import numpy as np import argparse import torch import copy import cv2 import os import moviepy.video.io.ImageSequenceClip from datetime import datetime import gc from huggingface_hub import hf_hub_download from pose.script.dwpose import DWposeDetector, draw_pose from pose.script.util import size_calculate, warpAffine_kps ''' Detect dwpose from img, then align it by scale parameters img: frame from the pose video detector: DWpose scales: scale parameters ''' class PoseAlignmentInference: def __init__(self): self.detector = None self.model_paths = { "det_ckpt": os.path.join("pretrained_weights", "dwpose", "yolox_l_8x8_300e_coco.pth"), "pose_ckpt": os.path.join("pretrained_weights", "dwpose", "dw-ll_ucoco_384.pth") } self.config_paths = { "pose_config": os.path.join("pose", "config", "dwpose-l_384x288.py"), "det_config": os.path.join("pose", "config", "yolox_l_8xb8-300e_coco.py"), } self.output_dir = os.path.join("assets", "videos") if not os.path.exists(self.output_dir): os.makedirs(self.output_dir) self.download_models() def align_pose( self, vidfn: str, imgfn_refer: str, detect_resolution: int, image_resolution: int, align_frame: int, max_frame: int, ): self.download_models() dt_file_name = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") outfn=os.path.abspath(os.path.join(self.output_dir, f'{dt_file_name}_demo.mp4')) outfn_align_pose_video=os.path.abspath(os.path.join(self.output_dir, f'{dt_file_name}.mp4')) video = cv2.VideoCapture(vidfn) width= video.get(cv2.CAP_PROP_FRAME_WIDTH) height= video.get(cv2.CAP_PROP_FRAME_HEIGHT) total_frame= video.get(cv2.CAP_PROP_FRAME_COUNT) fps= video.get(cv2.CAP_PROP_FPS) print("height:", height) print("width:", width) print("fps:", fps) H_in, W_in = height, width H_out, W_out = size_calculate(H_in,W_in, detect_resolution) H_out, W_out = size_calculate(H_out,W_out, image_resolution) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.detector = DWposeDetector( det_config = self.config_paths["det_config"], det_ckpt = self.model_paths["det_ckpt"], pose_config = self.config_paths["pose_config"], pose_ckpt = self.model_paths["pose_ckpt"], keypoints_only=False ) detector = self.detector.to(device) refer_img = cv2.imread(imgfn_refer) output_refer, pose_refer = detector(refer_img,detect_resolution=detect_resolution, image_resolution=image_resolution, output_type='cv2',return_pose_dict=True) body_ref_img = pose_refer['bodies']['candidate'] hands_ref_img = pose_refer['hands'] faces_ref_img = pose_refer['faces'] output_refer = cv2.cvtColor(output_refer, cv2.COLOR_RGB2BGR) skip_frames = align_frame max_frame = max_frame pose_list, video_frame_buffer, video_pose_buffer = [], [], [] cap = cv2.VideoCapture('2.mp4') # 读取视频 while cap.isOpened(): # 当视频被打开时： ret, frame = cap.read() # 读取视频，读取到的某一帧存储到frame，若是读取成功，ret为True，反之为False if ret: # 若是读取成功 cv2.imshow('frame', frame) # 显示读取到的这一帧画面 key = cv2.waitKey(25) # 等待一段时间，并且检测键盘输入 if key == ord('q'): # 若是键盘输入'q',则退出，释放视频 