NLFPoseExtract/nlf_render.py

import cv2
import numpy as np
import math
from PIL import Image
from render_3d.taichi_cylinder import render_whole
from NLFPoseExtract.nlf_draw import intrinsic_matrix_from_field_of_view, process_data_to_COCO_format, preview_nlf_2d, p3d_to_p2d
from concurrent.futures import ProcessPoolExecutor, as_completed
from pose_draw.draw_pose_utils import draw_pose_to_canvas_np, scale_image_hw_keep_size
import pose_draw.draw_utils as draw_utils
import torch.multiprocessing as mp
import os
os.environ['PYOPENGL_PLATFORM'] = 'osmesa'
import copy
import random
import torch
try:
    import moviepy.editor as mpy
except Exception:
    import moviepy as mpy

def p3d_single_p2d(points, intrinsic_matrix):
    X, Y, Z = points[0], points[1], points[2]
    u = (intrinsic_matrix[0, 0] * X / Z) + intrinsic_matrix[0, 2]
    v = (intrinsic_matrix[1, 1] * Y / Z) + intrinsic_matrix[1, 2]
    u_np = u.cpu().numpy()
    v_np = v.cpu().numpy()
    return np.array([u_np, v_np])

def scale_around_center(points, center, dim, scale=1.0):
    return (points[:, dim] - center[dim]) * scale + center[dim]

def shift_dwpose_according_to_nlf(smpl_poses, aligned_poses, ori_intrinstics, modified_intrinstics, height, width, scale_x = 1.0, scale_y = 1.0):
    ########## warning: 会改变body； shift 之后 body是不准的 ##########
    for i in range(len(smpl_poses)):
        persons_joints_list = smpl_poses[i]
        poses_list = aligned_poses[i]
        # 对里面每一个人，取关节并进行变形；并且修改2d；如果3d不存在，把2d的手/脸也去掉
        for person_idx, person_joints in enumerate(persons_joints_list):
            face = poses_list["faces"][person_idx]
            right_hand = poses_list["hands"][2 * person_idx]
            left_hand = poses_list["hands"][2 * person_idx + 1]
            candidate = poses_list["bodies"]["candidate"][person_idx]
            # 注意，这里不是coco format
            person_joint_15_2d_shift = p3d_single_p2d(person_joints[15], modified_intrinstics) - p3d_single_p2d(person_joints[15], ori_intrinstics) if person_joints[15, 2] > 0.01 else np.array([0.0, 0.0])  # face
            person_joint_20_2d_shift = p3d_single_p2d(person_joints[20], modified_intrinstics) - p3d_single_p2d(person_joints[20], ori_intrinstics) if person_joints[20, 2] > 0.01 else np.array([0.0, 0.0])  # right hand
            person_joint_21_2d_shift = p3d_single_p2d(person_joints[21], modified_intrinstics) - p3d_single_p2d(person_joints[21], ori_intrinstics) if person_joints[21, 2] > 0.01 else np.array([0.0, 0.0])  # left hand

            face[:, 0] += person_joint_15_2d_shift[0] / width
            face[:, 1] += person_joint_15_2d_shift[1] / height
            right_hand[:, 0] += person_joint_20_2d_shift[0] / width
            right_hand[:, 1] += person_joint_20_2d_shift[1] / height
            left_hand[:, 0] += person_joint_21_2d_shift[0] / width
            left_hand[:, 1] += person_joint_21_2d_shift[1] / height
            candidate[:, 0] += person_joint_15_2d_shift[0] / width
            candidate[:, 1] += person_joint_15_2d_shift[1] / height

            scales = [scale_x, scale_y]
            # apply camera scale around wrist (hand[0]). 
            for dim in [0,1]:
                right_hand[:, dim] = scale_around_center(right_hand, right_hand[0, :], dim=dim, scale=scales[dim])
                left_hand[:, dim] = scale_around_center(left_hand, left_hand[0, :], dim=dim, scale=scales[dim])

def get_single_pose_cylinder_specs(args):
    """渲染单个pose的辅助函数，用于并行处理"""
    idx, pose, focal, princpt, height, width, colors, limb_seq, draw_seq = args
    cylinder_specs = []
    
    for joints3d in pose:  # 多人
        joints3d = joints3d.cpu().numpy()
        joints3d = process_data_to_COCO_format(joints3d)
        for line_idx in draw_seq:
            line = limb_seq[line_idx]
            start, end = line[0], line[1]
            if np.sum(joints3d[start]) == 0 or np.sum(joints3d[end]) == 0:
                continue
            else:
                cylinder_specs.append((joints3d[start], joints3d[end], colors[line_idx]))
    return cylinder_specs

