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import os, sys, math
import json, time
import numpy as np
import argparse
from isaacgym import gymtorch, gymapi
import torch

CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
CSV_FRAME_RATE = 50.0
CSV_TOTAL_COLS = 36
CSV_ROOT_POS_SLICE = slice(0, 3)
CSV_ROOT_QUAT_SLICE = slice(3, 7)
CSV_DOF_SLICE = slice(7, 36)

def get_label_indices(labels, prefix):
    return [i for i, label in enumerate(labels) if label.startswith(prefix)]

def load_motion_data_json(json_path):
    with open(json_path, 'r') as f:
        motion = json.load(f)

    labels = motion.get('Labels', [])
    frames = np.array(motion['Frames'], dtype=np.float32)

    # 提取 DOF、root_pos、root_quat 的列索引
    dof_indices = get_label_indices(labels, "dof_pos/")
    root_pos_indices = get_label_indices(labels, "root_pos/")
    root_quat_indices = get_label_indices(labels, "root_quat/")

    print(f"Found {len(dof_indices)} DOF channels:")
    for idx in dof_indices:
        print(f"  {labels[idx]}")
    print(f"root_pos indices: {[labels[i] for i in root_pos_indices]}")
    print(f"root_quat indices: {[labels[i] for i in root_quat_indices]}")

    return (
        frames[:, dof_indices],
        frames[:, root_pos_indices],
        frames[:, root_quat_indices],
        motion['FrameDuration']
    )

def load_motion_data_csv(csv_path):
    frames = np.loadtxt(csv_path, delimiter=',', dtype=np.float32)
    if frames.ndim == 1:
        frames = frames[None, :]

    if frames.shape[1] < CSV_TOTAL_COLS:
        raise ValueError(
            f"CSV requires at least {CSV_TOTAL_COLS} columns, got {frames.shape[1]}: {csv_path}"
        )

    if frames.shape[1] > CSV_TOTAL_COLS:
        print(
            f"CSV has {frames.shape[1]} columns, using first {CSV_TOTAL_COLS} columns: {csv_path}"
        )
        frames = frames[:, :CSV_TOTAL_COLS]

    print(f"Loaded CSV motion: {csv_path}")
    print(f"root_pos columns: {CSV_ROOT_POS_SLICE.start + 1}~{CSV_ROOT_POS_SLICE.stop}")
    print(f"root_quat columns: {CSV_ROOT_QUAT_SLICE.start + 1}~{CSV_ROOT_QUAT_SLICE.stop}")
    print(f"dof columns: {CSV_DOF_SLICE.start + 1}~{CSV_DOF_SLICE.stop}")

    return (
        frames[:, CSV_DOF_SLICE],
        frames[:, CSV_ROOT_POS_SLICE],
        frames[:, CSV_ROOT_QUAT_SLICE],
        1.0 / CSV_FRAME_RATE
    )

def load_motion_data(motion_path):
    ext = os.path.splitext(motion_path)[1].lower()
    if ext == ".json":
        return load_motion_data_json(motion_path)
    if ext == ".csv":
        return load_motion_data_csv(motion_path)
    raise ValueError(f"Unsupported motion file format: {motion_path}")

def set_base_pose(gym, sim, env, actor, root_pos, root_quat):
    """
    设置机器人 base link 的位置和旋转
    :param root_pos: [x, y, z]
    :param root_quat: [x, y, z, w]
    """
    root_state_tensor = gym.acquire_actor_root_state_tensor(sim)
    root_state = gymtorch.wrap_tensor(root_state_tensor)

    # 获取 actor 在 tensor 中的索引（通常为 0，如果你只加载了一个 actor）
    actor_index = 0

    # 更新 base 位置和四元数
    root_state[actor_index, :3] = torch.tensor(root_pos, dtype=torch.float32)
    root_state[actor_index, 3:7] = torch.tensor(root_quat, dtype=torch.float32)

