# 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)
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
import cv2
import torch
import numpy as np
import json
import copy
import torch
import random
import argparse
import shutil
import tempfile
import subprocess
import numpy as np
import math
import torch.multiprocessing as mp
import torch.distributed as dist
import pickle
import logging
from io import BytesIO
import oss2 as oss
import os.path as osp
import sys
import dwpose.util as util
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
def get_logger(name="essmc2"):
logger = logging.getLogger(name)
logger.propagate = False
if len(logger.handlers) == 0:
std_handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
std_handler.setFormatter(formatter)
std_handler.setLevel(logging.INFO)
logger.setLevel(logging.INFO)
logger.addHandler(std_handler)
return logger
class DWposeDetector:
def __init__(self):
self.pose_estimation = Wholebody()
def __call__(self, oriImg):
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] > 0.3:
score[i][j] = int(18*i+j)
else:
score[i][j] = -1
un_visible = subset<0.3
candidate[un_visible] = -1
bodyfoot_score = subset[:,:24].copy()
for i in range(len(bodyfoot_score)):
for j in range(len(bodyfoot_score[i])):
if bodyfoot_score[i][j] > 0.3:
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)
pose = dict(bodies=bodies, hands=hands, faces=faces)
# return draw_pose(pose, H, W)
return pose
def draw_pose(pose, H, W):
bodies = pose['bodies']
faces = pose['faces']
hands = pose['hands']
candidate = bodies['candidate']
subset = bodies['subset']
canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)
canvas = util.draw_body_and_foot(canvas, candidate, subset)
canvas = util.draw_handpose(canvas, hands)
canvas_without_face = copy.deepcopy(canvas)
canvas = util.draw_facepose(canvas, faces)
return canvas_without_face, canvas
def dw_func(_id, frame, dwpose_model, dwpose_woface_folder='tmp_dwpose_wo_face', dwpose_withface_folder='tmp_dwpose_with_face'):
# frame = cv2.imread(frame_name, cv2.IMREAD_COLOR)
pose = dwpose_model(frame)
return pose
def mp_main(args):
if args.source_video_paths.endswith('mp4'):
video_paths = [args.source_video_paths]
else:
# video list
video_paths = [os.path.join(args.source_video_paths, frame_name) for frame_name in os.listdir(args.source_video_paths)]
logger.info("There are {} videos for extracting poses".format(len(video_paths)))
logger.info('LOAD: DW Pose Model')
dwpose_model = DWposeDetector()
results_vis = []
for i, file_path in enumerate(video_paths):
logger.info(f"{i}/{len(video_paths)}, {file_path}")
videoCapture = cv2.VideoCapture(file_path)
while videoCapture.isOpened():
# get a frame
ret, frame = videoCapture.read()
if ret:
pose = dw_func(i, frame, dwpose_model)
results_vis.append(pose)
else:
break
logger.info(f'all frames in {file_path} have been read.')
videoCapture.release()
# added
# results_vis = results_vis[8:]
print(len(results_vis))
ref_name = args.ref_name
save_motion = args.saved_pose_dir
os.system(f'rm -rf {save_motion}');
os.makedirs(save_motion, exist_ok=True)
save_warp = args.saved_pose_dir
# os.makedirs(save_warp, exist_ok=True)
ref_frame = cv2.imread(ref_name, cv2.IMREAD_COLOR)
pose_ref = dw_func(i, ref_frame, dwpose_model)
bodies = results_vis[0]['bodies']
faces = results_vis[0]['faces']
hands = results_vis[0]['hands']
candidate = bodies['candidate']
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, :]
for i in range(len(results_vis)):
dwpose_woface, dwpose_wface = draw_pose(results_vis[i], H=768, W=512)
img_path = save_motion+'/' + str(i).zfill(4) + '.jpg'
cv2.imwrite(img_path, dwpose_woface)
dwpose_woface, dwpose_wface = draw_pose(pose_ref, H=768, W=512)
img_path = save_warp+'/' + 'ref_pose.jpg'
cv2.imwrite(img_path, dwpose_woface)
logger = get_logger('dw pose extraction')
if __name__=='__main__':
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument("--ref_name", type=str, default="data/images/IMG_20240514_104337.jpg",)
parser.add_argument("--source_video_paths", type=str, default="data/videos/source_video.mp4",)
parser.add_argument("--saved_pose_dir", type=str, default="data/saved_pose/IMG_20240514_104337",)
args = parser.parse_args()
return args
args = parse_args()
mp_main(args)