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# coding: utf-8
"""
face detectoin and alignment using InsightFace
"""
import numpy as np
from .rprint import rlog as log
from .dependencies.insightface.app import FaceAnalysis
from .dependencies.insightface.app.common import Face
from .timer import Timer
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_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()
#log(f'FaceAnalysisDIY warmup time: {elapse:.3f}s')
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