auto_bbox.py

import os
import sys
import cv2
import json
import glob
import argparse
import subprocess
from typing import List, Tuple, Dict, Any

import numpy as np
from tqdm import tqdm


# ----------------- Args -----------------
def parse_args():
    ap = argparse.ArgumentParser("OWLv2 detection on JPG folders (Top-K per image), multi-GPU.")
    ap.add_argument("--input_dir", type=str, required=True, help="Root that contains subfolders of JPGs; if JPGs are directly under input_dir, it will be treated as a single set.")
    ap.add_argument("--startswith", type=str, default="", help="Filter folder name prefix (or input_dir basename if no subfolders).")
    ap.add_argument("--endswith", type=str, default="", help="Filter folder name ends (or input_dir basename if no subfolders).")
    ap.add_argument("--output_dir", type=str, required=True)
    ap.add_argument("--frame_stride", type=int, default=1, help="Sample every N-th image within a folder.")
    ap.add_argument("--top_k", type=int, default=5)
    ap.add_argument("--max_frames", type=int, default=0, help="Max processed images per folder; 0 means no limit.")
    ap.add_argument("--num_workers", type=int, default=1, help="#GPUs/#workers")
    ap.add_argument("--worker_idx", type=int, default=-1, help="internal; >=0 means child worker")
    ap.add_argument("--shard_file", type=str, default="", help="internal; JSON with folder paths for this worker")
    ap.add_argument("--scenic_root", type=str, default="/gz-data/Memory/owlv2/big_vision")
    ap.add_argument("--checkpoint_path", type=str, default="/gz-data/Memory/owlv2/owl2-b16-960-st-ngrams-curated-ft-lvisbase-ens-cold-weight-05_209b65b")
    return ap.parse_args()


# ----------------- Utils -----------------
def _has_jpgs(path: str) -> bool:
    exts = ("*.jpg", "*.jpeg", "*.JPG", "*.JPEG")
    for pat in exts:
        if glob.glob(os.path.join(path, pat)):
            return True
    return False


def iter_image_dirs(input_dir: str, startswith: str, endswith: str) -> List[str]:
    """
    Returns a list of directories to process.
    - If input_dir contains subfolders: return subfolders that contain JPGs and match startswith.
    - Else if input_dir itself contains JPGs and its basename matches startswith: return [input_dir].
    """
    input_dir = os.path.abspath(input_dir)
    subs = sorted([p for p in glob.glob(os.path.join(input_dir, "*")) if os.path.isdir(p)])
    # Prefer subfolders if any exist and contain jpgs
    dirs = [d for d in subs if os.path.basename(d).startswith(startswith) and os.path.basename(d).endswith(endswith) and _has_jpgs(d)]
    if dirs:
        return dirs

    # Fallback: treat input_dir itself as one set if it has jpgs
    base_ok = os.path.basename(os.path.normpath(input_dir)).startswith(startswith) and os.path.basename(d).endswith(endswith)
    if base_ok and _has_jpgs(input_dir):
        return [input_dir]
    return []


def ensure_dir(p: str):
    os.makedirs(p, exist_ok=True)


def draw_single_box(frame_bgr: np.ndarray, box: List[float], color=(0, 255, 0), thickness=2) -> np.ndarray:
    x1, y1, x2, y2 = map(int, box)
    out = frame_bgr.copy()
    cv2.rectangle(out, (x1, y1), (x2, y2), color, thickness)
    return out


def list_images_sorted(folder: str) -> List[str]:
    pats = ["*.jpg", "*.jpeg", "*.JPG", "*.JPEG"]
    files = []
    for pat in pats:
        files.extend(glob.glob(os.path.join(folder, pat)))
    # Sort by natural file name order
    return sorted(files)


# ----------------- Worker logic (imports JAX/Scenic inside) -----------------
def worker_run(args, dir_paths: List[str]):
    import sys as _sys
    if args.scenic_root not in _sys.path:
        _sys.path.append(args.scenic_root)

    # Free TF GPU to JAX in this process (why: avoid TF reserving VRAM)
    import tensorflow as tf
    tf.config.experimental.set_visible_devices([], "GPU")

    from scenic.projects.owl_vit import configs
    from scenic.projects.owl_vit import models
    import jax
    import functools
    import owlv2_helper as helper  # must be available in PYTHONPATH

    class OWLv2Objectness:
        def __init__(self, top_k: int = 5):
            self.top_k = top_k
            self.config = configs.owl_v2_clip_b16.get_config(init_mode="canonical_checkpoint")
            if args.checkpoint_path:
                self.config.init_from.checkpoint_path = os.path.abspath(args.checkpoint_path)
            
            print("[OWLv2] checkpoint_path =", self.config.init_from.checkpoint_path)

            print("cp path:", self.config.init_from.checkpoint_path)
            
            self.module = models.TextZeroShotDetectionModule(
                body_configs=self.config.model.body,
                objectness_head_configs=self.config.model.objectness_head,
                normalize=self.config.model.normalize,
                box_bias=self.config.model.box_bias,
            )
            self.variables = self.module.load_variables(self.config.init_from.checkpoint_path)


            self.image_embedder = jax.jit(
                functools.partial(self.module.apply, self.variables, train=False, method=self.module.image_embedder)
            )
            self.objectness_predictor = jax.jit(
                functools.partial(self.module.apply, self.variables, method=self.module.objectness_predictor)
            )
            self.box_predictor = jax.jit(
                functools.partial(self.module.apply, self.variables, method=self.module.box_predictor)
            )

        def detect(self, image_bgr: np.ndarray) -> List[Tuple[List[float], float]]:
            image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
            processed = helper.preprocess_images([image_rgb], self.config.dataset_configs.input_size)[0]
            feature_map = self.image_embedder(processed[None, ...])
            b, h, w, d = feature_map.shape
            image_features = feature_map.reshape(b, h * w, d)

