人脸识别三步：人脸检测、人脸对齐、特征提取。

一、依赖包

numpy==1.18.0
onnxruntime==1.13.1
onnxruntime_directml==1.10.0
opencv_python==4.6.0.66
scikit_image==0.19.3
scikit_learn==1.1.3

二、人脸检测(Face Detection)

from __future__ import division
import datetime
import numpy as np
import onnxruntime
import os.path
import cv2class SCRFD:def __init__(self, model_file, providers=None, options=None, nms_thresh=0.4):assert os.path.exists(model_file)if providers is None:providers = ['CPUExecutionProvider']if options is None:options = onnxruntime.SessionOptions()self.session = onnxruntime.InferenceSession(model_file, providers=providers, sess_options=options)input_cfg = self.session.get_inputs()[0]input_shape = input_cfg.shapeinput_name = input_cfg.nameself.input_size = tuple(input_shape[2:4][::-1])self.input_name = input_nameself.nms_thresh = nms_threshself.center_cache = {}def forward(self, img, thresh):scores_list = []bboxes_list = []kpss_list = []input_size = tuple(img.shape[0:2][::-1])blob = cv2.dnn.blobFromImage(img, 1.0 / 128, input_size, (127.5, 127.5, 127.5), swapRB=True)net_outs = self.session.run([], {self.input_name: blob})input_height = blob.shape[2]input_width = blob.shape[3]_feat_stride_fpn = [8, 16, 32]for idx, stride in enumerate(_feat_stride_fpn):scores = net_outs[idx][0]bbox_preds = net_outs[idx + 3 * 1][0] * stridekps_preds = net_outs[idx + 3 * 2][0] * strideheight = input_height // stridewidth = input_width // stridekey = (height, width, stride)if key in self.center_cache:anchor_centers = self.center_cache[key]else:anchor_centers = np.stack(np.mgrid[:height, :width][::-1], axis=-1).astype(np.float32)anchor_centers = (anchor_centers * stride).reshape((-1, 2))anchor_centers = np.stack([anchor_centers] * 2, axis=1).reshape((-1, 2))if len(self.center_cache) < 100:self.center_cache[key] = anchor_centerspos_inds = np.where(scores >= thresh)[0]if len(pos_inds) > 0:bboxes = self.distance2bbox(anchor_centers, bbox_preds)pos_scores = scores[pos_inds]pos_bboxes = bboxes[pos_inds]scores_list.append(pos_scores)bboxes_list.append(pos_bboxes)kpss = self.distance2kps(anchor_centers, kps_preds)kpss = kpss.reshape((kpss.shape[0], -1, 2))pos_kpss = kpss[pos_inds]kpss_list.append(pos_kpss)return scores_list, bboxes_list, kpss_listdef detect(self, img, threshold=0.5, max_num=0, metric='default'):input_size = self.input_sizeim_ratio = float(img.shape[0]) / img.shape[1]model_ratio = float(input_size[1]) / input_size[0]if im_ratio > model_ratio:new_height = input_size[1]new_width = int(new_height / im_ratio)else:new_width = input_size[0]new_height = int(new_width * im_ratio)det_scale = float(new_height) / img.shape[0]resized_img = cv2.resize(img, (new_width, new_height))det_img = np.zeros((input_size[1], input_size[0], 3), dtype=np.uint8)det_img[:new_height, :new_width, :] = resized_imgscores_list, bboxes_list, kpss_list = self.forward(det_img, threshold)if len(scores_list) == 0:return np.empty(0), np.empty(0)scores = np.vstack(scores_list)scores_ravel = scores.ravel()order = scores_ravel.argsort()[::-1]bboxes = np.vstack(bboxes_list) / det_scalekpss = np.vstack(kpss_list) / det_scalepre_det = np.hstack((bboxes, scores)).astype(np.float32, copy=False)pre_det = pre_det[order, :]keep = self.nms(pre_det)det = pre_det[keep, :]kpss = kpss[order, :, :]kpss = kpss[keep, :, :]if max_num > 0 and det.shape[0] > max_num:area = (det[:, 2] - det[:, 0]) * (det[:, 3] - det[:, 1])img_center = img.shape[0] // 2, img.shape[1] // 2offsets = np.vstack([(det[:, 0] + det[:, 2]) / 2 - img_center[1],(det[:, 1] + det[:, 3]) / 2 - img_center[0]])offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)if metric == 'max':values = areaelse:values = area - offset_dist_squared * 2.0  # some extra weight on the centeringbindex = np.argsort(values)[::-1]  # some extra weight on the centeringbindex = bindex[0:max_num]det = det[bindex, :]if kpss is not None:kpss = kpss[bindex, :]return det, kpssdef nms(self, dets):thresh = self.nms_threshx1 = dets[:, 0]y1 = dets[:, 1]x2 = dets[:, 2]y2 = dets[:, 3]scores = dets[:, 4]areas = (x2 - x1 + 1) * (y2 - y1 + 1)order = scores.argsort()[::-1]keep = []while order.size > 0:i = order[0]keep.append(i)xx1 = np.maximum(x1[i], x1[order[1:]])yy1 = np.maximum(y1[i], y1[order[1:]])xx2 = np.minimum(x2[i], x2[order[1:]])yy2 = np.minimum(y2[i], y2[order[1:]])w = np.maximum(0.0, xx2 - xx1 + 1)h = np.maximum(0.0, yy2 - yy1 + 1)inter = w * hovr = inter / (areas[i] + areas[order[1:]] - inter)inds = np.where(ovr <= thresh)[0]order = order[inds + 1]return keepdef distance2bbox(self, points, distance, max_shape=None):"""Decode distance prediction to bounding box.Args:points (Tensor): Shape (n, 2), [x, y].distance (Tensor): Distance from the given point to 4boundaries (left, top, right, bottom).max_shape (tuple): Shape of the image.Returns:Tensor: Decoded bboxes."""x1 = points[:, 0] - distance[:, 0]y1 = points[:, 1] - distance[:, 1]x2 = points[:, 0] + distance[:, 2]y2 = points[:, 1] + distance[:, 3]if max_shape is not None:x1 = x1.clamp(min=0, max=max_shape[1])y1 = y1.clamp(min=0, max=max_shape[0])x2 = x2.clamp(min=0, max=max_shape[1])y2 = y2.clamp(min=0, max=max_shape[0])return np.stack([x1, y1, x2, y2], axis=-1)def distance2kps(self, points, distance, max_shape=None):preds = []for i in range(0, distance.shape[1], 2):px = points[:, i % 2] + distance[:, i]py = points[:, i % 2 + 1] + distance[:, i + 1]if max_shape is not None:px = px.clamp(min=0, max=max_shape[1])py = py.clamp(min=0, max=max_shape[0])preds.append(px)preds.append(py)return np.stack(preds, axis=-1)

