process_idm_data.py

import os
import sys
import cv2
import json
import numpy as np
from scipy.spatial.transform import Rotation as R
from tqdm import tqdm
from multiprocessing import Pool, cpu_count


# =========================
# Config
# =========================
STEP = 5
ACTION_SEQ_LEN = 16
SKIP_CAMS = {"104422070044", "104422070042", "135122079702", "104122061850", "104122063678", "105422061350", "f0461559"}
WINDOW_STRIDE = 5
IMAGE_EXT = ".png"
FLOW_STEP = 3
FLOW_THRESHOLD = 0.005
MIN_CONSECUTIVE = 15
ACTION_THRESHOLD = 1e-3
FLOW_RESIZE_WIDTH = 320

# WINDOW_STRIDE = 10  


# =========================
# Helper Functions
# =========================
def pose7d_to_matrix(pose7d):
    x, y, z, qx, qy, qz, qw = pose7d
    T = np.eye(4, dtype=np.float64)
    T[:3, :3] = R.from_quat([qx, qy, qz, qw]).as_matrix()
    T[:3, 3] = [x, y, z]
    return T


def compute_action(pose_t, pose_tp1, grip_tp1):
    T_t = pose7d_to_matrix(pose_t)
    T_tp1 = pose7d_to_matrix(pose_tp1)
    T_rel = np.linalg.inv(T_t) @ T_tp1

    dxyz = T_rel[:3, 3]
    drot = R.from_matrix(T_rel[:3, :3]).as_euler("xyz", degrees=False)

    return np.concatenate([dxyz, drot, [grip_tp1]], axis=0).astype(float).tolist()


def get_eef_state_from_pose7d(pose7d):
    pose7d = np.asarray(pose7d, dtype=float)
    xyz = pose7d[:3]
    quat = pose7d[3:]
    rpy = R.from_quat(quat).as_euler("xyz", degrees=False)
    return np.concatenate([xyz, rpy], axis=0).astype(float).tolist()


def load_metadata(task_dir):
    candidates = [
        os.path.join(task_dir, "metadata.json"),
        os.path.join(task_dir, "metadata"),
    ]
    for p in candidates:
        if os.path.exists(p):
            try:
                with open(p, "r") as f:
                    return json.load(f)
            except Exception:
                pass

    npy_candidates = [
        os.path.join(task_dir, "metadata.npy"),
    ]
    for p in npy_candidates:
        if os.path.exists(p):
            obj = np.load(p, allow_pickle=True)
            if hasattr(obj, "item"):
                try:
                    return obj.item()
                except Exception:
                    pass

    raise FileNotFoundError(f"Cannot find readable metadata in {task_dir}")


_CALIB_CACHE = {}


def load_calibration(calib_root, calib_id):
    calib_id = str(calib_id)
    if calib_id in _CALIB_CACHE:
        return _CALIB_CACHE[calib_id]

    calib_dir = os.path.join(calib_root, calib_id)
    if not os.path.isdir(calib_dir):
        raise FileNotFoundError(f"Calibration folder not found: {calib_dir}")

    extrinsics_path = os.path.join(calib_dir, "extrinsics.npy")
    intrinsics_path = os.path.join(calib_dir, "intrinsics.npy")
    devices_path = os.path.join(calib_dir, "devices.npy")

    extrinsics = np.load(extrinsics_path, allow_pickle=True).item()
    intrinsics = None
    devices = None

    if os.path.exists(intrinsics_path):
        intrinsics = np.load(intrinsics_path, allow_pickle=True).item()
    if os.path.exists(devices_path):
        devices = np.load(devices_path, allow_pickle=True)

    result = (calib_dir, extrinsics, intrinsics, devices)
    _CALIB_CACHE[calib_id] = result
    return result


def normalize_tcp_stream(tcp_stream):
    if isinstance(tcp_stream, list):
        return tcp_stream

    if isinstance(tcp_stream, dict):
        keys = sorted(tcp_stream.keys(), key=lambda x: int(x))
        out = []
        for k in keys:
            v = tcp_stream[k]
            if isinstance(v, dict):
                item = dict(v)
                if "timestamp" not in item:
                    item["timestamp"] = int(k)
                out.append(item)
            else:
                raise ValueError("Unsupported tcp stream dict value format.")
        return out

    raise ValueError(f"Unsupported tcp stream format: {type(tcp_stream)}")


def normalize_gripper_stream(grip_stream):
    if isinstance(grip_stream, dict):
        out = {}
        for k, v in grip_stream.items():
            out[int(k)] = v
        return out

