1058 lines
44 KiB
Python
1058 lines
44 KiB
Python
# !/usr/bin/env python
|
||
# -*- coding: UTF-8 -*-
|
||
import datetime
|
||
import logging
|
||
import os
|
||
import torch
|
||
from PIL import Image
|
||
import numpy as np
|
||
import cv2
|
||
import sys
|
||
import pickle
|
||
import gc
|
||
import shutil
|
||
from huggingface_hub import hf_hub_download
|
||
try:
|
||
from moviepy.editor import VideoFileClip, AudioFileClip
|
||
except:
|
||
try:
|
||
from moviepy import VideoFileClip, AudioFileClip
|
||
except:
|
||
from moviepy import *
|
||
import random
|
||
from .src.utils.mp_utils import LMKExtractor
|
||
from .src.utils.motion_utils import motion_sync
|
||
from .src.utils.util import save_videos_grid, crop_and_pad,crop_and_pad_rectangle,center_crop
|
||
from .echomimic_v2.src.utils.dwpose_util import draw_pose_select_v2
|
||
from comfy.utils import common_upscale,ProgressBar
|
||
import folder_paths
|
||
from multiprocessing.pool import ThreadPool
|
||
weight_dtype = torch.float16
|
||
cur_path = os.path.dirname(os.path.abspath(__file__))
|
||
device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
|
||
tensorrt_lite= os.path.join(folder_paths.get_input_directory(),"tensorrt_lite")
|
||
MAX_SIZE = 768
|
||
|
||
def process_video_v2(ref_image_pil, uploaded_audio, width, height, length, seed,
|
||
context_frames, context_overlap, cfg, steps, sample_rate, fps, pipe,
|
||
save_video, pose_dir, audio_file_prefix,visualizer,video_images,face_detector):
|
||
origin_h = height
|
||
origin_w = width
|
||
# 处理输入图片的尺寸
|
||
panding_img=img_padding(height, width, ref_image_pil) # 不管输出图片是何种尺寸，为保证图片质量，将输入图片转为为正方形，横裁切，竖填充，长宽为输出尺寸最大
|
||
#### try input image Body alignment 暂时用sapiens
|
||
# 将高宽改成最大图幅，方便裁切
|
||
height = max(height, width)
|
||
width = max(height, width)
|
||
|
||
if visualizer and face_detector=="sapiens":
|
||
visualizer.move_to_cuda()
|
||
base_image=cv2.imread(os.path.join(cur_path,"echomimic_v2/assets/halfbody_demo/refimag/natural_bk_openhand/0222.png"))
|
||
base_image=cv2.cvtColor(base_image, cv2.COLOR_BGR2RGB)
|
||
|
||
_, base_image_key, base_image_box_xy = visualizer(np.asarray(base_image), None) #获取基准图片key 和xy数据 1024*1024
|
||
base_image_length, base_image_left_eye_y = estimate_ratio(base_image_key, base_image_box_xy)
|
||
|
||
panding_img_align = img_padding(1024, 1024, ref_image_pil) #裁切输入图片为1024*1024
|
||
|
||
_, input_img_key, input_img_box_xy = visualizer(np.asarray(panding_img_align), None) #获取实际输入图片的key 和人体box数据
|
||
input_img_length, input_img_left_eye_y = estimate_ratio(input_img_key, input_img_box_xy) #眼睛坐标为绝对值
|
||
|
||
print(base_image_length,base_image_left_eye_y,input_img_length,input_img_left_eye_y) #603 [201] 679 [220]
|
||
|
||
if base_image_length and base_image_left_eye_y and input_img_length and input_img_left_eye_y:
|
||
if abs(base_image_length / 1024 - input_img_length / 1024) > 0.005: # 比例不同须基于输入图片对齐
|
||
logging.info(
|
||
" *** Start input image align . 基于基准图片，开始输入图片的对齐! ***")
|
||
input_img_left_eye_y_ = input_img_left_eye_y[0] #基于1024的绝对值
|
||
base_image_left_eye_y_ = base_image_left_eye_y[0]#基于1024的绝对值
|
||
|
||
panding_img=align_img(base_image_length, input_img_length, 1024, 1024, panding_img_align, base_image_left_eye_y_,
|
||
input_img_left_eye_y_)
|
||
|
||
else: # 人体比例接近，但是高度不对，也需要对齐
|
||
logging.info(
|
||
"Starting the input image shift based on the base image . 基于基准图片，开始输入图片手势平移对齐 ! ***")
|
||
if abs(base_image_left_eye_y[0] / height - input_img_left_eye_y[
|
||
0] / height) > 0.005:
|
||
panding_img = affine_img(base_image_left_eye_y, input_img_left_eye_y, panding_img_align)
|
||
print("input image Body alignment is done")
|
||
panding_img=cv2.resize(panding_img, (width, height), interpolation=cv2.INTER_AREA) #基于1024做的对比，缩放回最大的输出尺寸
|
||
if not isinstance(video_images,torch.Tensor):#非视频驱动时，完成对齐后，卸载dino模型
|
||
visualizer.enable_model_cpu_offload()
|
||
gc.collect()
|
||
torch.cuda.empty_cache()
|
||
infer_image_pil=Image.fromarray(cv2.cvtColor(panding_img,cv2.COLOR_BGR2RGB))
|
||
|
||
if visualizer and isinstance(video_images,torch.Tensor):
|
||
logging.info("***** start infer video to npy files for drive pose ! ***** ")
|
||
video_len, _, _, _ = video_images.size()
|
||
if video_len < 50:
|
||
raise "input video has not much frames for driver,change your input video!"
