【项目】小帽简易人脸识别

小帽人脸识别

一、环境配置

• Python + Pycharm + opencv
  • pip install opencv-python

二、实操

1. 读取图片

# 导入cv模块
import cv2 as cv
# 读取图片
img = cv.imread('lq.jpg')
# 显示图片
cv.imshow('read_img', img)
# 等待
cv.waitKey(0)
# 释放内存
cv.destroyAllWindows()

2. 灰度转换

# 导入cv模块
import cv2 as cv
# 读取图片
img = cv.imread('lq.jpg')
# 灰度转换
gray_img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# 显示灰度
cv.imshow('gray', gray_img)
# 保存灰度图片
cv.imwrite('gray_face.jpg', gray_img)
# 等待
cv.waitKey(0)
# 释放内存
cv.destroyAllWindows()

3. 修改尺寸

# 导入cv模块
import cv2 as cv
# 读取图片
img = cv.imread('lq.jpg')
# 修改尺寸
resize_img = cv.resize(img, dsize=(200, 200))
# 显示原图
cv.imshow('img', img)
# 显示修改后的
cv.imshow('resize_img', resize_img)
# 打印原图尺寸大小
print('未修改：', img.shape)    # 未修改： (1080, 1616, 3)
# 打印修改后的大小
print('修改后：', resize_img.shape) # 修改后： (200, 200, 3)
# 等待，按下q退出
while True:
    if ord('q') == cv.waitKey(0):
        break
# 释放内存
cv.destroyAllWindows()

4. 绘制矩形

# 导入cv模块
import cv2 as cv
# 读取图片
img = cv.imread('lq.jpg')
# 坐标
x,y,w,h = 100,100,100,100
# 绘制矩形
cv.rectangle(img,(x,y,x+w,y+h),color=(0,0,255),thickness=1)
# 绘制圆形
cv.circle(img,center=(x+w,y+h),radius=100,color=(255,0,0),thickness=2)
# 显示
cv.imshow('re_img', img)
# 等待，按下q退出
while True:
    if ord('q') == cv.waitKey(0):
        break
# 释放内存
cv.destroyAllWindows()

5. 人脸检测

# 导入cv模块
import cv2 as cv
# 检测函数
def face_detect_demo():
    gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
    # 加载分类器
    face_detect = cv.CascadeClassifier('../opencv-4.5.5/data/haarcascades/haarcascade_frontalface_alt2.xml')
    face = face_detect.detectMultiScale(gray)
    for x,y,w,h in face:
        cv.rectangle(img, (x,y), (x+w, y+h), color=(0,0,255), thickness=2)
    cv.imshow('result', img)

# 读取图片
img = cv.imread('lq.jpg')
# 检测函数
face_detect_demo()
# 等待，按下q退出
while True:
    if ord('q') == cv.waitKey(0):
        break
# 释放内存
cv.destroyAllWindows()

6. 检测多个

# 导入cv模块
import cv2 as cv
# 检测函数
def face_detect_demo():
    gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
    # 加载分类器
    face_detect = cv.CascadeClassifier('../opencv-4.5.5/data/haarcascades/haarcascade_frontalface_default.xml')
    face = face_detect.detectMultiScale(gray)
    for x,y,w,h in face:
        cv.rectangle(img, (x,y), (x+w, y+h), color=(0,0,255), thickness=2)
    cv.imshow('result', img)

# 读取图片
img = cv.imread('multi_face.jpeg')
# 检测函数
face_detect_demo()
# 等待，按下q退出
while True:
    if ord('q') == cv.waitKey(0):
        break
# 释放内存
cv.destroyAllWindows()

7. 视频检测

# 导入cv模块
import cv2 as cv
# 检测函数
def face_detect_demo(img):
    gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
    # 加载分类器
    face_detect = cv.CascadeClassifier('../opencv-4.5.5/data/haarcascades/haarcascade_frontalface_default.xml')
    face = face_detect.detectMultiScale(gray)
    for x,y,w,h in face:
        cv.rectangle(img, (x,y), (x+w, y+h), color=(0,0,255), thickness=2)
    cv.imshow('result', img)

# 读取摄像头
cap = cv.VideoCapture(0)
# 循环，等待，按下q退出
while True:
    flag, frame = cap.read()
    if not flag:
        break
    face_detect_demo(frame)
    if ord('q') == cv.waitKey(0):
        break

# 释放内存
cv.destroyAllWindows()
# 释放摄像头
cap.release()

8. 拍照保存（信息录入）

# 导入cv模块
import cv2 as cv
# 检测函数
def face_detect_demo(img):
    gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
    # 加载分类器
    face_detect = cv.CascadeClassifier('../opencv-4.5.5/data/haarcascades/haarcascade_frontalface_default.xml')
    face = face_detect.detectMultiScale(gray)
    for x,y,w,h in face:
        cv.rectangle(img, (x,y), (x+w, y+h), color=(0,0,255), thickness=2)
    cv.imshow('result', img)