    if isinstance(grip_stream, list):
        out = {}
        for item in grip_stream:
            ts = int(item["timestamp"])
            out[ts] = item
        return out

    raise ValueError(f"Unsupported gripper stream format: {type(grip_stream)}")


def get_gripper_value(grip_dict, timestamp):
    if timestamp not in grip_dict:
        return 0.0

    g = grip_dict[timestamp]

    if isinstance(g, dict):
        if "gripper_info" in g:
            info = g["gripper_info"]
            if isinstance(info, (list, tuple, np.ndarray)) and len(info) > 0:
                return float(info[0])

        if "gripper_command" in g:
            cmd = g["gripper_command"]
            if isinstance(cmd, (list, tuple, np.ndarray)) and len(cmd) > 0:
                return float(cmd[0])
            return float(cmd)

        if "gripper" in g:
            val = g["gripper"]
            if isinstance(val, (list, tuple, np.ndarray)) and len(val) > 0:
                return float(val[0])
            return float(val)

    if isinstance(g, (list, tuple, np.ndarray)):
        return float(g[0])

    return float(g)


# =========================
# Merged: read video once, extract frames + compute optical flow
# =========================
def read_video_extract_and_flow(video_path, images_dir, flow_step, flow_resize_width=None):
    """
    Single-pass video reading that simultaneously:
      1) Extracts all frames as images to `images_dir`
      2) Computes optical flow on downscaled grayscale frames (sampled every `flow_step`)

    Args:
        video_path: path to video file
        images_dir: directory to write frame_XXXXXX.png
        flow_step: compute flow between every `flow_step`-th frame
        flow_resize_width: if set, resize frames to this width before flow computation.
                           Height is auto-calculated to preserve aspect ratio.

    Returns:
        n_frames: total number of frames
        flow_mags: np.array of mean flow magnitudes between sampled frames
        sampled_indices: list of original frame indices used for flow
    """
    os.makedirs(images_dir, exist_ok=True)

    cap = cv2.VideoCapture(video_path)
    if not cap.isOpened():
        raise ValueError(f"Cannot open video: {video_path}")

    n_frames = 0
    # For optical flow: keep track of the previous sampled gray frame
    prev_gray_small = None
    flow_mags = []
    sampled_indices = []

    while True:
        ret, frame = cap.read()
        if not ret:
            break

        idx = n_frames
        n_frames += 1

        # --- 1) Write frame image (skip if already exists) ---
        out_path = os.path.join(images_dir, f"frame_{idx:06d}{IMAGE_EXT}")
        if not os.path.exists(out_path):
            cv2.imwrite(out_path, frame)

        # --- 2) Optical flow on sampled frames ---
        if idx % flow_step == 0:
            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

            # Downscale for flow computation
            if flow_resize_width is not None and gray.shape[1] > flow_resize_width:
                scale = flow_resize_width / gray.shape[1]
                new_h = int(gray.shape[0] * scale)
                gray_small = cv2.resize(gray, (flow_resize_width, new_h),
                                        interpolation=cv2.INTER_AREA)
            else:
                gray_small = gray

            sampled_indices.append(idx)

            if prev_gray_small is not None:
                flow = cv2.calcOpticalFlowFarneback(
                    prev_gray_small, gray_small, None, 0.5, 3, 15, 3, 5, 1.2, 0
                )
                mag = np.mean(np.sqrt(flow[..., 0] ** 2 + flow[..., 1] ** 2))
                flow_mags.append(mag)

            prev_gray_small = gray_small

    cap.release()
    return n_frames, np.array(flow_mags), sampled_indices


# def compute_optical_flow_step(video_path, step):
#     cap = cv2.VideoCapture(video_path)
#     if not cap.isOpened():
#         raise ValueError(f"Cannot open video: {video_path}")

#     frames = []
#     while True:
#         ret, frame = cap.read()
#         if not ret:
#             break
#         frames.append(frame)
#     cap.release()

#     sampled_indices = list(range(0, len(frames), step))

#     flow_mags = []
#     for i in range(len(sampled_indices) - 1):
#         idx_t = sampled_indices[i]
#         idx_tp1 = sampled_indices[i + 1]
#         gray_t = cv2.cvtColor(frames[idx_t], cv2.COLOR_BGR2GRAY)
#         gray_tp1 = cv2.cvtColor(frames[idx_tp1], cv2.COLOR_BGR2GRAY)
#         flow = cv2.calcOpticalFlowFarneback(
#             gray_t, gray_tp1, None, 0.5, 3, 15, 3, 5, 1.2, 0
#         )
#         mag = np.mean(np.sqrt(flow[..., 0] ** 2 + flow[..., 1] ** 2))
#         flow_mags.append(mag)

