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https://github.com/KwaiVGI/LivePortrait.git
synced 2024-12-22 20:42:38 +00:00
feat: update crop configuration parameters for clarify (#97)
The crop configuration parameters in `crop_config.py` have been updated. The changes include: - Updating the paths for insightface_root and landmark_ckpt_path These changes aim to improve the cropping functionality of the application.
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longredzhong
GitHub
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@ -4,25 +4,26 @@
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parameters used for crop faces
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"""
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import os.path as osp
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from dataclasses import dataclass
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from typing import Union, List
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from .base_config import PrintableConfig
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@dataclass(repr=False) # use repr from PrintableConfig
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class CropConfig(PrintableConfig):
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device_id: int = 0 # gpu device id
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flag_force_cpu: bool = False # force cpu inference, WIP
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insightface_root: str = "../../pretrained_weights/insightface"
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landmark_ckpt_path: str = "../../pretrained_weights/liveportrait/landmark.onnx"
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device_id: int = 0 # gpu device id
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flag_force_cpu: bool = False # force cpu inference, WIP
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########## source image cropping option ##########
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dsize: int = 512 # crop size
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scale: float = 2.5 # scale factor
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vx_ratio: float = 0 # vx ratio
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vy_ratio: float = -0.125 # vy ratio +up, -down
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max_face_num: int = 0 # max face number, 0 mean no limit
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max_face_num: int = 0 # max face number, 0 mean no limit
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########## driving video auto cropping option ##########
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scale_crop_video: float = 2.2 #2.0 # scale factor for cropping video
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vx_ratio_crop_video: float = 0. # adjust y offset
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scale_crop_video: float = 2.2 # 2.0 # scale factor for cropping video
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vx_ratio_crop_video: float = 0.0 # adjust y offset
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vy_ratio_crop_video: float = -0.1 # adjust x offset
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direction: str = 'large-small' # direction of cropping
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direction: str = "large-small" # direction of cropping
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@ -1,17 +1,26 @@
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# coding: utf-8
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import numpy as np
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import os.path as osp
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from typing import List, Union, Tuple
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from dataclasses import dataclass, field
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import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False)
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from typing import List, Tuple, Union
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import cv2
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import numpy as np
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cv2.setNumThreads(0)
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cv2.ocl.setUseOpenCL(False)
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from ..config.crop_config import CropConfig
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from .landmark_runner import LandmarkRunner
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from .crop import (
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average_bbox_lst,
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crop_image,
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crop_image_by_bbox,
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parse_bbox_from_landmark,
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)
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from .face_analysis_diy import FaceAnalysisDIY
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from .crop import crop_image, crop_image_by_bbox, parse_bbox_from_landmark, average_bbox_lst
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from .rprint import rlog as log
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from .io import contiguous
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from .landmark_runner import LandmarkRunner
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from .rprint import rlog as log
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def make_abs_path(fn):
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@ -25,40 +34,44 @@ class Trajectory:
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lmk_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # lmk list
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bbox_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # bbox list
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frame_rgb_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # frame list
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frame_rgb_lst: Union[Tuple, List, np.ndarray] = field(
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default_factory=list
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) # frame list
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lmk_crop_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # lmk list
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frame_rgb_crop_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # frame crop list
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lmk_crop_lst: Union[Tuple, List, np.ndarray] = field(
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default_factory=list
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) # lmk list
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frame_rgb_crop_lst: Union[Tuple, List, np.ndarray] = field(
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default_factory=list
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) # frame crop list
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class Cropper(object):
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def __init__(self, **kwargs) -> None:
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device_id = kwargs.get('device_id', 0)
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flag_force_cpu = kwargs.get('flag_force_cpu', False)
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self.crop_cfg: CropConfig = kwargs.get("crop_cfg", None)
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device_id = kwargs.get("device_id", 0)
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flag_force_cpu = kwargs.get("flag_force_cpu", False)
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if flag_force_cpu:
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device = 'cpu'
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face_analysis_wrapper_provicer = ['CPUExecutionProvider']
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device = "cpu"
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face_analysis_wrapper_provicer = ["CPUExecutionProvider"]
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else:
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device = 'cuda'
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device = "cuda"
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face_analysis_wrapper_provicer = ["CUDAExecutionProvider"]
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self.landmark_runner = LandmarkRunner(
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ckpt_path=make_abs_path('../../pretrained_weights/liveportrait/landmark.onnx'),
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ckpt_path=make_abs_path(self.crop_cfg.landmark_ckpt_path),
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onnx_provider=device,
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device_id=device_id
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device_id=device_id,
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)
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self.landmark_runner.warmup()
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self.face_analysis_wrapper = FaceAnalysisDIY(
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name='buffalo_l',
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root=make_abs_path('../../pretrained_weights/insightface'),
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providers=face_analysis_wrapper_provicer
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name="buffalo_l",
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root=make_abs_path(self.crop_cfg.insightface_root),
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providers=face_analysis_wrapper_provicer,
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)
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self.face_analysis_wrapper.prepare(ctx_id=device_id, det_size=(512, 512))
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self.face_analysis_wrapper.warmup()
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self.crop_cfg: CropConfig = kwargs.get('crop_cfg', None)
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def update_config(self, user_args):
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for k, v in user_args.items():
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if hasattr(self.crop_cfg, k):
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@ -77,10 +90,12 @@ class Cropper(object):
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)
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if len(src_face) == 0:
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log('No face detected in the source image.')
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log("No face detected in the source image.")
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return None
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elif len(src_face) > 1:
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log(f'More than one face detected in the image, only pick one face by rule {crop_cfg.direction}.')
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log(
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f"More than one face detected in the image, only pick one face by rule {crop_cfg.direction}."
