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# -*- coding: utf-8 -*-
# @Organization : insightface.ai
# @Author : Jia Guo
# @Time : 2021-05-04
# @Function :
from __future__ import division
import onnxruntime
__all__ = ['FaceAnalysis']
from utils.common import Face
from models.arcface_onnx import ArcFaceONNX
from models.attribute import Attribute
from models.landmark import Landmark
from models.retinaface import RetinaFace
from huggingface_hub import hf_hub_download
REPO_ID = "leonelhs/insightface"
model_detector_path = hf_hub_download(repo_id=REPO_ID, filename="det_10g.onnx")
model_landmark_3d_68_path = hf_hub_download(repo_id=REPO_ID, filename="1k3d68.onnx")
model_landmark_2d_106_path = hf_hub_download(repo_id=REPO_ID, filename="2d106det.onnx")
model_genderage_path = hf_hub_download(repo_id=REPO_ID, filename="genderage.onnx")
model_recognition_path = hf_hub_download(repo_id=REPO_ID, filename="w600k_r50.onnx")
class FaceAnalysis:
def __init__(self):
onnxruntime.set_default_logger_severity(3)
self.detector = RetinaFace(model_file=model_detector_path, input_size=(640, 640), det_thresh=0.5)
self.landmark_3d_68 = Landmark(model_file=model_landmark_3d_68_path)
self.landmark_2d_106 = Landmark(model_file=model_landmark_2d_106_path)
self.genderage = Attribute(model_file=model_genderage_path)
self.recognition = ArcFaceONNX(model_file=model_recognition_path)
def get(self, img, max_num=0):
bboxes, kpss = self.detector.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)
self.landmark_3d_68.get(img, face)
self.landmark_2d_106.get(img, face)
self.genderage.get(img, face)
self.recognition.get(img, face)
ret.append(face)
return ret |