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Python笔记-OpenCV图像处理和人脸识别

一.简介

1.OpenCV:OpenCV的全称是Open Source Computer Vision Library,是一个开源的跨平台的计算机视觉库。可以运行在Linux、Windows、Android和macOS操作系统上,帮助人们快速构建复杂的视觉应用程序。

2.计算机视觉:计算机视觉(Computer Vision)就是利用计算机来处理图像,将来自静止或摄像机的数据转换成新的表示方式,获得我们想要的信息。

二.编程步骤

1.环境配置

2.读取图片

3.灰度转换

4.修改尺寸

5.绘制矩形

6.人脸检测

7.检测多个

8.视频检测

9.拍照保存

10.训练数据

11.人脸识别

三.简单实例

1.环境配置

python下编程,先安装库

pip install opencv-python
pip install opencv-contrib-python

通过pip下载的只是阉割版的,部分功能不全,要实现人脸识别还需在官网Home - OpenCV下载完整的库(官网有点慢,可以多等等)(在人脸检测的部分需要手动添加库的路径,我的是Mac所以路径是/Users/....)(主要用到haarcascade_frontalface_default.xml、haarcascade_frontalface_alt2.xml

2.读取图片

#导入cv模块
import cv2 as cv
#读取图片
img = cv.imread('face1.jpg')#图片路径
#显示图片
cv.imshow('read_img',img)
#等待
cv.waitKey(0)
#释放内存
cv.destroyAllWindows()

3.灰度转换

#导入cv模块
import cv2 as cv
#读取图片
img = cv.imread('face1.jpg')
#灰度转换
gray_img = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
#显示灰度图片
cv.imshow('gray',gray_img)
#保存灰度图片
cv.imwrite('gray_face1.jpg',gray_img)
#显示图片
cv.imshow('read_img',img)
#等待
cv.waitKey(0)
#释放内存
cv.destroyAllWindows()

4.修改尺寸

#导入cv模块
import cv2 as cv
#读取图片
img = cv.imread('face1.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)
#打印修改后的大小
print('修改后:',resize_img.shape)
#等待
while True:
    if ord('q') == cv.waitKey(0):
        break
#释放内存
cv.destroyAllWindows()

5.绘制矩形

#导入cv模块
import cv2 as cv
#读取图片
img = cv.imread('face1.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=5)
#显示
cv.imshow('re_img',img)
while True:
    if ord('q') == cv.waitKey(0):
        break
#释放内存
cv.destroyAllWindows()

6.人脸检测

# 导入cv模块
import cv2 as cv
# 检测函数
def face_detect_demo():
    gary = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
    face_detect = cv.CascadeClassifier('/Users/huangjw/Downloads/opencv-4.x/data/haarcascades/haarcascade_frontalface_alt2.xml')
    face = face_detect.detectMultiScale(gary, 1.01, 5, 0, (100, 100), (300, 300))
    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)

# 读取图像
img = cv.imread('face1.jpg')
#img = cv.imread('/Users/huangjw/Downloads/mycodetest/opencv/data/hjw/2.hjw.jpg')
# 检测函数
face_detect_demo()
# 等待
while True:
    if ord('q') == cv.waitKey(0):
        break
# 释放内存
cv.destroyAllWindows()

7.检测多个(同时检测多个人脸)

# 导入cv模块
import cv2 as cv

# 检测函数
def face_detect_demo():
    gary = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
    face_detect = cv.CascadeClassifier( '/Users/huangjw/Downloads/opencv-4.x/data/haarcascades/haarcascade_frontalface_default.xml')
    face = face_detect.detectMultiScale(gary)
    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)

# 读取图像
img = cv.imread('face2.jpg')
# 检测函数
face_detect_demo()
# 等待
while True:
    if ord('q') == cv.waitKey(0):
        break
# 释放内存
cv.destroyAllWindows()

8.视频检测

# 导入cv模块
import cv2 as cv

# 检测函数
def face_detect_demo(img):
    gary = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
    face_detect = cv.CascadeClassifier(
        '/Users/huangjw/Downloads/opencv-4.x/data/haarcascades/haarcascade_frontalface_default.xml')
    face = face_detect.detectMultiScale(gary)
    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
while True:
    flag, frame = cap.read()
    if not flag:
        break
    face_detect_demo(frame)
    if ord('q') == cv.waitKey(1):
        break
# 释放内存
cv.destroyAllWindows()
# 释放摄像头
cap.release()

9.拍照保存

调用摄像头拍照,按下‘s’键拍照,将照片保存在指定路径,空格键退出程序

# 导入模块
import cv2

# 摄像头
cap = cv2.VideoCapture(0)

#falg = 1
num = 1

while (cap.isOpened()):  # 检测是否在开启状态
    ret_flag, Vshow = cap.read()  # 得到每帧图像
    cv2.imshow("Capture_Test", Vshow)  # 显示图像
    k = cv2.waitKey(1) & 0xFF  # 按键判断
    if k == ord('s'):  # 保存
        cv2.imwrite("/Users/huangjw/Downloads/mycodetest/opencv/data/four/" + str(num) + ".hjc" + ".jpg", Vshow)
        print("success to save" + str(num) + ".jpg")
        num += 1
    elif k == ord(' '):  # 退出
        break
# 释放摄像头
cap.release()
# 释放内
cv2.destroyAllWindows()

10.训练数据

将所需人脸识别的图片训练,生成yml文件,以供人脸识别

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(
        '/Users/huangjw/Downloads/opencv-4.x/data/haarcascades/haarcascade_frontalface_alt2.xml')
    # 打印数组imagePaths
    print('数据排列:', imagePaths)
    # 遍历列表中的图片
    for imagePath in imagePaths:
        # 打开图片,黑白化
        PIL_img = Image.open(imagePath).convert('L')
        # 将图像转换为数组,以黑白深浅
        # PIL_img = cv2.resize(PIL_img, dsize=(400, 400))
        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('fs:', facesSamples)
        print('id:', id)
        # print('fs:', facesSamples[id])
    print('fs:', facesSamples)
    # print('脸部例子:',facesSamples[0])
    # print('身份信息:',ids[0])
    return facesSamples, ids

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

11.人脸识别

通过之前训练好的数据,对所需识别的图像、视频等加以分类(一下程序需要修改训练数据路径、训练图片路径(获取图片名字)、识别图像的方式)

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('/Users/huangjw/Downloads/mycodetest/opencv/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(
        '/Users/huangjw/Downloads/opencv-4.x/data/haarcascades/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 = '/Users/huangjw/Downloads/mycodetest/opencv/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()
# print(names)


本文转载自: https://blog.csdn.net/I_am_a_caiji/article/details/124565770
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