人脸识别三部曲

首先看目录结构
图像信息采集采集图片.py
模型训练训练模型.py
人脸识别人脸识别.py
- 效果

首先看目录结构

引用文121本

opencv
│   采集图片.py  
│    训练模型.py
│   人脸识别.py
│
└───trainer
│   │   trainer.yml
│   
└───data
│   └───00_Wang
│       │   0_00001.jpg
│       │   0_00002.jpg
│       │   ...
│       
│   └───01_Liu
│       │   1_00001.jpg
│       │   1_00001.jpg
│       │   ...
│

图像信息采集采集图片.py

开始运行时，输入待录入的人脸姓名。按下s键后，开始录入人脸图像，录入两百张后，结束程序。

import cv2
import os
"采集数据"def face_collecting(path):Num = 20  # 采集两百张图片file_num = len(os.listdir(path))name = input('input name:\n')name_dir = os.path.join(path, str(file_num).zfill(2) + "_" + name)os.makedirs(name_dir)print("按下s键开始录入人脸信息！！！")cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)face_detector = cv2.CascadeClassifier('haarcascade_frontalface_alt2.xml')count = 0break1 = 0while cap.isOpened():ret, frame = cap.read()if ret is True:gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)faces = face_detector.detectMultiScale(gray, 1.3, 5)for (x, y, w, h) in faces:cv2.rectangle(frame, (x, y), (x + w, y + w), (255, 0, 0))k = cv2.waitKey(1) & 0xFF  # 按键判断if k == ord('s') and count < Num:  # 保存count += 1cv2.imwrite(name_dir + "/" + str(file_num) + "_" + str(count).zfill(5) + ".jpg", gray[y:y+h,x:x+h])print("success to save  " + str(file_num) + "_" + str(count).zfill(5) + ".jpg")elif count >= Num or k == ord(' '):  # 200张照片break1 = 1breakif break1 :breakcv2.imshow('image', frame)else:breakcap.release()cv2.destroyAllWindows()if __name__ == '__main__':path = "./data/"face_collecting(path)print('PyCharm')

模型训练训练模型.py

import os
import cv2
import numpy as np
from PIL import Image
" 训练模型.py "
path = "./data/"
recognizer = cv2.face.LBPHFaceRecognizer_create()
detector = cv2.CascadeClassifier('haarcascade_frontalface_alt2.xml')def get_images_and_labels(path):image_paths = []name_dirs = [os.path.join(path, f) for f in os.listdir(path)]for i in range(0, len(name_dirs) ):print("name_dirs[{0}] : ".format(i) , name_dirs[i])image_paths += [os.path.join(name_dirs[i], f) for f in os.listdir(name_dirs[i])]face_samples = []ids = []for image_path in image_paths:img = Image.open(image_path).convert('L')img_np = np.array(img, 'uint8')if os.path.split(image_path)[-1].split(".")[-1] != 'jpg':continueid = int((os.path.split(image_path)[-1].split(".")[0])[0])faces = detector.detectMultiScale(img_np)for (x, y, w, h) in faces:face_samples.append(img_np[y:y + h, x:x + w])ids.append(id)return face_samples, idsfaces, ids = get_images_and_labels(path)
recognizer.train(faces, np.array(ids))
recognizer.save('trainer/trainer.yml')

人脸识别人脸识别.py

import cv2
import os
"人脸识别.py "
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt2.xml')
font = cv2.FONT_HERSHEY_SIMPLEX
idnum = 0cam = cv2.VideoCapture(0, cv2.CAP_DSHOW)
cam.set(6, cv2.VideoWriter.fourcc('M', 'J', 'P', 'G'))
minW = 0.1 * cam.get(3)
minH = 0.1 * cam.get(4)path = "./data/"
names = []
for name in os.listdir(path):names.append(name.split("_")[1])print(names)while True:ret, img = cam.read()gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)faces = face_cascade.detectMultiScale(gray,scaleFactor=1.2,minNeighbors=5,minSize=(int(minW), int(minH)))for (x, y, w, h) in faces:cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)idnum, confidence = recognizer.predict(gray[y:y + h, x:x + w])if confidence < 80:idum = names[idnum-1]confidence = "{0}%".format(round(100 - confidence))else:idum = "unknown"confidence = "{0}%".format(round(100 - confidence))cv2.putText(img, str(idum), (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(1) & 0xFF  # 按键判断if k == ord(' '):  # 退出breakcam.release()
cv2.destroyAllWindows()