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feat: update crop configuration parameters for clarify (#97)

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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.
This commit is contained in:
longredzhong 2024-07-11 16:30:57 +08:00 committed by GitHub
parent 1472379e77
commit 470c58fe5a
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2 changed files with 86 additions and 52 deletions
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9
src/config/crop_config.py
View File

@ -4,14 +4,15 @@
parameters used for crop faces
"""
import os.path as osp
from dataclasses import dataclass
from typing import Union, List
from .base_config import PrintableConfig
@dataclass(repr=False) # use repr from PrintableConfig
class CropConfig(PrintableConfig):
insightface_root: str = "../../pretrained_weights/insightface"
landmark_ckpt_path: str = "../../pretrained_weights/liveportrait/landmark.onnx"
device_id: int = 0 # gpu device id
flag_force_cpu: bool = False # force cpu inference, WIP
########## source image cropping option ##########
@ -23,6 +24,6 @@ class CropConfig(PrintableConfig):
########## driving video auto cropping option ##########
scale_crop_video: float = 2.2 # 2.0 # scale factor for cropping video
vx_ratio_crop_video: float = 0. # adjust y offset
vx_ratio_crop_video: float = 0.0 # adjust y offset
vy_ratio_crop_video: float = -0.1 # adjust x offset
direction: str = 'large-small' # direction of cropping
direction: str = "large-small" # direction of cropping