    # 写回物理引擎
    gym.set_actor_root_state_tensor(sim, root_state_tensor)

def set_dof_positions(gym, env, actor, target_pos):
    dof_states = np.zeros(len(target_pos), dtype=gymapi.DofState.dtype)
    dof_states['pos'] = target_pos
    dof_states['vel'] = np.zeros_like(target_pos)
    gym.set_actor_dof_states(env, actor, dof_states, gymapi.STATE_POS + gymapi.STATE_VEL)

def parse_args():
    parser = argparse.ArgumentParser(description="Isaac Gym Preview 4 Motion Visualizer")

    parser.add_argument('--file_name', type=str,
                        default="mixamo/low_jump.json",
                        help='Motion file name (.json or .csv). Default: "mixamo/low_jump.json"')

    parser.add_argument('--robot_type', type=str,
                        choices=['adam_lite', 'adam_sp'],
                        default='adam_lite',
                        help='Robot type to load. Default: "adam_lite"')

    return parser.parse_args()

def resolve_existing_path(candidates):
    for rel_path in candidates:
        abs_path = os.path.join(CURRENT_DIR, rel_path)
        if os.path.exists(abs_path):
            return rel_path
    raise FileNotFoundError(f"No valid path found in candidates: {candidates}")

def get_robot_paths(robot_type):
    if robot_type == "adam_lite":
        urdf_path = resolve_existing_path([
            "robot_description/adam_lite/urdf/adam_lite.urdf",
            "robot_description/adam_lite/adam_lite.urdf",
        ])
        motion_dir = "adam_lite"
    elif robot_type == "adam_sp":
        urdf_path = resolve_existing_path([
            "robot_description/adam_sp/urdf/adam_sp.urdf",
            "robot_description/adam_sp/adam_sp.urdf",
        ])
        motion_dir = "adam_sp"
    else:
        raise ValueError(f"Unknown robot_type: {robot_type}")

    return urdf_path, motion_dir

def main():
    args = parse_args()
    # 加载机器人资产
    asset_path, motion_dir = get_robot_paths(args.robot_type)
    asset_root = os.path.dirname(asset_path)
    asset_file = os.path.basename(asset_path)

    # 加载动作数据
    motion_path = os.path.join(CURRENT_DIR, motion_dir, args.file_name)
    frames_dof, frames_root_pos, frames_root_quat, frame_duration = load_motion_data(motion_path)
    num_frames = len(frames_dof)
    current_frame = 0
    # 初始化 gym
    gym = gymapi.acquire_gym()

    # 设置仿真参数
    sim_params = gymapi.SimParams()
    sim_params.dt = frame_duration  # 模拟频率
    sim_params.substeps = 2
    sim_params.up_axis = gymapi.UP_AXIS_Z
    sim_params.gravity = gymapi.Vec3(0.0, 0.0, 0.0)  # 关闭重力
    sim_params.use_gpu_pipeline = False              # 禁用 GPU pipeline（更简单）
    sim = gym.create_sim(0, 0, gymapi.SIM_PHYSX, sim_params)

    if sim is None:
        raise Exception("Failed to create sim")

    # 加载地面
    plane_params = gymapi.PlaneParams()
    plane_params.normal = gymapi.Vec3(0, 0, 1)
    gym.add_ground(sim, plane_params)


    asset_options = gymapi.AssetOptions()
    asset_options.fix_base_link = False
    asset_options.disable_gravity = True   
    asset_options.collapse_fixed_joints = True  

    robot_asset = gym.load_asset(sim, asset_root, asset_file, asset_options)

    # 获取 DOF 数量
    num_dof = gym.get_asset_dof_count(robot_asset)
    if frames_dof.shape[1] != num_dof:
        print(
            f"DOF count mismatch: motion has {frames_dof.shape[1]}, "
            f"asset requires {num_dof}. Auto-aligning."
        )
        if frames_dof.shape[1] > num_dof:
            frames_dof = frames_dof[:, :num_dof]
        else:
            pad = np.zeros((frames_dof.shape[0], num_dof - frames_dof.shape[1]), dtype=np.float32)
            frames_dof = np.concatenate([frames_dof, pad], axis=1)

    # 创建环境
    spacing = 1.0
    lower = gymapi.Vec3(-spacing, -spacing, 0.0)
    upper = gymapi.Vec3(spacing, spacing, spacing)
    env = gym.create_env(sim, lower, upper, 8)

    # 创建机器人 actor
    pose = gymapi.Transform()
    pose.p = gymapi.Vec3(0.0, 0.0, 1.0)
    pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)

    actor = gym.create_actor(env, robot_asset, pose, "actor", 0, 0)

    cam_props = gymapi.CameraProperties()
    viewer = gym.create_viewer(sim, cam_props)
    if viewer is None:
        raise Exception("Failed to create viewer")