cap.release() # 释放视频 break else: cap.release() cv2.destroyAllWindows() # 关闭所有窗口 for i in range(max_frame): ret, img = video.read() if img is None: break else: if i < skip_frames: continue video_frame_buffer.append(img) # estimate scale parameters by the 1st frame in the video if i==skip_frames: output_1st_img, pose_1st_img = detector(img, detect_resolution, image_resolution, output_type='cv2', return_pose_dict=True) body_1st_img = pose_1st_img['bodies']['candidate'] hands_1st_img = pose_1st_img['hands'] faces_1st_img = pose_1st_img['faces'] ''' 计算逻辑: 1. 先把 ref 和 pose 的高 resize 到一样，且都保持原来的长宽比。 2. 用点在图中的实际坐标来计算。 3. 实际计算中，把h的坐标归一化到 [0, 1], w为[0, W/H] 4. 由于 dwpose 的输出本来就是归一化的坐标，所以h不需要变，w要乘W/H 注意：dwpose 输出是 (w, h) ''' # h不变，w缩放到原比例 ref_H, ref_W = refer_img.shape[0], refer_img.shape[1] ref_ratio = ref_W / ref_H body_ref_img[:, 0] = body_ref_img[:, 0] * ref_ratio hands_ref_img[:, :, 0] = hands_ref_img[:, :, 0] * ref_ratio faces_ref_img[:, :, 0] = faces_ref_img[:, :, 0] * ref_ratio video_ratio = width / height body_1st_img[:, 0] = body_1st_img[:, 0] * video_ratio hands_1st_img[:, :, 0] = hands_1st_img[:, :, 0] * video_ratio faces_1st_img[:, :, 0] = faces_1st_img[:, :, 0] * video_ratio # scale align_args = dict() dist_1st_img = np.linalg.norm(body_1st_img[0]-body_1st_img[1]) # 0.078 dist_ref_img = np.linalg.norm(body_ref_img[0]-body_ref_img[1]) # 0.106 align_args["scale_neck"] = dist_ref_img / dist_1st_img # align / pose = ref / 1st dist_1st_img = np.linalg.norm(body_1st_img[16]-body_1st_img[17]) dist_ref_img = np.linalg.norm(body_ref_img[16]-body_ref_img[17]) align_args["scale_face"] = dist_ref_img / dist_1st_img dist_1st_img = np.linalg.norm(body_1st_img[2]-body_1st_img[5]) # 0.112 dist_ref_img = np.linalg.norm(body_ref_img[2]-body_ref_img[5]) # 0.174 align_args["scale_shoulder"] = dist_ref_img / dist_1st_img dist_1st_img = np.linalg.norm(body_1st_img[2]-body_1st_img[3]) # 0.895 dist_ref_img = np.linalg.norm(body_ref_img[2]-body_ref_img[3]) # 0.134 s1 = dist_ref_img / dist_1st_img dist_1st_img = np.linalg.norm(body_1st_img[5]-body_1st_img[6]) dist_ref_img = np.linalg.norm(body_ref_img[5]-body_ref_img[6]) s2 = dist_ref_img / dist_1st_img align_args["scale_arm_upper"] = (s1+s2)/2 # 1.548 dist_1st_img = np.linalg.norm(body_1st_img[3]-body_1st_img[4]) dist_ref_img = np.linalg.norm(body_ref_img[3]-body_ref_img[4]) s1 = dist_ref_img / dist_1st_img dist_1st_img = np.linalg.norm(body_1st_img[6]-body_1st_img[7]) dist_ref_img = np.linalg.norm(body_ref_img[6]-body_ref_img[7]) s2 = dist_ref_img / dist_1st_img align_args["scale_arm_lower"] = (s1+s2)/2 # hand dist_1st_img = np.zeros(10) dist_ref_img = np.zeros(10) dist_1st_img[0] = np.linalg.norm(hands_1st_img[0,0]-hands_1st_img[0,1]) dist_1st_img[1] = np.linalg.norm(hands_1st_img[0,0]-hands_1st_img[0,5]) dist_1st_img[2] = np.linalg.norm(hands_1st_img[0,0]-hands_1st_img[0,9]) dist_1st_img[3] = np.linalg.norm(hands_1st_img[0,0]-hands_1st_img[0,13]) dist_1st_img[4] = np.linalg.norm(hands_1st_img[0,0]-hands_1st_img[0,17]) dist_1st_img[5] = np.linalg.norm(hands_1st_img[1,0]-hands_1st_img[1,1]) dist_1st_img[6] = np.linalg.norm(hands_1st_img[1,0]-hands_1st_img[1,5]) dist_1st_img[7] = np.linalg.norm(hands_1st_img[1,0]-hands_1st_img[1,9]) dist_1st_img[8] = np.linalg.norm(hands_1st_img[1,0]-hands_1st_img[1,13]) dist_1st_img[9] = np.linalg.norm(hands_1st_img[1,0]-hands_1st_img[1,17]) dist_ref_img[0] = np.linalg.norm(hands_ref_img[0,0]-hands_ref_img[0,1]) dist_ref_img[1] = np.linalg.norm(hands_ref_img[0,0]-hands_ref_img[0,5]) dist_ref_img[2] = np.linalg.norm(hands_ref_img[0,0]-hands_ref_img[0,9]) dist_ref_img[3] = np.linalg.norm(hands_ref_img[0,0]-hands_ref_img[0,13]) dist_ref_img[4] = np.linalg.norm(hands_ref_img[0,0]-hands_ref_img[0,17]) dist_ref_img[5] = np.linalg.norm(hands_ref_img[1,0]-hands_ref_img[1,1]) dist_ref_img[6] = np.linalg.norm(hands_ref_img[1,0]-hands_ref_img[1,5]) dist_ref_img[7] = np.linalg.norm(hands_ref_img[1,0]-hands_ref_img[1,9]) dist_ref_img[8] = np.linalg.norm(hands_ref_img[1,0]-hands_ref_img[1,13]) dist_ref_img[9] = np.linalg.norm(hands_ref_img[1,0]-hands_ref_img[1,17]) ratio = 0 count = 0 for i in range (10): if dist_1st_img[i] != 0: ratio = ratio + dist_ref_img[i]/dist_1st_img[i] count = count + 1 if count!=0: align_args["scale_hand"] = (ratio/count+align_args["scale_arm_upper"]+align_args["scale_arm_lower"])/3 else: align_args["scale_hand"] = (align_args["scale_arm_upper"]+align_args["scale_arm_lower"])/2 # body dist_1st_img = np.linalg.norm(body_1st_img[1] - (body_1st_img[8] + body_1st_img[11])/2 ) dist_ref_img = np.linalg.norm(body_ref_img[1] - (body_ref_img[8] + body_ref_img[11])/2 ) align_args["scale_body_len"]=dist_ref_img / dist_1st_img dist_1st_img = np.linalg.norm(body_1st_img[8]-body_1st_img[9]) dist_ref_img = np.linalg.norm(body_ref_img[8]-body_ref_img[9]) s1 = dist_ref_img / dist_1st_img dist_1st_img = np.linalg.norm(body_1st_img[11]-body_1st_img[12]) dist_ref_img = np.linalg.norm(body_ref_img[11]-body_ref_img[12]) s2 = dist_ref_img / dist_1st_img align_args["scale_leg_upper"] = (s1+s2)/2 dist_1st_img = np.linalg.norm(body_1st_img[9]-body_1st_img[10]) dist_ref_img = np.linalg.norm(body_ref_img[9]-body_ref_img[10]) s1 = dist_ref_img / dist_1st_img dist_1st_img = np.linalg.norm(body_1st_img[12]-body_1st_img[13]) dist_ref_img = np.linalg.norm(body_ref_img[12]-body_ref_img[13]) s2 = dist_ref_img / dist_1st_img align_args["scale_leg_lower"] = (s1+s2)/2 #################### #################### # need adjust nan for k,v in align_args.items(): if np.isnan(v): align_args[k]=1 # centre offset (the offset of key point 1) offset = body_ref_img[1] - body_1st_img[1] # pose align pose_img, pose_ori = detector(img, detect_resolution, image_resolution, output_type='cv2', return_pose_dict=True) video_pose_buffer.append(pose_img) pose_align = self.align_img(img, pose_ori, align_args, detect_resolution, image_resolution) # add centre offset pose = pose_align