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Running
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Zero
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# coding: utf-8
import gradio as gr
import numpy as np
import os.path as osp
from typing import List, Union, Tuple
from dataclasses import dataclass, field
import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False)
from .landmark_runner import LandmarkRunner
from .face_analysis_diy import FaceAnalysisDIY
from .helper import prefix
from .crop import crop_image, crop_image_by_bbox, parse_bbox_from_landmark, average_bbox_lst
from .timer import Timer
from .rprint import rlog as log
from .io import load_image_rgb
from .video import VideoWriter, get_fps, change_video_fps
def make_abs_path(fn):
return osp.join(osp.dirname(osp.realpath(__file__)), fn)
@dataclass
class Trajectory:
start: int = -1 # 起始帧 闭区间
end: int = -1 # 结束帧 闭区间
lmk_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # lmk list
bbox_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # bbox list
frame_rgb_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # frame list
frame_rgb_crop_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # frame crop list
class Cropper(object):
def __init__(self, **kwargs) -> None:
device_id = kwargs.get('device_id', 0)
self.landmark_runner = LandmarkRunner(
ckpt_path=make_abs_path('../../pretrained_weights/liveportrait/landmark.onnx'),
onnx_provider='cpu',
device_id=device_id
)
self.landmark_runner.warmup()
self.face_analysis_wrapper = FaceAnalysisDIY(
name='buffalo_l',
root=make_abs_path('../../pretrained_weights/insightface'),
providers=["CPUExecutionProvider"]
)
self.face_analysis_wrapper.prepare(ctx_id=device_id, det_size=(512, 512))
self.face_analysis_wrapper.warmup()
self.crop_cfg = kwargs.get('crop_cfg', None)
def update_config(self, user_args):
for k, v in user_args.items():
if hasattr(self.crop_cfg, k):
setattr(self.crop_cfg, k, v)
def crop_single_image(self, obj, **kwargs):
direction = kwargs.get('direction', 'large-small')
# crop and align a single image
if isinstance(obj, str):
img_rgb = load_image_rgb(obj)
elif isinstance(obj, np.ndarray):
img_rgb = obj
src_face = self.face_analysis_wrapper.get(
img_rgb,
flag_do_landmark_2d_106=True,
direction=direction
)
if len(src_face) == 0:
log('No face detected in the source image.')
raise gr.Error("No face detected in the source image 💥!", duration=5)
raise Exception("No face detected in the source image!")
elif len(src_face) > 1:
log(f'More than one face detected in the image, only pick one face by rule {direction}.')
src_face = src_face[0]
pts = src_face.landmark_2d_106
# crop the face
ret_dct = crop_image(
img_rgb, # ndarray
pts, # 106x2 or Nx2
dsize=kwargs.get('dsize', 512),
scale=kwargs.get('scale', 2.3),
vy_ratio=kwargs.get('vy_ratio', -0.15),
)
# update a 256x256 version for network input or else
ret_dct['img_crop_256x256'] = cv2.resize(ret_dct['img_crop'], (256, 256), interpolation=cv2.INTER_AREA)
ret_dct['pt_crop_256x256'] = ret_dct['pt_crop'] * 256 / kwargs.get('dsize', 512)
recon_ret = self.landmark_runner.run(img_rgb, pts)
lmk = recon_ret['pts']
ret_dct['lmk_crop'] = lmk
return ret_dct
def get_retargeting_lmk_info(self, driving_rgb_lst):
# TODO: implement a tracking-based version
driving_lmk_lst = []
for driving_image in driving_rgb_lst:
ret_dct = self.crop_single_image(driving_image)
driving_lmk_lst.append(ret_dct['lmk_crop'])
return driving_lmk_lst
def make_video_clip(self, driving_rgb_lst, output_path, output_fps=30, **kwargs):
trajectory = Trajectory()
direction = kwargs.get('direction', 'large-small')
for idx, driving_image in enumerate(driving_rgb_lst):
if idx == 0 or trajectory.start == -1:
src_face = self.face_analysis_wrapper.get(
driving_image,
flag_do_landmark_2d_106=True,
direction=direction
)
if len(src_face) == 0:
# No face detected in the driving_image
continue
elif len(src_face) > 1:
log(f'More than one face detected in the driving frame_{idx}, only pick one face by rule {direction}.')
src_face = src_face[0]
pts = src_face.landmark_2d_106
lmk_203 = self.landmark_runner(driving_image, pts)['pts']
trajectory.start, trajectory.end = idx, idx
else:
lmk_203 = self.face_recon_wrapper(driving_image, trajectory.lmk_lst[-1])['pts']
trajectory.end = idx
trajectory.lmk_lst.append(lmk_203)
ret_bbox = parse_bbox_from_landmark(lmk_203, scale=self.crop_cfg.globalscale, vy_ratio=elf.crop_cfg.vy_ratio)['bbox']
bbox = [ret_bbox[0, 0], ret_bbox[0, 1], ret_bbox[2, 0], ret_bbox[2, 1]] # 4,
trajectory.bbox_lst.append(bbox) # bbox
trajectory.frame_rgb_lst.append(driving_image)
global_bbox = average_bbox_lst(trajectory.bbox_lst)
for idx, (frame_rgb, lmk) in enumerate(zip(trajectory.frame_rgb_lst, trajectory.lmk_lst)):
ret_dct = crop_image_by_bbox(
frame_rgb, global_bbox, lmk=lmk,
dsize=self.video_crop_cfg.dsize, flag_rot=self.video_crop_cfg.flag_rot, borderValue=self.video_crop_cfg.borderValue
)
frame_rgb_crop = ret_dct['img_crop']
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