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Running
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Zero
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# -*- 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 ensure_available
from .common import Face
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):
# 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(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)
#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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