刚开的一个小板块——AI智能，算是一个学习AI的过程记录吧，小白白白…白的不能再白了，人家叫人工智能，我这人工智障….
在网上看到个小项目，用Python写的一个人脸识别系统，基于OpenCV实现的，现在来学习下….完整项目联系本人QQ要，或者留言邮箱即可….
环境
数据库选一个就行了，模块缺啥就在上面安装什么即可
Python版本我用的3.7
下面这些模块是我暂时用到的，其他的遇到报错就直接添加安装即可
1.使用pycharm的在解释器插件搜索模块安装就行了，可视化操作
2.或者使用命令安装 pip install 模块名 （需要注意的地方是pip的版本要高，低版本可能安装不上）
加速使用国内镜像源 -i https://pypi.tuna.tsinghua.edu.cn/simple
import datetime import os import shutil import time import cv2 import sys import numpy as np import pymysql import pyttsx3 from PIL import Image
1.读取图片
#导入模块
import cv2 as cv
#读取图片
img=cv.imread('lena.jpg') #路径中不能有中文，否则加载图片失败
#显示图片
cv.imshow('read_img',img)
#等待键盘输入 单位毫秒 传入0 则就是无限等待
cv.waitKey(3000)
#释放内存 由于OpenCV底层是C++编写的
cv.destroyAllWindows()
2.将图片灰度转换
import cv2 as cv
img=cv.imread('lena.jpg')
cv.imshow('BGR_img',img)
#将图片灰度转换
gray_img=cv.cvtColor(img,cv.COLOR_BGR2GRAY)
cv.imshow('gray_img',gray_img)
#保存图片
cv.imwrite('gray_lena.jpg',gray_img)
cv.waitKey(0)
cv.destroyAllWindows()
3.修改图片的尺寸
import cv2 as cv
img=cv.imread('lena.jpg')
cv.imshow('img',img)
print('原来图片的形状',img.shape)
# resize_img=cv.resize(img,dsize=(200,240))
resize_img=cv.resize(img,dsize=(600,560))
print('修改后图片的形状：',resize_img.shape)
cv.imshow('resize_img',resize_img)
# cv.waitKey(0)
#只有输入q时候，退出
while True:
if ord('q')==cv.waitKey(0):
break
cv.destroyAllWindows()
4.绘制矩形
import cv2 as cv
img=cv.imread('lena.jpg')
#左上角的坐标是(x,y) 矩形的宽度和高度(w,h)
x,y,w,h=100,100,100,100
cv.rectangle(img,(x,y,x+w,y+h),color=(0,255,255),thickness=3) #BGR
#绘制圆center元组指圆点的坐标 radius：半径
x,y,r=200,200,100
cv.circle(img,center=(x,y),radius=r,color=(0,0,255),thickness=2)
#显示图片
cv.imshow('rectangle_img',img)
cv.waitKey(0)
cv.destroyAllWindows()
5.人脸检测
import cv2 as cv
def face_detect_demo():
#将图片转换为灰度图片
gray=cv.cvtColor(img,cv.COLOR_BGR2GRAY)
#加载特征数据
face_detector=cv.CascadeClassifier('./haarcascade_frontalface_default.xml')
#haarcascade_frontalface_default.xml OpenCV下载之后data/haarcascades目录下
faces=face_detector.detectMultiScale(gray)
for x,y,w,h in faces:
cv.rectangle(img,(x,y),(x+w,y+h),color=(0,255,0),thickness=2)
cv.imshow('result',img)
#加载图片
img=cv.imread('lena.jpg')
face_detect_demo()
cv.waitKey(0)
cv.destroyAllWindows()
6.检测多张人脸
import cv2 as cv
def face_detect_demo():
#将图片灰度
gray=cv.cvtColor(img,cv.COLOR_BGR2GRAY)
#加载特征数据
face_detector = cv.CascadeClassifier('./haarcascade_frontalface_default.xml')
faces = face_detector.detectMultiScale(gray)
for x,y,w,h in faces:
print(x,y,w,h)
cv.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2)
cv.circle(img,center=(x+w//2,y+h//2),radius=w//2,color=(0,255,0),thickness=2)
#显示图片
cv.imshow('result',img)
#加载图片
img=cv.imread('face3.jpg')
#调用人脸检测方法
face_detect_demo()
cv.waitKey(0)
cv.destroyAllWindows()
7.检测视频中的人脸
import cv2 as cv
def face_detect_demo(img):
#将图片灰度
gray=cv.cvtColor(img,cv.COLOR_BGR2GRAY)
#加载特征数据
face_detector = cv.CascadeClassifier('./haarcascade_frontalface_default.xml')
faces = face_detector.detectMultiScale(gray)
for x,y,w,h in faces:
cv.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2)
cv.circle(img,center=(x+w//2,y+h//2),radius=(w//2),color=(0,255,0),thickness=2)
cv.imshow('result',img)
#读取视频
#cap=cv.VideoCapture('video.mp4')
cap=cv.VideoCapture(0)
while True:
flag,frame=cap.read()
print('flag:',flag,'frame.shape:',frame.shape)
if not flag:
break