# 读取摄像头
cap = cv.VideoCapture(0)
# 循环，等待，按下q退出
while True:
    flag, frame = cap.read()
    if not flag:
        break
    face_detect_demo(frame)
    if ord('q') == cv.waitKey(0):
        break

# 释放内存
cv.destroyAllWindows()
# 释放摄像头
cap.release()

9. 数据训练

import os
import cv2
from PIL import Image
import numpy as np

def getImageAndLabels(path):
    # 储存人脸数据
    facesSamples = []
    # 储存姓名数据
    ids = []
    # 储存图片信息
    imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
    # 加载分类器
    face_detector = cv2.CascadeClassifier('../opencv-4.5.5/data/haarcascades/haarcascade_frontalface_alt2.xml')
    # 遍历列表中的图片
    for imagePath in imagePaths:
        # 打开图片，灰度化 PIL 有九种不同模式 1（黑白）,L（灰度）,P,RGB,RGBA,CMYK,YCbCr,I,F
        PIL_img = Image.open(imagePath).convert('L')
        # 将图像转换为数组，以黑白深浅
        img_numpy = np.array(PIL_img, 'uint8')
        # 获取图片人脸特征
        faces = face_detector.detectMultiScale(img_numpy)
        # 获取每张图片的id和姓名
        id = int(os.path.split(imagePath)[1].split('.')[0])
        # 预防无面容照片
        for x,y,w,h in faces:
            ids.append(id)
            facesSamples.append(img_numpy[y:y+h,x:x+w])
    # 打印脸部特征和id
    print('id:', id)
    print('fs:', facesSamples)
    return facesSamples, ids


if __name__ == '__main__':
    # 图片路径
    path='../data/'
    # 获取图像数组和id标签数组和姓名
    faces, ids = getImageAndLabels(path)
    # 加载识别器
    recognizer = cv2.face.LBPHFaceRecognizer_create()
    # 训练
    recognizer.train(faces,np.array(ids))
    # 保存文件
    recognizer.write('trainer.yml')

10. 人脸识别

import cv2
import numpy as np
import os
# coding=utf-8
import urllib
import urllib.request
import hashlib

#加载训练数据集文件
recogizer=cv2.face.LBPHFaceRecognizer_create()
recogizer.read('trainer.yml')
names=[]
warningtime = 0

def md5(str):
    import hashlib
    m = hashlib.md5()
    m.update(str.encode("utf8"))
    return m.hexdigest()

statusStr = {
    '0': '短信发送成功',
    '-1': '参数不全',
    '-2': '服务器空间不支持,请确认支持curl或者fsocket,联系您的空间商解决或者更换空间',
    '30': '密码错误',
    '40': '账号不存在',
    '41': '余额不足',
    '42': '账户已过期',
    '43': 'IP地址限制',
    '50': '内容含有敏感词'
}


def warning():
    smsapi = "http://api.smsbao.com/"
    # 短信平台账号
    user = '13******10'
    # 短信平台密码
    password = md5('*******')
    # 要发送的短信内容
    content = '【报警】\n原因：检测到未知人员\n地点：xxx'
    # 要发送短信的手机号码
    phone = '*******'

    data = urllib.parse.urlencode({'u': user, 'p': password, 'm': phone, 'c': content})
    send_url = smsapi + 'sms?' + data
    response = urllib.request.urlopen(send_url)
    the_page = response.read().decode('utf-8')
    print(statusStr[the_page])

#准备识别的图片
def face_detect_demo(img):
    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#转换为灰度
    face_detector=cv2.CascadeClassifier('../opencv-4.5.5/data/haarcascades/haarcascade_frontalface_alt2.xml')
    face=face_detector.detectMultiScale(gray,1.1,5,cv2.CASCADE_SCALE_IMAGE,(100,100),(300,300))
    #face=face_detector.detectMultiScale(gray)
    for x,y,w,h in face:
        cv2.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2)
        cv2.circle(img,center=(x+w//2,y+h//2),radius=w//2,color=(0,255,0),thickness=1)
        # 人脸识别
        ids, confidence = recogizer.predict(gray[y:y + h, x:x + w])
        #print('标签id:',ids,'置信评分：', confidence)
        if confidence > 80:
            global warningtime
            warningtime += 1
            if warningtime > 100:
               warning()
               warningtime = 0
            cv2.putText(img, 'unknown', (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
        else:
            cv2.putText(img,str(names[ids-1]), (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
    cv2.imshow('result',img)
    #print('bug:',ids)

def name():
    path = '../data/'
    #names = []
    imagePaths=[os.path.join(path,f) for f in os.listdir(path)]
    for imagePath in imagePaths:
       name = str(os.path.split(imagePath)[1].split('.',2)[1])
       names.append(name)


cap=cv2.VideoCapture(0)
name()
while True:
    flag,frame=cap.read()
    if not flag:
        break
    face_detect_demo(frame)
    if ord(' ') == cv2.waitKey(10):
        break
cv2.destroyAllWindows()
cap.release()
#print(names)