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aee2afa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | # -*- coding: utf-8 -*-
# @Organization : insightface.ai
# @Author : Jia Guo
# @Time : 2021-05-04
# @Function :
from __future__ import division
import glob
import os.path as osp
import numpy as np
import onnxruntime
from numpy.linalg import norm
from ..model_zoo import model_zoo
from ..utils import DEFAULT_MP_NAME, ensure_available
from .common import Face
__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):
print('find model:', onnx_file, model.taskname, model.input_shape, model.input_mean, model.input_std)
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, det_thresh=det_thresh)
else:
model.prepare(ctx_id)
def get_point_srcface(self, bboxes, kpss, img, max_num=0):
# bboxes, kpss = self.det_model.detect(img,
# max_num=max_num,
# metric='default')
# print("bboxes points la: ", bboxes)
# print("bbox type la: ", type(bboxes))
# print("kpss la: ", kpss)
# print("kpss type la: ", type(kpss))
# bboxes, kpss = None
if bboxes.shape[0] == 0:
return []
ret = []
for i in range(bboxes.shape[0]):
bbox = bboxes[i, 0:4]
det_score = None
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 get(self, img, max_num=0):
bboxes, kpss = self.det_model.detect(img,
max_num=max_num,
metric='default')
# print("bboxes points la: ", bboxes)
# print("bbox type la: ", type(bboxes))
# print("kpss la: ", kpss)
# print("kpss type la: ", type(kpss))
# bboxes, kpss = None
if bboxes.shape[0] == 0:
return []
ret = []
for i in range(bboxes.shape[0]):
bbox = bboxes[i, 0:4]
det_score = None
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 get(self, bboxes, kpss, img, max_num=0):
# bboxes, kpss = self.det_model.detect(img,
# max_num=max_num,
# metric='default')
# # print("bboxes points la: ", bboxes)
# # print("bbox type la: ", type(bboxes))
# # print("kpss la: ", kpss)
# # print("kpss type la: ", type(kpss))
# if bboxes.shape[0] == 0:
# return []
# ret = []
# for i in range(bboxes.shape[0]):
# bbox = bboxes[i, 0:4]
# det_score = None
# 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.int32)
# print("box face draw face:", box)
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.int32)
#print(landmark.shape)
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)
# for key, value in face.items():
# if key.startswith('landmark_3d'):
# print(key, value.shape)
# print(value[0:10,:])
# lmk = np.round(value).astype(np.int)
# for l in range(lmk.shape[0]):
# color = (255, 0, 0)
# cv2.circle(dimg, (lmk[l][0], lmk[l][1]), 1, color,
# 2)
return dimg
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