#     return np.array(flow_mags), sampled_indices, len(frames)


def find_active_segment(flow_mags, threshold, min_consecutive):
    is_active = flow_mags > threshold
    N = len(is_active)

    start = 0
    for i in range(N - min_consecutive + 1):
        if all(is_active[i:i + min_consecutive]):
            start = i
            break

    end = N - 1
    for i in range(N - 1, min_consecutive - 2, -1):
        check_start = max(0, i - min_consecutive + 1)
        if all(is_active[check_start:i + 1]):
            end = i
            break

    return start, end


def compute_action_norms_for_range(tcp_list, grip_dict, start_frame, end_frame, step):
    """
    For each original frame i in [start_frame, end_frame - step],
    compute action from i -> i+step, then take norm of first 6 dims.
    """
    frame_indices = []
    norms = []

    max_i = end_frame - step
    for i in range(start_frame, max_i + 1):
        pose_t = np.asarray(tcp_list[i]["tcp"], dtype=float)
        pose_tp1 = np.asarray(tcp_list[i + step]["tcp"], dtype=float)
        ts_tp1 = int(tcp_list[i + step]["timestamp"])
        grip_value = get_gripper_value(grip_dict, ts_tp1)

        action = compute_action(pose_t, pose_tp1, grip_value)
        norm = np.linalg.norm(np.asarray(action[:6], dtype=float))

        frame_indices.append(i)
        norms.append(norm)

    return frame_indices, norms


def trim_by_action_threshold(frame_indices, norms, threshold):
    """
    Trim leading and trailing low-action region.
    Returns valid_start, valid_end in original frame index.
    valid_end means the last allowed sample start/end reference boundary.
    """
    if len(frame_indices) == 0:
        return None, None

    left = 0
    while left < len(norms) and norms[left] <= threshold:
        left += 1

    right = len(norms) - 1
    while right >= 0 and norms[right] <= threshold:
        right -= 1

    if left > right:
        return None, None

    valid_start = frame_indices[left]
    valid_end = frame_indices[right] + STEP
    return valid_start, valid_end


def find_video_file(cam_dir):
    candidates = ["color.mp4", "color.avi", "color.video", "rgb.mp4"]
    for name in candidates:
        p = os.path.join(cam_dir, name)
        if os.path.exists(p):
            return p
    return None


# def extract_all_frames(video_path, images_dir):
#     """
#     Extract all frames once into images_dir:
#       frame_000000.png
#       frame_000001.png
#       ...
#     Returns total frame count extracted/found.
#     """
#     os.makedirs(images_dir, exist_ok=True)

#     cap = cv2.VideoCapture(video_path)
#     if not cap.isOpened():
#         raise ValueError(f"Cannot open video: {video_path}")

#     idx = 0
#     while True:
#         ret, frame = cap.read()
#         if not ret:
#             break

#         out_path = os.path.join(images_dir, f"frame_{idx:06d}{IMAGE_EXT}")
#         if not os.path.exists(out_path):
#             cv2.imwrite(out_path, frame)
#         idx += 1

#     cap.release()
#     return idx


def build_one_sample(
    task_id,
    cam_id,
    start_idx,
    tcp_list,
    grip_dict,
    camera_pose,
    cam_dir,
    dataset_root,
):
    last_frame_idx = start_idx + ACTION_SEQ_LEN * STEP
    if last_frame_idx >= len(tcp_list):
        return None

    images_dir = os.path.join(cam_dir, "images")

    num_frames = 5
    frame_interval = (ACTION_SEQ_LEN * STEP) // (num_frames - 1)  # = 20
    image_indices = [start_idx + i * frame_interval for i in range(num_frames)]

    image_rels = []
    for fidx in image_indices:
        abs_path = os.path.join(images_dir, f"frame_{fidx:06d}{IMAGE_EXT}")
        if not os.path.exists(abs_path):
            return None
        rel_path = os.path.relpath(abs_path, dataset_root).replace("\\", "/")
        image_rels.append(rel_path)

    pose0 = np.asarray(tcp_list[start_idx]["tcp"], dtype=float)
    eef_state = get_eef_state_from_pose7d(pose0)

    action_seq = []
    for k in range(ACTION_SEQ_LEN):
        idx_t = start_idx + k * STEP
        idx_tp1 = start_idx + (k + 1) * STEP