|
||
else:
|
||
tensor_list = list(torch.chunk(video_images, chunks=video_len))
|
||
input_frames_cv2 = [img_padding(height,width,i) for i in tensor_list] #不管输出图片是何种尺寸，为保证图片质量，将输入视频转为为正方形，横裁切，竖填充，长宽为输出尺寸最大
|
||
|
||
if face_detector=="sapiens":
|
||
audio_file_prefix=f"{audio_file_prefix}_sapiens"
|
||
pose_dir = os.path.join(tensorrt_lite, audio_file_prefix)
|
||
if not os.path.exists(pose_dir):
|
||
os.makedirs(pose_dir)
|
||
else:
|
||
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
|
||
pose_dir = os.path.join(tensorrt_lite, f"{audio_file_prefix}_{timestamp}")
|
||
os.makedirs(pose_dir)
|
||
|
||
#convert_fps(ori_video_path, ori_video_path_new)
|
||
# extract pose
|
||
if face_detector=="dwpose":
|
||
detected_poses, height_video, width_video, ori_frames = get_video_pose(visualizer, input_frames_cv2,
|
||
max_frame=None)
|
||
# parameterize pose
|
||
res_params = get_pose_params(detected_poses, MAX_SIZE)
|
||
# save pose to npy
|
||
pose_dir = save_pose_params(detected_poses, res_params['pose_params'],
|
||
res_params['draw_pose_params'], pose_dir)
|
||
USE_Default=True
|
||
print(f"All Finished,video frames number is {len(input_frames_cv2)}")
|
||
else:
|
||
# 首帧手势对齐输入图片，keypoint数据左眼，肩膀及手肘，
|
||
_, first_key, first_box_xy = visualizer(np.asarray(input_frames_cv2[0]), None)
|
||
if not first_box_xy: # first frame maybe empty or no preson,skip it,try find sceond
|
||
logging.info("*********first frame don't has person,skip it**********")
|
||
for i in range(len(input_frames_cv2)):
|
||
_, first_key, first_box_xy = visualizer(np.asarray(input_frames_cv2[i + 1]), None)
|
||
if first_box_xy:
|
||
break
|
||
|
||
_, input_key, input_box_xy = visualizer(np.asarray(panding_img), None)
|
||
first_length, first_left_eye_y = estimate_ratio(first_key, first_box_xy)
|
||
input_length, input_left_eye_y = estimate_ratio(input_key, input_box_xy)
|
||
# print(first_length,first_left_eye_y,input_length,input_left_eye_y) #160.0 [236.0] 158.0 [151.0] 眼睛高度为绝对值
|
||
if first_length and first_left_eye_y and input_length and input_left_eye_y:
|
||
if abs(input_length / height - first_length / height) > 0.005: # 比例不同须基于输入图片对齐
|
||
logging.info(
|
||
"Starting the first frame gesture alignment based on the input image *** 基于输入图片，开始首帧手势缩放对齐 !")
|
||
input_left_eye_y_ = input_left_eye_y[0]
|
||
first_left_eye_y_ = first_left_eye_y[0]
|
||
input_frames_cv2 = [align_img(input_length, first_length, height, width, i, input_left_eye_y_,
|
||
first_left_eye_y_) for i in input_frames_cv2]
|
||
else: # 人体比例接近，但是高度不对，也需要对齐
|
||
logging.info(
|
||
"Starting the first frame shift based on the input image *** 基于输入图片，开始首帧手势平移对齐 !")
|
||
if abs(input_left_eye_y[0] / height - first_left_eye_y[
|
||
0] / height) > 0.005:
|
||
input_frames_cv2 = [affine_img(input_left_eye_y, first_left_eye_y, i) for i in input_frames_cv2]
|
||
empty_index = []
|
||
for i, img in enumerate(input_frames_cv2):
|
||
pose_img, _, BOX_ = visualizer(np.asarray(img), [5])
|
||
if not BOX_:
|
||
pose_img = np.zeros((width, height, 3), np.uint8) # 防止空帧报错
|
||
empty_index.append(i) # 记录空帧索引
|
||
np.save(os.path.join(pose_dir, f"{i}"), pose_img)
|
||
# cv2.imwrite(f"{i}.png", pose_img)
|
||
|
||
if empty_index:
|
||
print(
|
||
f"********* The index of frames list : {empty_index} , which is no person find in images *********")
|
||
|
||
if len(empty_index) == 1:
|
||
if empty_index[0] != 0:
|
||
shutil.copy2(os.path.join(pose_dir, f"{empty_index[0]}.npy"),
|
||
os.path.join(pose_dir, f"{empty_index[0] - 1}.npy")) # 抽前帧覆盖
|
||
else:
|
||
shutil.copy2(os.path.join(pose_dir, f"{empty_index[0]}.npy"),
|
||
os.path.join(pose_dir, f"{empty_index[0] + 1}.npy")) # 抽前帧覆盖
|
||
else:
|
||
if 0 not in empty_index:
|
||
for i in empty_index:
|
||
shutil.copy2(os.path.join(pose_dir, f"{i}.npy"),
|
||
os.path.join(pose_dir, f"{empty_index[i] - 1}.npy")) # 抽前帧覆盖
|
||
|
||
else:
|
||
for i, x in enumerate(empty_index): # 先抽连续帧最末尾的后一帧盖0帧
|
||
if empty_index[i] != x: # [0,1,x]
|
||
shutil.copy2(os.path.join(pose_dir, f"{0}.npy"),
|
||
os.path.join(pose_dir, f"{i}.npy"))
|
||
break
|
||
else:
|
||
pass
|
||
|
||
for i, x in enumerate(empty_index): # 其他帧抽前帧覆盖
|
||
if i != 0:
|
||
shutil.copy2(os.path.join(pose_dir, f"{x}.npy"),
|
||
os.path.join(pose_dir, f"{empty_index[i] - 1}.npy")) # 抽前帧覆盖
|
||
|
||
USE_Default = False
|
||
visualizer.enable_model_cpu_offload()
|
||
gc.collect()
|
||
torch.cuda.empty_cache()
|
||
else:
|
||
if pose_dir in ["pose_01","pose_02","pose_03","pose_04","pose_fight","pose_good","pose_salute","pose_ultraman"]:
|
||
pose_d=pose_dir.split("_")[-1]
|
||
logging.info(f"use default pose {pose_dir} for running !")
