import torch import torch.nn as nn import os, copy, math import numpy as np from tqdm import tqdm from ..utils import log import comfy.model_management as mm from comfy.utils import ProgressBar import comfy.ops ops = comfy.ops.disable_weight_init def update_transformer(transformer, state_dict): concat_dim = 4 transformer.dwpose_embedding = nn.Sequential( ops.Conv3d(3, concat_dim * 4, (3,3,3), stride=(1,1,1), padding=(1,1,1)), nn.SiLU(), ops.Conv3d(concat_dim * 4, concat_dim * 4, (3,3,3), stride=(1,1,1), padding=(1,1,1)), nn.SiLU(), ops.Conv3d(concat_dim * 4, concat_dim * 4, (3,3,3), stride=(1,1,1), padding=(1,1,1)), nn.SiLU(), ops.Conv3d(concat_dim * 4, concat_dim * 4, (3,3,3), stride=(1,2,2), padding=(1,1,1)), nn.SiLU(), ops.Conv3d(concat_dim * 4, concat_dim * 4, 3, stride=(2,2,2), padding=1), nn.SiLU(), ops.Conv3d(concat_dim * 4, concat_dim * 4, 3, stride=(2,2,2), padding=1), nn.SiLU(), ops.Conv3d(concat_dim * 4, 5120, (1,2,2), stride=(1,2,2), padding=0)) randomref_dim = 20 transformer.randomref_embedding_pose = nn.Sequential( nn.Conv2d(3, concat_dim * 4, 3, stride=1, padding=1), nn.SiLU(), nn.Conv2d(concat_dim * 4, concat_dim * 4, 3, stride=1, padding=1), nn.SiLU(), nn.Conv2d(concat_dim * 4, concat_dim * 4, 3, stride=1, padding=1), nn.SiLU(), nn.Conv2d(concat_dim * 4, concat_dim * 4, 3, stride=2, padding=1), nn.SiLU(), nn.Conv2d(concat_dim * 4, concat_dim * 4, 3, stride=2, padding=1), nn.SiLU(), nn.Conv2d(concat_dim * 4, randomref_dim, 3, stride=2, padding=1), ) unianimate_sd = {} state_dict_new = {} for key in list(state_dict.keys()): if "dwpose_embedding" in key: state_dict_new[key] = state_dict.pop(key) unianimate_sd.update(state_dict_new) for key in list(state_dict.keys()): if "randomref_embedding_pose" in key: state_dict_new[key] = state_dict.pop(key) unianimate_sd.update(state_dict_new) del state_dict_new return transformer, unianimate_sd # Openpose # Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose # 2nd Edited by https://github.com/Hzzone/pytorch-openpose # 3rd Edited by ControlNet # 4th Edited by ControlNet (added face and correct hands) from .dwpose.wholebody import Wholebody def smoothing_factor(t_e, cutoff): r = 2 * math.pi * cutoff * t_e return r / (r + 1) def exponential_smoothing(a, x, x_prev): return a * x + (1 - a) * x_prev class OneEuroFilter: def __init__(self, t0, x0, dx0=0.0, min_cutoff=1.0, beta=0.0, d_cutoff=1.0): """Initialize the one euro filter.""" # The parameters. self.min_cutoff = float(min_cutoff) self.beta = float(beta) self.d_cutoff = float(d_cutoff) # Previous values. self.x_prev = x0 self.dx_prev = float(dx0) self.t_prev = float(t0) def __call__(self, t, x): """Compute the filtered signal.""" t_e = t - self.t_prev # The filtered derivative of the signal. a_d = smoothing_factor(t_e, self.d_cutoff) dx = (x - self.x_prev) / t_e dx_hat = exponential_smoothing(a_d, dx, self.dx_prev) # The filtered signal. cutoff = self.min_cutoff + self.beta * abs(dx_hat) a = smoothing_factor(t_e, cutoff) x_hat = exponential_smoothing(a, x, self.x_prev) # Memorize the previous values. self.x_prev = x_hat self.dx_prev = dx_hat self.t_prev = t return x_hat class DWposeDetector: def __init__(self, model_det, model_pose): self.pose_estimation = Wholebody(model_det, model_pose) def __call__(self, oriImg, score_threshold=0.3): oriImg = oriImg.copy() H, W, C = oriImg.shape with torch.no_grad(): candidate, subset = self.pose_estimation(oriImg) candidate = candidate[0][np.newaxis, :, :] subset = subset[0][np.newaxis, :] nums, keys, locs = candidate.shape candidate[..., 0] /= float(W) candidate[..., 1] /= float(H) body = candidate[:,:18].copy() body = body.reshape(nums*18, locs) score = subset[:,:18].copy() for i in range(len(score)): for j in range(len(score[i])): if score[i][j] > score_threshold: score[i][j] = int(18*i+j) else: score[i][j] = -1 un_visible = subset score_threshold: bodyfoot_score[i][j] = int(18*i+j) else: bodyfoot_score[i][j] = -1 if -1 not in bodyfoot_score[:,18] and -1 not in bodyfoot_score[:,19]: bodyfoot_score[:,18] = np.array([18.]) else: bodyfoot_score[:,18] = np.array([-1.]) if -1 not in bodyfoot_score[:,21] and -1 not in bodyfoot_score[:,22]: bodyfoot_score[:,19] = np.array([19.]) else: bodyfoot_score[:,19] = np.array([-1.]) bodyfoot_score = bodyfoot_score[:, :20] bodyfoot = candidate[:,:24].copy() for i in range(nums): if -1 not in bodyfoot[i][18] and -1 not in bodyfoot[i][19]: bodyfoot[i][18] = (bodyfoot[i][18]+bodyfoot[i][19])/2 else: bodyfoot[i][18] = np.array([-1., -1.]) if -1 not in bodyfoot[i][21] and -1 not in bodyfoot[i][22]: bodyfoot[i][19] = (bodyfoot[i][21]+bodyfoot[i][22])/2 else: bodyfoot[i][19] = np.array([-1., -1.]) bodyfoot = bodyfoot[:,:20,:] bodyfoot = bodyfoot.reshape(nums*20, locs) foot = candidate[:,18:24] faces = candidate[:,24:92] hands = candidate[:,92:113] hands = np.vstack([hands, candidate[:,113:]]) # bodies = dict(candidate=body, subset=score) bodies = dict(candidate=bodyfoot, subset=bodyfoot_score, score=bodyfoot_score) pose = dict(bodies=bodies, hands=hands, faces=faces) # return draw_pose(pose, H, W) return pose def draw_pose(pose, H, W, stick_width=4,draw_body=True, draw_hands=True, draw_feet=True, body_keypoint_size=4, hand_keypoint_size=4, draw_head=True): from .dwpose.util import draw_body_and_foot, draw_handpose, draw_facepose bodies = pose['bodies'] faces = pose['faces'] hands = pose['hands'] candidate = bodies['candidate'] subset = bodies['subset'] score=bodies['score'] canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8) canvas = draw_body_and_foot(canvas, candidate, subset, score, draw_body=draw_body, stick_width=stick_width, draw_feet=draw_feet, draw_head=draw_head, body_keypoint_size=body_keypoint_size) canvas = draw_handpose(canvas, hands, draw_hands=draw_hands, hand_keypoint_size=hand_keypoint_size) canvas_without_face = copy.deepcopy(canvas) canvas = draw_facepose(canvas, faces) return canvas_without_face, canvas def pose_extract(pose_images, ref_image, dwpose_model, height, width, score_threshold, stick_width, draw_body=True, draw_hands=True, hand_keypoint_size=4, draw_feet=True, body_keypoint_size=4, handle_not_detected="repeat", draw_head=True): results_vis = [] comfy_pbar = ProgressBar(len(pose_images)) if ref_image is not None: try: pose_ref = dwpose_model(ref_image.squeeze(0), score_threshold=score_threshold) except Exception: raise ValueError("No pose detected in reference image") prev_pose = None for img in tqdm(pose_images, desc="Pose Extraction", unit="image", total=len(pose_images)): try: pose = dwpose_model(img, score_threshold=score_threshold) if handle_not_detected == "repeat": prev_pose = pose except Exception: if prev_pose is not None: pose = prev_pose else: pose = np.zeros_like(img) results_vis.append(pose) comfy_pbar.update(1) bodies = results_vis[0]['bodies'] faces = results_vis[0]['faces'] hands = results_vis[0]['hands'] candidate = bodies['candidate'] if ref_image is not None: ref_bodies = pose_ref['bodies'] ref_faces = pose_ref['faces'] ref_hands = pose_ref['hands'] ref_candidate = ref_bodies['candidate'] ref_2_x = ref_candidate[2][0] ref_2_y = ref_candidate[2][1] ref_5_x = ref_candidate[5][0] ref_5_y = ref_candidate[5][1] ref_8_x = ref_candidate[8][0] ref_8_y = ref_candidate[8][1] ref_11_x = ref_candidate[11][0] ref_11_y = ref_candidate[11][1] ref_center1 = 0.5*(ref_candidate[2]+ref_candidate[5]) ref_center2 = 0.5*(ref_candidate[8]+ref_candidate[11]) zero_2_x = candidate[2][0] zero_2_y = candidate[2][1] zero_5_x = candidate[5][0] zero_5_y = candidate[5][1] zero_8_x = candidate[8][0] zero_8_y = candidate[8][1] zero_11_x = candidate[11][0] zero_11_y = candidate[11][1] zero_center1 = 0.5*(candidate[2]+candidate[5]) zero_center2 = 0.5*(candidate[8]+candidate[11]) x_ratio = (ref_5_x-ref_2_x)/(zero_5_x-zero_2_x) y_ratio = (ref_center2[1]-ref_center1[1])/(zero_center2[1]-zero_center1[1]) results_vis[0]['bodies']['candidate'][:,0] *= x_ratio results_vis[0]['bodies']['candidate'][:,1] *= y_ratio results_vis[0]['faces'][:,:,0] *= x_ratio results_vis[0]['faces'][:,:,1] *= y_ratio results_vis[0]['hands'][:,:,0] *= x_ratio results_vis[0]['hands'][:,:,1] *= y_ratio ########neck######## l_neck_ref = ((ref_candidate[0][0] - ref_candidate[1][0]) ** 2 + (ref_candidate[0][1] - ref_candidate[1][1]) ** 2) ** 0.5 l_neck_0 = ((candidate[0][0] - candidate[1][0]) ** 2 + (candidate[0][1] - candidate[1][1]) ** 2) ** 0.5 neck_ratio = l_neck_ref / l_neck_0 x_offset_neck = (candidate[1][0]-candidate[0][0])*(1.-neck_ratio) y_offset_neck = (candidate[1][1]-candidate[0][1])*(1.