def get_single_pose_cylinder_specs_mono(args):
    """渲染单个pose的辅助函数，用于并行处理"""
    idx, pose, ori_pose, binary_frame, intrinsic_matrix, height, width, limb_seq, draw_seq = args
    cylinder_specs = []
    
    for joints3d, ori_joint3d in zip(pose, ori_pose):  # 多人
        joints3d = joints3d.cpu().numpy()
        joints3d = process_data_to_COCO_format(joints3d)
        ori_joint3d = ori_joint3d.cpu().numpy()
        ori_joint3d = process_data_to_COCO_format(ori_joint3d)
        specific_color = locate_binary_color(binary_frame, ori_joint3d, height, width)     # 通过3D点的2D投影的像素位置，计算原本这个人对应的颜色
        for line_idx in draw_seq:
            line = limb_seq[line_idx]
            start, end = line[0], line[1]
            if np.sum(joints3d[start]) == 0 or np.sum(joints3d[end]) == 0:
                continue
            else:
                cylinder_specs.append((joints3d[start], joints3d[end], specific_color))
    return cylinder_specs

def get_single_pose_cylinder_specs_colored(args):
    """直接使用传入的每人颜色渲染，不做颜色查找。"""
    idx, pose, person_colors, limb_seq, draw_seq = args
    cylinder_specs = []
    for person_idx, joints3d in enumerate(pose):
        joints3d = joints3d.cpu().numpy()
        joints3d = process_data_to_COCO_format(joints3d)
        color = person_colors[person_idx] if person_idx < len(person_colors) else [0, 0, 0, 1]
        for line_idx in draw_seq:
            line = limb_seq[line_idx]
            start, end = line[0], line[1]
            if np.sum(joints3d[start]) == 0 or np.sum(joints3d[end]) == 0:
                continue
            cylinder_specs.append((joints3d[start], joints3d[end], color))
    return cylinder_specs


def locate_binary_color(binary_frame, ori_joint3d, height, width):
    """通过3D点的2D投影的像素位置，计算原本这个人对应的颜色。
    ori_joint3d: COCO format (18, 3) numpy array
    binary_frame: H x W x 3，BGR，像素颜色只有6种纯色之一
    """
    key_joint_indices = [1, 2, 5, 8, 11]  # neck, left shoulder, right shoulder, left pelvis, right pelvis
    key_joints_3d = ori_joint3d[key_joint_indices]          # (5, 3)
    valid_flag = key_joints_3d[:, 2] > 0.0001

    point_2d = p3d_to_p2d(key_joints_3d[np.newaxis], height, width)[0]  # (5, 3)

    sampled = []
    for is_valid, p2d in zip(valid_flag, point_2d):
        if not is_valid:
            continue
        u = int(round(p2d[0]))
        v = int(round(p2d[1]))
        if 0 <= u < width and 0 <= v < height:
            sampled.append(binary_frame[v, u].astype(np.float32))

    if len(sampled) == 0:
        return [0, 0, 0, 1]

    avg_color = np.mean(sampled, axis=0)
    binarized = (avg_color > 127).astype(np.float32) * 1.0

    return [binarized[0], binarized[1], binarized[2], 1]

def collect_smpl_poses(data):
    uncollected_smpl_poses = [item['nlfpose'] for item in data]
    smpl_poses = [[] for _ in range(len(uncollected_smpl_poses))]
    for frame_idx in range(len(uncollected_smpl_poses)):
        for person_idx in range(len(uncollected_smpl_poses[frame_idx])):  # 每个人（每个bbox）只给出一个pose
            if len(uncollected_smpl_poses[frame_idx][person_idx]) > 0:    # 有返回的骨骼
                smpl_poses[frame_idx].append(uncollected_smpl_poses[frame_idx][person_idx][0])
            else:
                smpl_poses[frame_idx].append(torch.zeros((24, 3), dtype=torch.float32))  # 没有检测到人，就放一个全0的

    return smpl_poses



def collect_smpl_poses_samurai(data):
    uncollected_smpl_poses = [item['nlfpose'] for item in data]
    smpl_poses_first = [[] for _ in range(len(uncollected_smpl_poses))]
    smpl_poses_second = [[] for _ in range(len(uncollected_smpl_poses))]

    for frame_idx in range(len(uncollected_smpl_poses)):
        for person_idx in range(len(uncollected_smpl_poses[frame_idx])):  # 每个人（每个bbox）只给出一个pose
            if len(uncollected_smpl_poses[frame_idx][person_idx]) > 0:    # 有返回的骨骼
                if person_idx == 0:
                    smpl_poses_first[frame_idx].append(uncollected_smpl_poses[frame_idx][person_idx][0]) 
                elif person_idx == 1:
                    smpl_poses_second[frame_idx].append(uncollected_smpl_poses[frame_idx][person_idx][0])
            else:
                if person_idx == 0:
                    smpl_poses_first[frame_idx].append(torch.zeros((24, 3), dtype=torch.float32))  # 没有检测到人，就放一个全0的
                elif person_idx == 1:
                    smpl_poses_second[frame_idx].append(torch.zeros((24, 3), dtype=torch.float32))