|
||
pose_dir = os.path.join(cur_path, f"echomimic_v2/assets/halfbody_demo/pose/{pose_d}")
|
||
USE_Default = True
|
||
else:
|
||
logging.info(
|
||
"Use NPY files for custom videos, which must be located in directory 'comfyui/input/tensorrt_lite'")
|
||
pose_dir = os.path.join(tensorrt_lite, pose_dir)
|
||
USE_Default=False if "sapiens" in pose_dir else True
|
||
|
||
|
||
if seed is not None and seed > -1:
|
||
generator = torch.manual_seed(seed)
|
||
else:
|
||
generator = torch.manual_seed(random.randint(100, 1000000))
|
||
|
||
#final_fps = fps
|
||
start_idx = 0
|
||
audio_clip = AudioFileClip(uploaded_audio)
|
||
|
||
L = min(int(audio_clip.duration * fps),length,len(os.listdir(pose_dir))) # if above will cause error
|
||
#L=min(length,L) #length is definitely
|
||
print(f"***** infer length is {L}")
|
||
|
||
pose_list = []
|
||
for index in range(start_idx, start_idx + L):
|
||
tgt_musk_path = os.path.join(pose_dir, "{}.npy".format(index))
|
||
if USE_Default:
|
||
detected_pose = np.load(tgt_musk_path, allow_pickle=True).tolist()
|
||
imh_new, imw_new, rb, re, cb, ce = detected_pose[
|
||
'draw_pose_params'] # print(imh_new, imw_new, rb, re, cb, ce) 官方示例蒙版的尺寸是768*768
|
||
im = draw_pose_select_v2(detected_pose, imh_new, imw_new, ref_w=800) # 缩放比例为1，im也是768 ref_w！=768
|
||
im = np.transpose(np.array(im), (1, 2, 0))
|
||
tgt_musk = np.zeros((imw_new, imh_new, 3)).astype('uint8')
|
||
tgt_musk[rb:re, cb:ce, :] = im
|
||
else:
|
||
tgt_musk = np.load(tgt_musk_path, allow_pickle=True)
|
||
|
||
tgt_musk = center_resize_pad(tgt_musk, width, height) # 缩放裁剪遮罩，防止遮罩非正方形
|
||
tgt_musk_pil = Image.fromarray(np.array(tgt_musk)).convert('RGB')
|
||
|
||
pose_list.append(
|
||
torch.Tensor(np.array(tgt_musk_pil)).to(dtype=weight_dtype, device=device).permute(2, 0, 1) / 255.0)
|
||
|
||
poses_tensor = torch.stack(pose_list, dim=1).unsqueeze(0)
|
||
print(f"poses_tensor:{poses_tensor.shape}")
|
||
# audio_clip = AudioFileClip(audio_clip)
|
||
#
|
||
# audio_clip = audio_clip.set_duration(L / fps)
|
||
|
||
video = pipe(
|
||
infer_image_pil,
|
||
uploaded_audio,
|
||
poses_tensor[:, :, :L, ...],
|
||
width,
|
||
height,
|
||
L,
|
||
steps,
|
||
cfg,
|
||
generator=generator,
|
||
audio_sample_rate=sample_rate,
|
||
context_frames=context_frames,
|
||
fps=fps,
|
||
context_overlap=context_overlap,
|
||
start_idx=start_idx,
|
||
).videos
|
||
|
||
final_length = min(video.shape[2],poses_tensor.shape[2], L)
|
||
video_sig = video[:, :, :final_length, :, :]
|
||
output_file = os.path.join(folder_paths.output_directory, f"{audio_file_prefix}_echo.mp4")
|
||
print(f"**** final_length is : {final_length} ****")
|
||
|
||
if origin_h!=origin_w:
|
||
ouput_list = save_videos_grid(video_sig, output_file, n_rows=1, fps=fps, save_video=save_video,size=(origin_w,origin_h),ref_image_pil=ref_image_pil)
|
||
else:
|
||
ouput_list = save_videos_grid(video_sig, output_file, n_rows=1, fps=fps, save_video=save_video)
|
||
|
||
if save_video:
|
||
output_video_path = os.path.join(folder_paths.output_directory, f"{audio_file_prefix}_audio.mp4")
|
||
video_clip = VideoFileClip(output_file)
|
||
audio_clip = AudioFileClip(uploaded_audio)
|
||
final_clip = video_clip.set_audio(audio_clip)
|
||
final_clip.write_videofile(
|
||
output_video_path,
|
||
codec="libx264", audio_codec="aac")
|
||
print(f"**** saving{output_file} at {output_video_path} ****")
|
||
video_clip.reader.close()
|
||
audio_clip.close()
|
||
final_clip.reader.close()
|
||
return ouput_list
|
||
|
||
|
||
def process_video(face_img, uploaded_audio, width, height, length, seed, facemask_dilation_ratio,
|
||
facecrop_dilation_ratio, context_frames, context_overlap, cfg, steps, sample_rate, fps, pipe,
|
||
face_detector, save_video, pose_dir, video_images, audio_file_prefix,
|
||
visualizer=None,):
|
||
if seed is not None and seed > -1:
|
||
generator = torch.manual_seed(seed)
|
||
else:
|
||
generator = torch.manual_seed(random.randint(100, 1000000))
|
||
|
||
#### face musk prepare
|
||
face_mask = np.zeros((face_img.shape[0], face_img.shape[1])).astype('uint8')
|
||
det_bboxes, probs = face_detector.detect(face_img)
|
||
select_bbox = select_face(det_bboxes, probs)
|
||
|
||
if select_bbox is None:
|
||
face_mask[:, :] = 255
|
||
else:
|
||
xyxy = select_bbox[:4].astype(float) # 面部处理出来是浮点数，无法实现整形
|
||
xyxy = np.round(xyxy).astype("int")
|
||
|
||
rb, re, cb, ce = xyxy[1], xyxy[3], xyxy[0], xyxy[2] #56 227 132 268
|
||
r_pad = int((re - rb) * facemask_dilation_ratio) # ratio：0.1 遮罩膨胀系数 17*2
|
||
c_pad = int((ce - cb) * facemask_dilation_ratio) # ratio：0.1 遮罩膨胀系数 14*2
|
||
face_mask[rb - r_pad: re + r_pad, cb - c_pad: ce + c_pad] = 255
|
||
|
||
#### face crop ####
|
||
if facecrop_dilation_ratio<1.0:
|
||
if facecrop_dilation_ratio==0:
|
||
facecrop_dilation_ratio=1
|
||
r_pad_crop = int((re - rb) * facecrop_dilation_ratio) # ratio 0.5 r_pad_crop：85,c_pad_crop:68
|
||
c_pad_crop = int((ce - cb) * facecrop_dilation_ratio) # ratio 1.0 r_pad_crop：171,c_pad_crop:136
|
||
|
||
crop_rect = [max(0, cb - c_pad_crop), max(0, rb - r_pad_crop), min(ce + c_pad_crop, face_img.shape[1]),
|
||
min(re + r_pad_crop, face_img.shape[0])]
|
||
|
||
if width == height:
|
||
# 输出图片指定尺寸，如果是非方形，则会变形
|
||
face_img_i, ori_face_rect_i = crop_and_pad(face_img, crop_rect)