-neck_ratio) results_vis[0]['bodies']['candidate'][0,0] += x_offset_neck results_vis[0]['bodies']['candidate'][0,1] += y_offset_neck results_vis[0]['bodies']['candidate'][14,0] += x_offset_neck results_vis[0]['bodies']['candidate'][14,1] += y_offset_neck results_vis[0]['bodies']['candidate'][15,0] += x_offset_neck results_vis[0]['bodies']['candidate'][15,1] += y_offset_neck results_vis[0]['bodies']['candidate'][16,0] += x_offset_neck results_vis[0]['bodies']['candidate'][16,1] += y_offset_neck results_vis[0]['bodies']['candidate'][17,0] += x_offset_neck results_vis[0]['bodies']['candidate'][17,1] += y_offset_neck ########shoulder2######## l_shoulder2_ref = ((ref_candidate[2][0] - ref_candidate[1][0]) ** 2 + (ref_candidate[2][1] - ref_candidate[1][1]) ** 2) ** 0.5 l_shoulder2_0 = ((candidate[2][0] - candidate[1][0]) ** 2 + (candidate[2][1] - candidate[1][1]) ** 2) ** 0.5 shoulder2_ratio = l_shoulder2_ref / l_shoulder2_0 x_offset_shoulder2 = (candidate[1][0]-candidate[2][0])*(1.-shoulder2_ratio) y_offset_shoulder2 = (candidate[1][1]-candidate[2][1])*(1.-shoulder2_ratio) results_vis[0]['bodies']['candidate'][2,0] += x_offset_shoulder2 results_vis[0]['bodies']['candidate'][2,1] += y_offset_shoulder2 results_vis[0]['bodies']['candidate'][3,0] += x_offset_shoulder2 results_vis[0]['bodies']['candidate'][3,1] += y_offset_shoulder2 results_vis[0]['bodies']['candidate'][4,0] += x_offset_shoulder2 results_vis[0]['bodies']['candidate'][4,1] += y_offset_shoulder2 results_vis[0]['hands'][1,:,0] += x_offset_shoulder2 results_vis[0]['hands'][1,:,1] += y_offset_shoulder2 ########shoulder5######## l_shoulder5_ref = ((ref_candidate[5][0] - ref_candidate[1][0]) ** 2 + (ref_candidate[5][1] - ref_candidate[1][1]) ** 2) ** 0.5 l_shoulder5_0 = ((candidate[5][0] - candidate[1][0]) ** 2 + (candidate[5][1] - candidate[1][1]) ** 2) ** 0.5 shoulder5_ratio = l_shoulder5_ref / l_shoulder5_0 x_offset_shoulder5 = (candidate[1][0]-candidate[5][0])*(1.-shoulder5_ratio) y_offset_shoulder5 = (candidate[1][1]-candidate[5][1])*(1.-shoulder5_ratio) results_vis[0]['bodies']['candidate'][5,0] += x_offset_shoulder5 results_vis[0]['bodies']['candidate'][5,1] += y_offset_shoulder5 results_vis[0]['bodies']['candidate'][6,0] += x_offset_shoulder5 results_vis[0]['bodies']['candidate'][6,1] += y_offset_shoulder5 results_vis[0]['bodies']['candidate'][7,0] += x_offset_shoulder5 results_vis[0]['bodies']['candidate'][7,1] += y_offset_shoulder5 results_vis[0]['hands'][0,:,0] += x_offset_shoulder5 results_vis[0]['hands'][0,:,1] += y_offset_shoulder5 ########arm3######## l_arm3_ref = ((ref_candidate[3][0] - ref_candidate[2][0]) ** 2 + (ref_candidate[3][1] - ref_candidate[2][1]) ** 2) ** 0.5 l_arm3_0 = ((candidate[3][0] - candidate[2][0]) ** 2 + (candidate[3][1] - candidate[2][1]) ** 2) ** 0.5 arm3_ratio = l_arm3_ref / l_arm3_0 x_offset_arm3 = (candidate[2][0]-candidate[3][0])*(1.-arm3_ratio) y_offset_arm3 = (candidate[2][1]-candidate[3][1])*(1.-arm3_ratio) results_vis[0]['bodies']['candidate'][3,0] += x_offset_arm3 results_vis[0]['bodies']['candidate'][3,1] += y_offset_arm3 results_vis[0]['bodies']['candidate'][4,0] += x_offset_arm3 results_vis[0]['bodies']['candidate'][4,1] += y_offset_arm3 results_vis[0]['hands'][1,:,0] += x_offset_arm3 results_vis[0]['hands'][1,:,1] += y_offset_arm3 ########arm4######## l_arm4_ref = ((ref_candidate[4][0] - ref_candidate[3][0]) ** 2 + (ref_candidate[4][1] - ref_candidate[3][1]) ** 2) ** 0.5 l_arm4_0 = ((candidate[4][0] - candidate[3][0]) ** 2 + (candidate[4][1] - candidate[3][1]) ** 2) ** 0.5 arm4_ratio = l_arm4_ref / l_arm4_0 x_offset_arm4 = (candidate[3][0]-candidate[4][0])*(1.-arm4_ratio) y_offset_arm4 = (candidate[3][1]-candidate[4][1])*(1.-arm4_ratio) results_vis[0]['bodies']['candidate'][4,0] += x_offset_arm4 results_vis[0]['bodies']['candidate'][4,1] += y_offset_arm4 results_vis[0]['hands'][1,:,0] += x_offset_arm4 results_vis[0]['hands'][1,:,1] += y_offset_arm4 ########arm6######## l_arm6_ref = ((ref_candidate[6][0] - ref_candidate[5][0]) ** 2 + (ref_candidate[6][1] - ref_candidate[5][1]) ** 2) ** 0.5 l_arm6_0 = ((candidate[6][0] - candidate[5][0]) ** 2 + (candidate[6][1] - candidate[5][1]) ** 2) ** 0.5 arm6_ratio = l_arm6_ref / l_arm6_0 x_offset_arm6 = (candidate[5][0]-candidate[6][0])*(1.-arm6_ratio) y_offset_arm6 = (candidate[5][1]-candidate[6][1])*(1.