opencv人脸识别实战1：初始化及录入人脸（PyCharm实现）_config.txt人脸识别

 一、初始化

开始前先导入必要的库，在conda中用 pip install package 指令安装。

import cv2
import numpy as np
import os
import shutil
import threading
import tkinter as tk
from PIL import Image, ImageTk

注意在运行时可能会出现AttributeError: module ‘cv2‘ has no attribute ‘face‘的报错，需要用 

pip install opencv-python

安装一个 opencv-python的包，如果此时还是无法运行，需要重新安装这两个包。

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

接下来用字典对存储关系键值对，再记录录入共计的人脸。

id_dict = {}  
Total_face_num = 999 

将config文件的信息存入字典中

def init(): 
    f = open('config.txt')
    global Total_face_num
    Total_face_num = int(f.readline())
 
    for i in range(int(Total_face_num)):
        line = f.readline()
        id_name = line.split(' ')
        id_dict[int(id_name[0])] = id_name[1]
    f.close()

加载检测器Haar和识别方法LBPH，接下来打开摄像头，并读取照片

face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
recognizer = cv2.face.LBPHFaceRecognizer_create()
 
 
camera = cv2.VideoCapture(0)  
success, img = camera.read()  
W_size = 0.1 * camera.get(3)
H_size = 0.1 * camera.get(4)
 
system_state_lock = 0  # 相当于mutex锁，用于线程同步

二、录入人脸

def Get_new_face():
    filepath = "data"
    if not os.path.exists(filepath):
        os.mkdir(filepath)
    else:
        shutil.rmtree(filepath)
        os.mkdir(filepath)
 
    sample_num = 0  
    while True:  
        global success
        global img  
        success, img = camera.read()
 
        # 转为灰度图片
        if success is True:
            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        else:
            break
 
        faces = face_cascade.detectMultiScale(gray, 1.3, 5)
        for (x, y, w, h) in faces:
            cv2.rectangle(img, (x, y), (x + w, y + w), (255, 0, 0))
            sample_num += 1
            T = Total_face_num
            cv2.imwrite("./data/User." + str(T) + '.' + str(sample_num) + '.jpg', gray[y:y + h, x:x + w])
 
        pictur_num = 1000  # 表示摄像头拍摄取样的数量,越多效果越好，但获取以及训练的越慢
 
        cv2.waitKey(1)
        if sample_num > pictur_num:
            break
        else:  
            l = int(sample_num / pictur_num * 50)
            r = int((pictur_num - sample_num) / pictur_num * 50)
            print("\r" + "%{:.1f}".format(sample_num / pictur_num * 100) + "=" * l + "->" + "_" * r, end="")
            var.set("%{:.1f}".format(sample_num / pictur_num * 100))  # 控件可视化进度信息
            window.update()  

创建一个函数，用于从数据集文件夹中获取训练图片并获取id

def get_images_and_labels(path):
    image_paths = [os.path.join(path, f) for f in os.listdir(path)]
    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':
            continue
        id = int(os.path.split(image_path)[-1].split(".")[1]) # 为了获取id，将图片和路径分裂并获取
 
        # 调用熟悉的人脸分类器
        detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
 
        faces = detector.detectMultiScale(img_np)
 
        # 将获取的图片和id添加到list中
        for (x, y, w, h) in faces:
            face_samples.append(img_np[y:y + h, x:x + w])
            ids.append(id)
    return face_samples, ids