from utils.dependencies.insightface.app import FaceAnalysis
from utils.dependencies.insightface.app.common import Face
from utils.timer import Timer
from utils.human_landmark_runner import LandmarkRunner as HumanLandmark
from utils.crop import crop_image
from typing import List, Tuple, Union
from dataclasses import dataclass, field
import numpy as np
import os.path as osp
import cv2
def contiguous(obj):
if not obj.flags.c_contiguous:
obj = obj.copy(order="C")
return obj
@dataclass
class Trajectory:
start: int = -1 # start frame
end: int = -1 # end frame
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
M_c2o_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list) # M_c2o 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
def make_abs_path(fn):
return osp.join(osp.dirname(osp.realpath(__file__)), fn)
def sort_by_direction(faces, direction: str = 'large-small', face_center=None):
if len(faces) <= 0:
return faces
if direction == 'left-right':
return sorted(faces, key=lambda face: face['bbox'][0])
if direction == 'right-left':
return sorted(faces, key=lambda face: face['bbox'][0], reverse=True)
if direction == 'top-bottom':
return sorted(faces, key=lambda face: face['bbox'][1])
if direction == 'bottom-top':
return sorted(faces, key=lambda face: face['bbox'][1], reverse=True)
if direction == 'small-large':
return sorted(faces, key=lambda face: (face['bbox'][2] - face['bbox'][0]) * (face['bbox'][3] - face['bbox'][1]))
if direction == 'large-small':
return sorted(faces, key=lambda face: (face['bbox'][2] - face['bbox'][0]) * (face['bbox'][3] - face['bbox'][1]), reverse=True)
if direction == 'distance-from-retarget-face':
return sorted(faces, key=lambda face: (((face['bbox'][2]+face['bbox'][0])/2-face_center[0])**2+((face['bbox'][3]+face['bbox'][1])/2-face_center[1])**2)**0.5)
return faces
class FaceAnalysisDIY(FaceAnalysis):
def __init__(self, name='buffalo_l', root='~/.insightface', allowed_modules=None, **kwargs):
super().__init__(name=name, root=root, allowed_modules=allowed_modules, **kwargs)
self.timer = Timer()
def get(self, img_bgr, **kwargs):
max_num = kwargs.get('max_face_num', 0) # the number of the detected faces, 0 means no limit
flag_do_landmark_2d_106 = kwargs.get('flag_do_landmark_2d_106', True) # whether to do 106-point detection
direction = kwargs.get('direction', 'large-small') # sorting direction
face_center = None
bboxes, kpss = self.det_model.detect(img_bgr, max_num=max_num, metric='default')
if bboxes.shape[0] == 0:
return []
ret = []
for i in range(bboxes.shape[0]):
bbox = bboxes[i, 0:4]
det_score = bboxes[i, 4]
kps = None
if kpss is not None:
kps = kpss[i]
face = Face(bbox=bbox, kps=kps, det_score=det_score)
for taskname, model in self.models.items():
if taskname == 'detection':
continue
if (not flag_do_landmark_2d_106) and taskname == 'landmark_2d_106':
continue
# print(f'taskname: {taskname}')
model.get(img_bgr, face)
ret.append(face)
ret = sort_by_direction(ret, direction, face_center)
return ret
def warmup(self):
self.timer.tic()
img_bgr = np.zeros((512, 512, 3), dtype=np.uint8)
self.get(img_bgr)
elapse = self.timer.toc()
print(f'FaceAnalysisDIY warmup time: {elapse:.3f}s')
class Cropper(object):
def __init__(self, ):
self.face_analysis_wrapper_provider = ["CPUExecutionProvider"]
self.insightface_root: str = make_abs_path("./pretrained_weights/insightface")
self.device_id = 0
self.landmark_ckpt_path: str = make_abs_path("./pretrained_weights/liveportrait/landmark.onnx")
self.det_thresh: float = 0.1 # detection threshold
self.device = "cpu"
self.image_type = "human_face"
self.direction: str = "large-small" # direction of cropping
self.max_face_num: int = 0 # max face number, 0 mean no limit
self.dsize: int = 512 # crop size
self.scale: float = 2.3 # scale factor
self.vx_ratio: float = 0 # vx ratio
self.vy_ratio: float = -0.125 # vy ratio +up, -down
self.flag_do_rot: bool = True # whether to conduct the rotation when flag_do_crop is True
self.face_analysis_wrapper = FaceAnalysisDIY(
name="buffalo_l",
root=self.insightface_root,
providers=self.face_analysis_wrapper_provider,
)
self.face_analysis_wrapper.prepare(ctx_id=self.device_id, det_size=(512, 512), det_thresh=self.det_thresh)
self.face_analysis_wrapper.warmup()
self.human_landmark_runner = HumanLandmark(
ckpt_path=self.landmark_ckpt_path,
onnx_provider=self.device,
device_id=self.device_id,
)
self.human_landmark_runner.warmup()
def crop_source_image(self, img_rgb_: np.ndarray):
# crop a source image and get neccessary information
img_rgb = img_rgb_.copy() # copy it
img_bgr = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR)
if self.image_type == "human_face":
src_face = self.face_analysis_wrapper.get(
img_bgr,
flag_do_landmark_2d_106=True,
direction=self.direction,
max_face_num=self.max_face_num,
)
if len(src_face) == 0:
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 {self.direction}.")
