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| from __future__ import division | |
| import collections | |
| import numpy as np | |
| import glob | |
| import os | |
| import os.path as osp | |
| import cv2 | |
| from insightface.model_zoo import model_zoo | |
| from insightface.utils import face_align | |
| __all__ = ['Face_detect_crop', 'Face'] | |
| Face = collections.namedtuple('Face', [ | |
| 'bbox', 'kps', 'det_score', 'embedding', 'gender', 'age', | |
| 'embedding_norm', 'normed_embedding', | |
| 'landmark' | |
| ]) | |
| Face.__new__.__defaults__ = (None, ) * len(Face._fields) | |
| class Face_detect_crop: | |
| def __init__(self, name, root='~/.insightface_func/models'): | |
| self.models = {} | |
| root = os.path.expanduser(root) | |
| onnx_files = glob.glob(osp.join(root, name, '*.onnx')) | |
| onnx_files = sorted(onnx_files) | |
| for onnx_file in onnx_files: | |
| if onnx_file.find('_selfgen_')>0: | |
| #print('ignore:', onnx_file) | |
| continue | |
| model = model_zoo.get_model(onnx_file) | |
| if model.taskname not in self.models: | |
| print('find model:', onnx_file, model.taskname) | |
| self.models[model.taskname] = model | |
| else: | |
| print('duplicated model task type, ignore:', onnx_file, model.taskname) | |
| del model | |
| assert 'detection' in self.models | |
| self.det_model = self.models['detection'] | |
| def prepare(self, ctx_id, det_thresh=0.5, det_size=(640, 640)): | |
| self.det_thresh = det_thresh | |
| assert det_size is not None | |
| print('set det-size:', det_size) | |
| self.det_size = det_size | |
| for taskname, model in self.models.items(): | |
| if taskname=='detection': | |
| model.prepare(ctx_id, input_size=det_size) | |
| else: | |
| model.prepare(ctx_id) | |
| def get(self, img, crop_size, max_num=0): | |
| bboxes, kpss = self.det_model.detect(img, | |
| threshold=self.det_thresh, | |
| max_num=max_num, | |
| metric='default') | |
| if bboxes.shape[0] == 0: | |
| return None | |
| 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] | |
| # M, _ = face_align.estimate_norm(kps, crop_size, mode ='None') | |
| # align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0) | |
| # align_img_list = [] | |
| # M_list = [] | |
| kps_list = [] | |
| for i in range(bboxes.shape[0]): | |
| kps = None | |
| if kpss is not None: | |
| kps = kpss[i] | |
| # M, _ = face_align.estimate_norm(kps, crop_size, mode ='None') | |
| # align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0) | |
| # align_img_list.append(align_img) | |
| # M_list.append(M) | |
| kps_list.append(kps) | |
| # det_score = bboxes[..., 4] | |
| # best_index = np.argmax(det_score) | |
| # kps = None | |
| # if kpss is not None: | |
| # kps = kpss[best_index] | |
| # M, _ = face_align.estimate_norm(kps, crop_size, mode ='None') | |
| # align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0) | |
| return kps_list | |