pose['bodies']['candidate'] = pose['bodies']['candidate'] + offset pose['hands'] = pose['hands'] + offset pose['faces'] = pose['faces'] + offset # h不变，w从绝对坐标缩放回0-1 注意这里要回到ref的坐标系 pose['bodies']['candidate'][:, 0] = pose['bodies']['candidate'][:, 0] / ref_ratio pose['hands'][:, :, 0] = pose['hands'][:, :, 0] / ref_ratio pose['faces'][:, :, 0] = pose['faces'][:, :, 0] / ref_ratio pose_list.append(pose) # stack body_list = [pose['bodies']['candidate'][:18] for pose in pose_list] body_list_subset = [pose['bodies']['subset'][:1] for pose in pose_list] hands_list = [pose['hands'][:2] for pose in pose_list] faces_list = [pose['faces'][:1] for pose in pose_list] body_seq = np.stack(body_list , axis=0) body_seq_subset = np.stack(body_list_subset, axis=0) hands_seq = np.stack(hands_list , axis=0) faces_seq = np.stack(faces_list , axis=0) # concatenate and paint results H = 768 # paint height W1 = int((H/ref_H * ref_W)//2 *2) W2 = int((H/height * width)//2 *2) result_demo = [] # = Writer(args, None, H, 3*W1+2*W2, outfn, fps) result_pose_only = [] # Writer(args, None, H, W1, args.outfn_align_pose_video, fps) for i in range(len(body_seq)): pose_t={} pose_t["bodies"]={} pose_t["bodies"]["candidate"]=body_seq[i] pose_t["bodies"]["subset"]=body_seq_subset[i] pose_t["hands"]=hands_seq[i] pose_t["faces"]=faces_seq[i] ref_img = cv2.cvtColor(refer_img, cv2.COLOR_RGB2BGR) ref_img = cv2.resize(ref_img, (W1, H)) ref_pose= cv2.resize(output_refer, (W1, H)) output_transformed = draw_pose( pose_t, int(H_in*1024/W_in), 1024, draw_face=False, ) output_transformed = cv2.cvtColor(output_transformed, cv2.COLOR_BGR2RGB) output_transformed = cv2.resize(output_transformed, (W1, H)) video_frame = cv2.resize(video_frame_buffer[i], (W2, H)) video_pose = cv2.resize(video_pose_buffer[i], (W2, H)) res = np.concatenate([ref_img, ref_pose, output_transformed, video_frame, video_pose], axis=1) result_demo.append(res) result_pose_only.append(output_transformed) print(f"pose_list len: {len(pose_list)}") clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(result_demo, fps=fps) clip.write_videofile(outfn, fps=fps) clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(result_pose_only, fps=fps) clip.write_videofile(outfn_align_pose_video, fps=fps) print('pose align done') self.release_vram() return outfn_align_pose_video, outfn def download_models(self): repo_id = 'jhj0517/MusePose' local_model_dir = os.path.join("pretrained_weights") for name, file_path in self.model_paths.items(): local_dir, filename = os.path.dirname(file_path), os.path.basename(file_path) if not os.path.exists(local_dir): os.makedirs(local_dir) remote_filepath = f"dwpose/{filename}" if not os.path.exists(file_path): hf_hub_download(repo_id=repo_id, filename=remote_filepath, local_dir=local_model_dir, local_dir_use_symlinks=False) def release_vram(self): if self.detector is not None: del self.detector self.detector = None if torch.cuda.is_available(): torch.cuda.empty_cache() gc.collect() @staticmethod def align_img(img, pose_ori, scales, detect_resolution, image_resolution): body_pose = copy.deepcopy(pose_ori['bodies']['candidate']) hands = copy.deepcopy(pose_ori['hands']) faces = copy.deepcopy(pose_ori['faces']) ''' 计算逻辑: 0. 