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)
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# NOTE: temporarily only pick the first face, to support multiple face in the future
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src_face = src_face[0]
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@ -97,30 +112,34 @@ class Cropper(object):
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)
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lmk = self.landmark_runner.run(img_rgb, lmk)
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ret_dct['lmk_crop'] = lmk
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ret_dct["lmk_crop"] = lmk
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# update a 256x256 version for network input
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ret_dct['img_crop_256x256'] = cv2.resize(ret_dct['img_crop'], (256, 256), interpolation=cv2.INTER_AREA)
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ret_dct['lmk_crop_256x256'] = ret_dct['lmk_crop'] * 256 / crop_cfg.dsize
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ret_dct["img_crop_256x256"] = cv2.resize(
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ret_dct["img_crop"], (256, 256), interpolation=cv2.INTER_AREA
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)
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ret_dct["lmk_crop_256x256"] = ret_dct["lmk_crop"] * 256 / crop_cfg.dsize
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return ret_dct
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def crop_driving_video(self, driving_rgb_lst, **kwargs):
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"""Tracking based landmarks/alignment and cropping"""
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trajectory = Trajectory()
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direction = kwargs.get('direction', 'large-small')
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direction = kwargs.get("direction", "large-small")
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for idx, frame_rgb in enumerate(driving_rgb_lst):
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if idx == 0 or trajectory.start == -1:
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src_face = self.face_analysis_wrapper.get(
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contiguous(frame_rgb[..., ::-1]),
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flag_do_landmark_2d_106=True,
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direction=direction
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direction=direction,
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)
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if len(src_face) == 0:
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log(f'No face detected in the frame #{idx}')
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log(f"No face detected in the frame #{idx}")
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continue
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elif len(src_face) > 1:
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log(f'More than one face detected in the driving frame_{idx}, only pick one face by rule {direction}.')
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log(
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f"More than one face detected in the driving frame_{idx}, only pick one face by rule {direction}."
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)
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src_face = src_face[0]
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lmk = src_face.landmark_2d_106
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lmk = self.landmark_runner.run(frame_rgb, lmk)
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@ -130,47 +149,61 @@ class Cropper(object):
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trajectory.end = idx
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trajectory.lmk_lst.append(lmk)
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ret_bbox = parse_bbox_from_landmark(lmk, scale=self.crop_cfg.scale_crop_video, vx_ratio_crop_video=self.crop_cfg.vx_ratio_crop_video, vy_ratio=self.crop_cfg.vy_ratio_crop_video)['bbox']
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bbox = [ret_bbox[0, 0], ret_bbox[0, 1], ret_bbox[2, 0], ret_bbox[2, 1]] # 4,
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ret_bbox = parse_bbox_from_landmark(
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lmk,
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scale=self.crop_cfg.scale_crop_video,
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vx_ratio_crop_video=self.crop_cfg.vx_ratio_crop_video,
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vy_ratio=self.crop_cfg.vy_ratio_crop_video,
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)["bbox"]
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bbox = [
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ret_bbox[0, 0],
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ret_bbox[0, 1],
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ret_bbox[2, 0],
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ret_bbox[2, 1],
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] # 4,
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trajectory.bbox_lst.append(bbox) # bbox
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trajectory.frame_rgb_lst.append(frame_rgb)
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global_bbox = average_bbox_lst(trajectory.bbox_lst)
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for idx, (frame_rgb, lmk) in enumerate(zip(trajectory.frame_rgb_lst, trajectory.lmk_lst)):
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for idx, (frame_rgb, lmk) in enumerate(
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zip(trajectory.frame_rgb_lst, trajectory.lmk_lst)
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):
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ret_dct = crop_image_by_bbox(
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frame_rgb,
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global_bbox,
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lmk=lmk,
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dsize=kwargs.get('dsize', 512),
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dsize=kwargs.get("dsize", 512),
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flag_rot=False,
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borderValue=(0, 0, 0),
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)
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trajectory.frame_rgb_crop_lst.append(ret_dct['img_crop'])
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trajectory.lmk_crop_lst.append(ret_dct['lmk_crop'])
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trajectory.frame_rgb_crop_lst.append(ret_dct["img_crop"])
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trajectory.lmk_crop_lst.append(ret_dct["lmk_crop"])
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return {
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'frame_crop_lst': trajectory.frame_rgb_crop_lst,
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'lmk_crop_lst': trajectory.lmk_crop_lst,
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"frame_crop_lst": trajectory.frame_rgb_crop_lst,
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"lmk_crop_lst": trajectory.lmk_crop_lst,
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}
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def calc_lmks_from_cropped_video(self, driving_rgb_crop_lst, **kwargs):
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"""Tracking based landmarks/alignment"""
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trajectory = Trajectory()
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direction = kwargs.get('direction', 'large-small')
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direction = kwargs.get("direction", "large-small")
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for idx, frame_rgb_crop in enumerate(driving_rgb_crop_lst):
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if idx == 0 or trajectory.start == -1:
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src_face = self.face_analysis_wrapper.get(
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contiguous(frame_rgb_crop[..., ::-1]), # convert to BGR
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flag_do_landmark_2d_106=True,
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direction=direction
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direction=direction,
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)
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if len(src_face) == 0:
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log(f'No face detected in the frame #{idx}')
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raise Exception(f'No face detected in the frame #{idx}')
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log(f"No face detected in the frame #{idx}")
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raise Exception(f"No face detected in the frame #{idx}")
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elif len(src_face) > 1:
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log(f'More than one face detected in the driving frame_{idx}, only pick one face by rule {direction}.')
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log(
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f"More than one face detected in the driving frame_{idx}, only pick one face by rule {direction}."
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)
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src_face = src_face[0]
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lmk = src_face.landmark_2d_106
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lmk = self.landmark_runner.run(frame_rgb_crop, lmk)
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