    # 设置相机参数
    CAMERA_DISTANCE = -3.0    # 距离 base 的距离
    CAMERA_HEIGHT = 1.0      # 高度
    CAMERA_ANGLE = math.radians(90)  # 视角角度（弧度）
    while not gym.query_viewer_has_closed(viewer):
        if current_frame < num_frames:
            target_dof = frames_dof[current_frame]
            target_root_pos = frames_root_pos[current_frame]
            target_root_quat = frames_root_quat[current_frame]

            # 设置 Base 位姿
            set_base_pose(gym, sim, env, actor, target_root_pos, target_root_quat)

            # 设置关节角度
            set_dof_positions(gym, env, actor, target_dof)

            current_frame += 1
        else:
            print("Animation finished.")
            break

        # Step simulation and render
        gym.simulate(sim)
        gym.fetch_results(sim, True)
        gym.step_graphics(sim)
        # 获取机器人 base 位置
        root_state_tensor = gym.acquire_actor_root_state_tensor(sim)
        root_state = gymtorch.wrap_tensor(root_state_tensor)
        root_pos = root_state[0, :3].cpu().numpy()

        # 使用球面坐标系计算相机位置
        cam_x = root_pos[0] + CAMERA_DISTANCE * math.cos(CAMERA_ANGLE)
        cam_y = root_pos[1] + CAMERA_DISTANCE * math.sin(CAMERA_ANGLE)
        cam_z = root_pos[2] + CAMERA_HEIGHT

        cam_pos = gymapi.Vec3(cam_x, cam_y, cam_z)
        cam_target = gymapi.Vec3(root_pos[0], root_pos[1], root_pos[2])

        # 更新相机视角
        gym.viewer_camera_look_at(viewer, None, cam_pos, cam_target)

        gym.draw_viewer(viewer, sim, True)
        gym.sync_frame_time(sim)
        time.sleep(0.001)

if __name__ == "__main__":
    main()

	import os, sys, math
	import json, time
	import numpy as np
	import argparse
	from isaacgym import gymtorch, gymapi
	import torch

	CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
	CSV_FRAME_RATE = 50.0
	CSV_TOTAL_COLS = 36
	CSV_ROOT_POS_SLICE = slice(0, 3)
	CSV_ROOT_QUAT_SLICE = slice(3, 7)
	CSV_DOF_SLICE = slice(7, 36)

	def get_label_indices(labels, prefix):
	return [i for i, label in enumerate(labels) if label.startswith(prefix)]

	def load_motion_data_json(json_path):
	with open(json_path, 'r') as f:
	motion = json.load(f)

	labels = motion.get('Labels', [])
	frames = np.array(motion['Frames'], dtype=np.float32)

	# 提取 DOF、root_pos、root_quat 的列索引
	dof_indices = get_label_indices(labels, "dof_pos/")
	root_pos_indices = get_label_indices(labels, "root_pos/")
	root_quat_indices = get_label_indices(labels, "root_quat/")

	print(f"Found {len(dof_indices)} DOF channels:")
	for idx in dof_indices:
	print(f" {labels[idx]}")
	print(f"root_pos indices: {[labels[i] for i in root_pos_indices]}")
	print(f"root_quat indices: {[labels[i] for i in root_quat_indices]}")

	return (
	frames[:, dof_indices],
	frames[:, root_pos_indices],
	frames[:, root_quat_indices],
	motion['FrameDuration']
	)

	def load_motion_data_csv(csv_path):
	frames = np.loadtxt(csv_path, delimiter=',', dtype=np.float32)
	if frames.ndim == 1:
	frames = frames[None, :]

	if frames.shape[1] < CSV_TOTAL_COLS:
	raise ValueError(
	f"CSV requires at least {CSV_TOTAL_COLS} columns, got {frames.shape[1]}: {csv_path}"
	)

	if frames.shape[1] > CSV_TOTAL_COLS:
	print(
	f"CSV has {frames.shape[1]} columns, using first {CSV_TOTAL_COLS} columns: {csv_path}"
	)
	frames = frames[:, :CSV_TOTAL_COLS]

