face_recognition实现人脸检测+活体检测（通过眨眼时的帧数判断）

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import cv2
import face_recognition
import numpy as np
from PIL import ImageFont, ImageDraw, Image
 
 
class FaceRecognition(object):
    # 已知姓名的人脸编码
    known_face_encodings = []
    # 人脸对应名称
    known_face_names = []
    # 初始化眨眼的连续帧数
    blink_counter = 0
    # 初始化眨眼次数总和
    blink_total = 0
    # 眼长宽比例值
    EAR_THRESH = 0.15
    EAR_CONSEC_FRAMES_MIN = 1
    # 当EAR小于阈值时，接连多少帧一定发生眨眼动作
    EAR_CONSEC_FRAMES_MAX = 3
 
    def gel_konw_face_encodings(self):
        """
        获取已知姓名人脸照片的128维向量
        :return:
        """
        face_locations, fcj_face_encoding = self.get_face_info(file_path="小明.png")
        self.known_face_encodings = [fcj_face_encoding[0]]
        self.known_face_names = ["小明"]
 
    def get_face_info(self, img_ndarry=None, file_path="", only_first_face=True):
        """
        获取图片中第一个人脸位置和128位的人脸特征向量
        :param img_ndarry:
        :param file_path:
        :return:
        """
        if file_path:
            img_ndarry = face_recognition.load_image_file("付晨杰.png")
 
        # 缩小图片为原图的四分之一提高检测速度
        small_frame = cv2.resize(img_ndarry, (0, 0), fx=0.25, fy=0.25)
        rgb_small_frame = small_frame
        smaill_face_locations = face_recognition.face_locations(rgb_small_frame)
        if smaill_face_locations:
            if only_first_face:
                smaill_face_locations = [smaill_face_locations[0]]
            face_locations = [(top*4, right*4, bottom*4, left*4) for (top, right, bottom, left) in smaill_face_locations]
            face_encodings = face_recognition.face_encodings(rgb_small_frame, smaill_face_locations)
            return face_locations, face_encodings
        else:
            return [], []
 
    def compare_faces(self, known_face_encodings, unkonwn_face_encoding):
        """人脸对比"""
        results = face_recognition.compare_faces(known_face_encodings, unkonwn_face_encoding, tolerance=0.4)
        for index, item in enumerate(results):
            if item:
                return self.known_face_names[index]
        return "unkonwn"
 
    def cv2_img_add_text(self, img, text, left, top, textColor=(0, 255, 0), textSize=20):
        """
        cv2无法显示中文，因此需要通过PIL并定义字体库显示，然后重新转化为OpenCV格式
        :param img:
        :param text:
        :param left:
        :param top:
        :param textColor:
        :param textSize:
        :return:
        """
        if (isinstance(img, np.ndarray)):  # 判断是否OpenCV图片类型
            img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
        # 创建一个可以在给定图像上绘图的对象
        draw = ImageDraw.Draw(img)
        # 字体的格式
        fontStyle = ImageFont.truetype(
            "font/Alimama_ShuHeiTi_Bold.ttf", textSize, encoding="utf-8")
        # 绘制文本
        draw.text((left, top), text, textColor, font=fontStyle)
        # 转换回OpenCV格式
        return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
 
    def eye_aspect_ratio(self, eye):
        """
        获取眼睛长宽比
        :param eye:
        :return:
        """
        # (|e1-e5|+|e2-e4|) / (2|e0-e3|)
        A = np.linalg.norm(eye[1] - eye[5])
        B = np.linalg.norm(eye[2] - eye[4])
        C = np.linalg.norm(eye[0] - eye[3])
        ear = (A + B) / (2.0 * C)
        return ear
 
    def biopsy_by_blink(self, img_ndarry, face_location, check_eye=True):
        """
        活体检测：眨眼检测
        :return:
        """
        face_landmarks_list = face_recognition.face_landmarks(img_ndarry, [face_location])[0]
        left_eyebrow = face_landmarks_list['left_eye']
        right_eyebrow = face_landmarks_list['right_eye']
 
        if check_eye:
            left_eyebrow = np.array(left_eyebrow)
            right_eyebrow = np.array(right_eyebrow)
            left_ear = self.eye_aspect_ratio(left_eyebrow)  # 计算左眼EAR
            right_ear = self.eye_aspect_ratio(right_eyebrow)  # 计算右眼EAR
            ear = (left_ear + right_ear) / 2.0  # 求左右眼EAR的均值
            # EAR低于阈值，有可能发生眨眼，眨眼连续帧数加一次
            if ear < self.EAR_THRESH:
                self.blink_counter += 1
 
            # EAR高于阈值，判断前面连续闭眼帧数，如果在合理范围内，说明发生眨眼
            else:
                if self.EAR_CONSEC_FRAMES_MIN <= self.blink_counter <= self.EAR_CONSEC_FRAMES_MAX:
                    self.blink_total += 1
                self.blink_counter = 0
 
            # # for item in  left_eyebrow:
            # cv2.circle(img_ndarry, left_eyebrow[0], 1, (255, 0, 0), 2)
            # cv2.circle(img_ndarry, left_eyebrow[1], 1, (255, 0, 0), 2)
 
    def test(self):
        capture = cv2.VideoCapture(0)
        while True:
            ret, frame = capture.read()
            if ret is False:
                print("未检测到摄像头")
                exit(0)
            face_locations, face_encodings = self.get_face_info(img_ndarry=frame)
            if face_locations:
                # 循环处理个个人脸
                for index, face_encoding in enumerate(face_encodings):
                    # 对比人脸显示人员名称
                    name = self.compare_faces(self.known_face_encodings, face_encoding)
 
                    # 活体检测-眨眼
                    self.biopsy_by_blink(frame, face_locations[index])
                    if self.blink_total > 1:
                        name += "检测到眨眼"
 
                    # 长方形显示人脸区域
                    top, right, bottom, left = face_locations[index]
                    cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
 
                    # 显示人员名称
                    frame = self.cv2_img_add_text(frame, name, left + 6, bottom - 6)
 
                cv2.imshow('Video', frame)
                if cv2.waitKey(1) & 0xFF == ord('q'):
                    break
            else:
                print("未检测到人脸")
        # 释放摄像头
        capture.release()
        capture.destroyAllWindows()
 
 
if __name__ == "__main__":
    a = FaceRecognition()
    a.gel_konw_face_encodings()
    a.test()