# NOTE: temporarily only pick the first face, to support multiple face in the future
src_face = src_face[0]
lmk = src_face.landmark_2d_106 # this is the 106 landmarks from insightface
else:
tmp_dct = {
'animal_face_9': 'animal_face',
'animal_face_68': 'face'
}
img_rgb_pil = Image.fromarray(img_rgb)
lmk = self.animal_landmark_runner.run(
img_rgb_pil,
'face',
tmp_dct[self.animal_face_type],
0,
0
)
# crop the face
ret_dct = crop_image(
img_rgb, # ndarray
lmk, # 106x2 or Nx2
dsize=self.dsize,
scale=self.scale,
vx_ratio=self.vx_ratio,
vy_ratio=self.vy_ratio,
flag_do_rot=self.flag_do_rot,
)
# update a 256x256 version for network input
ret_dct["img_crop_256x256"] = cv2.resize(ret_dct["img_crop"], (256, 256), interpolation=cv2.INTER_AREA)
cv2.imwrite("/data/tmp/yongqiang/LLM/projects/zr/liveportrait_onnx/img_crop.jpg", cv2.cvtColor(ret_dct["img_crop"], cv2.COLOR_BGR2RGB))
cv2.imwrite("/data/tmp/yongqiang/LLM/projects/zr/liveportrait_onnx/img_crop_256x256.jpg", cv2.cvtColor(ret_dct["img_crop_256x256"], cv2.COLOR_BGR2RGB))
if self.image_type == "human_face":
lmk = self.human_landmark_runner.run(img_rgb, lmk)
ret_dct["lmk_crop"] = lmk
ret_dct["lmk_crop_256x256"] = ret_dct["lmk_crop"] * 256 / self.dsize
else:
# 68x2 or 9x2
ret_dct["lmk_crop"] = lmk
return ret_dct
def calc_lmk_from_cropped_image(self, img_rgb_, **kwargs):
direction = kwargs.get("direction", "large-small")
src_face = self.face_analysis_wrapper.get(
contiguous(img_rgb_[..., ::-1]), # convert to BGR
flag_do_landmark_2d_106=True,
direction=direction,
)
if len(src_face) == 0:
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 {direction}.")
src_face = src_face[0]
lmk = src_face.landmark_2d_106
lmk = self.human_landmark_runner.run(img_rgb_, lmk)
return lmk
def calc_lmks_from_cropped_video(self, driving_rgb_crop_lst, **kwargs):
"""Tracking based landmarks/alignment"""
trajectory = Trajectory()
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,
)
if len(src_face) == 0:
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}.")
src_face = src_face[0]
lmk = src_face.landmark_2d_106
lmk = self.human_landmark_runner.run(frame_rgb_crop, lmk)
trajectory.start, trajectory.end = idx, idx
else:
lmk = self.human_landmark_runner.run(frame_rgb_crop, trajectory.lmk_lst[-1])
trajectory.end = idx
trajectory.lmk_lst.append(lmk)
return trajectory.lmk_lst