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# -*- coding: utf-8 -*-
# @Time : 2024/9/13 0:23
# @Project : FasterLivePortrait
# @FileName: api.py
import pdb
import shutil
from typing import Optional, Dict, Any
import io
import os
import subprocess
import uvicorn
import cv2
import time
import numpy as np
import os
import datetime
import platform
import pickle
from tqdm import tqdm
from pydantic import BaseModel
from fastapi import APIRouter, Depends, FastAPI, Request, Response, UploadFile
from fastapi import File, Body, Form
from omegaconf import OmegaConf
from fastapi.responses import StreamingResponse
from zipfile import ZipFile
from src.pipelines.faster_live_portrait_pipeline import FasterLivePortraitPipeline
from src.utils.utils import video_has_audio
from src.utils import logger
# model dir
project_dir = os.path.dirname(__file__)
checkpoints_dir = os.environ.get("FLIP_CHECKPOINT_DIR", os.path.join(project_dir, "checkpoints"))
log_dir = os.path.join(project_dir, "logs")
os.makedirs(log_dir, exist_ok=True)
result_dir = os.path.join(project_dir, "results")
os.makedirs(result_dir, exist_ok=True)
logger_f = logger.get_logger("faster_liveportrait_api", log_file=os.path.join(log_dir, "log_run.log"))
app = FastAPI()
global pipe
if platform.system().lower() == 'windows':
FFMPEG = "third_party/ffmpeg-7.0.1-full_build/bin/ffmpeg.exe"
else:
FFMPEG = "ffmpeg"
def check_all_checkpoints_exist(infer_cfg):
"""
check whether all checkpoints exist
:return:
"""
ret = True
for name in infer_cfg.models:
if not isinstance(infer_cfg.models[name].model_path, str):
for i in range(len(infer_cfg.models[name].model_path)):
infer_cfg.models[name].model_path[i] = infer_cfg.models[name].model_path[i].replace("./checkpoints",
checkpoints_dir)
if not os.path.exists(infer_cfg.models[name].model_path[i]) and not os.path.exists(
infer_cfg.models[name].model_path[i][:-4] + ".onnx"):
return False
else:
infer_cfg.models[name].model_path = infer_cfg.models[name].model_path.replace("./checkpoints",
checkpoints_dir)
if not os.path.exists(infer_cfg.models[name].model_path) and not os.path.exists(
infer_cfg.models[name].model_path[:-4] + ".onnx"):
return False
for name in infer_cfg.animal_models:
if not isinstance(infer_cfg.animal_models[name].model_path, str):
for i in range(len(infer_cfg.animal_models[name].model_path)):
infer_cfg.animal_models[name].model_path[i] = infer_cfg.animal_models[name].model_path[i].replace(
"./checkpoints",
checkpoints_dir)
if not os.path.exists(infer_cfg.animal_models[name].model_path[i]) and not os.path.exists(
infer_cfg.animal_models[name].model_path[i][:-4] + ".onnx"):
return False
else:
infer_cfg.animal_models[name].model_path = infer_cfg.animal_models[name].model_path.replace("./checkpoints",
checkpoints_dir)
if not os.path.exists(infer_cfg.animal_models[name].model_path) and not os.path.exists(
infer_cfg.animal_models[name].model_path[:-4] + ".onnx"):
return False
# XPOSE
xpose_model_path = os.path.join(checkpoints_dir, "liveportrait_animal_onnx/xpose.pth")
if not os.path.exists(xpose_model_path):
return False
embeddings_cache_9_path = os.path.join(checkpoints_dir, "liveportrait_animal_onnx/clip_embedding_9.pkl")
if not os.path.exists(embeddings_cache_9_path):
return False
embeddings_cache_68_path = os.path.join(checkpoints_dir, "liveportrait_animal_onnx/clip_embedding_68.pkl")
if not os.path.exists(embeddings_cache_68_path):
return False
return ret
