#from __future__ import absolute_import import sys import io import os sys.argv = ['GPT_eval_multi.py'] # 将项目根目录添加到sys.path中 PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(1, PROJECT_ROOT) CKPT_ROOT="/cfs-datasets/public_models/motion" from .options import option_transformer as option_trans import sys print(sys.path[0]) import clip import torch import cv2 import numpy as np from .models import vqvae as vqvae from .models import t2m_trans as trans import warnings from .visualization import plot_3d_global as plot_3d import matplotlib.pyplot as plt import numpy as np import matplotlib.colors as mcolors from tqdm import tqdm from mpl_toolkits.mplot3d import Axes3D from PIL import Image import time import random warnings.filterwarnings('ignore') from matplotlib.axes._axes import _log as matplotlib_axes_logger matplotlib_axes_logger.setLevel('ERROR') from math import cos,sin,radians args = option_trans.get_args_parser() args.dataname = 't2m' args.resume_pth = os.path.join(CKPT_ROOT,'pretrained/VQVAE/net_last.pth') args.resume_trans = os.path.join(CKPT_ROOT,'pretrained/VQTransformer_corruption05/net_best_fid.pth') args.down_t = 2 args.depth = 3 args.block_size = 51 def replace_space_with_underscore(s): return s.replace(' ', '_') def Rz(angle): theta=radians(angle) return np.array([[cos(theta), -sin(theta), 0], [sin(theta), cos(theta), 0], [0, 0, 1]]) def Rx(angle): theta=radians(angle) return np.array( [[1, 0, 0], [0 , cos(theta), -sin(theta)], [0, sin(theta), cos(theta)]]) def generate_cuid(): timestamp = hex(int(time.time() * 1000))[2:] random_str = hex(random.randint(0, 0xfffff))[2:] return (timestamp + random_str).zfill(10) def smpl_to_openpose18(smpl_keypoints): ''' 22关键点SMPL对应关系解释 [0, 2, 5, 8, 11] 这个列表表示SMPL模型中左腿的连接方式，从骨盆（0号关键点）开始，连接左大腿（2号关键点）、左小腿（5号关键点）、左脚（8号关键点）和左脚尖（11号关键点）。 [0, 1, 4, 7, 10] 这个列表表示SMPL模型中右腿的连接方式，从骨盆（0号关键点）开始，连接右大腿（1号关键点）、右小腿（4号关键点）、右脚（7号关键点）和右脚尖（10号关键点）。 [0, 3, 6, 9, 12, 15] 这个列表表示SMPL模型中躯干的连接方式，从骨盆（0号关键点）开始，连接脊柱（3号关键点）、颈部（6号关键点）、头部（9号关键点）、左肩膀（12号关键点）、右肩膀（15号关键点）。 [9, 14, 17, 19, 21] 这个列表表示SMPL模型中左臂的连接方式，从左肩膀（9号关键点）开始，连接左上臂（14号关键点）、左前臂（17号关键点）、左手腕（19号关键点）和左手（21号关键点）。 [9, 13, 16, 18, 20] 这个列表表示SMPL模型中右臂的连接方式，从右肩膀（9号关键点）开始，连接右上臂（13号关键点）、右前臂（16号关键点）、右手腕（18号关键点）和右手（20号关键点）。 目前转Openpose忽略掉了SMPL的肩膀关键点 ''' openpose_keypoints = np.zeros((18, 3)) openpose_keypoints[0] = smpl_keypoints[9] # nose openpose_keypoints[0][1] = openpose_keypoints[0][1]+0.3 # openpose_keypoints[1] = smpl_keypoints[6] # neck openpose_keypoints[2] = smpl_keypoints[16] # right shoulder openpose_keypoints[3] = smpl_keypoints[18] # right elbow openpose_keypoints[4] = smpl_keypoints[20] # right wrist openpose_keypoints[5] = smpl_keypoints[17] # left shoulder openpose_keypoints[6] = smpl_keypoints[19] # left elbow openpose_keypoints[7] = smpl_keypoints[21] # left wrist #TODO: Experiment,将neck的关键点抬高&&将nose的关键点相对高度关系与neck保持一致 openpose_keypoints[1][0]=(openpose_keypoints[2][0]+openpose_keypoints[5][0])/2 openpose_keypoints[1][1]=(openpose_keypoints[2][1]+openpose_keypoints[5][1])/2 