毫升 |使用带有 scikit-learn 的 k-NN 实现人脸识别

k-最近邻：

k-NN是机器学习中最基本的分类算法之一。属于机器学习的监督学习范畴。 k-NN 通常用于您正在寻找“相似”项目的搜索应用程序中。我们测量相似度的方法是创建项目的向量表示，然后使用适当的距离度量（例如欧几里德距离）比较向量。

它通常用于数据挖掘、模式识别、推荐系统和入侵检测。

Libraries used are:

OpenCV2
Pandas
Numpy
Scikit-learn

使用的数据集：
我们使用了haarcascade_frontalface_default.xml数据集，该数据集很容易在线获得，您也可以从此链接下载。

Scikit-学习：
scikit-learn通过Python中的一致接口提供了一系列有监督和无监督学习算法。
该库基于 SciPy 构建，必须安装在您的设备上才能使用 scikit_learn。

人脸识别 ：
这包括三个Python文件，其中第一个用于检测面部并将其以列表格式存储，第二个用于以“.csv”文件格式存储数据，第三个用于识别面部。

facedetect.py——

# this file is used to detect face 
# and then store the data of the face
import cv2
import numpy as np
  
# import the file where data is
# stored in a csv file format
import npwriter
  
name = input("Enter your name: ")
  
# this is used to access the web-cam
# in order to capture frames
cap = cv2.VideoCapture(0)
  
classifier = cv2.CascadeClassifier("../dataset/haarcascade_frontalface_default.xml")
  
# this is class used to detect the faces as provided
# with a haarcascade_frontalface_default.xml file as data
f_list = []
  
while True:
    ret, frame = cap.read()
      
    # converting the image into gray
    # scale as it is easy for detection
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
      
    # detect multiscale, detects the face and its coordinates
    faces = classifier.detectMultiScale(gray, 1.5, 5)
      
    # this is used to detect the face which
    # is closest to the web-cam on the first position
    faces = sorted(faces, key = lambda x: x[2]*x[3],
                                     reverse = True)
  
    # only the first detected face is used
    faces = faces[:1]  
       
    # len(faces) is the number of
    # faces showing in a frame
    if len(faces) == 1:   
        # this is removing from tuple format      
        face = faces[0]   
        
        # storing the coordinates of the
        # face in different variables
        x, y, w, h = face 
  
        # this is will show the face
        # that is being detected     
        im_face = frame[y:y + h, x:x + w] 
  
        cv2.imshow("face", im_face)
  
  
    if not ret:
        continue
  
    cv2.imshow("full", frame)
  
    key = cv2.waitKey(1)
  
    # this will break the execution of the program
    # on pressing 'q' and will click the frame on pressing 'c'
    if key & 0xFF == ord('q'):
        break
    elif key & 0xFF == ord('c'):
        if len(faces) == 1:
            gray_face = cv2.cvtColor(im_face, cv2.COLOR_BGR2GRAY)
            gray_face = cv2.resize(gray_face, (100, 100))
            print(len(f_list), type(gray_face), gray_face.shape)
  
            # this will append the face's coordinates in f_list
            f_list.append(gray_face.reshape(-1)) 
        else:
            print("face not found")
  
        # this will store the data for detected
        # face 10 times in order to increase accuracy
        if len(f_list) == 10:
            break
  
# declared in npwriter
npwriter.write(name, np.array(f_list)) 
  
  
cap.release()
cv2.destroyAllWindows()

npwriter.py –创建/更新“.csv”：文件

import pandas as pd
import numpy as np
import os.path
  
f_name = "face_data.csv"
  
# storing the data into a csv file
def write(name, data):
  
    if os.path.isfile(f_name):
  
        df = pd.read_csv(f_name, index_col = 0)
  
        latest = pd.DataFrame(data, columns = map(str, range(10000)))
        latest["name"] = name
  
        df = pd.concat((df, latest), ignore_index = True, sort = False)
  
    else:
  
        # Providing range only because the data
        # here is already flattened for when
        # it was store in f_list
        df = pd.DataFrame(data, columns = map(str, range(10000)))
        df["name"] = name
  
    df.to_csv(f_name)

recog.py –人脸识别器

# this one is used to recognize the 
# face after training the model with
# our data stored using knn
import cv2
import numpy as np
import pandas as pd
  
from npwriter import f_name
from sklearn.neighbors import KNeighborsClassifier
  
  
# reading the data
data = pd.read_csv(f_name).values
  
# data partition
X, Y = data[:, 1:-1], data[:, -1]
  
print(X, Y)
  
# Knn function calling with k = 5
model = KNeighborsClassifier(n_neighbors = 5)
  
# fdtraining of model
model.fit(X, Y)
  
cap = cv2.VideoCapture(0)
  
classifier = cv2.CascadeClassifier("../dataset/haarcascade_frontalface_default.xml")
  
f_list = []
  
while True:
  
    ret, frame = cap.read()
  
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
  
    faces = classifier.detectMultiScale(gray, 1.5, 5)
  
    X_test = []
  
    # Testing data
    for face in faces:
        x, y, w, h = face
        im_face = gray[y:y + h, x:x + w]
        im_face = cv2.resize(im_face, (100, 100))
        X_test.append(im_face.reshape(-1))
  
    if len(faces)>0:
        response = model.predict(np.array(X_test))
        # prediction of result using knn
  
        for i, face in enumerate(faces):
            x, y, w, h = face
  
            # drawing a rectangle on the detected face
            cv2.rectangle(frame, (x, y), (x + w, y + h),
                                         (255, 0, 0), 3)
  
            # adding detected/predicted name for the face
            cv2.putText(frame, response[i], (x-50, y-50),
                              cv2.FONT_HERSHEY_DUPLEX, 2,
                                         (0, 255, 0), 3)
     
    cv2.imshow("full", frame)
  
    key = cv2.waitKey(1)
  
    if key & 0xFF == ord("q") :
        break
  
cap.release()
cv2.destroyAllWindows()

输出：