该函数内进行绝对变换，始终保持人体中心点 body_pose[1] 不变 1. 先把 ref 和 pose 的高 resize 到一样，且都保持原来的长宽比。 2. 用点在图中的实际坐标来计算。 3. 实际计算中，把h的坐标归一化到 [0, 1], w为[0, W/H] 4. 由于 dwpose 的输出本来就是归一化的坐标，所以h不需要变，w要乘W/H 注意：dwpose 输出是 (w, h) ''' # h不变，w缩放到原比例 H_in, W_in, C_in = img.shape video_ratio = W_in / H_in body_pose[:, 0] = body_pose[:, 0] * video_ratio hands[:, :, 0] = hands[:, :, 0] * video_ratio faces[:, :, 0] = faces[:, :, 0] * video_ratio # scales of 10 body parts scale_neck = scales["scale_neck"] scale_face = scales["scale_face"] scale_shoulder = scales["scale_shoulder"] scale_arm_upper = scales["scale_arm_upper"] scale_arm_lower = scales["scale_arm_lower"] scale_hand = scales["scale_hand"] scale_body_len = scales["scale_body_len"] scale_leg_upper = scales["scale_leg_upper"] scale_leg_lower = scales["scale_leg_lower"] scale_sum = 0 count = 0 scale_list = [scale_neck, scale_face, scale_shoulder, scale_arm_upper, scale_arm_lower, scale_hand, scale_body_len, scale_leg_upper, scale_leg_lower] for i in range(len(scale_list)): if not np.isinf(scale_list[i]): scale_sum = scale_sum + scale_list[i] count = count + 1 for i in range(len(scale_list)): if np.isinf(scale_list[i]): scale_list[i] = scale_sum / count # offsets of each part offset = dict() offset["14_15_16_17_to_0"] = body_pose[[14, 15, 16, 17], :] - body_pose[[0], :] offset["3_to_2"] = body_pose[[3], :] - body_pose[[2], :] offset["4_to_3"] = body_pose[[4], :] - body_pose[[3], :] offset["6_to_5"] = body_pose[[6], :] - body_pose[[5], :] offset["7_to_6"] = body_pose[[7], :] - body_pose[[6], :] offset["9_to_8"] = body_pose[[9], :] - body_pose[[8], :] offset["10_to_9"] = body_pose[[10], :] - body_pose[[9], :] offset["12_to_11"] = body_pose[[12], :] - body_pose[[11], :] offset["13_to_12"] = body_pose[[13], :] - body_pose[[12], :] offset["hand_left_to_4"] = hands[1, :, :] - body_pose[[4], :] offset["hand_right_to_7"] = hands[0, :, :] - body_pose[[7], :] # neck c_ = body_pose[1] cx = c_[0] cy = c_[1] M = cv2.getRotationMatrix2D((cx, cy), 0, scale_neck) neck = body_pose[[0], :] neck = warpAffine_kps(neck, M) body_pose[[0], :] = neck # body_pose_up_shoulder c_ = body_pose[0] cx = c_[0] cy = c_[1] M = cv2.getRotationMatrix2D((cx, cy), 0, scale_face) body_pose_up_shoulder = offset["14_15_16_17_to_0"] + body_pose[[0], :] body_pose_up_shoulder = warpAffine_kps(body_pose_up_shoulder, M) body_pose[[14, 15, 16, 17], :] = body_pose_up_shoulder # shoulder c_ = body_pose[1] cx = c_[0] cy = c_[1] M = cv2.getRotationMatrix2D((cx, cy), 0, scale_shoulder) body_pose_shoulder = body_pose[[2, 5], :] body_pose_shoulder = warpAffine_kps(body_pose_shoulder, M) body_pose[[2, 5], :] = body_pose_shoulder # arm upper left c_ = body_pose[2] cx = c_[0] cy = c_[1] M = cv2.getRotationMatrix2D((cx, cy), 0, scale_arm_upper) elbow = offset["3_to_2"] + body_pose[[2], :] elbow = warpAffine_kps(elbow, M) body_pose[[3], :] = elbow # arm lower left c_ = body_pose[3] cx = c_[0] cy = c_[1] M = cv2.getRotationMatrix2D((cx, cy), 0, scale_arm_lower) wrist = offset["4_to_3"] + body_pose[[3], :] wrist = warpAffine_kps(wrist, M) body_pose[[4], :] = wrist # hand left c_ = body_pose[4] cx = c_[0] cy = c_[1] M = cv2.getRotationMatrix2D((cx, cy), 0, scale_hand) hand = offset["hand_left_to_4"] + body_pose[[4], :] hand = warpAffine_kps(hand, M) hands[1, :, :] = hand # arm upper right c_ = body_pose[5] cx = c_[0] cy = c_[1] M = cv2.getRotationMatrix2D((cx, cy), 0, scale_arm_upper) elbow = offset["6_to_5"] + body_pose[[5], :] elbow = warpAffine_kps(elbow, M) body_pose[[6], :] = elbow # arm lower right c_ = body_pose[6] cx = c_[0] cy = c_[1] M = cv2.getRotationMatrix2D((cx, cy), 0, scale_arm_lower) wrist = offset["7_to_6"] + body_pose[[6], :] wrist = warpAffine_kps(wrist, M) body_pose[[7], :] = wrist # hand right c_ = body_pose[7] cx = c_[0] cy = c_[1] M = cv2.getRotationMatrix2D((cx, cy), 0, scale_hand) hand = offset["hand_right_to_7"] + body_pose[[7], :] hand = warpAffine_kps(hand, M) hands[0, :, :] = hand # body len c_ = body_pose[1] cx = c_[0] cy = c_[1] M = cv2.getRotationMatrix2D((cx, cy), 0, scale_body_len) body_len = body_pose[[8, 11], :] body_len = warpAffine_kps(body_len, M) body_pose[[8, 11], :] = body_len # leg upper left c_ = body_pose[8] cx = c_[0] cy = c_[1] M = cv2.getRotationMatrix2D((cx, cy), 0, scale_leg_upper) knee = offset["9_to_8"] + body_pose[[8], :] knee = warpAffine_kps(knee, M) body_pose[[9], :] = knee # leg lower left c_ = body_pose[9] cx = c_[0] cy = c_[1] M = cv2.getRotationMatrix2D((cx, cy), 0, scale_leg_lower) ankle = offset["10_to_9"] + body_pose[[9], :] ankle = warpAffine_kps(ankle, M) body_pose[[10], :] = ankle # leg upper right c_ = body_pose[11] cx = c_[0] cy = c_[1] M = cv2.getRotationMatrix2D((cx, cy), 0, scale_leg_upper) knee = offset["12_to_11"] + body_pose[[11], :] knee = warpAffine_kps(knee, M) body_pose[[12], :] = knee # leg lower right c_ = body_pose[12] cx = c_[0] cy = c_[1] M = cv2.getRotationMatrix2D((cx, cy), 0, scale_leg_lower) ankle = offset["13_to_12"] + body_pose[[12], :] ankle = warpAffine_kps(ankle, M) body_pose[[13], :] = ankle # none part body_pose_none = pose_ori['bodies']['candidate'] == -1. hands_none = pose_ori['hands'] == -1. faces_none = pose_ori['faces'] == -1. body_pose[body_pose_none] = -1. hands[hands_none] = -1. nan = float('nan') if len(hands[np.isnan(hands)]) > 0: print('nan') faces[faces_none] = -1. # last check nan -> -1. body_pose = np.nan_to_num(body_pose, nan=-1.) hands = np.nan_to_num(hands, nan=-1.) faces = np.nan_to_num(faces, nan=-1.) # return pose_align = copy.deepcopy(pose_ori) pose_align['bodies']['candidate'] = body_pose pose_align['hands'] = hands pose_align['faces'] = faces return pose_align