openpose_keypoints[1][2]=(openpose_keypoints[2][2]+openpose_keypoints[5][2])/2 openpose_keypoints[0][1] = openpose_keypoints[1][1]+0.3 # openpose_keypoints[8] = smpl_keypoints[1] # right hip openpose_keypoints[9] = smpl_keypoints[4] # right knee openpose_keypoints[10] = smpl_keypoints[7] # right ankle openpose_keypoints[11] = smpl_keypoints[2] # left hip openpose_keypoints[12] = smpl_keypoints[5] # left knee openpose_keypoints[13] = smpl_keypoints[8] # left ankle #TODO: Experiment,手工指定脸部关键点测试是否能够指定身体朝向 #openpose_keypoints[0][0] = openpose_keypoints[0][0]+0.3#测试0坐标轴方向(水平向右) #openpose_keypoints[0][2] = openpose_keypoints[0][2]#测试2坐标轴方向（向外 #openpose_keypoints[0][1] = openpose_keypoints[0][1]+0.5#测试1坐标轴方向（垂直向上 openpose_keypoints[14] = openpose_keypoints[0] # right eye openpose_keypoints[14][1]=openpose_keypoints[14][1]+0.05 openpose_keypoints[14][0]=openpose_keypoints[14][0]+0.3*(openpose_keypoints[2][0]-openpose_keypoints[1][0]) openpose_keypoints[14][2]=openpose_keypoints[14][2]+0.3*(openpose_keypoints[2][2]-openpose_keypoints[1][2]) openpose_keypoints[15] = openpose_keypoints[0] # left eye openpose_keypoints[15][1]=openpose_keypoints[15][1]+0.05 openpose_keypoints[15][0]=openpose_keypoints[15][0]+0.3*(openpose_keypoints[5][0]-openpose_keypoints[1][0]) openpose_keypoints[15][2]=openpose_keypoints[15][2]+0.3*(openpose_keypoints[5][2]-openpose_keypoints[1][2]) openpose_keypoints[16] = openpose_keypoints[0] # right ear openpose_keypoints[16][0]=openpose_keypoints[16][0]+0.7*(openpose_keypoints[2][0]-openpose_keypoints[1][0]) openpose_keypoints[16][2]=openpose_keypoints[16][2]+0.7*(openpose_keypoints[2][2]-openpose_keypoints[1][2]) openpose_keypoints[17] = openpose_keypoints[0] # left ear openpose_keypoints[17][0]=openpose_keypoints[17][0]+0.7*(openpose_keypoints[5][0]-openpose_keypoints[1][0]) openpose_keypoints[17][2]=openpose_keypoints[17][2]+0.7*(openpose_keypoints[5][2]-openpose_keypoints[1][2]) return openpose_keypoints # TODO: debug only, need to be deleted before unload ## load clip model and datasets clip_model, clip_preprocess = clip.load("ViT-B/32", device=torch.device('cuda'), jit=False, download_root=CKPT_ROOT) # Must set jit=False for training clip.model.convert_weights(clip_model) # Actually this line is unnecessary since clip by default already on float16 clip_model.eval() for p in clip_model.parameters(): p.requires_grad = False print("loaded CLIP model") net = vqvae.HumanVQVAE(args, ## use args to define different parameters in different quantizers args.nb_code, args.code_dim, args.output_emb_width, args.down_t, args.stride_t, args.width, args.depth, args.dilation_growth_rate) trans_encoder = trans.Text2Motion_Transformer(num_vq=args.nb_code, embed_dim=1024, clip_dim=args.clip_dim, block_size=args.block_size, num_layers=9, n_head=16, drop_out_rate=args.drop_out_rate, fc_rate=args.ff_rate) print ('loading checkpoint from {}'.format(args.resume_pth)) ckpt = torch.load(args.resume_pth, map_location='cpu') net.load_state_dict(ckpt['net'], strict=True) net.eval() net.cuda() print ('loading transformer checkpoint from {}'.format(args.resume_trans)) ckpt = torch.load(args.resume_trans, map_location='cpu') trans_encoder.load_state_dict(ckpt['trans'], strict=True) trans_encoder.eval() trans_encoder.cuda() mean = torch.from_numpy(np.load(os.path.join(CKPT_ROOT,'./checkpoints/t2m/VQVAEV3_CB1024_CMT_H1024_NRES3/meta/mean.npy'))).cuda() std = torch.from_numpy(np.load(os.path.join(CKPT_ROOT,'./checkpoints/t2m/VQVAEV3_CB1024_CMT_H1024_NRES3/meta/std.npy'))).cuda() def get_open_pose(text,height,width,save_path,video_length): CKPT_ROOT = os.path.dirname(os.path.abspath(__file__)) clip_text=[text] print(f"Motion Prompt: {text}") # cuid=generate_cuid() # print(f"Motion Generation cuid: {cuid}") # clip_text = ["the person jump and spin twice,then running straght and sit down. "] #支持单个token的生成 # change the text here text = clip.tokenize(clip_text, truncate=False).cuda() feat_clip_text = clip_model.encode_text(text).float() index_motion = trans_encoder.sample(feat_clip_text[0:1], False) pred_pose = net.forward_decoder(index_motion) from utils.motion_process import recover_from_ric pred_xyz = recover_from_ric((pred_pose*std+mean).float(), 22) xyz = pred_xyz.reshape(1, -1, 22, 3) np.save('motion.npy', xyz.detach().cpu().numpy()) pose_vis = plot_3d.draw_to_batch(xyz.detach().cpu().numpy(),clip_text, ['smpl.gif']) res=xyz.detach().cpu().numpy() points_3d_list=res[0] frame_num=points_3d_list.shape[0] open_pose_list=np.array(points_3d_list) print("The total SMPL sequence shape is : "+str(open_pose_list.shape)) max_val = np.max(open_pose_list, axis=(0, 1)) min_val = np.min(open_pose_list, axis=(0, 1)) print("三维坐标在坐标系上的最大值：", max_val) print("三维坐标在坐标系上的最小值：", min_val) check= smpl_to_openpose18(open_pose_list[0]) # 18个关键点 print("********SMPL_2_OpenPose_List(14/18)********") print(check) print("*************************") print(f"Total Frame Number: {frame_num}") img_list=[] for step in tqdm(range(0,frame_num)): # 生成图像 dpi=84 fig =plt.figure(figsize=(width/dpi, height/dpi), dpi=dpi) ax = fig.add_subplot(111, projection='3d') limits=2 ax.set_xlim(-limits*0.7, limits*0.7) ax.set_ylim(0, limits*1.5)#上下 ax.set_zlim(0, limits*1.5)# 前后 ax.grid(b=False) #ax.dist = 1 ax.set_box_aspect([1.4, 1.5, 1.5],zoom=3.5)# 坐标轴比例 TODO:这个比例可能有问题，会出现超出坐标范围的bug # 关键点坐标，每行包含(x, y, z) keypoints = smpl_to_openpose18(open_pose_list[step]) # 18个关键点 # 运动学链 目前只用到body部分 kinematic_chain = [(0, 1), (1, 2), (2, 3), (3, 4), (1, 5), (5, 6), (6, 7), (1, 8), (8, 9), (9, 10), (1, 11), (11, 12), (12, 13), (0, 14), (14, 16), (0, 15), (15, 17)] #kinematic_chain = [(0, 1), (1, 2), (2, 3), (3, 4), (1, 5), (5, 6), (6, 7), (1, 8), (8, 9), (9, 10), (1, 11), (11, 12), (12, 13)] # 颜色RGB colors = [(0, 0, 255), (0, 255, 255), (0, 255, 0), (255, 0, 0), (255, 0, 255), (255, 192, 203), (0, 165, 255), (19, 69, 139), (173, 216, 230), (34, 139, 34), (0, 0, 128), (184, 134, 11), (139, 0, 139), (0, 100, 0), (0, 255, 255), (0, 255, 0), (216, 191, 216), (255, 255, 224)] #colors=[(0, 