-arm6_ratio) results_vis[0]['bodies']['candidate'][6,0] += x_offset_arm6 results_vis[0]['bodies']['candidate'][6,1] += y_offset_arm6 results_vis[0]['bodies']['candidate'][7,0] += x_offset_arm6 results_vis[0]['bodies']['candidate'][7,1] += y_offset_arm6 results_vis[0]['hands'][0,:,0] += x_offset_arm6 results_vis[0]['hands'][0,:,1] += y_offset_arm6 ########arm7######## l_arm7_ref = ((ref_candidate[7][0] - ref_candidate[6][0]) ** 2 + (ref_candidate[7][1] - ref_candidate[6][1]) ** 2) ** 0.5 l_arm7_0 = ((candidate[7][0] - candidate[6][0]) ** 2 + (candidate[7][1] - candidate[6][1]) ** 2) ** 0.5 arm7_ratio = l_arm7_ref / l_arm7_0 x_offset_arm7 = (candidate[6][0]-candidate[7][0])*(1.-arm7_ratio) y_offset_arm7 = (candidate[6][1]-candidate[7][1])*(1.-arm7_ratio) results_vis[0]['bodies']['candidate'][7,0] += x_offset_arm7 results_vis[0]['bodies']['candidate'][7,1] += y_offset_arm7 results_vis[0]['hands'][0,:,0] += x_offset_arm7 results_vis[0]['hands'][0,:,1] += y_offset_arm7 ########head14######## l_head14_ref = ((ref_candidate[14][0] - ref_candidate[0][0]) ** 2 + (ref_candidate[14][1] - ref_candidate[0][1]) ** 2) ** 0.5 l_head14_0 = ((candidate[14][0] - candidate[0][0]) ** 2 + (candidate[14][1] - candidate[0][1]) ** 2) ** 0.5 head14_ratio = l_head14_ref / l_head14_0 x_offset_head14 = (candidate[0][0]-candidate[14][0])*(1.-head14_ratio) y_offset_head14 = (candidate[0][1]-candidate[14][1])*(1.-head14_ratio) results_vis[0]['bodies']['candidate'][14,0] += x_offset_head14 results_vis[0]['bodies']['candidate'][14,1] += y_offset_head14 results_vis[0]['bodies']['candidate'][16,0] += x_offset_head14 results_vis[0]['bodies']['candidate'][16,1] += y_offset_head14 ########head15######## l_head15_ref = ((ref_candidate[15][0] - ref_candidate[0][0]) ** 2 + (ref_candidate[15][1] - ref_candidate[0][1]) ** 2) ** 0.5 l_head15_0 = ((candidate[15][0] - candidate[0][0]) ** 2 + (candidate[15][1] - candidate[0][1]) ** 2) ** 0.5 head15_ratio = l_head15_ref / l_head15_0 x_offset_head15 = (candidate[0][0]-candidate[15][0])*(1.-head15_ratio) y_offset_head15 = (candidate[0][1]-candidate[15][1])*(1.-head15_ratio) results_vis[0]['bodies']['candidate'][15,0] += x_offset_head15 results_vis[0]['bodies']['candidate'][15,1] += y_offset_head15 results_vis[0]['bodies']['candidate'][17,0] += x_offset_head15 results_vis[0]['bodies']['candidate'][17,1] += y_offset_head15 ########head16######## l_head16_ref = ((ref_candidate[16][0] - ref_candidate[14][0]) ** 2 + (ref_candidate[16][1] - ref_candidate[14][1]) ** 2) ** 0.5 l_head16_0 = ((candidate[16][0] - candidate[14][0]) ** 2 + (candidate[16][1] - candidate[14][1]) ** 2) ** 0.5 head16_ratio = l_head16_ref / l_head16_0 x_offset_head16 = (candidate[14][0]-candidate[16][0])*(1.-head16_ratio) y_offset_head16 = (candidate[14][1]-candidate[16][1])*(1.-head16_ratio) results_vis[0]['bodies']['candidate'][16,0] += x_offset_head16 results_vis[0]['bodies']['candidate'][16,1] += y_offset_head16 ########head17######## l_head17_ref = ((ref_candidate[17][0] - ref_candidate[15][0]) ** 2 + (ref_candidate[17][1] - ref_candidate[15][1]) ** 2) ** 0.5 l_head17_0 = ((candidate[17][0] - candidate[15][0]) ** 2 + (candidate[17][1] - candidate[15][1]) ** 2) ** 0.5 head17_ratio = l_head17_ref / l_head17_0 x_offset_head17 = (candidate[15][0]-candidate[17][0])*(1.-head17_ratio) y_offset_head17 = (candidate[15][1]-candidate[17][1])*(1.-head17_ratio) results_vis[0]['bodies']['candidate'][17,0] += x_offset_head17 results_vis[0]['bodies']['candidate'][17,1] += y_offset_head17 ########MovingAverage######## ########left leg######## l_ll1_ref = ((ref_candidate[8][0] - ref_candidate[9][0]) ** 2 + (ref_candidate[8][1] - ref_candidate[9][1]) ** 2) ** 0.5 l_ll1_0 = ((candidate[8][0] - candidate[9][0]) ** 2 + (candidate[8][1] - candidate[9][1]) ** 2) ** 0.5 ll1_ratio = l_ll1_ref / l_ll1_0 x_offset_ll1 = (candidate[9][0]-candidate[8][0])*(ll1_ratio-1.) y_offset_ll1 = (candidate[9][1]-candidate[8][1])*(ll1_ratio-1.) results_vis[0]['bodies']['candidate'][9,0] += x_offset_ll1 results_vis[0]['bodies']['candidate'][9,1] += y_offset_ll1 results_vis[0]['bodies']['candidate'][10,0] += x_offset_ll1 results_vis[0]['bodies']['candidate'][10,1] += y_offset_ll1 results_vis[0]['bodies']['candidate'][19,0] += x_offset_ll1 results_vis[0]['bodies']['candidate'][19,1] += y_offset_ll1 l_ll2_ref = ((ref_candidate[9][0] - ref_candidate[10][0]) ** 2 + (ref_candidate[9][1] - ref_candidate[10][1]) ** 2) ** 0.5 l_ll2_0 = ((candidate[9][0] - candidate[10][0]) ** 2 + (candidate[9][1] - candidate[10][1]) ** 2) ** 0.5 ll2_ratio = l_ll2_ref / l_ll2_0 x_offset_ll2 = (candidate[10][0]-candidate[9][0])*(ll2_ratio-1.) y_offset_ll2 = (candidate[10][1]-candidate[9][1])*(ll2_ratio-1.) results_vis[0]['bodies']['candidate'][10,0] += x_offset_ll2 results_vis[0]['bodies']['candidate'][10,1] += y_offset_ll2 results_vis[0]['bodies']['candidate'][19,0] += x_offset_ll2 results_vis[0]['bodies']['candidate'][19,1] += y_offset_ll2 ########right leg######## l_rl1_ref = ((ref_candidate[11][0] - ref_candidate[12][0]) ** 2 + (ref_candidate[11][1] - ref_candidate[12][1]) ** 2) ** 0.5 l_rl1_0 = ((candidate[11][0] - candidate[12][0]) ** 2 + (candidate[11][1] - candidate[12][1]) ** 2) ** 0.5 rl1_ratio = l_rl1_ref / l_rl1_0 x_offset_rl1 = (candidate[12][0]-candidate[11][0])*(rl1_ratio-1.) y_offset_rl1 = (candidate[12][1]-candidate[11][1])*(rl1_ratio-1.) results_vis[0]['bodies']['candidate'][12,0] += x_offset_rl1 results_vis[0]['bodies']['candidate'][12,1] += y_offset_rl1 results_vis[0]['bodies']['candidate'][13,0] += x_offset_rl1 results_vis[0]['bodies']['candidate'][13,1] += y_offset_rl1 results_vis[0]['bodies']['candidate'][18,0] += x_offset_rl1 results_vis[0]['bodies']['candidate'][18,1] += y_offset_rl1 l_rl2_ref = ((ref_candidate[12][0] - ref_candidate[13][0]) ** 2 + (ref_candidate[12][1] - ref_candidate[13][1]) ** 2) ** 0.5 l_rl2_0 = ((candidate[12][0] - candidate[13][0]) ** 2 + (candidate[12][1] - candidate[13][1]) ** 2) ** 0.5 rl2_ratio = l_rl2_ref / l_rl2_0 x_offset_rl2 = (candidate[13][0]-candidate[12][0])*(rl2_ratio-1.) y_offset_rl2 = (candidate[13][1]-candidate[12][1])*(rl2_ratio-1.) results_vis[0]['bodies']['candidate'][13,0] += x_offset_rl2 results_vis[0]['bodies']['candidate'][13,1] += y_offset_rl2 results_vis[0]['bodies']['candidate'][18,0] += x_offset_rl2 results_vis[0]['bodies']['candidate'][18,1] += y_offset_rl2 offset = ref_candidate[1] - results_vis[0]['bodies']['candidate'][1] results_vis[0]['bodies']['candidate'] += offset[np.newaxis, :] results_vis[0]['faces'] += offset[np.newaxis, np.newaxis, :] results_vis[0]['hands'] += offset[np.newaxis, np.newaxis, :] for i in range(1, len(results_vis)): results_vis[i]['bodies']['candidate'][:,0] *= x_ratio results_vis[i]['bodies']['candidate'][:,1] *= y_ratio results_vis[i]['faces'][:,:,0] *= x_ratio results_vis[i]['faces'][:,:,1] *= y_ratio results_vis[i]['hands'][:,:,0] *= x_ratio results_vis[i]['hands'][:,:,1] *= y_ratio ########neck######## x_offset_neck = (results_vis[i]['bodies']['candidate'][1][0]-results_vis[i]['bodies']['candidate'][0][0])*(1.-neck_ratio) y_offset_neck = (results_vis[i]['bodies']['candidate'][1][1]-results_vis[i]['bodies']['candidate'][0][1])*(1.-neck_ratio) results_vis[i]['bodies']['candidate'][0,0] += x_offset_neck results_vis[i]['bodies']['candidate'][0,1] += y_offset_neck results_vis[i]['bodies']['candidate'][14,0] += x_offset_neck results_vis[i]['bodies']['candidate'][14,1] += y_offset_neck results_vis[i]['bodies']['candidate'][15,0] += x_offset_neck results_vis[i]['bodies']['candidate'][15,1] += y_offset_neck results_vis[i]['bodies']['candidate'][16,0] += x_offset_neck results_vis[i]['bodies']['candidate'][16,1] += y_offset_neck results_vis[i]['bodies']['candidate'][17,0] += x_offset_neck results_vis[i]['bodies']['candidate'][17,1] += y_offset_neck ########shoulder2######## x_offset_shoulder2 = (results_vis[i]['bodies']['candidate'][1][0]-results_vis[i]['bodies']['candidate'][2][0])*(1.-shoulder2_ratio) y_offset_shoulder2 = (results_vis[i]['bodies']['candidate'][1][1]-results_vis[i]['bodies']['candidate'][2][1])*(1.-shoulder2_ratio) results_vis[i]['bodies']['candidate'][2,0] += x_offset_shoulder2 results_vis[i]['bodies']['candidate'][2,1] += y_offset_shoulder2 results_vis[i]['bodies']['candidate'][3,0] += x_offset_shoulder2 results_vis[i]['bodies']['candidate'][3,1] += y_offset_shoulder2 results_vis[i]['bodies']['candidate'][4,0] += x_offset_shoulder2 results_vis[i]['bodies']['candidate'][4,1] += y_offset_shoulder2 results_vis[i]['hands'][1,:,0] += x_offset_shoulder2 results_vis[i]['hands'][1,:,1] += y_offset_shoulder2 ########shoulder5######## x_offset_shoulder5 = (results_vis[i]['bodies']['candidate'][1][0]-results_vis[i]['bodies']['candidate'][5][0])*(1.-shoulder5_ratio) y_offset_shoulder5 = (results_vis[i]['bodies']['candidate'][1][1]-results_vis[i]['bodies']['candidate'][5][1])*(1.