| ''' |
| Author: Naiyuan liu |
| Github: https://github.com/NNNNAI |
| Date: 2021-11-23 17:03:58 |
| LastEditors: Naiyuan liu |
| LastEditTime: 2021-11-24 16:46:04 |
| Description: |
| ''' |
| 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_func.utils import face_align_ffhqandnewarc as 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: |
| |
| 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), mode ='None'): |
| self.det_thresh = det_thresh |
| self.mode = mode |
| 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 |
|
|
| det_score = bboxes[..., 4] |
| best_index = np.argmax(det_score) |
|
|
| kps = None |
| if kpss is not None: |
| kps = kpss[best_index] |
|
|
| |
| |
| h, w, _ = img.shape |
| face_h = bboxes[best_index][3] - bboxes[best_index][1] |
| kps[:,1] += int(0.1 * face_h) |
|
|
| |
| M, _ = face_align.estimate_norm(kps, crop_size, mode=self.mode) |
| align_img = cv2.warpAffine(img, M, (crop_size, crop_size), borderValue=0.0) |
| |
| return [align_img], [M] |
|
|