    return smpl_poses_first, smpl_poses_second
    



def render_nlf_as_images(data, poses, reshape_pool=None, intrinsic_matrix=None, draw_2d=True, aug_2d=False, aug_cam=False, binary_mask=None, person_colors=None, palette_offset=0):
    """ return a list of images """
    height, width = data[0]['video_height'], data[0]['video_width']
    video_length = len(data)

    base_colors_255_dict = {
        # Warm Colors for Right Side (R.) - Red, Orange, Yellow
        "Red": [255, 0, 0],
        "Orange": [255, 85, 0],
        "Golden Orange": [255, 170, 0],
        "Yellow": [255, 240, 0],
        "Yellow-Green": [180, 255, 0],
        # Cool Colors for Left Side (L.) - Green, Blue, Purple
        "Bright Green": [0, 255, 0],
        "Light Green-Blue": [0, 255, 85],
        "Aqua": [0, 255, 170],
        "Cyan": [0, 255, 255],
        "Sky Blue": [0, 170, 255],
        "Medium Blue": [0, 85, 255],
        "Pure Blue": [0, 0, 255],
        "Purple-Blue": [85, 0, 255],
        "Medium Purple": [170, 0, 255],
        # Neutral/Central Colors (e.g., for Neck, Nose, Eyes, Ears)
        "Grey": [150, 150, 150],
        "Pink-Magenta": [255, 0, 170],
        "Dark Pink": [255, 0, 85],
        "Violet": [100, 0, 255],
        "Dark Violet": [50, 0, 255],
    }

    ordered_colors_255 = [
        base_colors_255_dict["Red"],              # Neck -> R. Shoulder (Red)
        base_colors_255_dict["Cyan"],             # Neck -> L. Shoulder (Cyan)
        base_colors_255_dict["Orange"],           # R. Shoulder -> R. Elbow (Orange)
        base_colors_255_dict["Golden Orange"],    # R. Elbow -> R. Wrist (Golden Orange)
        base_colors_255_dict["Sky Blue"],         # L. Shoulder -> L. Elbow (Sky Blue)
        base_colors_255_dict["Medium Blue"],      # L. Elbow -> L. Wrist (Medium Blue)
        base_colors_255_dict["Yellow-Green"],       # Neck -> R. Hip ( Yellow-Green)
        base_colors_255_dict["Bright Green"],     # R. Hip -> R. Knee (Bright Green - transitioning warm to cool spectrum)
        base_colors_255_dict["Light Green-Blue"], # R. Knee -> R. Ankle (Light Green-Blue - transitioning)
        base_colors_255_dict["Pure Blue"],        # Neck -> L. Hip (Pure Blue)
        base_colors_255_dict["Purple-Blue"],      # L. Hip -> L. Knee (Purple-Blue)
        base_colors_255_dict["Medium Purple"],    # L. Knee -> L. Ankle (Medium Purple)
        base_colors_255_dict["Grey"],             # Neck -> Nose (Grey)
        base_colors_255_dict["Pink-Magenta"],     # Nose -> R. Eye (Pink/Magenta)
        base_colors_255_dict["Dark Violet"],        # R. Eye -> R. Ear (Dark Pink)
        base_colors_255_dict["Pink-Magenta"],           # Nose -> L. Eye (Violet)
        base_colors_255_dict["Dark Violet"],      # L. Eye -> L. Ear (Dark Violet)
    ]

    limb_seq = [
        [1, 2],    # 0 Neck -> R. Shoulder
        [1, 5],    # 1 Neck -> L. Shoulder
        [2, 3],    # 2 R. Shoulder -> R. Elbow
        [3, 4],    # 3 R. Elbow -> R. Wrist
        [5, 6],    # 4 L. Shoulder -> L. Elbow
        [6, 7],    # 5 L. Elbow -> L. Wrist
        [1, 8],    # 6 Neck -> R. Hip
        [8, 9],    # 7 R. Hip -> R. Knee
        [9, 10],   # 8 R. Knee -> R. Ankle
        [1, 11],   # 9 Neck -> L. Hip
        [11, 12],  # 10 L. Hip -> L. Knee
        [12, 13],  # 11 L. Knee -> L. Ankle
        [1, 0],    # 12 Neck -> Nose
        [0, 14],   # 13 Nose -> R. Eye
        [14, 16],  # 14 R. Eye -> R. Ear
        [0, 15],   # 15 Nose -> L. Eye
        [15, 17],  # 16 L. Eye -> L. Ear
    ]