            obj_logits = self.objectness_predictor(image_features)["objectness_logits"]
            raw_boxes = self.box_predictor(image_features=image_features, feature_map=feature_map)["pred_boxes"]

            obj = np.array(obj_logits[0], dtype=np.float32)
            raw_boxes = np.array(raw_boxes[0], dtype=np.float32)
            boxes = helper.rescale_detection_box(raw_boxes, image_rgb)

            if len(obj) == 0:
                return []

            k = min(self.top_k, len(obj))
            thresh = np.partition(obj, -k)[-k]

            filtered: List[Tuple[List[float], float]] = []
            H, W = image_rgb.shape[:2]
            for box, score in zip(boxes, obj):
                if score < thresh:
                    continue
                if helper.too_small(box) or helper.too_large(box, image_rgb):
                    continue
                x1, y1, x2, y2 = box
                x1 = max(0, min(float(x1), W - 1))
                y1 = max(0, min(float(y1), H - 1))
                x2 = max(0, min(float(x2), W - 1))
                y2 = max(0, min(float(y2), H - 1))
                filtered.append(([x1, y1, x2, y2], float(score)))

            kept_boxes = helper.remove_overlapping_bboxes([b for b, _ in filtered])

            def _match_score(bb: List[float]) -> float:
                arr = np.array([b for b, _ in filtered], dtype=np.float32)
                idx = int(np.argmin(np.abs(arr - np.array(bb, dtype=np.float32)).sum(axis=1)))
                return filtered[idx][1]

            return [(bb, _match_score(bb)) for bb in kept_boxes]

    detector = OWLv2Objectness(top_k=args.top_k)

    for dpath in tqdm(dir_paths, desc=f"Worker{args.worker_idx}", unit="set"):
        stem = os.path.basename(os.path.normpath(dpath))
        images = list_images_sorted(dpath)
        if not images:
            print(f"[WARN][w{args.worker_idx}] No JPGs under: {dpath}")
            continue

        saved_cnt = 0
        pbar = tqdm(total=len(images), desc=f"{stem}[w{args.worker_idx}]", unit="img", leave=False)

        for idx, ipath in enumerate(images):
            pbar.update(1)
            if args.frame_stride > 1 and (idx % args.frame_stride) != 0:
                continue

            frame = cv2.imread(ipath, cv2.IMREAD_COLOR)
            if frame is None:
                print(f"[WARN][w{args.worker_idx}] Cannot read: {ipath}")
                continue

            boxes_scores = detector.detect(frame)
            if boxes_scores:
                boxes_scores = sorted(boxes_scores, key=lambda x: x[1], reverse=True)[:args.top_k]

            fname = os.path.basename(ipath)
            for i, (box, score) in enumerate(boxes_scores):
                out_dir = os.path.join(args.output_dir, stem, f"object_{i}")
                ensure_dir(out_dir)
                vis = draw_single_box(frame, box, color=(0, 255, 0), thickness=2)
                cv2.imwrite(os.path.join(out_dir, fname), vis)

            saved_cnt += 1
            if args.max_frames and saved_cnt >= args.max_frames:
                break

        pbar.close()


# ----------------- Master -----------------
def main():
    args = parse_args()

    # Child worker path
    if args.worker_idx >= 0:
        if not args.shard_file or not os.path.exists(args.shard_file):
            raise RuntimeError("Worker requires --shard_file with JSON list of folder paths.")
        with open(args.shard_file, "r", encoding="utf-8") as f:
            dir_paths = json.load(f)
        worker_run(args, dir_paths)
        return

    # Master path
    dir_paths = iter_image_dirs(args.input_dir, args.startswith, args.endswith)
    if not dir_paths:
        print(f"[INFO] No JPG folders (or JPGs) startwith '{args.startswith}' and endwith '{args.endswith}' under {args.input_dir}")
        return

    num_workers = max(1, int(args.num_workers))
    shards: List[List[str]] = [[] for _ in range(num_workers)]
    for i, d in enumerate(dir_paths):
        shards[i % num_workers].append(d)

    procs = []
    tmp_dir = os.path.join(args.output_dir, "_shards_tmp")
    ensure_dir(tmp_dir)

    for w in range(num_workers):
        shard_path = os.path.join(tmp_dir, f"shard_{w}.json")
        with open(shard_path, "w", encoding="utf-8") as f:
            json.dump(shards[w], f, ensure_ascii=False, indent=0)

        # Bind GPU: cycle through available GPU ids [0..num_workers-1]
        env = os.environ.copy()
        env["CUDA_VISIBLE_DEVICES"] = str(w)  # one GPU per worker

        cmd = [
            sys.executable, __file__,
            "--input_dir", args.input_dir,
            "--startswith", args.startswith,
            "--output_dir", args.output_dir,
            "--frame_stride", str(args.frame_stride),
            "--top_k", str(args.top_k),
            "--max_frames", str(args.max_frames),
            "--num_workers", str(num_workers),
            "--worker_idx", str(w),
            "--shard_file", shard_path,
            "--scenic_root", args.scenic_root,
            "--checkpoint_path", args.checkpoint_path,
        ]
        print(f"[Master] Launch worker {w}: GPU={env['CUDA_VISIBLE_DEVICES']}  folders={len(shards[w])}")
        procs.append(subprocess.Popen(cmd, env=env))

    # wait
    rc = 0
    for p in procs:
        p.wait()
        rc |= p.returncode

    if rc != 0:
        print("[Master] Some workers failed. Return code:", rc)
    else:
        print("[Master] All workers done. Output:", args.output_dir)


if __name__ == "__main__":
    main()