-shoulder5_ratio) results_vis[i]['bodies']['candidate'][5,0] += x_offset_shoulder5 results_vis[i]['bodies']['candidate'][5,1] += y_offset_shoulder5 results_vis[i]['bodies']['candidate'][6,0] += x_offset_shoulder5 results_vis[i]['bodies']['candidate'][6,1] += y_offset_shoulder5 results_vis[i]['bodies']['candidate'][7,0] += x_offset_shoulder5 results_vis[i]['bodies']['candidate'][7,1] += y_offset_shoulder5 results_vis[i]['hands'][0,:,0] += x_offset_shoulder5 results_vis[i]['hands'][0,:,1] += y_offset_shoulder5 ########arm3######## x_offset_arm3 = (results_vis[i]['bodies']['candidate'][2][0]-results_vis[i]['bodies']['candidate'][3][0])*(1.-arm3_ratio) y_offset_arm3 = (results_vis[i]['bodies']['candidate'][2][1]-results_vis[i]['bodies']['candidate'][3][1])*(1.-arm3_ratio) results_vis[i]['bodies']['candidate'][3,0] += x_offset_arm3 results_vis[i]['bodies']['candidate'][3,1] += y_offset_arm3 results_vis[i]['bodies']['candidate'][4,0] += x_offset_arm3 results_vis[i]['bodies']['candidate'][4,1] += y_offset_arm3 results_vis[i]['hands'][1,:,0] += x_offset_arm3 results_vis[i]['hands'][1,:,1] += y_offset_arm3 ########arm4######## x_offset_arm4 = (results_vis[i]['bodies']['candidate'][3][0]-results_vis[i]['bodies']['candidate'][4][0])*(1.-arm4_ratio) y_offset_arm4 = (results_vis[i]['bodies']['candidate'][3][1]-results_vis[i]['bodies']['candidate'][4][1])*(1.-arm4_ratio) results_vis[i]['bodies']['candidate'][4,0] += x_offset_arm4 results_vis[i]['bodies']['candidate'][4,1] += y_offset_arm4 results_vis[i]['hands'][1,:,0] += x_offset_arm4 results_vis[i]['hands'][1,:,1] += y_offset_arm4 ########arm6######## x_offset_arm6 = (results_vis[i]['bodies']['candidate'][5][0]-results_vis[i]['bodies']['candidate'][6][0])*(1.-arm6_ratio) y_offset_arm6 = (results_vis[i]['bodies']['candidate'][5][1]-results_vis[i]['bodies']['candidate'][6][1])*(1.-arm6_ratio) results_vis[i]['bodies']['candidate'][6,0] += x_offset_arm6 results_vis[i]['bodies']['candidate'][6,1] += y_offset_arm6 results_vis[i]['bodies']['candidate'][7,0] += x_offset_arm6 results_vis[i]['bodies']['candidate'][7,1] += y_offset_arm6 results_vis[i]['hands'][0,:,0] += x_offset_arm6 results_vis[i]['hands'][0,:,1] += y_offset_arm6 ########arm7######## x_offset_arm7 = (results_vis[i]['bodies']['candidate'][6][0]-results_vis[i]['bodies']['candidate'][7][0])*(1.-arm7_ratio) y_offset_arm7 = (results_vis[i]['bodies']['candidate'][6][1]-results_vis[i]['bodies']['candidate'][7][1])*(1.-arm7_ratio) results_vis[i]['bodies']['candidate'][7,0] += x_offset_arm7 results_vis[i]['bodies']['candidate'][7,1] += y_offset_arm7 results_vis[i]['hands'][0,:,0] += x_offset_arm7 results_vis[i]['hands'][0,:,1] += y_offset_arm7 ########head14######## x_offset_head14 = (results_vis[i]['bodies']['candidate'][0][0]-results_vis[i]['bodies']['candidate'][14][0])*(1.-head14_ratio) y_offset_head14 = (results_vis[i]['bodies']['candidate'][0][1]-results_vis[i]['bodies']['candidate'][14][1])*(1.-head14_ratio) results_vis[i]['bodies']['candidate'][14,0] += x_offset_head14 results_vis[i]['bodies']['candidate'][14,1] += y_offset_head14 results_vis[i]['bodies']['candidate'][16,0] += x_offset_head14 results_vis[i]['bodies']['candidate'][16,1] += y_offset_head14 ########head15######## x_offset_head15 = (results_vis[i]['bodies']['candidate'][0][0]-results_vis[i]['bodies']['candidate'][15][0])*(1.-head15_ratio) y_offset_head15 = (results_vis[i]['bodies']['candidate'][0][1]-results_vis[i]['bodies']['candidate'][15][1])*(1.-head15_ratio) results_vis[i]['bodies']['candidate'][15,0] += x_offset_head15 results_vis[i]['bodies']['candidate'][15,1] += y_offset_head15 results_vis[i]['bodies']['candidate'][17,0] += x_offset_head15 results_vis[i]['bodies']['candidate'][17,1] += y_offset_head15 ########head16######## x_offset_head16 = (results_vis[i]['bodies']['candidate'][14][0]-results_vis[i]['bodies']['candidate'][16][0])*(1.-head16_ratio) y_offset_head16 = (results_vis[i]['bodies']['candidate'][14][1]-results_vis[i]['bodies']['candidate'][16][1])*(1.-head16_ratio) results_vis[i]['bodies']['candidate'][16,0] += x_offset_head16 results_vis[i]['bodies']['candidate'][16,1] += y_offset_head16 ########head17######## x_offset_head17 = (results_vis[i]['bodies']['candidate'][15][0]-results_vis[i]['bodies']['candidate'][17][0])*(1.-head17_ratio) y_offset_head17 = (results_vis[i]['bodies']['candidate'][15][1]-results_vis[i]['bodies']['candidate'][17][1])*(1.