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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') | |