|
||
face_mask_m, ori_mask_rect_m = crop_and_pad(face_mask, crop_rect) # (0, 7, 384, 391)
|
||
face_img = cv2.resize(face_img_i, (width, height))
|
||
face_mask = cv2.resize(face_mask_m, (width, height))
|
||
else:
|
||
face_img,face_mask=crop_and_pad_rectangle(face_img,face_mask,crop_rect)
|
||
face_img= cv2tensor(face_img).permute(0, 2, 3, 1)#[1, 3, 357, 245] =>[[1,357, 245,3]]
|
||
face_mask = cv2tensor(face_mask).permute(0, 2, 3, 1)
|
||
face_img=tensor_upscale(face_img, width, height)
|
||
face_img=tensor2cv(face_img)
|
||
face_mask = tensor_upscale(face_mask, width, height)
|
||
face_mask=cv2.cvtColor(tensor2cv(face_mask), cv2.COLOR_BGR2GRAY)#二值化
|
||
ret, face_mask = cv2.threshold(face_mask, 0, 255, cv2.THRESH_BINARY)
|
||
|
||
else: #when ratio=1 no crop
|
||
print("when facecrop_ratio=1.0,The maximum image size will be obtained, but there may be edge deformation.** 选择最大裁切为1.0时，边缘可能会出现形变！")
|
||
|
||
ref_image_pil = Image.fromarray(face_img[:, :, [2, 1, 0]])
|
||
|
||
|
||
if visualizer:
|
||
if pose_dir == "none": # motion sync
|
||
if isinstance(video_images,torch.Tensor):
|
||
print("**** Use video pose drive video! ****")
|
||
pose_dir_path,video_len = motion_sync_main(visualizer, width, height, video_images, face_img,facecrop_dilation_ratio,
|
||
audio_file_prefix)
|
||
else:
|
||
raise ("**** You need link video_images for drive video ****")
|
||
#pose_dir = os.path.join(cur_path, "assets", "test_pose_demo_pose") # default
|
||
else:
|
||
print("**** Use pkl drive video! ****")
|
||
pose_dir_path = os.path.join(tensorrt_lite, pose_dir)
|
||
files_and_directories = os.listdir(pose_dir_path)
|
||
# 过滤出文件，排除子目录
|
||
files = [f for f in files_and_directories if os.path.isfile(os.path.join(pose_dir_path, f))]
|
||
video_len=len(files)
|
||
if length>video_len:
|
||
print(f"**** video length {video_len} is less than length,use {video_len} as {length} ****")
|
||
length=video_len
|
||
pose_list = []
|
||
for index in range(len(os.listdir(pose_dir_path))):
|
||
tgt_musk_path = os.path.join(pose_dir_path, f"{index}.pkl")
|
||
with open(tgt_musk_path, "rb") as f:
|
||
tgt_kpts = pickle.load(f)
|
||
tgt_musk = visualizer.draw_landmarks((width, height), tgt_kpts,facecrop_dilation_ratio)
|
||
tgt_musk_pil = Image.fromarray(np.array(tgt_musk).astype(np.uint8)).convert('RGB')
|
||
pose_list.append(
|
||
torch.Tensor(np.array(tgt_musk_pil)).to(dtype=weight_dtype, device="cuda").permute(2, 0, 1) / 255.0)
|
||
face_mask_tensor = torch.stack(pose_list, dim=1).unsqueeze(0)
|
||
else:
|
||
print("**** Use audio drive video! ****")
|
||
face_mask_tensor = torch.Tensor(face_mask).to(dtype=weight_dtype, device="cuda").unsqueeze(0).unsqueeze(
|
||
0).unsqueeze(0) / 255.0
|
||
|
||
video = pipe(
|
||
ref_image_pil,
|
||
uploaded_audio,
|
||
face_mask_tensor,
|
||
width,
|
||
height,
|
||
length,
|
||
steps,
|
||
cfg,
|
||
generator=generator,
|
||
audio_sample_rate=sample_rate,
|
||
context_frames=context_frames,
|
||
fps=fps,
|
||
context_overlap=context_overlap
|
||
).videos
|
||
|
||
final_length = min(video.shape[2], face_mask_tensor.shape[2], length)
|
||
output_file = os.path.join(folder_paths.output_directory, f"{audio_file_prefix}_echo.mp4")
|
||
print(f"**** final_length is : {final_length} ****")
|
||
|
||
ouput_list = save_videos_grid(video, output_file, n_rows=1, fps=fps, save_video=save_video)
|
||
|
||
if save_video:
|
||
output_video_path = os.path.join(folder_paths.output_directory, f"{audio_file_prefix}_audio.mp4")
|
||
video_clip = VideoFileClip(output_file)
|
||
audio_clip = AudioFileClip(uploaded_audio)
|
||
final_clip = video_clip.set_audio(audio_clip)
|
||
final_clip.write_videofile(
|
||
output_video_path,
|
||
codec="libx264", audio_codec="aac")
|
||
print(f"**** saving{output_file} at {output_video_path} ****")
|
||
video_clip.reader.close()
|
||
audio_clip.close()
|
||
final_clip.reader.close()
|
||
|
||
return ouput_list
|
||
|
||
def motion_sync_main(vis, width, height, video_images, face_img,facecrop_dilation_ratio, audio_file_prefix):
|
||
lmk_extractor = LMKExtractor()
|
||
ref_det = lmk_extractor(face_img)
|
||
|
||
#driver_video = os.path.join(folder_paths.input_directory, driver_video)
|
||
# if audio_form_video:
|
||
# audio_path = os.path.join(folder_paths.input_directory, f"{audio_file_prefix}_audio.wav")
|
||
# video_clip = VideoFileClip(driver_video)
|
||
# audio_clip = video_clip.audio
|
||
# audio_clip.write_audiofile(audio_path)
|
||
# video_clip.close()
|
||
# audio_clip.close()
|
||
|
||
video_len,_,_,_=video_images.size()
|
||
if video_len<25:
|
||
raise "input video has not much frames for driver,change your input video!"
|
||
else:
|
||
tensor_list = list(torch.chunk(video_images, chunks=video_len))
|
||
input_frames_cv2=[tensor2cv(tensor_upscale(i, width, height)) for i in tensor_list]
|
||
|
||
# print(ref_det)
|
||
sequence_driver_det = []
|
||
if input_frames_cv2:
|
||
try:
|
||
print("**** Starting process video ****")
|
||
for frame in input_frames_cv2:
|
||
result = lmk_extractor(frame)
|
||
assert result is not None, "bad video, face not detected"
|
||
sequence_driver_det.append(result)
|
||
except:
|
||
print("face detection failed")
|
||
else:
|
||
raise "input video error,change your input video!"