        if idx_tp1 >= len(tcp_list):
            return None

        pose_t = np.asarray(tcp_list[idx_t]["tcp"], dtype=float)
        pose_tp1 = np.asarray(tcp_list[idx_tp1]["tcp"], dtype=float)
        ts_tp1 = int(tcp_list[idx_tp1]["timestamp"])
        grip_value = get_gripper_value(grip_dict, ts_tp1)

        action = compute_action(pose_t, pose_tp1, grip_value)
        action_seq.append(action)

    item = {
        "id": f"{task_id}/{cam_id}/{start_idx:06d}",
        "image_0": image_rels[0],
        "image_1": image_rels[1],
        "image_2": image_rels[2],
        "image_3": image_rels[3],
        "image_4": image_rels[4],
        "camera_pose": np.asarray(camera_pose, dtype=float).tolist(),
        "eef_state": eef_state,
        "action": action_seq,
    }
    return item


def process_camera(
    task_dir,
    task_id,
    cam_dir_name,
    tcp_all,
    grip_all,
    extrinsics,
    metadata,
    dataset_root,
):
    cam_id = cam_dir_name.replace("cam_", "")
    cam_dir = os.path.join(task_dir, cam_dir_name)

    if cam_id in SKIP_CAMS:
        print(f"  [{cam_id}] skip: in SKIP_CAMS")
        return []

    bad_views = set(metadata.get("bad_calib_view", []))
    if cam_id in bad_views:
        print(f"  [{cam_id}] skip: in metadata.bad_calib_view")
        return []

    if cam_id not in tcp_all:
        print(f"  [{cam_id}] skip: missing in tcp_base.npy")
        return []
    if cam_id not in grip_all:
        print(f"  [{cam_id}] skip: missing in gripper.npy")
        return []
    if cam_id not in extrinsics:
        print(f"  [{cam_id}] skip: missing in extrinsics")
        return []
    if extrinsics[cam_id] is None:
        print(f"  [{cam_id}] skip: extrinsics is None")
        return []

    video_path = find_video_file(cam_dir)
    if video_path is None:
        print(f"  [{cam_id}] skip: no color video")
        return []

    tcp_list = normalize_tcp_stream(tcp_all[cam_id])
    grip_dict = normalize_gripper_stream(grip_all[cam_id])
    images_dir = os.path.join(cam_dir, "images")
    print(f"  [{cam_id}] reading video (extract frames + optical flow) -> {images_dir}")
    n_frames, flow_mags, sampled_indices = read_video_extract_and_flow(
        video_path=video_path,
        images_dir=images_dir,
        flow_step=FLOW_STEP,
        flow_resize_width=FLOW_RESIZE_WIDTH,
    )

    n_tcp = len(tcp_list)
    if n_frames != n_tcp:
        raise ValueError(
            f"{task_id}/{cam_id}: extracted video frames ({n_frames}) != tcp length ({n_tcp})"
        )

    if n_frames < ACTION_SEQ_LEN * STEP + 1:
        print(f"  [{cam_id}] skip: too short, video/tcp={n_frames}")
        return []

    # =========================
    # 1) optical flow filter
    # =========================
    flow_start_idx, flow_end_idx = find_active_segment(
        flow_mags, FLOW_THRESHOLD, MIN_CONSECUTIVE
    )

    flow_start_frame = sampled_indices[flow_start_idx]
    flow_end_frame = sampled_indices[min(flow_end_idx + 1, len(sampled_indices) - 1)]

    print(f"  [{cam_id}] flow valid range: [{flow_start_frame}, {flow_end_frame}]")

    # =========================
    # 2) action filter inside flow-valid range
    # =========================
    frame_indices, norms = compute_action_norms_for_range(
        tcp_list=tcp_list,
        grip_dict=grip_dict,
        start_frame=flow_start_frame,
        end_frame=flow_end_frame,
        step=STEP,
    )

    valid_start, valid_end = trim_by_action_threshold(
        frame_indices, norms, ACTION_THRESHOLD
    )

    if valid_start is None or valid_end is None:
        print(f"  [{cam_id}] skip: no valid segment after action filtering")
        return []

    print(f"  [{cam_id}] final valid range after action filter: [{valid_start}, {valid_end}]")

    # Need at least 16 actions = 80 frames span
    if valid_start + ACTION_SEQ_LEN * STEP > valid_end:
        print(f"  [{cam_id}] skip: filtered segment too short")
        return []