def convert_onnx_to_trt_models(infer_cfg):
ret = True
for name in infer_cfg.models:
if not isinstance(infer_cfg.models[name].model_path, str):
for i in range(len(infer_cfg.models[name].model_path)):
trt_path = infer_cfg.models[name].model_path[i]
onnx_path = trt_path[:-4] + ".onnx"
if not os.path.exists(trt_path):
convert_cmd = f"python scripts/onnx2trt.py -o {onnx_path}"
logger_f.info(f"convert onnx model: {onnx_path}")
result = subprocess.run(convert_cmd, shell=True, check=True)
# 检查结果
if result.returncode == 0:
logger_f.info(f"convert onnx model: {onnx_path} successful")
else:
logger_f.error(f"convert onnx model: {onnx_path} failed")
return False
else:
trt_path = infer_cfg.models[name].model_path
onnx_path = trt_path[:-4] + ".onnx"
if not os.path.exists(trt_path):
convert_cmd = f"python scripts/onnx2trt.py -o {onnx_path}"
logger_f.info(f"convert onnx model: {onnx_path}")
result = subprocess.run(convert_cmd, shell=True, check=True)
# 检查结果
if result.returncode == 0:
logger_f.info(f"convert onnx model: {onnx_path} successful")
else:
logger_f.error(f"convert onnx model: {onnx_path} failed")
return False
for name in infer_cfg.animal_models:
if not isinstance(infer_cfg.animal_models[name].model_path, str):
for i in range(len(infer_cfg.animal_models[name].model_path)):
trt_path = infer_cfg.animal_models[name].model_path[i]
onnx_path = trt_path[:-4] + ".onnx"
if not os.path.exists(trt_path):
convert_cmd = f"python scripts/onnx2trt.py -o {onnx_path}"
logger_f.info(f"convert onnx model: {onnx_path}")
result = subprocess.run(convert_cmd, shell=True, check=True)
# 检查结果
if result.returncode == 0:
logger_f.info(f"convert onnx model: {onnx_path} successful")
else:
logger_f.error(f"convert onnx model: {onnx_path} failed")
return False
else:
trt_path = infer_cfg.animal_models[name].model_path
onnx_path = trt_path[:-4] + ".onnx"
if not os.path.exists(trt_path):
convert_cmd = f"python scripts/onnx2trt.py -o {onnx_path}"
logger_f.info(f"convert onnx model: {onnx_path}")
result = subprocess.run(convert_cmd, shell=True, check=True)
# 检查结果
if result.returncode == 0:
logger_f.info(f"convert onnx model: {onnx_path} successful")
else:
logger_f.error(f"convert onnx model: {onnx_path} failed")
return False
return ret
@app.on_event("startup")
async def startup_event():
global pipe
# default use trt model
cfg_file = os.path.join(project_dir, "configs/trt_infer.yaml")
infer_cfg = OmegaConf.load(cfg_file)
checkpoints_exist = check_all_checkpoints_exist(infer_cfg)
# first: download model if not exist
if not checkpoints_exist:
download_cmd = f"huggingface-cli download warmshao/FasterLivePortrait --local-dir {checkpoints_dir}"
logger_f.info(f"download model: {download_cmd}")
result = subprocess.run(download_cmd, shell=True, check=True)
# 检查结果
if result.returncode == 0:
logger_f.info(f"Download checkpoints to {checkpoints_dir} successful")
else:
logger_f.error(f"Download checkpoints to {checkpoints_dir} failed")
exit(1)
# second: convert onnx model to trt
convert_ret = convert_onnx_to_trt_models(infer_cfg)
if not convert_ret:
logger_f.error(f"convert onnx model to trt failed")
exit(1)
infer_cfg.infer_params.flag_pasteback = True
pipe = FasterLivePortraitPipeline(cfg=infer_cfg, is_animal=True)
def run_with_video(source_image_path, driving_video_path, save_dir):
global pipe
ret = pipe.prepare_source(source_image_path, realtime=False)
if not ret:
logger_f.warning(f"no face in {source_image_path}! exit!")
