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| from __future__ import division |
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| import glob |
| import os.path as osp |
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| import numpy as np |
| import onnxruntime |
| from numpy.linalg import norm |
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| from ..model_zoo import model_zoo |
| from ..utils import ensure_available |
| from .common import Face |
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| DEFAULT_MP_NAME = 'buffalo_l' |
| __all__ = ['FaceAnalysis'] |
|
|
| class FaceAnalysis: |
| def __init__(self, name=DEFAULT_MP_NAME, root='~/.insightface', allowed_modules=None, **kwargs): |
| onnxruntime.set_default_logger_severity(3) |
| self.models = {} |
| self.model_dir = ensure_available('models', name, root=root) |
| onnx_files = glob.glob(osp.join(self.model_dir, '*.onnx')) |
| onnx_files = sorted(onnx_files) |
| for onnx_file in onnx_files: |
| model = model_zoo.get_model(onnx_file, **kwargs) |
| if model is None: |
| print('model not recognized:', onnx_file) |
| elif allowed_modules is not None and model.taskname not in allowed_modules: |
| print('model ignore:', onnx_file, model.taskname) |
| del model |
| elif model.taskname not in self.models and (allowed_modules is None or model.taskname in allowed_modules): |
| |
| 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'] |
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|
| def prepare(self, ctx_id, det_thresh=0.5, det_size=(640, 640)): |
| self.det_thresh = det_thresh |
| assert det_size is not None |
| |
| self.det_size = det_size |
| for taskname, model in self.models.items(): |
| if taskname=='detection': |
| model.prepare(ctx_id, input_size=det_size, det_thresh=det_thresh) |
| else: |
| model.prepare(ctx_id) |
|
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| def get(self, img, max_num=0): |
| bboxes, kpss = self.det_model.detect(img, |
| 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 |
| model.get(img, face) |
| ret.append(face) |
| return ret |
|
|
| def draw_on(self, img, faces): |
| import cv2 |
| dimg = img.copy() |
| for i in range(len(faces)): |
| face = faces[i] |
| box = face.bbox.astype(np.int) |
| color = (0, 0, 255) |
| cv2.rectangle(dimg, (box[0], box[1]), (box[2], box[3]), color, 2) |
| if face.kps is not None: |
| kps = face.kps.astype(np.int) |
| |
| for l in range(kps.shape[0]): |
| color = (0, 0, 255) |
| if l == 0 or l == 3: |
| color = (0, 255, 0) |
| cv2.circle(dimg, (kps[l][0], kps[l][1]), 1, color, |
| 2) |
| if face.gender is not None and face.age is not None: |
| cv2.putText(dimg,'%s,%d'%(face.sex,face.age), (box[0]-1, box[1]-4),cv2.FONT_HERSHEY_COMPLEX,0.7,(0,255,0),1) |
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| return dimg |
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