python人脸识别

# 导入模块
import cv2 as cv
# 读取图片
img=cv.imread('E:/girl.jpg') # 路径中不能有中文，否则加载图片失败
# 将图片缩小至原来的1/2
height, width = img.shape[:2]
reSize = cv.resize(img, (int(width/3), int(height/3)), interpolation=cv.INTER_CUBIC)
# 显示图片
cv.imshow('read_reSize',reSize)
# 等待键盘输入 单位毫秒  传入0 则就是无限等待
cv.waitKey(0)
# 释放内存  由于OpenCV底层是C++编写的
cv.destroyAllWindows()

import cv2 as cv

img=cv.imread('E:/girl.jpg')
cv.imshow('BGR_img',img)

# 将图片缩小至原来的1/2
height, width = img.shape[:2]
reSize = cv.resize(img, (int(width/3), int(height/3)), interpolation=cv.INTER_CUBIC)

#将图片灰度转换
gray_img=cv.cvtColor(reSize,cv.COLOR_BGR2GRAY)
cv.imshow('gray_img',gray_img)

#保存图片
cv.imwrite('gray_girl.jpg',gray_img)
cv.waitKey(0)
cv.destroyAllWindows()

import cv2 as cv

img=cv.imread('E:/girl.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()

import cv2 as cv

img = cv.imread('E:/girl.jpg')
# 左上角的坐标是(x,y) 矩形的宽度和高度(w,h)

# 将图片缩小至原来的1/2
height, width = img.shape[:2]
reSize = cv.resize(img, (int(width/3), int(height/3)), interpolation=cv.INTER_CUBIC)

x, y, w, h = 95, 66, 90, 90
cv.rectangle(reSize, (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 = 188, 138, 88
cv.circle(reSize, center=(x, y),radius=r, color=(0, 0, 255), thickness=2)

# 显示图片
cv.imshow('rectangle_img',reSize)
cv.waitKey(0)
cv.destroyAllWindows()

import cv2 as cv

def face_detect_demo():
    #将图片转换为灰度图片
    gray=cv.cvtColor(reSize,cv.COLOR_BGR2GRAY)
    #加载特征数据
    face_detector=cv.CascadeClassifier(r'E:\software\python3.8.2\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml')
    faces=face_detector.detectMultiScale(gray)
    for x,y,w,h in faces:
        cv.rectangle(reSize,(x,y),(x+w,y+h),color=(0,255,0),thickness=2)
    cv.imshow('result',reSize)

# 加载图片
img=cv.imread('E:/girl.jpg')

# 将图片缩小至原来的1/2
height, width = img.shape[:2]
reSize = cv.resize(img, (int(width / 3), int(height / 3)), interpolation=cv.INTER_CUBIC)

face_detect_demo()
cv.waitKey(0)
cv.destroyAllWindows()

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(r'E:\software\python3.8.2\Lib\site-packages\cv2\data\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('E:/girls.jpeg')

# 调用人脸检测方法
face_detect_demo()
cv.waitKey(0)
cv.destroyAllWindows()

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(
        'E:\software\python3.8.2\Lib\site-packages\cv2\data\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('E:/video.mp4')
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()

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(
        'E:/software/python3.8.2/Lib/site-packages/cv2/data/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 = 'C:/Users/单纯小男子/Downloads/代码/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.yml')

import cv2
import numpy as np
import os

# 加载训练数据集文件
recogizer = cv2.face.LBPHFaceRecognizer_create()
recogizer.read('./trainer.yml')

# 准备识别的图片
img = cv2.imread(r'E:/girl.jpg')
# 将图片缩小至原来的1/2
height, width = img.shape[:2]
reSize = cv2.resize(img, (int(width / 3), int(height / 3)), interpolation=cv2.INTER_CUBIC)

gray = cv2.cvtColor(reSize, cv2.COLOR_BGR2GRAY)
face_detector = cv2.CascadeClassifier(
    r'E:\software\python3.8.2\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml')
faces = face_detector.detectMultiScale(gray)
for x, y, w, h in faces:
    cv2.rectangle(reSize, (x, y), (x+w, y+h), (0, 255, 0), 2)
    # 人脸识别
    id, confidence=recogizer.predict(gray[y:y+h, x:x+w])
    print('标签id:', id,'置信评分：', confidence)

cv2.imshow('result', reSize)
cv2.waitKey(0)
cv2.destroyAllWindows()