三、人脸对齐(Face Alignment)

import numpy as np
import cv2
from skimage import transform################### 标准脸的关键点 ###################
REFERENCE_FACIAL_POINTS = np.array([# 112×112[38.2946, 51.6963],[73.5318, 51.5014],[56.0252, 71.7366],[41.5493, 92.3655],[70.7299, 92.2041]
], np.float32)def align(image, landmark, dsize=(112, 112)):# 变换矩阵trans = transform.SimilarityTransform()trans.estimate(landmark, REFERENCE_FACIAL_POINTS)M = trans.params[:2, :]# 此方法等同上面变换# Q = np.zeros((10, 4))# S = REFERENCE_FACIAL_POINTS.reshape(-1, 1)# for i in range(5):#     x, y = landmark[i]#     Q[i * 2 + 0] = x, y, 1, 0#     Q[i * 2 + 1] = y, -x, 0, 1# M = (np.linalg.inv(Q.T @ Q) @ Q.T @ S).squeeze()# M = np.array([#     [M[0], M[1], M[2]],#     [-M[1], M[0], M[3]]# ])face_img = cv2.warpAffine(image, M, dsize, borderValue=3.0)return face_img

四、特征提取(Feature Extraction)

import numpy as np
import cv2
import os.path
from sklearn import preprocessing
import onnxruntime
import alignfaceclass ArcFace:def __init__(self, model_file, providers=None, options=None):assert os.path.exists(model_file)self.input_mean = 127.5self.input_std = 127.5if providers is None:providers = ['CPUExecutionProvider']if options is None:options = onnxruntime.SessionOptions()self.session = onnxruntime.InferenceSession(model_file, providers=providers, sess_options=options)input_cfg = self.session.get_inputs()[0]input_shape = input_cfg.shapeinput_name = input_cfg.nameself.input_size = tuple(input_shape[2:4][::-1])self.input_name = input_namedef get_feature(self, img, landmark):_img = alignface.align(img, landmark=landmark)embedding = self.forward(_img).flatten()embedding = np.array(embedding).reshape((1, -1))embedding = preprocessing.normalize(embedding)return embeddingdef forward(self, imgs):if not isinstance(imgs, list):imgs = [imgs]input_size = self.input_sizeblob = cv2.dnn.blobFromImages(imgs, 1.0 / self.input_std, input_size,(self.input_mean, self.input_mean, self.input_mean), swapRB=True)net_out = self.session.run([], {self.input_name: blob})[0]return net_out