    # =========================
    # 3) sliding window on filtered range
    # =========================
    camera_pose = extrinsics[cam_id]
    items = []

    start_idx = valid_start
    while start_idx + ACTION_SEQ_LEN * STEP <= valid_end:
        sample = build_one_sample(
            task_id=task_id,
            cam_id=cam_id,
            start_idx=start_idx,
            tcp_list=tcp_list,
            grip_dict=grip_dict,
            camera_pose=camera_pose,
            cam_dir=cam_dir,
            dataset_root=dataset_root,
        )
        if sample is not None:
            items.append(sample)
        start_idx += WINDOW_STRIDE

    print(f"  [{cam_id}] generated {len(items)} samples")
    return items


def process_task(task_dir, dataset_root, calib_root):
    task_id = os.path.basename(task_dir.rstrip("/"))
    print(f"\nProcessing task: {task_id}")

    metadata = load_metadata(task_dir)

    calib_id = metadata["calib"]
    calib_dir, extrinsics, intrinsics, devices = load_calibration(calib_root, calib_id)

    print(f"  calib_id: {calib_id}")
    print(f"  calib_dir: {calib_dir}")
    print(f"  calib_quality: {metadata.get('calib_quality', 'N/A')}")

    transform_dir = os.path.join(task_dir, "transformed")
    # tcp_path = os.path.join(transform_dir, "tcp_base.npy")
    tcp_path = os.path.join(transform_dir, "tcp.npy")
    grip_path = os.path.join(transform_dir, "gripper.npy")

    if not os.path.exists(tcp_path) or not os.path.exists(grip_path):
        print("  skip task: missing transformed/tcp_base.npy or gripper.npy")
        return []

    tcp_all = np.load(tcp_path, allow_pickle=True).item()
    grip_all = np.load(grip_path, allow_pickle=True).item()

    cam_dirs = sorted([
        d for d in os.listdir(task_dir)
        if d.startswith("cam_") and os.path.isdir(os.path.join(task_dir, d))
    ])

    task_items = []
    for cam_dir_name in cam_dirs:
        cam_items = process_camera(
            task_dir=task_dir,
            task_id=task_id,
            cam_dir_name=cam_dir_name,
            tcp_all=tcp_all,
            grip_all=grip_all,
            extrinsics=extrinsics,
            metadata=metadata,
            dataset_root=dataset_root,
        )
        task_items.extend(cam_items)

    print(f"  task total samples: {len(task_items)}")
    return task_items


def find_task_dirs(dataset_root):
    task_dirs = []
    for name in sorted(os.listdir(dataset_root)):
        if "human" in name:
            continue
        path = os.path.join(dataset_root, name)
        if os.path.isdir(path) and "task_" in name:
            task_dirs.append(path)
    return task_dirs


def process_task_wrapper(args):
    task_dir, dataset_root, calib_root = args
    try:
        return process_task(task_dir, dataset_root, calib_root)
    except Exception as e:
        print(f"Error processing {task_dir}: {e}")
        return []


def main(dataset_root, calib_root, output_root):
    os.makedirs(output_root, exist_ok=True)

    all_items = []

    task_dirs = find_task_dirs(dataset_root)
    print(f"Found {len(task_dirs)} task folders")

    n_workers = min(cpu_count(), len(task_dirs))
    args = [(t, dataset_root, calib_root) for t in task_dirs]
    
    import random
    random.seed(42)
    task_dirs = random.sample(task_dirs, min(60, len(task_dirs)))

    data_jsonl_path = os.path.join(output_root, "data.jsonl")
    total = 0

    with open(data_jsonl_path, "a") as f: 
        with Pool(processes=n_workers) as pool:
            for task_items in tqdm(pool.imap_unordered(process_task_wrapper, args),
                                   total=len(task_dirs)):
                for item in task_items:
                    f.write(json.dumps(item) + "\n")
                f.flush()  
                total += len(task_items)

    print("\n" + "=" * 80)
    print(f"\nDone. Total samples: {total}")
    print(f"Saved: {data_jsonl_path}")
    print("=" * 80)


if __name__ == "__main__":
    if len(sys.argv) != 4:
        print("Usage:")
        print("  python build_idm_data.py <dataset_root> <calib_root> <output_root>")
        sys.exit(1)

    dataset_root = sys.argv[1]
    calib_root = sys.argv[2]
    output_root = sys.argv[3]

    if not os.path.isdir(dataset_root):
        print(f"dataset_root does not exist: {dataset_root}")
        sys.exit(1)

    if not os.path.isdir(calib_root):
        print(f"calib_root does not exist: {calib_root}")
        sys.exit(1)

    main(dataset_root, calib_root, output_root)