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https://github.com/KwaiVGI/LivePortrait.git
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chore: format code
This commit is contained in:
guojianzhu
parent
89676189b9
commit
c3f01d3f3b
14
inference.py
14
inference.py
@ -2,11 +2,11 @@
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import os.path as osp
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import tyro
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import subprocess
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from src.config.argument_config import ArgumentConfig
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from src.config.inference_config import InferenceConfig
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from src.config.crop_config import CropConfig
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from src.live_portrait_pipeline import LivePortraitPipeline
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import subprocess
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def partial_fields(target_class, kwargs):
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@ -37,19 +37,17 @@ def main():
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raise ImportError(
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"FFmpeg is not installed. Please install FFmpeg before running this script. https://ffmpeg.org/download.html"
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)
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# fast check the args
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fast_check_args(args)
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# specify configs for inference
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inference_cfg = partial_fields(
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InferenceConfig, args.__dict__
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) # use attribute of args to initial InferenceConfig
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crop_cfg = partial_fields(
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CropConfig, args.__dict__
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) # use attribute of args to initial CropConfig
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inference_cfg = partial_fields(InferenceConfig, args.__dict__) # use attribute of args to initial InferenceConfig
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crop_cfg = partial_fields(CropConfig, args.__dict__) # use attribute of args to initial CropConfig
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live_portrait_pipeline = LivePortraitPipeline(
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inference_cfg=inference_cfg, crop_cfg=crop_cfg
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inference_cfg=inference_cfg,
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crop_cfg=crop_cfg
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)
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# run
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@ -59,17 +59,19 @@ conda activate LivePortrait
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pip install -r requirements.txt
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```
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Make sure your system has [FFmpeg](https://ffmpeg.org/)
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**Note:** make sure your system has [FFmpeg](https://ffmpeg.org/) installed!
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### 2. Download pretrained weights
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Download the pretrained weights from HuggingFace:
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The easiest way to download the pretrained weights is from HuggingFace:
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```bash
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# you may need to run `git lfs install` first
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git clone https://huggingface.co/KwaiVGI/liveportrait pretrained_weights
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```
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Or, download all pretrained weights from [Google Drive](https://drive.google.com/drive/folders/1UtKgzKjFAOmZkhNK-OYT0caJ_w2XAnib) or [Baidu Yun](https://pan.baidu.com/s/1MGctWmNla_vZxDbEp2Dtzw?pwd=z5cn). We have packed all weights in one directory 😊. Unzip and place them in `./pretrained_weights` ensuring the directory structure is as follows:
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Alternatively, you can download all pretrained weights from [Google Drive](https://drive.google.com/drive/folders/1UtKgzKjFAOmZkhNK-OYT0caJ_w2XAnib) or [Baidu Yun](https://pan.baidu.com/s/1MGctWmNla_vZxDbEp2Dtzw?pwd=z5cn). Unzip and place them in `./pretrained_weights`.
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Ensuring the directory structure is as follows, or contains:
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```text
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pretrained_weights
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├── insightface
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@ -4,12 +4,9 @@ import os.path as osp
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from dataclasses import dataclass, field
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from typing import List, Tuple, Union
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import cv2
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import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False)
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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 .crop import (
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average_bbox_lst,
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@ -17,10 +14,10 @@ from .crop import (
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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 .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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from .face_analysis_diy import FaceAnalysisDIY
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from .landmark_runner import LandmarkRunner
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def make_abs_path(fn):
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@ -34,16 +31,9 @@ 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(
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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(
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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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frame_rgb_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # 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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class Cropper(object):
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@ -93,9 +83,7 @@ class Cropper(object):
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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(
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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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log(f"More than one face detected in the image, only pick one face by rule {crop_cfg.direction}.")
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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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@ -115,9 +103,7 @@ class Cropper(object):
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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(
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ret_dct["img_crop"], (256, 256), interpolation=cv2.INTER_AREA
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)
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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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return ret_dct
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@ -137,9 +123,7 @@ class Cropper(object):
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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(
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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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log(f"More than one face detected in the driving frame_{idx}, only pick one face by rule {direction}.")
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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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@ -166,9 +150,7 @@ class Cropper(object):
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global_bbox = average_bbox_lst(trajectory.bbox_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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for idx, (frame_rgb, lmk) in enumerate(zip(trajectory.frame_rgb_lst, trajectory.lmk_lst)):
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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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@ -201,9 +183,7 @@ class Cropper(object):
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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(
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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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log(f"More than one face detected in the driving frame_{idx}, only pick one face by rule {direction}.")
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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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