    draw_seq = [0, 2, 3, # Neck -> R. Shoulder -> R. Elbow -> R. Wrist
                1, 4, 5, # Neck -> L. Shoulder -> L. Elbow -> L. Wrist
                6, 7, 8, # Neck -> R. Hip -> R. Knee -> R. Ankle
                9, 10, 11, # Neck -> L. Hip -> L. Knee -> L. Ankle
                12, # Neck -> Nose
                13, 14, # Nose -> R. Eye -> R. Ear
                15, 16, # Nose -> L. Eye -> L. Ear
                ]   # 从近心端往外扩展

    colors = [[c / 300 + 0.15 for c in color_rgb] + [0.8] for color_rgb in ordered_colors_255]
    


    # smpl_poses 会在这里被修改
    if poses is not None or binary_mask is not None or person_colors is not None:
        # 重新收集poses
        smpl_poses = collect_smpl_poses(data)
        if binary_mask is not None:
            original_smpl_poses = copy.deepcopy(smpl_poses)
        if poses is not None:
            aligned_poses = copy.deepcopy(poses)    # 2d poses
            if reshape_pool is not None:
                for i in range(video_length):
                    persons_joints_list = smpl_poses[i]
                    poses_list = aligned_poses[i]
                    # 对里面每一个人，取关节并进行变形；并且修改2d；如果3d不存在，把2d的手/脸也去掉
                    for person_idx, person_joints in enumerate(persons_joints_list):
                        candidate = poses_list['bodies']['candidate'][person_idx]
                        subset = poses_list['bodies']['subset'][person_idx]
                        face = poses_list["faces"][person_idx]
                        right_hand = poses_list["hands"][2 * person_idx]
                        left_hand = poses_list["hands"][2 * person_idx + 1]
                        reshape_pool.apply_random_reshapes(person_joints, candidate, left_hand, right_hand, face, subset)
    else:
        smpl_poses = [item['nlfpose'] for item in data]      # 主要为了兼容多人评测集；搭配process_video_nlf_original


    if intrinsic_matrix is None:
        intrinsic_matrix = intrinsic_matrix_from_field_of_view((height, width))
    focal_x = intrinsic_matrix[0,0]
    focal_y = intrinsic_matrix[1,1]
    princpt = (intrinsic_matrix[0,2], intrinsic_matrix[1,2])  # 主点 (cx, cy)
    if aug_cam and random.random() < 0.3:
        w_shift_factor = random.uniform(-0.04, 0.04)
        h_shift_factor = random.uniform(-0.04, 0.04)
        princpt = (princpt[0] - w_shift_factor * width, princpt[1] - h_shift_factor * height)   # princpt变化和点的变化相反
        new_intrinsic_matrix = copy.deepcopy(intrinsic_matrix)
        new_intrinsic_matrix[0,2] = princpt[0]
        new_intrinsic_matrix[1,2] = princpt[1]
        shift_dwpose_according_to_nlf(smpl_poses, aligned_poses, intrinsic_matrix, new_intrinsic_matrix, height, width)
                
    # person_colors 传入时，为每人生成独立肢体颜色方案（同 render_multi_nlf_as_images 的两套配色）
    if person_colors is not None:
        _palettes_255 = [
            # Person 0: 浅色调
            [[255,150,150],[180,230,240],[255,180,140],[255,215,150],[160,200,255],[100,120,255],
             [200,255,100],[100,255,100],[140,255,180],[120,140,255],[180, 90,255],[190,120,255],
             [210,210,210],[255,120,200],[130, 80,255],[255,120,200],[130, 80,255]],
            # Person 1: 饱和色调
            [[255, 20, 20],[  0,230,255],[255, 60,  0],[255,110,  0],[  0,130,255],[  0, 70,255],
             [160,255, 40],[  0,255, 50],[  0,255,100],[  0,  0,255],[ 80,  0,255],[160,  0,255],
             [130,130,130],[255,  0,150],[ 60,  0,255],[255,  0,150],[ 60,  0,255]],
        ]
        colors_per_person = [
            [[c / 300 + 0.15 for c in rgb] + [0.8]
             for rgb in _palettes_255[(p + palette_offset) % len(_palettes_255)]]
            for p in range(len(person_colors))
        ]