121
src/utils/cropper.py
View File

@ -1,17 +1,26 @@
# coding: utf-8
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 typing import List, Tuple, Union
import cv2
import numpy as np
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
from ..config.crop_config import CropConfig
from .landmark_runner import LandmarkRunner
from .crop import (
average_bbox_lst,
crop_image,
crop_image_by_bbox,
parse_bbox_from_landmark,
)
from .face_analysis_diy import FaceAnalysisDIY
from .crop import crop_image, crop_image_by_bbox, parse_bbox_from_landmark, average_bbox_lst
from .rprint import rlog as log
from .io import contiguous
from .landmark_runner import LandmarkRunner
from .rprint import rlog as log
def make_abs_path(fn):
@ -25,40 +34,44 @@ class Trajectory:
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_lst: Union[Tuple, List, np.ndarray] = field(
default_factory=list
) # frame list
lmk_crop_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # lmk list
frame_rgb_crop_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # frame crop list
lmk_crop_lst: Union[Tuple, List, np.ndarray] = field(
default_factory=list
) # lmk 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)
flag_force_cpu = kwargs.get('flag_force_cpu', False)
self.crop_cfg: CropConfig = kwargs.get("crop_cfg", None)
device_id = kwargs.get("device_id", 0)
flag_force_cpu = kwargs.get("flag_force_cpu", False)
if flag_force_cpu:
device = 'cpu'
face_analysis_wrapper_provicer = ['CPUExecutionProvider']
device = "cpu"
face_analysis_wrapper_provicer = ["CPUExecutionProvider"]
else:
device = 'cuda'
device = "cuda"
face_analysis_wrapper_provicer = ["CUDAExecutionProvider"]
self.landmark_runner = LandmarkRunner(
ckpt_path=make_abs_path('../../pretrained_weights/liveportrait/landmark.onnx'),
ckpt_path=make_abs_path(self.crop_cfg.landmark_ckpt_path),
onnx_provider=device,
device_id=device_id
device_id=device_id,
)
self.landmark_runner.warmup()
self.face_analysis_wrapper = FaceAnalysisDIY(
name='buffalo_l',
root=make_abs_path('../../pretrained_weights/insightface'),
providers=face_analysis_wrapper_provicer
name="buffalo_l",
root=make_abs_path(self.crop_cfg.insightface_root),
providers=face_analysis_wrapper_provicer,
)
self.face_analysis_wrapper.prepare(ctx_id=device_id, det_size=(512, 512))
self.face_analysis_wrapper.warmup()
self.crop_cfg: CropConfig = kwargs.get('crop_cfg', None)
def update_config(self, user_args):
for k, v in user_args.items():
if hasattr(self.crop_cfg, k):
@ -77,10 +90,12 @@ class Cropper(object):
)
if len(src_face) == 0:
log('No face detected in the source image.')
log("No face detected in the source image.")
return None
elif len(src_face) > 1:
log(f'More than one face detected in the image, only pick one face by rule {crop_cfg.direction}.')
log(
f"More than one face detected in the image, only pick one face by rule {crop_cfg.direction}."
)
# NOTE: temporarily only pick the first face, to support multiple face in the future
src_face = src_face[0]
@ -97,30 +112,34 @@ class Cropper(object):
)
lmk = self.landmark_runner.run(img_rgb, lmk)
ret_dct['lmk_crop'] = lmk
ret_dct["lmk_crop"] = lmk
# update a 256x256 version for network input
ret_dct['img_crop_256x256'] = cv2.resize(ret_dct['img_crop'], (256, 256), interpolation=cv2.INTER_AREA)
ret_dct['lmk_crop_256x256'] = ret_dct['lmk_crop'] * 256 / crop_cfg.dsize
ret_dct["img_crop_256x256"] = cv2.resize(
ret_dct["img_crop"], (256, 256), interpolation=cv2.INTER_AREA
)
ret_dct["lmk_crop_256x256"] = ret_dct["lmk_crop"] * 256 / crop_cfg.dsize
return ret_dct
def crop_driving_video(self, driving_rgb_lst, **kwargs):
"""Tracking based landmarks/alignment and cropping"""
trajectory = Trajectory()
direction = kwargs.get('direction', 'large-small')
direction = kwargs.get("direction", "large-small")
for idx, frame_rgb in enumerate(driving_rgb_lst):
if idx == 0 or trajectory.start == -1:
src_face = self.face_analysis_wrapper.get(
contiguous(frame_rgb[..., ::-1]),
flag_do_landmark_2d_106=True,
direction=direction
direction=direction,
)
if len(src_face) == 0:
log(f'No face detected in the frame #{idx}')
log(f"No face detected in the frame #{idx}")
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}.')
log(
f"More than one face detected in the driving frame_{idx}, only pick one face by rule {direction}."
)
src_face = src_face[0]
lmk = src_face.landmark_2d_106
lmk = self.landmark_runner.run(frame_rgb, lmk)
@ -130,47 +149,61 @@ class Cropper(object):
trajectory.end = idx
trajectory.lmk_lst.append(lmk)
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']
bbox = [ret_bbox[0, 0], ret_bbox[0, 1], ret_bbox[2, 0], ret_bbox[2, 1]] # 4,
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"]
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(frame_rgb)
global_bbox = average_bbox_lst(trajectory.bbox_lst)
for idx, (frame_rgb, lmk) in enumerate(zip(trajectory.frame_rgb_lst, trajectory.lmk_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=kwargs.get('dsize', 512),
dsize=kwargs.get("dsize", 512),
flag_rot=False,
borderValue=(0, 0, 0),
)
trajectory.frame_rgb_crop_lst.append(ret_dct['img_crop'])
trajectory.lmk_crop_lst.append(ret_dct['lmk_crop'])
trajectory.frame_rgb_crop_lst.append(ret_dct["img_crop"])
trajectory.lmk_crop_lst.append(ret_dct["lmk_crop"])
return {
'frame_crop_lst': trajectory.frame_rgb_crop_lst,
'lmk_crop_lst': trajectory.lmk_crop_lst,
"frame_crop_lst": trajectory.frame_rgb_crop_lst,
"lmk_crop_lst": trajectory.lmk_crop_lst,
}
def calc_lmks_from_cropped_video(self, driving_rgb_crop_lst, **kwargs):
"""Tracking based landmarks/alignment"""
trajectory = Trajectory()
direction = kwargs.get('direction', 'large-small')
direction = kwargs.get("direction", "large-small")
for idx, frame_rgb_crop in enumerate(driving_rgb_crop_lst):
if idx == 0 or trajectory.start == -1:
src_face = self.face_analysis_wrapper.get(
contiguous(frame_rgb_crop[..., ::-1]), # convert to BGR
flag_do_landmark_2d_106=True,
direction=direction
direction=direction,
)
if len(src_face) == 0:
log(f'No face detected in the frame #{idx}')
raise Exception(f'No face detected in the frame #{idx}')
log(f"No face detected in the frame #{idx}")
raise Exception(f"No face detected in the frame #{idx}")
elif len(src_face) > 1:
log(f'More than one face detected in the driving frame_{idx}, only pick one face by rule {direction}.')
log(
f"More than one face detected in the driving frame_{idx}, only pick one face by rule {direction}."
)
src_face = src_face[0]
lmk = src_face.landmark_2d_106
lmk = self.landmark_runner.run(frame_rgb_crop, lmk)