return
vcap = cv2.VideoCapture(driving_video_path)
fps = int(vcap.get(cv2.CAP_PROP_FPS))
h, w = pipe.src_imgs[0].shape[:2]
# render output video
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
vsave_crop_path = os.path.join(save_dir,
f"{os.path.basename(source_image_path)}-{os.path.basename(driving_video_path)}-crop.mp4")
vout_crop = cv2.VideoWriter(vsave_crop_path, fourcc, fps, (512 * 2, 512))
vsave_org_path = os.path.join(save_dir,
f"{os.path.basename(source_image_path)}-{os.path.basename(driving_video_path)}-org.mp4")
vout_org = cv2.VideoWriter(vsave_org_path, fourcc, fps, (w, h))
infer_times = []
motion_lst = []
c_eyes_lst = []
c_lip_lst = []
frame_ind = 0
while vcap.isOpened():
ret, frame = vcap.read()
if not ret:
break
t0 = time.time()
first_frame = frame_ind == 0
dri_crop, out_crop, out_org, dri_motion_info = pipe.run(frame, pipe.src_imgs[0], pipe.src_infos[0],
first_frame=first_frame)
frame_ind += 1
if out_crop is None:
logger_f.warning(f"no face in driving frame:{frame_ind}")
continue
motion_lst.append(dri_motion_info[0])
c_eyes_lst.append(dri_motion_info[1])
c_lip_lst.append(dri_motion_info[2])
infer_times.append(time.time() - t0)
# print(time.time() - t0)
dri_crop = cv2.resize(dri_crop, (512, 512))
out_crop = np.concatenate([dri_crop, out_crop], axis=1)
out_crop = cv2.cvtColor(out_crop, cv2.COLOR_RGB2BGR)
vout_crop.write(out_crop)
out_org = cv2.cvtColor(out_org, cv2.COLOR_RGB2BGR)
vout_org.write(out_org)
vcap.release()
vout_crop.release()
vout_org.release()
if video_has_audio(driving_video_path):
vsave_crop_path_new = os.path.splitext(vsave_crop_path)[0] + "-audio.mp4"
subprocess.call(
[FFMPEG, "-i", vsave_crop_path, "-i", driving_video_path,
"-b:v", "10M", "-c:v",
"libx264", "-map", "0:v", "-map", "1:a",
"-c:a", "aac",
"-pix_fmt", "yuv420p", vsave_crop_path_new, "-y", "-shortest"])
vsave_org_path_new = os.path.splitext(vsave_org_path)[0] + "-audio.mp4"
subprocess.call(
[FFMPEG, "-i", vsave_org_path, "-i", driving_video_path,
"-b:v", "10M", "-c:v",
"libx264", "-map", "0:v", "-map", "1:a",
"-c:a", "aac",
"-pix_fmt", "yuv420p", vsave_org_path_new, "-y", "-shortest"])
logger_f.info(vsave_crop_path_new)
logger_f.info(vsave_org_path_new)
else:
logger_f.info(vsave_crop_path)
logger_f.info(vsave_org_path)
logger_f.info(
"inference median time: {} ms/frame, mean time: {} ms/frame".format(np.median(infer_times) * 1000,
np.mean(infer_times) * 1000))
# save driving motion to pkl
template_dct = {
'n_frames': len(motion_lst),
'output_fps': fps,
'motion': motion_lst,
'c_eyes_lst': c_eyes_lst,
'c_lip_lst': c_lip_lst,
}
template_pkl_path = os.path.join(save_dir,
f"{os.path.basename(driving_video_path)}.pkl")
with open(template_pkl_path, "wb") as fw:
pickle.dump(template_dct, fw)
logger_f.info(f"save driving motion pkl file at : {template_pkl_path}")
def run_with_pkl(source_image_path, driving_pickle_path, save_dir):
global pipe
ret = pipe.prepare_source(source_image_path, realtime=False)
if not ret:
logger_f.warning(f"no face in {source_image_path}! exit!")