|
||
|
||
print("**** motion sync lenght " f"{len(sequence_driver_det)} ****")
|
||
|
||
if vis:
|
||
if facecrop_dilation_ratio==0:
|
||
facecrop_dilation_ratio=1
|
||
pose_frames_driver = [vis.draw_landmarks((width, height), i["lmks"],facecrop_dilation_ratio, normed=True) for i in sequence_driver_det]
|
||
poses_add_driver = [(i * 0.5 + j * 0.5).clip(0, 255).astype(np.uint8) for i, j in
|
||
zip(input_frames_cv2, pose_frames_driver)]
|
||
#print(f"**** poses_add_driver is done in len : {len(poses_add_driver)} ****")
|
||
|
||
save_dir = os.path.join(tensorrt_lite, audio_file_prefix)
|
||
|
||
if not os.path.exists(save_dir):
|
||
os.makedirs(save_dir)
|
||
sequence_det_ms = motion_sync(sequence_driver_det, ref_det)
|
||
for i in range(len(sequence_det_ms)):
|
||
with open('{}/{}.pkl'.format(save_dir, i), 'wb') as file:
|
||
pickle.dump(sequence_det_ms[i], file)
|
||
print(f"**** motion_sync {save_dir} is done ****")
|
||
else: #即便有文件夹，还是重新生成，避免出错
|
||
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
|
||
save_dir=os.path.join(tensorrt_lite,f"{audio_file_prefix}_{timestamp}")
|
||
os.makedirs(save_dir)
|
||
sequence_det_ms = motion_sync(sequence_driver_det, ref_det)
|
||
for i in range(len(sequence_det_ms)):
|
||
with open('{}/{}.pkl'.format(save_dir, i), 'wb') as file:
|
||
pickle.dump(sequence_det_ms[i], file)
|
||
print(f"**** motion_sync {save_dir} is done ****")
|
||
return save_dir,video_len
|
||
|
||
|
||
def select_face(det_bboxes, probs):
|
||
## max face from faces that the prob is above 0.8
|
||
## box: xyxy
|
||
if det_bboxes is None or probs is None:
|
||
return None
|
||
filtered_bboxes = []
|
||
for bbox_i in range(len(det_bboxes)):
|
||
if probs[bbox_i] > 0.8:
|
||
filtered_bboxes.append(det_bboxes[bbox_i])
|
||
if len(filtered_bboxes) == 0:
|
||
return None
|
||
sorted_bboxes = sorted(filtered_bboxes, key=lambda x: (x[3] - x[1]) * (x[2] - x[0]), reverse=True)
|
||
return sorted_bboxes[0]
|
||
|
||
|
||
def find_directories(base_path):
|
||
directories = []
|
||
for root, dirs, files in os.walk(base_path):
|
||
for name in dirs:
|
||
directories.append(name)
|
||
return directories
|
||
|
||
def download_weights(file_dir,repo_id,subfolder="",pt_name=""):
|
||
if subfolder:
|
||
file_path = os.path.join(file_dir,subfolder, pt_name)
|
||
sub_dir=os.path.join(file_dir,subfolder)
|
||
if not os.path.exists(sub_dir):
|
||
os.makedirs(sub_dir)
|
||
if not os.path.exists(file_path):
|
||
pt_path = hf_hub_download(
|
||
repo_id=repo_id,
|
||
subfolder=subfolder,
|
||
filename=pt_name,
|
||
local_dir = file_dir,
|
||
)
|
||
else:
|
||
pt_path=get_instance_path(file_path)
|
||
return pt_path
|
||
else:
|
||
file_path = os.path.join(file_dir, pt_name)
|
||
if not os.path.exists(file_dir):
|
||
os.makedirs(file_dir)
|
||
if not os.path.exists(file_path):
|
||
pt_path = hf_hub_download(
|
||
repo_id=repo_id,
|
||
filename=pt_name,
|
||
local_dir=file_dir,
|
||
)
|
||
else:
|
||
pt_path=get_instance_path(file_path)
|
||
return pt_path
|
||
|
||
|
||
def pil2narry(img):
|
||
img = torch.from_numpy(np.array(img).astype(np.float32) / 255.0).unsqueeze(0)
|
||
return img
|
||
|
||
|
||
def narry_list(list_in):
|
||
for i in range(len(list_in)):
|
||
value = list_in[i]
|
||
modified_value = pil2narry(value)
|
||
list_in[i] = modified_value
|
||
return list_in
|
||
|
||
|
||
def get_video_img(tensor):
|
||
if tensor == None:
|
||
return None
|
||
outputs = []
|
||
for x in tensor:
|
||
x = tensor_to_pil(x)
|
||
outputs.append(x)
|
||
yield outputs
|
||
|
||
def instance_path(path, repo):
|
||
if repo == "":
|
||
if path == "none":
|
||
repo = "none"
|
||
else:
|
||
model_path = get_local_path(folder_paths.base_path, path)
|
||
repo = get_instance_path(model_path)
|
||
return repo
|
||
|
||
|
||
def gen_img_form_video(tensor):
|
||
pil = []
|
||
for x in tensor:
|
||
pil[x] = tensor_to_pil(x)
|
||
yield pil
|
||
|
||
|
||
def phi_list(list_in):
|
||
for i in range(len(list_in)):
|
||
value = list_in[i]
|
||
list_in[i] = value
|
||
return list_in
|
||
|
||
def tensor_to_pil(tensor):
|
||
image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
|
||
image = Image.fromarray(image_np, mode='RGB')
|
||
return image
|
||
|
||
def nomarl_upscale(img_tensor, width, height):
|
||
samples = img_tensor.movedim(-1, 1)
|
||
img = common_upscale(samples, width, height, "nearest-exact", "center")
|
||
samples = img.movedim(1, -1)
|
||
img_pil = tensor_to_pil(samples)
|
||
return img_pil
|
||
|
||
def tensor_upscale(img_tensor, width, height):
|
||
samples = img_tensor.movedim(-1, 1)
|
||
img = common_upscale(samples, width, height, "nearest-exact", "center")
|
||
samples = img.movedim(1, -1)
|
||
return samples
|
||
|
||
def get_local_path(comfy_file_path, model_path):
|
||
path = os.path.join(comfy_file_path, "models", "diffusers", model_path)
|
||
model_path = os.path.normpath(path)
|
||
if sys.platform == 'win32':
|
||
model_path = model_path.replace('\\', "/")
|
||
return model_path
|
||
|
||
def get_instance_path(path):
|
||
instance_path = os.path.normpath(path)
|
||
if sys.platform == 'win32':
|
||
instance_path = instance_path.replace('\\', "/")
|
||
return instance_path
|
||
|
||
|
||
def tensor2cv(tensor_image):
|
||
if len(tensor_image.shape)==4:# b hwc to hwc
|
||
tensor_image=tensor_image.squeeze(0)
|
||
if tensor_image.is_cuda:
|
||
tensor_image = tensor_image.cpu()
|
||
tensor_image=tensor_image.numpy()
|
||
#反归一化
|
||
maxValue=tensor_image.max()
|
||
tensor_image=tensor_image*255/maxValue
|
||
img_cv2=np.uint8(tensor_image)#32 to uint8
|
||
img_cv2=cv2.cvtColor(img_cv2,cv2.COLOR_RGB2BGR)
|
||
return img_cv2
|
||
|
||
def cvargb2tensor(img):
|
||
assert type(img) == np.ndarray, 'the img type is {}, but ndarry expected'.format(type(img))
|
||
img = torch.from_numpy(img.transpose((2, 0, 1)))
|
||
return img.float().div(255).unsqueeze(0) # 255也可以改为256
|
||
|
||
def cv2tensor(img):
|
||
assert type(img) == np.ndarray, 'the img type is {}, but ndarry expected'.format(type(img))
|
||
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
||
img = torch.from_numpy(img.transpose((2, 0, 1)))
|
||
return img.float().div(255).unsqueeze(0) # 255也可以改为256
|
||
|
||
def images_generator(img_list: list,):
|
||
#get img size
|
||
sizes = {}
|
||
for image_ in img_list:
|
||
if isinstance(image_,Image.Image):
|
||
count = sizes.get(image_.size, 0)
|
||
sizes[image_.size] = count + 1
|
||
elif isinstance(image_,np.ndarray):
|
||
count = sizes.get(image_.shape[:2][::-1], 0)
|
||
sizes[image_.shape[:2][::-1]] = count + 1
|
||
else:
|
||
raise "unsupport image list,must be pil or cv2!!!"