face_detect_demo(frame)
if ord('q') == cv.waitKey(10):
break
cv.destroyAllWindows()
cap.release()
8.训练数据
import os
import cv2
import sys
from PIL import Image
import numpy as np
def getImageAndLabels(path):
facesSamples=[]
ids=[]
imagePaths=[os.path.join(path,f) for f in os.listdir(path)]
#检测人脸
face_detector = cv2.CascadeClassifier('./haarcascade_frontalface_default.xml')
#遍历列表中的图片
for imagePath in imagePaths:
#打开图片
PIL_img=Image.open(imagePath).convert('L')
#将图像转换为数组
img_numpy=np.array(PIL_img,'uint8')
faces = face_detector.detectMultiScale(img_numpy)
#获取每张图片的id
id=int(os.path.split(imagePath)[1].split('.')[0])
for x,y,w,h in faces:
facesSamples.append(img_numpy[y:y+h,x:x+w])
ids.append(id)
return facesSamples,ids
if __name__ == '__main__':
#图片路径
path='./data/jm/'
#获取图像数组和id标签数组
faces,ids=getImageAndLabels(path)
#获取训练对象
recognizer=cv2.face.LBPHFaceRecognizer_create()
recognizer.train(faces,np.array(ids))
#保存文件
recognizer.write('trainer/trainer.yml')
9.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/trainer.yml')
# recogizer.read('trainer/trainer.yml')
names = []
warningtime = 0
# 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(
# '/Users/huangjw/Downloads/opencv-4.x/data/haarcascades/haarcascade_frontalface_alt2.xml')
# 加载分类器（opencv已经训练好了）
face_detector = cv2.CascadeClassifier(
'./haarcascade_frontalface_default.xml')
face = face_detector.detectMultiScale(gray, 1.1, 5, cv2.CASCADE_SCALE_IMAGE, (100, 100), (500, 500))
# 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 > 70:
global warningtime
warningtime += 1
if warningtime > 100:
# 发送警报
# warning()
print("陌生人")
warningtime = 0
cv2.putText(img, 'unkonw', (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
else:
# print(ids - 1)
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/jm/'
# names = []
imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
print('数据排列：', imagePaths)
for imagePath in imagePaths:
name = str(os.path.split(imagePath)[1].split('.', 2)[1])
names.append(name)
#print(names)
# , cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# 读取视频
# cap = cv2.VideoCapture('1.mp4')
# 读取摄像头
cap = cv2.VideoCapture(0)
name()
print(names)
# names.reverse() # 队列反转
# print(names)
while True:
flag, frame = cap.read()
if not flag:
break
face_detect_demo(frame)
if ord(' ') == cv2.waitKey(10):
break
cv2.destroyAllWindows()
cap.release()
10.录入人脸数据
import cv2
#摄像头
cap=cv2.VideoCapture(0)
falg = 1
num = 1
while(cap.isOpened()):
ret_flag,Vshow = cap.read()
cv2.imshow("Capture_Test",Vshow)
k = cv2.waitKey(1) & 0xFF
if k == ord('s'):
cv2.imwrite("./data/jm/"+str(num)+".lixian"+".jpg",Vshow)
print("success to save"+str(num)+".jpg")
print("--------------------")
num +=1
elif k == ord(' '):
break
#释放摄像头
cap.release()
#释放内存
cv2.destroyAllWindows()
调用网络视频
import cv2
class CaptureVideo(object):
def net_video(self):
# 获取网络视频流
cam = cv2.VideoCapture("rtmp://58.200.131.2:1935/livetv/cctv2")
while cam.isOpened():
sucess, frame = cam.read()
cv2.imshow("Network", frame)
cv2.waitKey(1)
if __name__ == "__main__":
capture_video = CaptureVideo()
capture_video.net_video()
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转载请注明原文链接：Python基于opencv实现AI人脸识别（一）Opencv基础模块搭建和demo例子【显哥出品，必为精品】
转载请注明原文链接：Python基于opencv实现AI人脸识别（一）Opencv基础模块搭建和demo例子【显哥出品，必为精品】