	print(f"Loaded CSV motion: {csv_path}")
	print(f"root_pos columns: {CSV_ROOT_POS_SLICE.start + 1}~{CSV_ROOT_POS_SLICE.stop}")
	print(f"root_quat columns: {CSV_ROOT_QUAT_SLICE.start + 1}~{CSV_ROOT_QUAT_SLICE.stop}")
	print(f"dof columns: {CSV_DOF_SLICE.start + 1}~{CSV_DOF_SLICE.stop}")

	return (
	frames[:, CSV_DOF_SLICE],
	frames[:, CSV_ROOT_POS_SLICE],
	frames[:, CSV_ROOT_QUAT_SLICE],
	1.0 / CSV_FRAME_RATE
	)

	def load_motion_data(motion_path):
	ext = os.path.splitext(motion_path)[1].lower()
	if ext == ".json":
	return load_motion_data_json(motion_path)
	if ext == ".csv":
	return load_motion_data_csv(motion_path)
	raise ValueError(f"Unsupported motion file format: {motion_path}")

	def set_base_pose(gym, sim, env, actor, root_pos, root_quat):
	"""
	设置机器人 base link 的位置和旋转
	:param root_pos: [x, y, z]
	:param root_quat: [x, y, z, w]
	"""
	root_state_tensor = gym.acquire_actor_root_state_tensor(sim)
	root_state = gymtorch.wrap_tensor(root_state_tensor)

	# 获取 actor 在 tensor 中的索引（通常为 0，如果你只加载了一个 actor）
	actor_index = 0

	# 更新 base 位置和四元数
	root_state[actor_index, :3] = torch.tensor(root_pos, dtype=torch.float32)
	root_state[actor_index, 3:7] = torch.tensor(root_quat, dtype=torch.float32)

	# 写回物理引擎
	gym.set_actor_root_state_tensor(sim, root_state_tensor)

	def set_dof_positions(gym, env, actor, target_pos):
	dof_states = np.zeros(len(target_pos), dtype=gymapi.DofState.dtype)
	dof_states['pos'] = target_pos
	dof_states['vel'] = np.zeros_like(target_pos)
	gym.set_actor_dof_states(env, actor, dof_states, gymapi.STATE_POS + gymapi.STATE_VEL)

	def parse_args():
	parser = argparse.ArgumentParser(description="Isaac Gym Preview 4 Motion Visualizer")

	parser.add_argument('--file_name', type=str,
	default="mixamo/low_jump.json",
	help='Motion file name (.json or .csv). Default: "mixamo/low_jump.json"')

	parser.add_argument('--robot_type', type=str,
	choices=['adam_lite', 'adam_sp'],
	default='adam_lite',
	help='Robot type to load. Default: "adam_lite"')

	return parser.parse_args()

	def resolve_existing_path(candidates):
	for rel_path in candidates:
	abs_path = os.path.join(CURRENT_DIR, rel_path)
	if os.path.exists(abs_path):
	return rel_path
	raise FileNotFoundError(f"No valid path found in candidates: {candidates}")

	def get_robot_paths(robot_type):
	if robot_type == "adam_lite":
	urdf_path = resolve_existing_path([
	"robot_description/adam_lite/urdf/adam_lite.urdf",
	"robot_description/adam_lite/adam_lite.urdf",
	])
	motion_dir = "adam_lite"
	elif robot_type == "adam_sp":
	urdf_path = resolve_existing_path([
	"robot_description/adam_sp/urdf/adam_sp.urdf",
	"robot_description/adam_sp/adam_sp.urdf",
	])
	motion_dir = "adam_sp"
	else:
	raise ValueError(f"Unknown robot_type: {robot_type}")

	return urdf_path, motion_dir

	def main():
	args = parse_args()
	# 加载机器人资产
	asset_path, motion_dir = get_robot_paths(args.robot_type)
	asset_root = os.path.dirname(asset_path)
	asset_file = os.path.basename(asset_path)

	# 加载动作数据
	motion_path = os.path.join(CURRENT_DIR, motion_dir, args.file_name)
	frames_dof, frames_root_pos, frames_root_quat, frame_duration = load_motion_data(motion_path)
	num_frames = len(frames_dof)
	current_frame = 0
	# 初始化 gym
	gym = gymapi.acquire_gym()