|
||
size = max(sizes.items(), key=lambda x: x[1])[0]
|
||
yield size[0], size[1]
|
||
|
||
# any to tensor
|
||
def load_image(img_in):
|
||
if isinstance(img_in, Image.Image):
|
||
img_in=img_in.convert("RGB")
|
||
i = np.array(img_in, dtype=np.float32)
|
||
i = torch.from_numpy(i).div_(255)
|
||
if i.shape[0] != size[1] or i.shape[1] != size[0]:
|
||
i = torch.from_numpy(i).movedim(-1, 0).unsqueeze(0)
|
||
i = common_upscale(i, size[0], size[1], "lanczos", "center")
|
||
i = i.squeeze(0).movedim(0, -1).numpy()
|
||
return i
|
||
elif isinstance(img_in,np.ndarray):
|
||
i=cv2.cvtColor(img_in,cv2.COLOR_BGR2RGB).astype(np.float32)
|
||
i = torch.from_numpy(i).div_(255)
|
||
#print(i.shape)
|
||
return i
|
||
else:
|
||
raise "unsupport image list,must be pil,cv2 or tensor!!!"
|
||
|
||
total_images = len(img_list)
|
||
processed_images = 0
|
||
pbar = ProgressBar(total_images)
|
||
images = map(load_image, img_list)
|
||
try:
|
||
prev_image = next(images)
|
||
while True:
|
||
next_image = next(images)
|
||
yield prev_image
|
||
processed_images += 1
|
||
pbar.update_absolute(processed_images, total_images)
|
||
prev_image = next_image
|
||
except StopIteration:
|
||
pass
|
||
if prev_image is not None:
|
||
yield prev_image
|
||
|
||
def load_images(img_list: list,):
|
||
gen = images_generator(img_list)
|
||
(width, height) = next(gen)
|
||
images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (height, width, 3)))))
|
||
if len(images) == 0:
|
||
raise FileNotFoundError(f"No images could be loaded .")
|
||
return images
|
||
|
||
def tensor2pil(tensor):
|
||
image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
|
||
image = Image.fromarray(image_np, mode='RGB')
|
||
return image
|
||
|
||
|
||
def cf_tensor2cv(tensor,width, height):
|
||
d1, _, _, _ = tensor.size()
|
||
if d1 > 1:
|
||
tensor_list = list(torch.chunk(tensor, chunks=d1))
|
||
tensor = [tensor_list][0]
|
||
cr_tensor=tensor_upscale(tensor,width, height)
|
||
cv_img=tensor2cv(cr_tensor)
|
||
return cv_img
|
||
|
||
def center_resize_pad(img, new_width, new_height):#为简化，new已是正方形
|
||
h, w = img.shape[:2]
|
||
if w == h:
|
||
if w == new_width:
|
||
return img
|
||
else:
|
||
return cv2.resize(img, (new_width, new_height))
|
||
else: #蒙版也有可能不是正方形
|
||
if h > w: # 竖直图左右填充
|
||
s = max(h, w)
|
||
f = np.zeros((s, s, 3), np.uint8)
|
||
ax, ay = (s - img.shape[1]) // 2, (s - img.shape[0]) // 2
|
||
f[ay:img.shape[0] + ay, ax:ax + img.shape[1]] = img
|
||
else:
|
||
f = center_crop(img, h, h)
|
||
return cv2.resize(f, (new_width, new_height))
|
||
|
||
|
||
def img_padding(height,width,ref_image_pil):
|
||
output_max = max(height, width)
|
||
img = tensor2cv(ref_image_pil)
|
||
h, w = img.shape[:2]
|
||
if h==w:
|
||
return cv2.resize(img, (output_max,output_max), interpolation=cv2.INTER_AREA)
|
||
else:
|
||
if h > w: #竖直图左右填充
|
||
s = max(h, w)
|
||
f = np.zeros((s, s, 3), np.uint8)
|
||
ax, ay = (s - w) // 2, (s - h) // 2
|
||
f[ay:h + ay, ax:ax + w] = img
|
||
else:
|
||
f=center_crop(img, h, h)
|
||
|
||
return cv2.resize(f, (output_max,output_max), interpolation=cv2.INTER_AREA)
|
||
|
||
def estimate_ratio(keypoint: list,box_xy,length=None):
|
||
x1, y1, x2, y2=box_xy
|
||
bbox_width, bbox_height = x2 - x1, y2 - y1
|
||
left_eye_y = []
|
||
left_shoulder_y = []
|
||
left_elbow_y = []
|
||
for i, (name, (x, y, conf)) in enumerate(keypoint[0].items()):
|
||
if name == "left_eye":
|
||
if conf > 0.3:
|
||
y_coord =int(y * bbox_height / 256) + y1 #获取坐标点在原图的绝对值
|
||
left_eye_y.append(y_coord)
|
||
if name == "left_shoulder":
|
||
if conf > 0.3:
|
||
y_coord = int(y * bbox_height / 256) + y1
|
||
left_shoulder_y.append(y_coord)
|
||
if name == "left_elbow":
|
||
if conf > 0.3:
|
||
y_coord = int(y * bbox_height / 256) + y1
|
||
left_elbow_y.append(y_coord)
|
||
|
||
if left_eye_y and left_elbow_y:
|
||
length=left_elbow_y[0] - left_eye_y[0]
|
||
elif left_eye_y and left_shoulder_y and not left_elbow_y :
|
||
length = left_shoulder_y[0] - left_eye_y[0]
|
||
else:
|
||
pass
|
||
if left_eye_y:
|
||
left_eye_y=[left_eye_y[0]]
|
||
return length,left_eye_y
|
||
|
||
|
||
def align_img(input_length, first_length, height, width, input_frames_cv2_first, input_left_eye_y, first_left_eye_y):
