人脸识别算法之Fisherface_python实现
有关Fisherface的人脸识别代码网上实现较少，因此，分享一下自己的代码实现
1.从摄像头获取照片
# -*- coding: utf-8 -*-
"""
Created on Wed May 6 11:00:25 2020
@author: Leonardragon
"""
import cv2
# 调用笔记本内置摄像头，所以参数为0，如果有其他的摄像头可以调整参数为1，2
cap = cv2.VideoCapture(0)
face_detector = cv2.CascadeClassifier('F:\\Useful App\opencv-2.4.9\opencv\sources\data\haarcascades\haarcascade_frontalface_default.xml')
face_id = input('\n enter user id:')
print('\n Initializing face capture. Look at the camera and wait ...')
count = 0
while True:
# 从摄像头读取图片
sucess, img = cap.read()
# 转为灰度图片
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 检测人脸
faces = face_detector.detectMultiScale( gray, 1.3 , 5 )
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0))
count += 1
h = cv2.resize(img[y:y+h,x:x+w],(150,150))
# 保存图像
cv2.imwrite("C:\\Users\\Administrator\\Desktop\\Facedata\\User." + str(face_id) + '.' + str(count) + '.jpg', h)
cv2.imshow('image', img)
# 保持画面的持续。
k = cv2.waitKey(1)
if k == 27: # 通过esc键退出摄像
break
elif count >= 50: # 得到30个样本后退出摄像
break
# 关闭摄像头
cap.release()
cv2.destroyAllWindows()
2.图像训练
# -*- coding: utf-8 -*-
"""
Created on Mon May 4 17:22:46 2020
@author: Leonardragon
"""
import numpy as np
from PIL import Image
import os
import cv2
# 人脸数据路径
#path = 'C:\\Users\\Administrator\\.spyder-py3\Spyder_Python\OpenCV\FaceRecognition\Facedata_6'
path = 'C:\\Users\\Administrator\\Desktop\\u'
recognizer = cv2.face.FisherFaceRecognizer_create() # 图像归一化
# detector = cv2.CascadeClassifier("F:\\Useful App\opencv-2.4.9\opencv\sources\data\haarcascades\haarcascade_frontalface_default.xml")
def getImagesAndLabels(path):
imagePaths = [os.path.join(path, f) for f in os.listdir(path)] # join函数的作用？
faceSamples = []
ids = []
for imagePath in imagePaths:
PIL_img = Image.open(imagePath).convert('L') # convert it to grayscale
img_numpy = np.array(PIL_img, 'uint8')
x = os.path.split(imagePath)[-1]
y = os.path.split(imagePath)[-1].split(".")[1]
id = int( os.path.split(imagePath)[-1].split(".")[1] )
ids.append(id)
"""
faces = detector.detectMultiScale(img_numpy)
for (x, y, w, h) in faces:
faceSamples.append(img_numpy[y:y + h, x: x + w])
ids.append(id)
"""
# return faceSamples, ids
return ids
images=[]
for i in range(1,51):
images.append(cv2.imread("C:\\Users\\Administrator\\Desktop\\u\\User.0"+'.'+ str(i) +'.jpg' ,cv2.IMREAD_GRAYSCALE))
images.append(cv2.imread("C:\\Users\\Administrator\\Desktop\\u\\User.1.1.bmp",cv2.IMREAD_GRAYSCALE))
images.append(cv2.imread("C:\\Users\\Administrator\\Desktop\\u\\User.2.1.bmp",cv2.IMREAD_GRAYSCALE))
images.append(cv2.imread("C:\\Users\\Administrator\\Desktop\\u\\User.2.2.bmp",cv2.IMREAD_GRAYSCALE))
images.append(cv2.imread("C:\\Users\\Administrator\\Desktop\\u\\User.2.3.bmp",cv2.IMREAD_GRAYSCALE))
images.append(cv2.imread("C:\\Users\\Administrator\\Desktop\\u\\User.2.4.bmp",cv2.IMREAD_GRAYSCALE))
images.append(cv2.imread("C:\\Users\\Administrator\\Desktop\\u\\User.2.5.bmp",cv2.IMREAD_GRAYSCALE))
images.append(cv2.imread("C:\\Users\\Administrator\\Desktop\\u\\User.3.1.bmp",cv2.IMREAD_GRAYSCALE))
#images.append(cv2.imread("C:\\Users\\Administrator\\Desktop\\u\\User.4.1.jpg",cv2.IMREAD_GRAYSCALE))
for i in range(1,11):
images.append(cv2.imread("C:\\Users\\Administrator\\Desktop\\u\\User.4"+'.'+ str(i) +'.jpg' ,cv2.IMREAD_GRAYSCALE))
print('Training faces. It will take a few seconds. Wait ...')
# faces, ids = getImagesAndLabels(path)
ids = getImagesAndLabels(path)
#recognizer.train(faces, np.array(ids))
recognizer.train(images, np.array(ids))
recognizer.write(r'face_trainer\trainer.yml')
#recognizer.write("C:\\Users\\Administrator\\.spyder-py3\\Spyder_Python\\OpenCV\\FaceRecognition\\face_trainer")
print("{0} faces trained. Exiting Program".format(len(np.unique(ids))))3.人脸识别
# -*- coding: utf-8 -*-
"""
Created on Mon May 4 18:03:00 2020
@author: Leonardragon
"""
import cv2
recognizer = cv2.face.FisherFaceRecognizer_create()
recognizer.read("C:\\Users\\Administrator\\Desktop\\face_trainer\\trainer.yml")
cascadePath = "F:\\Useful App\\opencv-2.4.9\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath)
font = cv2.FONT_HERSHEY_SIMPLEX
idnum = 0
names = [ 'LiMike','Mike','Cindy','Lucy','yaohuiqin']
cam = cv2.VideoCapture(0)
cam.set(3,500) #宽
cam.set(4,500) #高
minW = cam.get(3)
minH = cam.get(4)
minW = 0.1*cam.get(3)
minH = 0.1*cam.get(4)
while True:
ret, img = cam.read() # 从摄像头读取图片
# 转为灰度图片
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.2,
minNeighbors=5,
minSize=(int(minW), int(minH)),
#minSize = (int(100),int(100)),
)
# predict_image=cv2.imread("C:\\Users\\Administrator\\Desktop\\U\\User.0.10.jpg",cv2.IMREAD_GRAYSCALE)
for (x, y, w, h) in faces:
#if w >= 150 & h >= 150 :
u = cv2.rectangle( img, (x, y), (x+w, y+h), (0, 255, 0), 2 ) #酸橙色
g=gray [y:y+ h, x:x+ w]
out = cv2.resize(g, (150,150))
idnum, confidence = recognizer.predict(out)
# idnum, confidence = recognizer.predict(gray[y:y+h,x:x+w])
# idnum, confidence = recognizer.predict(predict_image)
if confidence < 3000 :
print(confidence,names[idnum])
confidence = ((100-0)/(4000-100))*(confidence - 0) + 0 # 投影至【0,100】区间
print(confidence)
#if confidence < 100:
idnum = names[idnum]
confidence = "{0}%".format(round(100 - confidence))
else:
print(confidence,"unknow")
idnum = "unknown"
confidence = "{0}%".format(round(100 - confidence))
cv2.putText(img, str(idnum), (x+5, y-5), font, 1, (0, 0, 255), 1)
cv2.putText(img, str(confidence), (x+5, y+h-5), font, 1, (0, 0, 0), 1)
cv2.imshow('camera', img)
k = cv2.waitKey(10)
if k == 27:
break
cam.release()
cv2.destroyAllWindows()4.识别效果