0, 255), (0, 255, 255), (0, 255, 0), (255, 0, 0), (255, 0, 255), (255, 192, 203), (0, 165, 255), (19, 69, 139), (173, 216, 230), (34, 139, 34), (0, 0, 128), (184, 134, 11), (139, 0, 139), (0, 100, 0)] #18点 joint_colors=[(255,0,0),(255,85,0),(255,170,0),(255,255,0),(170,255,0),(85,255,0),(0,255,0),(0,255,85),(0,255,170),(0,255,255),(0,170,255),(0,85,255),(0,0,255),(85,0,255),(170,0,255),(255,0,255),(255,0,170),(255,0,85),(255,0,0)] #14点主干 #joint_colors=[(255,0,0),(255,85,0),(255,170,0),(255,255,0),(170,255,0),(85,255,0),(0,255,0),(0,255,85),(0,255,170),(0,255,255),(0,170,255),(0,85,255),(0,0,255),(85,0,255),(170,0,255)] #运动链连线是joint颜色的60% #plt颜色在0-1之间 rgb_color2=[] joint_rgb_color2=[] kinematic_chain_rgb_color2=[] for color in joint_colors: joint_rgb_color2.append(tuple([x/255 for x in color])) kinematic_chain_rgb_color2.append(tuple([x*0.6/255 for x in color])) #运动链连线是joint颜色的60% # 可视化结果 for i in range(0,18): # 绘制关键点 ax.scatter(keypoints[i][0], keypoints[i][1], keypoints[i][2], s=50, c=joint_rgb_color2[i], marker='o') # 绘制运动学链 for j in range(len(kinematic_chain)): if kinematic_chain[j][1] == i: ax.plot([keypoints[kinematic_chain[j][0]][0], keypoints[kinematic_chain[j][1]][0]], [keypoints[kinematic_chain[j][0]][1], keypoints[kinematic_chain[j][1]][1]], [keypoints[kinematic_chain[j][0]][2], keypoints[kinematic_chain[j][1]][2]], c=kinematic_chain_rgb_color2[i], linewidth=5) # 调整视角 ax.view_init(elev=110, azim=-90) plt.axis('off') # 保存图片 # 将图像数据输出为图像数组 if not os.path.exists(save_path): os.makedirs(save_path) image_tmp_path=str(f"{save_path}/{str(step)}.jpg") plt.savefig(os.path.join(CKPT_ROOT,image_tmp_path))#RGB img=cv2.imread(os.path.join(CKPT_ROOT,image_tmp_path)) img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB) img_list.append(img) res=[] if len(img_list)>=video_length: key_frame_sample_step=int(len(img_list)/video_length) else: print("ERROR: video length is too long") key_frame_sample_step=1 for i in range(0,len(img_list),key_frame_sample_step): res.append(img_list[i]) return res def offline_get_open_pose(text,motion_text,height,width,save_path): #motion_text=text clip_text=[text] print(f"Motion Prompt: {text}") cuid=generate_cuid() print(f"Motion Generation cuid: {cuid}") # clip_text = ["the person jump and spin twice,then running straght and sit down. "] #支持单个token的生成 # change the text here text = clip.tokenize(clip_text, truncate=False).cuda() feat_clip_text = clip_model.encode_text(text).float() index_motion = trans_encoder.sample(feat_clip_text[0:1], False) pred_pose = net.forward_decoder(index_motion) from utils.motion_process import recover_from_ric pred_xyz = recover_from_ric((pred_pose*std+mean).float(), 22) xyz = pred_xyz.reshape(1, -1, 22, 3) res=xyz.detach().cpu().numpy() np.save(f'{save_path}/{replace_space_with_underscore(motion_text)}.npy', res) pose_vis = plot_3d.draw_to_batch(res,clip_text, ['smpl.gif']) if __name__ == "__main__": text="walk around, jump, run straght." pose = get_open_pose(text,512,512) #pdb.set_trace()