    # 串行获取每一帧的cylinder_specs
    cylinder_specs_list = []
    cylinder_specs_list_mono = []
    for i in range(video_length):
        if person_colors is not None:
            cylinder_specs = []
            for p_idx, person_pose in enumerate(smpl_poses[i]):
                p_limb_colors = colors_per_person[p_idx] if p_idx < len(colors_per_person) else colors
                cylinder_specs.extend(get_single_pose_cylinder_specs(
                    (i, [person_pose], None, None, None, None, p_limb_colors, limb_seq, draw_seq)))
        else:
            cylinder_specs = get_single_pose_cylinder_specs((i, smpl_poses[i], None, None, None, None, colors, limb_seq, draw_seq))
        cylinder_specs_list.append(cylinder_specs)
        if person_colors is not None:
            cylinder_specs_colored = get_single_pose_cylinder_specs_colored((i, smpl_poses[i], person_colors, limb_seq, draw_seq))
            cylinder_specs_list_mono.append(cylinder_specs_colored)
        elif binary_mask is not None:
            cylinder_specs_mono = get_single_pose_cylinder_specs_mono((i, smpl_poses[i], original_smpl_poses[i], binary_mask[i], intrinsic_matrix, height, width, limb_seq, draw_seq))
            cylinder_specs_list_mono.append(cylinder_specs_mono)


    frames_np_rgba = render_whole(cylinder_specs_list, H=height, W=width, fx=focal_x, fy=focal_y, cx=princpt[0], cy=princpt[1])
    frames_np_rgba_mono = render_whole(cylinder_specs_list_mono, H=height, W=width, fx=focal_x, fy=focal_y, cx=princpt[0], cy=princpt[1], use_specular=False) if (binary_mask is not None or person_colors is not None) else None

    bg_color = np.array([0, 0, 0], dtype=np.uint8)
    for frame in frames_np_rgba:
        bg_mask = frame[:, :, 3] == 0
        frame[:, :, :3][bg_mask] = bg_color

    scale_h = random.uniform(0.85, 1.15)
    scale_w = random.uniform(0.85, 1.15)
    rescale_flag = random.random() < 0.4 if reshape_pool is not None else False

    if poses is not None and draw_2d:
        canvas_2d = draw_pose_to_canvas_np(aligned_poses, pool=None, H=height, W=width, reshape_scale=0, show_feet_flag=False, show_body_flag=False, show_cheek_flag=True, dw_hand=True)
        for i in range(len(frames_np_rgba)):
            frame_img = frames_np_rgba[i]
            canvas_img = canvas_2d[i]
            mask = canvas_img != 0
            frame_img[:, :, :3][mask] = canvas_img[mask]
            frames_np_rgba[i] = frame_img       # no alpha blending
            # 在 mono 版上用每人的颜色画 cheek/hand/face 2D 关键点
            if frames_np_rgba_mono is not None and person_colors is not None:
                poses_list = aligned_poses[i]
                n_draw = min(len(poses_list['bodies']['candidate']), len(person_colors))
                for p_idx in range(n_draw):
                    temp_canvas = np.zeros((height, width, 3), dtype=np.uint8)
                    p_candidate = poses_list['bodies']['candidate'][p_idx]
                    p_subset = poses_list['bodies']['subset'][p_idx:p_idx+1]
                    p_faces = poses_list['faces'][p_idx:p_idx+1]
                    p_hands = poses_list['hands'][2*p_idx:2*p_idx+2]
                    temp_canvas = draw_utils.draw_bodypose_augmentation(temp_canvas, p_candidate, p_subset, drop_aug=False, shift_aug=False, all_cheek_aug=True)
                    temp_canvas = draw_utils.draw_handpose(temp_canvas, p_hands)
                    temp_canvas = draw_utils.draw_facepose(temp_canvas, p_faces, optimized_face=True)
                    mask_2d = np.any(temp_canvas != 0, axis=-1)
                    mono_color = [int(c * 255) for c in person_colors[p_idx][:3]]
                    frames_np_rgba_mono[i][:, :, :3][mask_2d] = mono_color
            if aug_2d:
                if rescale_flag:
                    frames_np_rgba[i] = scale_image_hw_keep_size(frames_np_rgba[i], scale_h, scale_w)
                    border_mask = frames_np_rgba[i][:, :, 3] == 0
                    frames_np_rgba[i][:, :, :3][border_mask] = bg_color
                if reshape_pool is not None and random.random() < 0.04:
                    # 4%的概率完全消除某些帧，两组同步
                    frames_np_rgba[i][:, :, :3] = bg_color
                    if frames_np_rgba_mono is not None:
                        frames_np_rgba_mono[i][:, :, 0:3] = 0
                if frames_np_rgba_mono is not None and rescale_flag:
                    frames_np_rgba_mono[i] = scale_image_hw_keep_size(frames_np_rgba_mono[i], scale_h, scale_w)
    else:
        for i in range(len(frames_np_rgba)):
            if aug_2d:
                if rescale_flag:
                    frames_np_rgba[i] = scale_image_hw_keep_size(frames_np_rgba[i], scale_h, scale_w)
                    border_mask = frames_np_rgba[i][:, :, 3] == 0
                    frames_np_rgba[i][:, :, :3][border_mask] = bg_color
                if reshape_pool is not None and random.random() < 0.04:
                    # 4%的概率完全消除某些帧，两组同步
                    frames_np_rgba[i][:, :, :3] = bg_color
                    if frames_np_rgba_mono is not None:
                        frames_np_rgba_mono[i][:, :, 0:3] = 0
                if frames_np_rgba_mono is not None and rescale_flag:
                    frames_np_rgba_mono[i] = scale_image_hw_keep_size(frames_np_rgba_mono[i], scale_h, scale_w)

