Python在人臉識別方面功能很強(qiáng)大，程序語言簡單高效，下面小編來編程實(shí)現(xiàn)一下如何實(shí)現(xiàn)人臉識別。如有錯點(diǎn)，還望斧正

識別圖片中的人臉位置

#人臉識別分類器路徑
tool_url = r'C:\Users\86188\AppData\Local\Programs\Python\Python36-32\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml'
#人臉定位函數(shù)
def face_detect(img):
gray = cv.cvtColor(img,cv.COLOR_BGRA2BGR)
face = cv.CascadeClassifier(tool_url)
#將圖片轉(zhuǎn)化為灰度圖
faces = face.detectMultiScale(gray)
#確定人臉部分
for x,y,w,h in faces:
reg = cv.rectangle(img, (x, y),( x + w, y + h), color=(0, 255, 0), thickness=2)
cv.imshow("reg",reg)

根據(jù)訓(xùn)練集訓(xùn)練數(shù)據(jù)并保存(dataTraining.py)

import os
import cv2 as cvimport numpyfrom PIL import Image'''
訓(xùn)練數(shù)據(jù)來自s2和s9
'''
def getImageAndIds(path):
facesSimples = [] ids = [] img_paths = [os.path.join(path,f) for f in os.listdir(path)]
#檢測人臉 tool_url = r'C:\Users\86188\AppData\Local\Programs\Python\Python36-32\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml'
# 調(diào)用插件獲取圖像特征 face = cv.CascadeClassifier(tool_url) for i in img_paths:
# print(i)
img = Image.open(i).convert("L")
# Image._show(img) #將圖片轉(zhuǎn)換為數(shù)組 img_np = numpy.array(img,'uint8')
# print(img_np)
# 獲取圖像的人臉信息和對應(yīng)id faces = face.detectMultiScale(img_np) id = int(i.split(path)[1].split('.')[0])
for x, y, w, h in faces:
facesSimples.append(img_np[y:y+h,x:x+w]) ids.append(id) return facesSimples,ids
if __name__ == "__main__":
#圖片路徑 path = "./data/"
faces,ids = getImageAndIds(path)
#獲取循環(huán)對象 reg = cv.face.LBPHFaceRecognizer_create() reg.train(faces,numpy.array(ids)) #保存文件 reg.write("trainer/trainer.yml")

人臉識別(faceChecking.py)

import cv2 as cv
import os,numpy
#檢測人臉tool_url = r'C:\Users\86188\AppData\Local\Programs\Python\Python36-32\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml'
#圖片數(shù)據(jù)路徑path = './data/'
# 調(diào)用插件獲取圖像特征face = cv.CascadeClassifier(tool_url)#加載訓(xùn)練數(shù)據(jù)reg = cv.face.LBPHFaceRecognizer_create()reg.read("trainer/trainer.yml")
img= cv.imread("3.jpg")
#將圖像的人臉特征圈出來gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)faces = face.detectMultiScale(gray)for x, y, w, h in faces:
# print(x,y,w,h)
cv.rectangle(gray, (x, y), (x + w, y + h), color=(0, 0, 255), thickness=2)
#檢測出識別后的圖片和id id,configence = reg.predict(gray[y:y+h,x:x+w]) #顯示對應(yīng)圖片 print("編號:{},置信度:{}".format(id,configence))
aim_img = cv.imread(os.path.join(path,str(id)+'.jpg'))
#顯示目標(biāo)圖片 cv.imshow("aim_img",aim_img)
cv.imshow("result",gray)
cv.waitKey(0)
cv.destroyAllWindows()

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