	# 设置仿真参数
	sim_params = gymapi.SimParams()
	sim_params.dt = frame_duration # 模拟频率
	sim_params.substeps = 2
	sim_params.up_axis = gymapi.UP_AXIS_Z
	sim_params.gravity = gymapi.Vec3(0.0, 0.0, 0.0) # 关闭重力
	sim_params.use_gpu_pipeline = False # 禁用 GPU pipeline（更简单）
	sim = gym.create_sim(0, 0, gymapi.SIM_PHYSX, sim_params)

	if sim is None:
	raise Exception("Failed to create sim")

	# 加载地面
	plane_params = gymapi.PlaneParams()
	plane_params.normal = gymapi.Vec3(0, 0, 1)
	gym.add_ground(sim, plane_params)


	asset_options = gymapi.AssetOptions()
	asset_options.fix_base_link = False
	asset_options.disable_gravity = True
	asset_options.collapse_fixed_joints = True

	robot_asset = gym.load_asset(sim, asset_root, asset_file, asset_options)

	# 获取 DOF 数量
	num_dof = gym.get_asset_dof_count(robot_asset)
	if frames_dof.shape[1] != num_dof:
	print(
	f"DOF count mismatch: motion has {frames_dof.shape[1]}, "
	f"asset requires {num_dof}. Auto-aligning."
	)
	if frames_dof.shape[1] > num_dof:
	frames_dof = frames_dof[:, :num_dof]
	else:
	pad = np.zeros((frames_dof.shape[0], num_dof - frames_dof.shape[1]), dtype=np.float32)
	frames_dof = np.concatenate([frames_dof, pad], axis=1)

	# 创建环境
	spacing = 1.0
	lower = gymapi.Vec3(-spacing, -spacing, 0.0)
	upper = gymapi.Vec3(spacing, spacing, spacing)
	env = gym.create_env(sim, lower, upper, 8)

	# 创建机器人 actor
	pose = gymapi.Transform()
	pose.p = gymapi.Vec3(0.0, 0.0, 1.0)
	pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)

	actor = gym.create_actor(env, robot_asset, pose, "actor", 0, 0)

	cam_props = gymapi.CameraProperties()
	viewer = gym.create_viewer(sim, cam_props)
	if viewer is None:
	raise Exception("Failed to create viewer")

	# 设置相机参数
	CAMERA_DISTANCE = -3.0 # 距离 base 的距离
	CAMERA_HEIGHT = 1.0 # 高度
	CAMERA_ANGLE = math.radians(90) # 视角角度（弧度）
	while not gym.query_viewer_has_closed(viewer):
	if current_frame < num_frames:
	target_dof = frames_dof[current_frame]
	target_root_pos = frames_root_pos[current_frame]
	target_root_quat = frames_root_quat[current_frame]

	# 设置 Base 位姿
	set_base_pose(gym, sim, env, actor, target_root_pos, target_root_quat)

	# 设置关节角度
	set_dof_positions(gym, env, actor, target_dof)

	current_frame += 1
	else:
	print("Animation finished.")
	break

	# Step simulation and render
	gym.simulate(sim)
	gym.fetch_results(sim, True)
	gym.step_graphics(sim)
	# 获取机器人 base 位置
	root_state_tensor = gym.acquire_actor_root_state_tensor(sim)
	root_state = gymtorch.wrap_tensor(root_state_tensor)
	root_pos = root_state[0, :3].cpu().numpy()

	# 使用球面坐标系计算相机位置
	cam_x = root_pos[0] + CAMERA_DISTANCE * math.cos(CAMERA_ANGLE)
	cam_y = root_pos[1] + CAMERA_DISTANCE * math.sin(CAMERA_ANGLE)
	cam_z = root_pos[2] + CAMERA_HEIGHT

	cam_pos = gymapi.Vec3(cam_x, cam_y, cam_z)
	cam_target = gymapi.Vec3(root_pos[0], root_pos[1], root_pos[2])

	# 更新相机视角
	gym.viewer_camera_look_at(viewer, None, cam_pos, cam_target)

	gym.draw_viewer(viewer, sim, True)
	gym.sync_frame_time(sim)
	time.sleep(0.001)

	if __name__ == "__main__":
	main()