    if binary_mask is not None or person_colors is not None:
        return frames_np_rgba, frames_np_rgba_mono
    return frames_np_rgba







def render_multi_nlf_as_images(data, poses, reshape_pool=None, intrinsic_matrix=None, draw_2d=True, aug_2d=False, aug_cam=False):
    """ return a list of images """
    height, width = data[0]['video_height'], data[0]['video_width']
    video_length = len(data)

    second_person_base_colors_255_dict = {
        # Warm Colors for Right Side (R.) - Red, Orange, Yellow
        "Red": [255, 20, 20],
        "Orange": [255, 60, 0],
        "Golden Orange": [255, 110, 0],
        "Yellow": [255, 200, 0],
        "Yellow-Green": [160, 255, 40],
        
        # Cool Colors for Left Side (L.) - Green, Blue, Purple
        "Bright Green": [0, 255, 50],
        "Light Green-Blue": [0, 255, 100],
        "Aqua": [0, 255, 200],
        "Cyan": [0, 230, 255],
        "Sky Blue": [0, 130, 255],
        "Medium Blue": [0, 70, 255],
        "Pure Blue": [0, 0, 255],
        "Purple-Blue": [80, 0, 255],
        "Medium Purple": [160, 0, 255],
        
        # Neutral/Central Colors (e.g., for Neck, Nose, Eyes, Ears)
        "Grey": [130, 130, 130],
        "Pink-Magenta": [255, 0, 150],
        "Dark Pink": [255, 0, 100],
        "Violet": [120, 0, 255],
        "Dark Violet": [60, 0, 255],
    }

    first_person_base_colors_255_dict = {
        # Warm Colors for Right Side (R.) - Red, Orange, Yellow
        "Red": [255, 150, 150],
        "Orange": [255, 180, 140],
        "Golden Orange": [255, 215, 150],
        "Yellow": [255, 240, 170],
        "Yellow-Green": [200, 255, 100],
        
        # Cool Colors for Left Side (L.) - Green, Blue, Purple
        "Bright Green": [100, 255, 100],
        "Light Green-Blue": [140, 255, 180],
        "Aqua": [150, 240, 200],
        "Cyan": [180, 230, 240],
        "Sky Blue": [160, 200, 255],
        "Medium Blue": [100, 120, 255],
        "Pure Blue": [120, 140, 255],
        "Purple-Blue": [180, 90, 255],
        "Medium Purple": [190, 120, 255],
        
        # Neutral/Central Colors (e.g., for Neck, Nose, Eyes, Ears)
        "Grey": [210, 210, 210],
        "Pink-Magenta": [255, 120, 200],
        "Dark Pink": [255, 150, 180],
        "Violet": [200, 90, 255],
        "Dark Violet": [130, 80, 255],
    }

    base_colors_255_dict_list = [first_person_base_colors_255_dict, second_person_base_colors_255_dict]
    ordered_colors_255_list = [[
        base_colors_255_dict["Red"],              # Neck -> R. Shoulder (Red)
        base_colors_255_dict["Cyan"],             # Neck -> L. Shoulder (Cyan)
        base_colors_255_dict["Orange"],           # R. Shoulder -> R. Elbow (Orange)
        base_colors_255_dict["Golden Orange"],    # R. Elbow -> R. Wrist (Golden Orange)
        base_colors_255_dict["Sky Blue"],         # L. Shoulder -> L. Elbow (Sky Blue)
        base_colors_255_dict["Medium Blue"],      # L. Elbow -> L. Wrist (Medium Blue)
        base_colors_255_dict["Yellow-Green"],       # Neck -> R. Hip ( Yellow-Green)
        base_colors_255_dict["Bright Green"],     # R. Hip -> R. Knee (Bright Green - transitioning warm to cool spectrum)
        base_colors_255_dict["Light Green-Blue"], # R. Knee -> R. Ankle (Light Green-Blue - transitioning)
        base_colors_255_dict["Pure Blue"],        # Neck -> L. Hip (Pure Blue)
        base_colors_255_dict["Purple-Blue"],      # L. Hip -> L. Knee (Purple-Blue)
        base_colors_255_dict["Medium Purple"],    # L. Knee -> L. Ankle (Medium Purple)
        base_colors_255_dict["Grey"],             # Neck -> Nose (Grey)
        base_colors_255_dict["Pink-Magenta"],     # Nose -> R. Eye (Pink/Magenta)
        base_colors_255_dict["Dark Violet"],        # R. Eye -> R. Ear (Dark Pink)
        base_colors_255_dict["Pink-Magenta"],           # Nose -> L. Eye (Violet)
        base_colors_255_dict["Dark Violet"],      # L. Eye -> L. Ear (Dark Violet)
    ] for base_colors_255_dict in base_colors_255_dict_list]