return
with open(driving_pickle_path, "rb") as fin:
dri_motion_infos = pickle.load(fin)
fps = int(dri_motion_infos["output_fps"])
h, w = pipe.src_imgs[0].shape[:2]
# render output video
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
vsave_crop_path = os.path.join(save_dir,
f"{os.path.basename(source_image_path)}-{os.path.basename(driving_pickle_path)}-crop.mp4")
vout_crop = cv2.VideoWriter(vsave_crop_path, fourcc, fps, (512, 512))
vsave_org_path = os.path.join(save_dir,
f"{os.path.basename(source_image_path)}-{os.path.basename(driving_pickle_path)}-org.mp4")
vout_org = cv2.VideoWriter(vsave_org_path, fourcc, fps, (w, h))
infer_times = []
motion_lst = dri_motion_infos["motion"]
c_eyes_lst = dri_motion_infos["c_eyes_lst"] if "c_eyes_lst" in dri_motion_infos else dri_motion_infos[
"c_d_eyes_lst"]
c_lip_lst = dri_motion_infos["c_lip_lst"] if "c_lip_lst" in dri_motion_infos else dri_motion_infos["c_d_lip_lst"]
frame_num = len(motion_lst)
for frame_ind in tqdm(range(frame_num)):
t0 = time.time()
first_frame = frame_ind == 0
dri_motion_info_ = [motion_lst[frame_ind], c_eyes_lst[frame_ind], c_lip_lst[frame_ind]]
out_crop, out_org = pipe.run_with_pkl(dri_motion_info_, pipe.src_imgs[0], pipe.src_infos[0],
first_frame=first_frame)
if out_crop is None:
logger_f.warning(f"no face in driving frame:{frame_ind}")
continue
infer_times.append(time.time() - t0)
# print(time.time() - t0)
out_crop = cv2.cvtColor(out_crop, cv2.COLOR_RGB2BGR)
vout_crop.write(out_crop)
out_org = cv2.cvtColor(out_org, cv2.COLOR_RGB2BGR)
vout_org.write(out_org)
vout_crop.release()
vout_org.release()
logger_f.info(vsave_crop_path)
logger_f.info(vsave_org_path)
logger_f.info(
"inference median time: {} ms/frame, mean time: {} ms/frame".format(np.median(infer_times) * 1000,
np.mean(infer_times) * 1000))
class LivePortraitParams(BaseModel):
flag_pickle: bool = False
flag_relative_input: bool = True
flag_do_crop_input: bool = True
flag_remap_input: bool = True
driving_multiplier: float = 1.0
flag_stitching: bool = True
flag_crop_driving_video_input: bool = True
flag_video_editing_head_rotation: bool = False
flag_is_animal: bool = True
scale: float = 2.3
vx_ratio: float = 0.0
vy_ratio: float = -0.125
scale_crop_driving_video: float = 2.2
vx_ratio_crop_driving_video: float = 0.0
vy_ratio_crop_driving_video: float = -0.1
driving_smooth_observation_variance: float = 1e-7
@app.post("/predict/")
async def upload_files(
source_image: Optional[UploadFile] = File(None),
driving_video: Optional[UploadFile] = File(None),
driving_pickle: Optional[UploadFile] = File(None),
flag_is_animal: bool = Form(...),
flag_pickle: bool = Form(...),
flag_relative_input: bool = Form(...),
flag_do_crop_input: bool = Form(...),
flag_remap_input: bool = Form(...),
driving_multiplier: float = Form(...),
flag_stitching: bool = Form(...),
flag_crop_driving_video_input: bool = Form(...),
flag_video_editing_head_rotation: bool = Form(...),
scale: float = Form(...),
vx_ratio: float = Form(...),
vy_ratio: float = Form(...),
scale_crop_driving_video: float = Form(...),
vx_ratio_crop_driving_video: float = Form(...),
vy_ratio_crop_driving_video: float = Form(...),
driving_smooth_observation_variance: float = Form(...)