-head17_ratio) results_vis[i]['bodies']['candidate'][17,0] += x_offset_head17 results_vis[i]['bodies']['candidate'][17,1] += y_offset_head17 # ########MovingAverage######## ########left leg######## x_offset_ll1 = (results_vis[i]['bodies']['candidate'][9][0]-results_vis[i]['bodies']['candidate'][8][0])*(ll1_ratio-1.) y_offset_ll1 = (results_vis[i]['bodies']['candidate'][9][1]-results_vis[i]['bodies']['candidate'][8][1])*(ll1_ratio-1.) results_vis[i]['bodies']['candidate'][9,0] += x_offset_ll1 results_vis[i]['bodies']['candidate'][9,1] += y_offset_ll1 results_vis[i]['bodies']['candidate'][10,0] += x_offset_ll1 results_vis[i]['bodies']['candidate'][10,1] += y_offset_ll1 results_vis[i]['bodies']['candidate'][19,0] += x_offset_ll1 results_vis[i]['bodies']['candidate'][19,1] += y_offset_ll1 x_offset_ll2 = (results_vis[i]['bodies']['candidate'][10][0]-results_vis[i]['bodies']['candidate'][9][0])*(ll2_ratio-1.) y_offset_ll2 = (results_vis[i]['bodies']['candidate'][10][1]-results_vis[i]['bodies']['candidate'][9][1])*(ll2_ratio-1.) results_vis[i]['bodies']['candidate'][10,0] += x_offset_ll2 results_vis[i]['bodies']['candidate'][10,1] += y_offset_ll2 results_vis[i]['bodies']['candidate'][19,0] += x_offset_ll2 results_vis[i]['bodies']['candidate'][19,1] += y_offset_ll2 ########right leg######## x_offset_rl1 = (results_vis[i]['bodies']['candidate'][12][0]-results_vis[i]['bodies']['candidate'][11][0])*(rl1_ratio-1.) y_offset_rl1 = (results_vis[i]['bodies']['candidate'][12][1]-results_vis[i]['bodies']['candidate'][11][1])*(rl1_ratio-1.) results_vis[i]['bodies']['candidate'][12,0] += x_offset_rl1 results_vis[i]['bodies']['candidate'][12,1] += y_offset_rl1 results_vis[i]['bodies']['candidate'][13,0] += x_offset_rl1 results_vis[i]['bodies']['candidate'][13,1] += y_offset_rl1 results_vis[i]['bodies']['candidate'][18,0] += x_offset_rl1 results_vis[i]['bodies']['candidate'][18,1] += y_offset_rl1 x_offset_rl2 = (results_vis[i]['bodies']['candidate'][13][0]-results_vis[i]['bodies']['candidate'][12][0])*(rl2_ratio-1.) y_offset_rl2 = (results_vis[i]['bodies']['candidate'][13][1]-results_vis[i]['bodies']['candidate'][12][1])*(rl2_ratio-1.) results_vis[i]['bodies']['candidate'][13,0] += x_offset_rl2 results_vis[i]['bodies']['candidate'][13,1] += y_offset_rl2 results_vis[i]['bodies']['candidate'][18,0] += x_offset_rl2 results_vis[i]['bodies']['candidate'][18,1] += y_offset_rl2 results_vis[i]['bodies']['candidate'] += offset[np.newaxis, :] results_vis[i]['faces'] += offset[np.newaxis, np.newaxis, :] results_vis[i]['hands'] += offset[np.newaxis, np.newaxis, :] dwpose_woface_list = [] for i in range(len(results_vis)): #try: dwpose_woface, dwpose_wface = draw_pose(results_vis[i], H=height, W=width, stick_width=stick_width, draw_body=draw_body, draw_hands=draw_hands, hand_keypoint_size=hand_keypoint_size, draw_feet=draw_feet, body_keypoint_size=body_keypoint_size, draw_head=draw_head) result = torch.from_numpy(dwpose_woface) #except Exception: # result = torch.zeros((height, width, 3), dtype=torch.uint8) dwpose_woface_list.append(result) dwpose_woface_tensor = torch.stack(dwpose_woface_list, dim=0) dwpose_woface_ref_tensor = None if ref_image is not None: dwpose_woface_ref, dwpose_wface_ref = draw_pose(pose_ref, H=height, W=width, stick_width=stick_width, draw_body=draw_body, draw_hands=draw_hands, hand_keypoint_size=hand_keypoint_size, draw_feet=draw_feet, body_keypoint_size=body_keypoint_size, draw_head=draw_head) dwpose_woface_ref_tensor = torch.from_numpy(dwpose_woface_ref) return dwpose_woface_tensor, dwpose_woface_ref_tensor class WanVideoUniAnimateDWPoseDetector: @classmethod def INPUT_TYPES(s): return {"required": { "pose_images": ("IMAGE", {"tooltip": "Pose images"}), "score_threshold": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Score threshold for pose detection"}), "stick_width": ("INT", {"default": 4, "min": 1, "max": 100, "step": 1, "tooltip": "Stick width for drawing keypoints"}), "draw_body": ("BOOLEAN", {"default": True, "tooltip": "Draw body keypoints"}), "body_keypoint_size": ("INT", {"default": 4, "min": 0, "max": 100, "step": 1, "tooltip": "Body keypoint size"}), "draw_feet": ("BOOLEAN", {"default": True, "tooltip": "Draw feet keypoints"}), "draw_hands": ("BOOLEAN", {"default": True, "tooltip": "Draw hand keypoints"}), "hand_keypoint_size": ("INT", {"default": 4, "min": 0, "max": 100, "step": 1, "tooltip": "Hand keypoint size"}), "colorspace": (["RGB", "BGR"], {"tooltip": "Color