    limb_seq = [
        [1, 2],    # 0 Neck -> R. Shoulder
        [1, 5],    # 1 Neck -> L. Shoulder
        [2, 3],    # 2 R. Shoulder -> R. Elbow
        [3, 4],    # 3 R. Elbow -> R. Wrist
        [5, 6],    # 4 L. Shoulder -> L. Elbow
        [6, 7],    # 5 L. Elbow -> L. Wrist
        [1, 8],    # 6 Neck -> R. Hip
        [8, 9],    # 7 R. Hip -> R. Knee
        [9, 10],   # 8 R. Knee -> R. Ankle
        [1, 11],   # 9 Neck -> L. Hip
        [11, 12],  # 10 L. Hip -> L. Knee
        [12, 13],  # 11 L. Knee -> L. Ankle
        [1, 0],    # 12 Neck -> Nose
        [0, 14],   # 13 Nose -> R. Eye
        [14, 16],  # 14 R. Eye -> R. Ear
        [0, 15],   # 15 Nose -> L. Eye
        [15, 17],  # 16 L. Eye -> L. Ear
    ]

    draw_seq = [0, 2, 3, # Neck -> R. Shoulder -> R. Elbow -> R. Wrist
                1, 4, 5, # Neck -> L. Shoulder -> L. Elbow -> L. Wrist
                6, 7, 8, # Neck -> R. Hip -> R. Knee -> R. Ankle
                9, 10, 11, # Neck -> L. Hip -> L. Knee -> L. Ankle
                12, # Neck -> Nose
                13, 14, # Nose -> R. Eye -> R. Ear
                15, 16, # Nose -> L. Eye -> L. Ear
                ]   # 从近心端往外扩展

    colors_first = [[c / 300 + 0.15 for c in color_rgb] + [0.8] for color_rgb in ordered_colors_255_list[0]]
    colors_second = [[c / 300 + 0.15 for c in color_rgb] + [0.8] for color_rgb in ordered_colors_255_list[1]]

    smpl_poses_first, smpl_poses_second = collect_smpl_poses_samurai(data)


    if intrinsic_matrix is None:
        intrinsic_matrix = intrinsic_matrix_from_field_of_view((height, width))
    focal_x = intrinsic_matrix[0,0]
    focal_y = intrinsic_matrix[1,1]
    princpt = (intrinsic_matrix[0,2], intrinsic_matrix[1,2])  # 主点 (cx, cy)

    # 串行获取每一帧的cylinder_specs
    cylinder_specs_list = []
    for i in range(video_length):
        cylinder_specs_first = get_single_pose_cylinder_specs((i, smpl_poses_first[i], None, None, None, None, colors_first, limb_seq, draw_seq))
        cylinder_specs_second = get_single_pose_cylinder_specs((i, smpl_poses_second[i], None, None, None, None, colors_second, limb_seq, draw_seq))
        cylinder_specs = cylinder_specs_first + cylinder_specs_second
        cylinder_specs_list.append(cylinder_specs)


    frames_np_rgba = render_whole(cylinder_specs_list, H=height, W=width, fx=focal_x, fy=focal_y, cx=princpt[0], cy=princpt[1])
    if poses is not None and draw_2d:
        aligned_poses = copy.deepcopy(poses)
        canvas_2d = draw_pose_to_canvas_np(aligned_poses, pool=None, H=height, W=width, reshape_scale=0, show_feet_flag=False, show_body_flag=False, show_cheek_flag=True, dw_hand=True)
        for i in range(len(frames_np_rgba)):
            frame_img = frames_np_rgba[i]
            canvas_img = canvas_2d[i]
            mask = canvas_img != 0
            frame_img[:, :, :3][mask] = canvas_img[mask]
            frames_np_rgba[i] = frame_img

    return frames_np_rgba


def run_nlf_from_masks(video_frames, masks, colors, model_nlf, nlf_render_path,
                       nlf_render_mask_path, fps=16, detector=None):
    """对每个人用墨绿色背景隔离后提取 NLF 姿态，再分别渲染普通和 mono 结果并保存为 MP4。