):
# 根据传入的表单参数构建 infer_params
infer_params = LivePortraitParams(
flag_is_animal=flag_is_animal,
flag_pickle=flag_pickle,
flag_relative_input=flag_relative_input,
flag_do_crop_input=flag_do_crop_input,
flag_remap_input=flag_remap_input,
driving_multiplier=driving_multiplier,
flag_stitching=flag_stitching,
flag_crop_driving_video_input=flag_crop_driving_video_input,
flag_video_editing_head_rotation=flag_video_editing_head_rotation,
scale=scale,
vx_ratio=vx_ratio,
vy_ratio=vy_ratio,
scale_crop_driving_video=scale_crop_driving_video,
vx_ratio_crop_driving_video=vx_ratio_crop_driving_video,
vy_ratio_crop_driving_video=vy_ratio_crop_driving_video,
driving_smooth_observation_variance=driving_smooth_observation_variance
)
global pipe
pipe.init_vars()
if infer_params.flag_is_animal != pipe.is_animal:
pipe.init_models(is_animal=infer_params.flag_is_animal)
args_user = {
'flag_relative_motion': infer_params.flag_relative_input,
'flag_do_crop': infer_params.flag_do_crop_input,
'flag_pasteback': infer_params.flag_remap_input,
'driving_multiplier': infer_params.driving_multiplier,
'flag_stitching': infer_params.flag_stitching,
'flag_crop_driving_video': infer_params.flag_crop_driving_video_input,
'flag_video_editing_head_rotation': infer_params.flag_video_editing_head_rotation,
'src_scale': infer_params.scale,
'src_vx_ratio': infer_params.vx_ratio,
'src_vy_ratio': infer_params.vy_ratio,
'dri_scale': infer_params.scale_crop_driving_video,
'dri_vx_ratio': infer_params.vx_ratio_crop_driving_video,
'dri_vy_ratio': infer_params.vy_ratio_crop_driving_video,
}
# update config from user input
update_ret = pipe.update_cfg(args_user)
# 保存 source_image 到指定目录
temp_dir = os.path.join(result_dir, f"temp-{datetime.datetime.now().strftime('%Y-%m-%d-%H%M%S')}")
os.makedirs(temp_dir, exist_ok=True)
if source_image and source_image.filename:
source_image_path = os.path.join(temp_dir, source_image.filename)
with open(source_image_path, "wb") as buffer:
buffer.write(await source_image.read()) # 将内容写入文件
else:
source_image_path = None
if driving_video and driving_video.filename:
driving_video_path = os.path.join(temp_dir, driving_video.filename)
with open(driving_video_path, "wb") as buffer:
buffer.write(await driving_video.read()) # 将内容写入文件
else:
driving_video_path = None
if driving_pickle and driving_pickle.filename:
driving_pickle_path = os.path.join(temp_dir, driving_pickle.filename)
with open(driving_pickle_path, "wb") as buffer:
buffer.write(await driving_pickle.read()) # 将内容写入文件
else:
driving_pickle_path = None
save_dir = os.path.join(result_dir, f"{datetime.datetime.now().strftime('%Y-%m-%d-%H%M%S')}")
os.makedirs(save_dir, exist_ok=True)
if infer_params.flag_pickle:
if source_image_path and driving_pickle_path:
run_with_pkl(source_image_path, driving_pickle_path, save_dir)
else:
if source_image_path and driving_video_path:
run_with_video(source_image_path, driving_video_path, save_dir)
# zip all files and return
# 使用 BytesIO 在内存中创建一个字节流
zip_buffer = io.BytesIO()
# 使用 ZipFile 将文件夹内容压缩到 zip_buffer 中
with ZipFile(zip_buffer, "w") as zip_file:
for root, dirs, files in os.walk(save_dir):
for file in files:
file_path = os.path.join(root, file)
# 添加文件到 ZIP 文件中
zip_file.write(file_path, arcname=os.path.relpath(file_path, save_dir))
# 确保缓冲区指针在开始位置,以便读取整个内容
zip_buffer.seek(0)
shutil.rmtree(temp_dir)
shutil.rmtree(save_dir)
# 通过 StreamingResponse 返回 zip 文件
return StreamingResponse(zip_buffer, media_type="application/zip",
headers={"Content-Disposition": "attachment; filename=output.zip"})
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host=os.environ.get("FLIP_IP", "127.0.0.1"), port=os.environ.get("FLIP_PORT", 9871))
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