|
||
ratio = input_length / first_length #603 [201] 679 [220]
|
||
input_frames_cv2_first = cv2.resize(input_frames_cv2_first, (int(height * ratio), int(height * ratio)),
|
||
interpolation=cv2.INTER_AREA) # 缩小
|
||
base_image=np.zeros((height, width,3), np.uint8)
|
||
if input_length / height < first_length / height: # 输入图的人物占比要小，pose图需要缩小0.88对齐,#603 [201] 679 [220]
|
||
reduced_image,pad_size=center_paste(base_image, input_frames_cv2_first) #中心粘贴，pad为黑边尺寸
|
||
eye_y=int(first_left_eye_y*ratio+pad_size[1]) #图片缩放后的绝对值
|
||
|
||
else: # pose图里人物的比例小于输入图，pose要放大
|
||
reduced_image=center_crop(input_frames_cv2_first, height, width) #中心裁切
|
||
h, w = input_frames_cv2_first.shape[:2]
|
||
shift_y=(h-height)//2 #图片放大后裁切掉的边界
|
||
eye_y= int(first_left_eye_y*ratio-shift_y)
|
||
|
||
move_ = -int(eye_y - input_left_eye_y) if eye_y>= int(input_left_eye_y) else int(input_left_eye_y- eye_y) #对齐眼睛
|
||
translation_matrix = np.float32([[1, 0, 0], [0, 1, move_]])
|
||
shifted_image = cv2.warpAffine(reduced_image, translation_matrix, (width, height))
|
||
|
||
return shifted_image
|
||
|
||
def center_paste(img_b,img_f):
|
||
b_h,b_w=img_b.shape[:2]
|
||
f_h, f_w = img_f.shape[:2]
|
||
x = (b_w - f_w) // 2
|
||
y = (b_h - f_h) // 2
|
||
# 确保坐标不会是负数
|
||
x = max(0, x)
|
||
y = max(0, y)
|
||
img_b[y:y + f_h, x:x + f_w] = img_f
|
||
return img_b,(x,y)
|
||
|
||
|
||
def affine_img(input_left_eye_y, first_left_eye_y, img):
|
||
height,width=img.shape[:2]
|
||
move_ = int(input_left_eye_y[0] - first_left_eye_y[0])
|
||
translation_matrix = np.float32([[1, 0, 0], [0, 1, move_]]) # y轴位移
|
||
shifted_image = cv2.warpAffine(img, translation_matrix, (width, height))
|
||
return shifted_image
|
||
|
||
|
||
def convert_fps(src_path, tgt_path, tgt_fps=24, tgt_sr=16000):
|
||
clip = VideoFileClip(src_path)
|
||
new_clip = clip.set_fps(tgt_fps)
|
||
if tgt_fps is not None:
|
||
audio = new_clip.audio
|
||
audio = audio.set_fps(tgt_sr)
|
||
new_clip = new_clip.set_audio(audio)
|
||
if '.mov' in tgt_path:
|
||
tgt_path = tgt_path.replace('.mov', '.mp4')
|
||
new_clip.write_videofile(tgt_path, codec='libx264', audio_codec='aac')
|
||
|
||
|
||
def get_video_pose(visualizer,
|
||
frames,
|
||
sample_stride: int = 1,
|
||
max_frame=None):
|
||
# read input video
|
||
#vr = decord.VideoReader(video_path, ctx=decord.cpu(0))
|
||
#sample_stride *= max(1, int(vr.get_avg_fps() / 24))
|
||
|
||
#frames = vr.get_batch(list(range(0, len(vr), sample_stride))).asnumpy()
|
||
#frames=vr.get_batch(list(range(0, len(vr), sample_stride))).asnumpy()
|
||
# print(frames[0])
|
||
if max_frame is not None:
|
||
frames = frames[:max_frame]
|
||
height, width, _ = frames[0].shape
|
||
print("start get video pose list")
|
||
detected_poses = [visualizer(frm) for frm in frames]
|
||
visualizer.release_memory()
|
||
|
||
return detected_poses, height, width, frames
|
||
|
||
|
||
def resize_and_pad(img, max_size):
|
||
img_new = np.zeros((max_size, max_size, 3)).astype('uint8')
|
||
imh, imw = img.shape[0], img.shape[1]
|
||
half = max_size // 2
|
||
if imh > imw:
|
||
imh_new = max_size
|
||
imw_new = int(round(imw / imh * imh_new))
|
||
half_w = imw_new // 2
|
||
rb, re = 0, max_size
|
||
cb = half - half_w
|
||
ce = cb + imw_new
|
||
else:
|
||
imw_new = max_size
|
||
imh_new = int(round(imh / imw * imw_new))
|
||
half_h = imh_new // 2
|
||
cb, ce = 0, max_size
|
||
rb = half - half_h
|
||
re = rb + imh_new
|
||
|
||
img_resize = cv2.resize(img, (imw_new, imh_new))
|
||
img_new[rb:re, cb:ce, :] = img_resize
|
||
return img_new
|
||
|
||
|
||
def resize_and_pad_param(imh, imw, max_size):
|
||
half = max_size // 2
|
||
if imh > imw:
|
||
imh_new = max_size
|
||
imw_new = int(round(imw / imh * imh_new))
|
||
half_w = imw_new // 2
|
||
rb, re = 0, max_size
|
||
cb = half - half_w
|
||
ce = cb + imw_new
|
||
else:
|
||
imw_new = max_size
|
||
imh_new = int(round(imh / imw * imw_new))
|
||
imh_new = max_size
|
||
|
||
half_h = imh_new // 2
|
||
cb, ce = 0, max_size
|
||
rb = half - half_h
|
||
re = rb + imh_new
|
||
|
||
return imh_new, imw_new, rb, re, cb, ce
|
||
|
||
|
||
def get_pose_params(detected_poses, max_size):
|
||
|
||
print('get_pose_params...')
