【飞凌RK3588开发板试用】超级人脸识别——哪怕你戴着口罩

今天完成了口罩识别，这个板子太强大了吧。哪怕你戴口罩也是可以识别出来的，上代码：
1、识别代码

import numpy as np
import cv2 as cv
import os

face_detection_model = "../models/yunet.onnx"
face_recognition_model = "../models/face_recognizer_fast.onnx"
images_path = "../images"

faceDetector = cv.FaceDetectorYN.create(face_detection_model, "", input_size=(640, 480)) # 初始化FaceRecognizerYN
recognizer = cv.FaceRecognizerSF.create(face_recognition_model, "") # 初始化FaceRecognizerSF

cosine_similarity_threshold = 0.363
l2_similarity_threshold = 1.128

# 获取图片中的人脸特征,将获取到的人脸特征和ID分别添加到不同的列表中
def Gets_Facial_Features(images_path):
    images_feature_list = []
    ID_list = []
    for image_name in os.listdir(images_path):
        ID_list.append(image_name.split('.')[0])
        image = cv.imread(images_path + '/' + image_name)
        faces1 = faceDetector.detect(image)
        # 在人脸检测部分的基础上, 对齐检测到的首个人脸(faces[1][0]), 保存至aligned_face。
        aligned_face1 = recognizer.alignCrop(image, faces1[1][0])
        # 在上文的基础上, 获取对齐人脸的特征feature。
        image_feature = recognizer.feature(aligned_face1)
        images_feature_list.append(image_feature)
    return ID_list, images_feature_list

if __name__ == '__main__':
    capture = cv.VideoCapture(74)
    capture.set(3, 640)  # 设置摄像头的帧的高为640
    capture.set(4, 480)  # 设置摄像头的帧的宽为480

    ID_list, images_feature_list = Gets_Facial_Features(images_path)
    while True:
        ret, frame = capture.read()
        if ret is not True:
            print('摄像头未打开')
            break

        frame_faces = faceDetector.detect(frame)

        if frame_faces[1] is not None:
            for idx, face in enumerate(frame_faces[1]):
                coords = face[:-1].astype(np.int32)
                cv.rectangle(frame, (coords[0], coords[1]), (coords[0] + coords[2], coords[1] + coords[3]), (255, 0, 0), thickness=2)
                cv.circle(frame, (coords[4], coords[5]), 2, (255, 0, 0), thickness=2)
                cv.circle(frame, (coords[6], coords[7]), 2, (0, 0, 255), thickness=2)
                cv.circle(frame, (coords[8], coords[9]), 2, (0, 255, 0), thickness=2)
                cv.circle(frame, (coords[10], coords[11]), 2, (255, 0, 255), thickness=2)
                cv.circle(frame, (coords[12], coords[13]), 2, (0, 255, 255), thickness=2)

                aligned_face = recognizer.alignCrop(frame, frame_faces[1][idx])
                frame_feature = recognizer.feature(aligned_face)
                for index, image_feature in enumerate(images_feature_list):
                    cosine_score = recognizer.match(frame_feature, image_feature, 0)
                    l2_score = recognizer.match(frame_feature, image_feature, 1)
                    if (cosine_score >= cosine_similarity_threshold):
                        cv.putText(frame, ID_list[index], (coords[0] + 5, coords[1] - 5), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
                        i = 1
                        break
                    elif (l2_score <= l2_similarity_threshold):
                        cv.putText(frame, ID_list[index], (coords[0] + 5, coords[1] - 5), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
                        i = 1
                        break
                    # else:
                    #     cv.putText(frame, 'unkown', (coords[0] + 5, coords[1] - 5), cv.FONT_HERSHEY_SIMPLEX, 0.5,
                    #                (0, 255, 0), 2)

            cv.imshow('RP3588_face_DEMO', frame)
            c = cv.waitKey(1)
            # 按esc退出视频
            if c == 27:
                break

        else:
            cv.putText(frame, 'face is not detected', (1, 16), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
            cv.imshow('photo-collection-demo', frame)
            c = cv.waitKey(1)
            # 按esc退出视频
            if c == 27:
                break

2、人脸采集代码

# -*- coding: utf-8 -*-

import cv2
import sys

from PIL import Image


def CatchPICFromVideo(window_name, camera_idx, catch_pic_num, path_name):
    cv2.namedWindow(window_name)

    # 视频来源，可以来自一段已存好的视频，也可以直接来自USB摄像头
    cap = cv2.VideoCapture(camera_idx)

    # 告诉OpenCV使用人脸识别分类器
    classfier = cv2.CascadeClassifier("./static/haarcascade_frontalface_default.xml")

    # 识别出人脸后要画的边框的颜色，RGB格式
    color = (0, 255, 0)

    num = 0
    while cap.isOpened():
        ok, frame = cap.read()  # 读取一帧数据
        if not ok:
            break

        grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # 将当前桢图像转换成灰度图像

        # 人脸检测，1.2和2分别为图片缩放比例和需要检测的有效点数
        faceRects = classfier.detectMultiScale(grey, scaleFactor=1.2, minNeighbors=3, minSize=(32, 32))
        if len(faceRects) > 0:  # 大于0则检测到人脸
            for faceRect in faceRects:  # 单独框出每一张人脸
                x, y, w, h = faceRect

                # 将当前帧保存为图片
                img_name = '%s/%d.jpg' % (path_name, num)
                image = frame[y - 10: y + h + 10, x - 10: x + w + 10]
                cv2.imwrite(img_name, image)

                num += 1
                if num > (catch_pic_num):  # 如果超过指定最大保存数量退出循环
                    break

                # 画出矩形框
                cv2.rectangle(frame, (x - 10, y - 10), (x + w + 10, y + h + 10), color, 2)

                # 显示当前捕捉到了多少人脸图片了，这样站在那里被拍摄时心里有个数，不用两眼一抹黑傻等着
                font = cv2.FONT_HERSHEY_SIMPLEX
                cv2.putText(frame, 'num:%d' % (num), (x + 30, y + 30), font, 1, (255, 0, 255), 4)

        # 超过指定最大保存数量结束程序
        if num > (catch_pic_num): break

        # 显示图像
        cv2.imshow(window_name, frame)
        c = cv2.waitKey(10)
        if c & 0xFF == ord('q'):
            break

    # 释放摄像头并销毁所有窗口
    cap.release()
    cv2.destroyAllWindows()


if __name__ == '__main__':
    if len(sys.argv) != 4:
        print("Usage:%s camera_id face_num_max path_name\r\n" % (sys.argv[0]))
    else:
        CatchPICFromVideo("截取人脸", int(sys.argv[1]), int(sys.argv[2]), sys.argv[3])

验证：
1、打开人脸采集程序，检测到人脸后，按空格键，然后输入名字，回车保存，可继续采集，如果采集完毕按esc退出。
2、采集好人脸后，就可以打开人脸识别程序，进行人脸识别。

测脸

侧脸部分遮盖

戴口罩侧脸：

戴口罩、逆光：

【小结】这开发板太强大了，下一步，做一个人脸识别的门禁系统。

参考资料
https://docs.opencv.org/4.x/d0/dd4/tutorial_dnn_face.html  
https://zhuanlan.zhihu.com/p/423625566  
https://github.com/opencv/opencv/tree/master/samples/dnn  
https://github.com/opencv/opencv/tree/master/modules/objdetect