总计分为三个步骤
一、捕获人脸照片
二、对捕获的照片进行训练
三、加载训练的数据，识别
使用python3.6.8，opencv，numpy，pil
第一步：通过笔记本前置摄像头捕获脸部图片
将捕获的照片存在picData文件夹中，并格式为user.id.num.jpg。id在识别时和人名数组一一对应。
import numpy as np
import cv2 cap = cv2.VideoCapture(0)
face_cascade = cv2.CascadeClassifier("data/haarcascade_frontalface_default.xml")
sampleNum = 0
Id = input('请输入id：') while True:
ret, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
# 增加例子数
sampleNum = sampleNum + 1 # 把照片保存到数据集文件夹
cv2.imwrite(
"picData/user." + str(Id) + "." + str(sampleNum) + ".jpg",
gray[y : y + h, x : x + w],
)
cv2.imshow("img", img)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
if sampleNum == 3000:
break cap.release()
cv2.destroyAllWindows()
第二步：训练数据
将训练好的数据存储在data/trainner.yml中
import cv2
import os
import numpy as np
from PIL import Image recognizer = cv2.face.LBPHFaceRecognizer_create()
detector = cv2.CascadeClassifier("data/haarcascade_frontalface_default.xml") 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:
image = Image.open(image_path).convert("L")
image_np = np.array(image, "uint8")
if os.path.split(image_path)[-1].split(".")[-1] != "jpg":
continue
image_id = int(os.path.split(image_path)[-1].split(".")[1])
faces = detector.detectMultiScale(image_np)
for (x, y, w, h) in faces:
face_samples.append(image_np[y : y + h, x : x + w])
ids.append(image_id) return face_samples, ids faces, Ids = get_images_and_labels("picData")
recognizer.train(faces, np.array(Ids))
recognizer.save("data/trainner.yml")
第三步：人脸识别
加载第二步训练的数据，定义需要识别的人名。
import cv2 recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('data/trainner.yml')
faceCascade = cv2.CascadeClassifier("data/haarcascade_frontalface_default.xml")
font = cv2.FONT_HERSHEY_SIMPLEX idnum = 0 names = ['kAng'] cam = cv2.VideoCapture(0)
minW = 0.1*cam.get(3)
minH = 0.1*cam.get(4) while True:
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = faceCascade.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 >50:
idnum = names[idnum]
confidence = "{0}%".format(round(confidence))
else:
idnum = "unknown"
confidence = "{0}%".format(round(confidence)) cv2.putText(img, str(idnum), (x+5, y-5), font, 1, (0, 0, 255), 1)
cv2.putText(img, str(confidence), (x+5, y+h-5), font, 1, (255, 255, 0), 1) cv2.imshow('camera', img)
k = cv2.waitKey(10)
if k == 27:
break cam.release()
cv2.destroyAllWindows()
效果图：