|
||
# pose rescale
|
||
height = 768
|
||
width=768
|
||
|
||
w_min_all, w_max_all, h_min_all, h_max_all = [], [], [], []
|
||
mid_all = []
|
||
for num, detected_pose in enumerate(detected_poses):
|
||
detected_poses[num]['num'] = num
|
||
candidate_body = detected_pose['bodies']['candidate']
|
||
score_body = detected_pose['bodies']['score']
|
||
candidate_face = detected_pose['faces']
|
||
score_face = detected_pose['faces_score']
|
||
candidate_hand = detected_pose['hands']
|
||
score_hand = detected_pose['hands_score']
|
||
|
||
# face
|
||
if candidate_face.shape[0] > 1:
|
||
index = 0
|
||
candidate_face = candidate_face[index]
|
||
score_face = score_face[index]
|
||
detected_poses[num]['faces'] = candidate_face.reshape(1, candidate_face.shape[0], candidate_face.shape[1])
|
||
detected_poses[num]['faces_score'] = score_face.reshape(1, score_face.shape[0])
|
||
else:
|
||
candidate_face = candidate_face[0]
|
||
score_face = score_face[0]
|
||
|
||
# body
|
||
if score_body.shape[0] > 1:
|
||
tmp_score = []
|
||
for k in range(0, score_body.shape[0]):
|
||
tmp_score.append(score_body[k].mean())
|
||
index = np.argmax(tmp_score)
|
||
candidate_body = candidate_body[index * 18:(index + 1) * 18, :]
|
||
score_body = score_body[index]
|
||
score_hand = score_hand[(index * 2):(index * 2 + 2), :]
|
||
candidate_hand = candidate_hand[(index * 2):(index * 2 + 2), :, :]
|
||
else:
|
||
score_body = score_body[0]
|
||
all_pose = np.concatenate((candidate_body, candidate_face))
|
||
all_score = np.concatenate((score_body, score_face))
|
||
all_pose = all_pose[all_score > 0.8]
|
||
|
||
body_pose = np.concatenate((candidate_body,))
|
||
mid_ = body_pose[1, 0]
|
||
|
||
face_pose = candidate_face
|
||
hand_pose = candidate_hand
|
||
|
||
h_min, h_max = np.min(face_pose[:, 1]), np.max(body_pose[:7, 1])
|
||
|
||
h_ = h_max - h_min
|
||
|
||
mid_w = mid_
|
||
w_min = mid_w - h_ // 2
|
||
w_max = mid_w + h_ // 2
|
||
|
||
w_min_all.append(w_min)
|
||
w_max_all.append(w_max)
|
||
h_min_all.append(h_min)
|
||
h_max_all.append(h_max)
|
||
mid_all.append(mid_w)
|
||
|
||
w_min = np.min(w_min_all)
|
||
w_max = np.max(w_max_all)
|
||
h_min = np.min(h_min_all)
|
||
h_max = np.max(h_max_all)
|
||
mid = np.mean(mid_all)
|
||
|
||
margin_ratio = 0.25
|
||
h_margin = (h_max - h_min) * margin_ratio
|
||
|
||
h_min = max(h_min - h_margin * 0.8, 0)
|
||
h_max = min(h_max + h_margin * 0.1, 1)
|
||
|
||
h_new = h_max - h_min
|
||
|
||
h_min_real = int(h_min * height)
|
||
h_max_real = int(h_max * height)
|
||
mid_real = int(mid * width)
|
||
|
||
height_new = h_max_real - h_min_real + 1
|
||
width_new = height_new
|
||
w_min_real = mid_real - width_new // 2
|
||
if w_min_real < 0:
|
||
w_min_real = 0
|
||
width_new = mid_real * 2
|
||
|
||
w_max_real = w_min_real + width_new
|
||
w_min = w_min_real / width
|
||
w_max = w_max_real / width
|
||
|
||
imh_new, imw_new, rb, re, cb, ce = resize_and_pad_param(height_new, width_new, max_size)
|
||
res = {'draw_pose_params': [imh_new, imw_new, rb, re, cb, ce],
|
||
'pose_params': [w_min, w_max, h_min, h_max],
|
||
'video_params': [h_min_real, h_max_real, w_min_real, w_max_real],
|
||
}
|
||
return res
|
||
|
||
|
||
def save_pose_params_item(input_items):
|
||
detected_pose, pose_params, draw_pose_params, save_dir = input_items
|
||
w_min, w_max, h_min, h_max = pose_params
|
||
num = detected_pose['num']
|
||
candidate_body = detected_pose['bodies']['candidate']
|
||
candidate_face = detected_pose['faces'][0]
|
||
candidate_hand = detected_pose['hands']
|
||
candidate_body[:, 0] = (candidate_body[:, 0] - w_min) / (w_max - w_min)
|
||
candidate_body[:, 1] = (candidate_body[:, 1] - h_min) / (h_max - h_min)
|
||
candidate_face[:, 0] = (candidate_face[:, 0] - w_min) / (w_max - w_min)
|
||
candidate_face[:, 1] = (candidate_face[:, 1] - h_min) / (h_max - h_min)
|
||
candidate_hand[:, :, 0] = (candidate_hand[:, :, 0] - w_min) / (w_max - w_min)
|
||
candidate_hand[:, :, 1] = (candidate_hand[:, :, 1] - h_min) / (h_max - h_min)
|
||
detected_pose['bodies']['candidate'] = candidate_body
|
||
detected_pose['faces'] = candidate_face.reshape(1, candidate_face.shape[0], candidate_face.shape[1])
|
||
detected_pose['hands'] = candidate_hand
|
||
detected_pose['draw_pose_params'] = draw_pose_params
|
||
np.save(save_dir + '/' + str(num) + '.npy', detected_pose)
|
||
|
||
|
||
def save_pose_params(detected_poses, pose_params, draw_pose_params, save_dir):
|
||
#save_dir = ori_video_path.replace('video', 'pose/')
|
||
if not os.path.exists(save_dir):
|
||
os.makedirs(save_dir)
|
||
|
||
input_list = []
|
||
|
||
for i, detected_pose in enumerate(detected_poses):
|
||
input_list.append([detected_pose, pose_params, draw_pose_params, save_dir])
|
||
|
||
pool = ThreadPool(8)
|
||
pool.map(save_pose_params_item, input_list)
|
||
pool.close()
|
||
pool.join()
|
||
return save_dir
|
||
|
||
|
||
def get_img_pose(visualizer,
|
||
img_path: str,
|
||
sample_stride: int = 1,
|
||
max_frame=None):
|
||
# read input img
|
||
frame = cv2.imread(img_path)
|
||
height, width, _ = frame.shape
|
||
short_size = min(height, width)
|
||
resize_ratio = max(MAX_SIZE / short_size, 1.0)
|
||
frame = cv2.resize(frame, (int(resize_ratio * width), int(resize_ratio * height)))
|
||
height, width, _ = frame.shape
|
||
detected_poses = [visualizer(frame)]
|
||
visualizer.release_memory()
|
||
|
||
return detected_poses, height, width, frame
|
||
|
||
|
||
def save_aligned_img(ori_frame, video_params, max_size):
|
||
h_min_real, h_max_real, w_min_real, w_max_real = video_params
|
||
img = ori_frame[h_min_real:h_max_real, w_min_real:w_max_real, :]
|
||
img_aligened = resize_and_pad(img, max_size=max_size)
|
||
print('aligned img shape:', img_aligened.shape)
|
||
save_dir = './assets/refimg_aligned'
|
||
|
||
os.makedirs(save_dir, exist_ok=True)
|
||
save_path = os.path.join(save_dir, 'aligned.png')
|
||
cv2.imwrite(save_path, img_aligened)
|
||
return save_path
|