    Args:
        video_frames: (T, H, W, 3) uint8 numpy array, RGB
        masks:  list of (T, H, W) bool ndarray，每人一个
        colors: list of BGR color tuples，与 masks 一一对应
        model_nlf: TorchScript NLF 模型
        nlf_render_path: 普通渲染输出路径（含 2D 关键点叠加）
        nlf_render_mask_path: mono 渲染输出路径
        fps: 输出帧率
        detector: DWposeDetector，对原始帧提取多人 2D 关键点
    """
    from NLFPoseExtract.extract_nlfpose_batch import process_video_multi_nlf

    if len(masks) == 0:
        print("No masks provided, skipping.")
        return

    T, H, W, C = video_frames.shape
    dark_green = np.array([0, 100, 0], dtype=np.uint8)

    vr_frames_list = []
    for mask in masks:
        person_frames = np.full((T, H, W, C), dark_green, dtype=np.uint8)
        person_frames[mask] = video_frames[mask]
        vr_frames_list.append(torch.from_numpy(person_frames))

    nlf_results = process_video_multi_nlf(model_nlf, vr_frames_list)

    poses = None
    if detector is not None:
        # Per-person DWpose: run detector on each SAM3 person's dark_green-bg crop so the
        # 2D keypoints (face/hands/body) align with SAM3 person order. Stack the per-person
        # single-person dicts back into multi-person dicts per frame, in SAM3 order.
        N = len(masks)
        EMPTY_BODY = np.full((24, 2), -1.0, dtype=np.float32)
        EMPTY_SUBSET = np.full((24,), -1.0, dtype=np.float32)
        EMPTY_FACE = np.full((68, 2), -1.0, dtype=np.float32)
        EMPTY_HAND = np.full((21, 2), -1.0, dtype=np.float32)

        per_person_per_frame = [[None] * T for _ in range(N)]
        for p_idx in range(N):
            person_frames_np = vr_frames_list[p_idx].numpy()  # (T, H, W, 3) RGB, dark_green bg
            for t in range(T):
                pose_dict, _, _ = detector(Image.fromarray(person_frames_np[t]))
                per_person_per_frame[p_idx][t] = pose_dict

        poses = []
        for t in range(T):
            cand_rows, sub_rows, face_rows = [], [], []
            hand_rows = []
            for p_idx in range(N):
                pd = per_person_per_frame[p_idx][t]
                cands = pd['bodies']['candidate']
                if cands is not None and len(cands) > 0:
                    cand_rows.append(cands[0])
                    sub_rows.append(pd['bodies']['subset'][0])
                    face_rows.append(pd['faces'][0])
                    hand_rows.append(pd['hands'][0])
                    hand_rows.append(pd['hands'][1])
                else:
                    cand_rows.append(EMPTY_BODY)
                    sub_rows.append(EMPTY_SUBSET)
                    face_rows.append(EMPTY_FACE)
                    hand_rows.append(EMPTY_HAND)
                    hand_rows.append(EMPTY_HAND)
            poses.append({
                'bodies': {
                    'candidate': np.stack(cand_rows, axis=0),
                    'subset':    np.stack(sub_rows, axis=0),
                },
                'faces': np.stack(face_rows, axis=0),
                'hands': np.stack(hand_rows, axis=0),
            })

    person_colors_rgba = []
    for bgr in colors:
        b, g, r = bgr[0] / 255.0, bgr[1] / 255.0, bgr[2] / 255.0
        person_colors_rgba.append([r, g, b, 1.0])

    palette_offset = 1 if len(masks) == 1 else 0
    frames_regular, frames_mono = render_nlf_as_images(
        copy.deepcopy(nlf_results), poses=copy.deepcopy(poses),
        reshape_pool=None, intrinsic_matrix=None,
        draw_2d=True, aug_2d=False, aug_cam=False,
        person_colors=person_colors_rgba, palette_offset=palette_offset,
    )

    for out_path in (nlf_render_path, nlf_render_mask_path):
        out_dir = os.path.dirname(out_path)
        if out_dir:
            os.makedirs(out_dir, exist_ok=True)

    frames_regular_rgb = [f[:, :, :3] for f in frames_regular]
    frames_mono_rgb = [f[:, :, :3] for f in frames_mono]

    mpy.ImageSequenceClip(frames_regular_rgb, fps=fps).write_videofile(nlf_render_path)
    mpy.ImageSequenceClip(frames_mono_rgb, fps=fps).write_videofile(nlf_render_mask_path)