五、人脸识别(Face Recognition)

import cv2
import os
import numpy as np
import onnxruntime
from scrfd import SCRFD
from arcface import ArcFaceclass FaceRecognition:def __init__(self, dete_model=None, reco_model=None, ctx_id=0, dete_threshold=0.50, reco_threshold=1.24):# 人脸识别工具类# param ctx_id: 非负数为GPU的ID，负数为使用CPU# param reco_threshold: 人脸识别阈值# param reco_threshold: 人脸检测阈值providers = ['CPUExecutionProvider']options = onnxruntime.SessionOptions()if ctx_id >= 0:providers = ['DmlExecutionProvider']options.enable_mem_pattern = Falseoptions.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIALself.detector = SCRFD(model_file=dete_model, providers=providers, options=options)self.recognizer = ArcFace(model_file=reco_model, providers=providers, options=options)self.reco_threshold = reco_thresholdself.dete_threshold = dete_thresholdself.faces_embedding = []# 加载人脸库中的人脸def load_faces(self, face_db_path):if not os.path.exists(face_db_path):os.makedirs(face_db_path)for root, dirs, files in os.walk(face_db_path):for file in files:input_image = cv2.imdecode(np.fromfile(os.path.join(root, file), dtype=np.uint8), 1)user_id = file.split(".")[0]result = self.register(input_image, user_id)if result != 'success':print(file + ':' + result)# 人脸识别def recognize(self, image):dets, landmarks = self.detector.detect(image, threshold=self.dete_threshold)results = list()for det, landmark in zip(dets, landmarks):embedding = self.recognizer.get_feature(image, landmark)for com_face in self.faces_embedding:r = self.feature_compare(embedding, com_face["feature"])print("recognize: {}/{}".format(com_face["user_id"], r))if r < self.reco_threshold:result = dict()result["user_id"] = com_face["user_id"]result["bbox"] = (np.array(det)[:4]).astype(np.int32).tolist()result["landmark"] = np.array(landmark).astype(np.int32).tolist()results.append(result)return results@staticmethoddef feature_compare(feature1, feature2):diff = np.subtract(feature1, feature2)dist = np.sum(np.square(diff), 1)return dist# 注册人脸库def register(self, image, user_id):bboxes, landmarks = self.detector.detect(image, threshold=self.dete_threshold)if bboxes.shape[0] == 0:return '检测不到人脸'if bboxes.shape[0] > 1:return '检测到多个人脸'# 判断人脸是否存在embedding = self.recognizer.get_feature(image, landmarks[0])for com_face in self.faces_embedding:r = self.feature_compare(embedding, com_face["feature"])if r < self.reco_threshold:return com_face["user_id"] + ' 已存在'# 符合注册条件保存图片，同时把特征添加到人脸特征库中# cv2.imencode('.png', image)[1].tofile(os.path.join(self.face_db, '%s.png' % user_id))self.faces_embedding.append({"user_id": user_id,"feature": embedding})return "success"

注意：providers可以更具自己硬件环境修改。

六、示例

if __name__ == '__main__':dete_model = r'X:\xxx\scrfd_10g_bnkps_shape640x640.onnx'reco_model = r'X:\xxx\w600k_r50.onnx'face_db_path = r'X:\xxx\face_db'face_reco = FaceRecognition(dete_model=dete_model, reco_model=reco_model, ctx_id=0)face_reco.load_faces(face_db_path=face_db_path)img = cv2.imread(r'X:\xxx\test.jpg')ta = datetime.datetime.now()results = face_reco.recognize(img)print('all cost(ms):', (datetime.datetime.now() - ta).total_seconds() * 1000)for result in results:x1, y1, x2, y2 = result["bbox"]cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)cv2.putText(img=img, text=result["user_id"], org=(x1, y1 - 10),fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.5, color=(0, 255, 0), thickness=1)for landmark in result["landmark"]:cv2.circle(img, tuple(landmark), 1, (0, 0, 255), 2)cv2.imshow('Image', img)cv2.waitKey()cv2.destroyAllWindows()

说明：

SCRFD模型下载：https://onedrive.live.com/redir?resid=4A83B6B633B029CC!5543&authkey=!ACwXX1RtoJZotbE&e=F6i5Vm，也可在此下载。
ArcFace模型下载：https://drive.google.com/file/d/1qXsQJ8ZT42_xSmWIYy85IcidpiZudOCB/view?usp=sharing，也可在此下载。

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基于ONNX人脸识别实例(SCRFD/ArcFace)-Python版

一、依赖包

二、人脸检测(Face Detection)

三、人脸对齐(Face Alignment)

四、特征提取(Feature Extraction)

五、人脸识别(Face Recognition)

六、示例

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