processor, grounding_model, predictor, device, |
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box_threshold=0.25, text_threshold=0.3): |
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image_pil = Image.open(image_path).convert("RGB") |
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image_np = np.array(image_pil) |
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text = text_prompt.strip().lower() |
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if not text.endswith("."): |
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text += "." |
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inputs = processor(images=image_pil, text=text, return_tensors="pt").to(device) |
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outputs = grounding_model(**inputs) |
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results = processor.post_process_grounded_object_detection( |
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outputs, |
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inputs.input_ids, |
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box_threshold=box_threshold, |
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text_threshold=text_threshold, |
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target_sizes=[image_pil.size[::-1]] |
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) |
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boxes = results[0]["boxes"].detach().cpu().numpy() |
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if len(boxes) == 0: |
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return None, False |
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predictor.set_image(image_np) |
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masks, _, _ = predictor.predict( |
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point_coords=None, |
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point_labels=None, |
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box=boxes, |
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multimask_output=False |
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) |
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if masks.ndim == 4: |
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masks = masks.squeeze(1) |
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final_mask = np.any(masks, axis=0) |
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return final_mask, True |
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def main(): |
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import argparse |
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p = argparse.ArgumentParser() |
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p.add_argument("--prompts_json", default="/mnt/prev_nas/qhy_1/datasets/unedit_image_prompts/genspace_prompts_vlm.json") |
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p.add_argument("--dir_jsonl", default="/mnt/5T_nas/cwl/wan/OmniGen2/data_configs/train/example/edit/qwen_direction_merged.jsonl") |
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p.add_argument("--img_root", default="/mnt/prev_nas/qhy_1/datasets/flux_gen_images_edit") |
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p.add_argument("--img_ext", default=".png", help="when image file is {video_id}{img_ext}") |
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p.add_argument("--out_dir", default="/mnt/prev_nas/qhy_1/datasets/flux_gen_images_masks") |
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p.add_argument("--overwrite", action="store_true") |
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p.add_argument("--start", type=int, default=0) |
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p.add_argument("--end", type=int, default=-1) |
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p.add_argument("--dino_dir", default="/mnt/prev_nas/qhy/MagicMotion/trajectory_construction/Grounded_SAM2/checkpoints/grounding-dino-tiny") |
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p.add_argument("--sam2_ckpt", default="/mnt/prev_nas/qhy/MagicMotion/trajectory_construction/Grounded_SAM2/checkpoints/sam2_hiera_large.pt") |
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p.add_argument("--sam2_cfg", default="sam2_hiera_l.yaml") |
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args = p.parse_args() |
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dir_map = load_dir_map(args.dir_jsonl) |
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with open(args.prompts_json, "r", encoding="utf-8") as f: |
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data = json.load(f) |
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samples = data["samples"] |
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start = max(0, args.start) |
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end = len(samples) if args.end < 0 else min(len(samples), args.end) |
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samples = samples[start:end] |
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print(f"samples: [{start}:{end}) -> {len(samples)}") |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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processor, grounding_model, predictor = build_models( |
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device=device, |
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dino_dir=args.dino_dir, |
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sam2_checkpoint=args.sam2_ckpt, |
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model_cfg=args.sam2_cfg, |
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) |
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for s in samples: |
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video_id = s.get("sample_id") |
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obj_class = s.get("object_class") |
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if not video_id or not obj_class: |
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continue |
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direction = dir_map.get(video_id, None) |
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if direction is None: |
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direction = dir_map.get(video_id + ".png", None) |
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if direction is None: |
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continue |
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img_path = os.path.join(args.img_root, video_id) |
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if not os.path.exists(img_path): |
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img_path2 = os.path.join(args.img_root, video_id + args.img_ext) |
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if os.path.exists(img_path2): |
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img_path = img_path2 |
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else: |
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print("missing image:", img_path, "or", img_path2) |
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continue |
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out_name = video_id |
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if not out_name.lower().endswith(".png"): |
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out_name = out_name + ".png" |
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out_path = os.path.join(args.out_dir, out_name) |
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if (not args.overwrite) and os.path.exists(out_path): |
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continue |
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mask, ok = segment_single_image( |
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img_path, obj_class, processor, grounding_model, predictor, device |
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) |
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if not ok or mask is None: |
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print(f"no detection: {video_id} text={obj_class}") |
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continue |
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save_mask(mask, out_path) |
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print("saved:", out_path) |
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if __name__ == "__main__": |
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main() |
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