| | |
| |
|
| | """ |
| | 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_face_num', 0) |
| | flag_do_landmark_2d_106 = kwargs.get('flag_do_landmark_2d_106', True) |
| | direction = kwargs.get('direction', 'large-small') |
| | 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 |
| |
|
| | |
| | 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') |
| |
|