space for the output image"}), "handle_not_detected": (["empty", "repeat"], {"default": "empty", "tooltip": "How to handle undetected poses, empty inserts black and repeat inserts previous detection"}), "draw_head": ("BOOLEAN", {"default": True, "tooltip": "Draw head keypoints"}), }, "optional": { "reference_pose_image": ("IMAGE", {"tooltip": "Reference pose image"}), }, } RETURN_TYPES = ("IMAGE", "IMAGE", ) RETURN_NAMES = ("poses", "reference_pose",) FUNCTION = "process" CATEGORY = "WanVideoWrapper" def process(self, pose_images, score_threshold, stick_width, reference_pose_image=None, draw_body=True, body_keypoint_size=4, draw_feet=True, draw_hands=True, hand_keypoint_size=4, colorspace="RGB", handle_not_detected="empty", draw_head=True): device = mm.get_torch_device() #model loading dw_pose_model = "dw-ll_ucoco_384_bs5.torchscript.pt" yolo_model = "yolox_l.torchscript.pt" script_directory = os.path.dirname(os.path.abspath(__file__)) model_base_path = os.path.join(script_directory, "models", "DWPose") model_det=os.path.join(model_base_path, yolo_model) model_pose=os.path.join(model_base_path, dw_pose_model) if not os.path.exists(model_det): log.info(f"Downloading yolo model to: {model_base_path}") from huggingface_hub import snapshot_download snapshot_download(repo_id="hr16/yolox-onnx", allow_patterns=[f"*{yolo_model}*"], local_dir=model_base_path, local_dir_use_symlinks=False) if not os.path.exists(model_pose): log.info(f"Downloading dwpose model to: {model_base_path}") from huggingface_hub import snapshot_download snapshot_download(repo_id="hr16/DWPose-TorchScript-BatchSize5", allow_patterns=[f"*{dw_pose_model}*"], local_dir=model_base_path, local_dir_use_symlinks=False) if not hasattr(self, "det") or not hasattr(self, "pose"): self.det = torch.jit.load(model_det, map_location=device) self.pose = torch.jit.load(model_pose, map_location=device) self.dwpose_detector = DWposeDetector(self.det, self.pose) #model inference height, width = pose_images.shape[1:3] pose_np = pose_images.cpu().numpy() * 255 ref_np = None if reference_pose_image is not None: ref = reference_pose_image ref_np = ref.cpu().numpy() * 255 prev_fuser_state = torch._C._jit_texpr_fuser_enabled() torch._C._jit_set_texpr_fuser_enabled(False) # removes warmup delay, may want to enable later poses, reference_pose = pose_extract(pose_np, ref_np, self.dwpose_detector, height, width, score_threshold, stick_width=stick_width, draw_body=draw_body, body_keypoint_size=body_keypoint_size, draw_feet=draw_feet, draw_hands=draw_hands, hand_keypoint_size=hand_keypoint_size, handle_not_detected=handle_not_detected, draw_head=draw_head) poses = poses / 255.0 torch._C._jit_set_texpr_fuser_enabled(prev_fuser_state) if reference_pose_image is not None: reference_pose = reference_pose.unsqueeze(0) / 255.0 else: reference_pose = torch.zeros(1, 64, 64, 3, device=torch.device("cpu")) if colorspace == "BGR": poses=torch.flip(poses, dims=[-1]) return (poses, reference_pose, ) class WanVideoUniAnimatePoseInput: @classmethod def INPUT_TYPES(s): return {"required": { "pose_images": ("IMAGE", {"tooltip": "Pose images"}), "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Strength of the pose control"}), "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percentage for the pose control"}), "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percentage for the pose control"}), }, "optional": { "reference_pose_image": ("IMAGE", {"tooltip": "Reference pose image"}), }, } RETURN_TYPES = ("UNIANIMATE_POSE", ) RETURN_NAMES = ("unianimate_poses",) FUNCTION = "process" CATEGORY = "WanVideoWrapper" def process(self, pose_images, strength, start_percent, end_percent, reference_pose_image=None): pose = pose_images.permute(3, 0, 1, 2).unsqueeze(0).contiguous() ref = None if reference_pose_image is not None: ref = reference_pose_image.permute(0, 3, 1, 2).contiguous() unianim_poses = { "pose": pose, "ref": ref, "strength": strength, "start_percent": start_percent, "end_percent": end_percent } return (unianim_poses,) NODE_CLASS_MAPPINGS = { "WanVideoUniAnimatePoseInput": WanVideoUniAnimatePoseInput, "WanVideoUniAnimateDWPoseDetector": WanVideoUniAnimateDWPoseDetector, } NODE_DISPLAY_NAME_MAPPINGS = { "WanVideoUniAnimatePoseInput": "WanVideo UniAnimate Pose Input", "WanVideoUniAnimateDWPoseDetector": "WanVideo UniAnimate DWPose Detector", }