ML |使用带 scikit-learn 的 k-NN 实现人脸识别
原文:https://www . geesforgeks . org/ml-机具-人脸识别-使用-k-nn-with-scikit-learn/
k-最近邻居:
k-NN 是机器学习中最基本的分类算法之一。它属于机器学习的监督学习范畴。k-NN 通常用于搜索应用程序，在这些应用程序中，您正在寻找“相似”的项目。我们测量相似性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，像欧几里德距离)来比较向量。
它通常用于数据挖掘、模式识别、推荐系统和入侵检测。
使用的库有:
OpenCV2 熊猫 Numpy Scikit-learn
使用的数据集:
我们使用了haarcascade_frontalface_default.xml数据集，这个数据集在网上很容易找到，你也可以从这个链接下载。
Scikit-learn: Scikit-learn通过 Python 中一致的接口提供了一系列有监督和无监督的学习算法。 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人脸并将其存储为列表格式，第二个第一个用于存储数据。csv '文件格式和第三个一个用来识别人脸。
人脸检测. 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)
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:
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