OpenCv 基础函数
# 读取图片
image = cv2.imread("test01.jpg")
# 转灰度
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 修改尺寸
resize_image = cv2.resize(image,(300,400))
# 绘制矩形
cv2.rectangle(image, (x , y),(x + width, y + height),(0,255,0),2)
# 显示图片
cv2.imshow("title",image)
# 关闭窗口
cv2.waitKey(0)
cv2.destroyAllWindows()
识别流程
依赖库
import cv2
import os
from PIL import Image
import numpy as np
录入脸部特征信息
# 选择分类器
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
# 使用默认摄像头，参数为0
video_capture = cv2.VideoCapture(0)
# 输入人脸录入者的姓名
person_name = input("请输入人脸录入者的姓名: ")
# 初始化样本计数器
sample_count = 0
while True:
# 读取帧数
ret, frame = video_capture.read()
# 转灰度
gray_image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 提取特征
faces = face_cascade.detectMultiScale(gray_image, 1.1, 5)
for (x, y, w, h) in faces:
# 绘制矩形
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
# 将人脸样本保存到本地文件
sample_count += 1
# 提取人脸ROI
face_roi = gray_image[y:y + h, x:x + w]
# 创建储存样本的文件夹
person_fodler = f'./face_roi/{person_name}/'
if not os.path.exists(person_fodler):
os.mkdir(person_fodler)
# 样本图片命名
sample_path = person_fodler + f'{person_name}_{sample_count}.jpg'
# 储存到文件夹
cv2.imwrite(sample_path, face_roi)
print(f"保存样本: {sample_path}")
cv2.imshow("Video", frame)
# 记录数据特征样本数量 10张测试
if cv2.waitKey(1) & 0xFF == ord('q') or sample_count >= 10:
break
video_capture.release()
cv2.destroyAllWindows()
分类器训练数据
# 提取样本的 人脸数据集和标签
def get_ImgaesLabels(path):
facesSamplies = [] # 储存人脸数据
ids = [] # 储存标签数据
imagesPath = [os.path.join(path,f) for f in os.listdir(path)] # 储存每个图片的相对路径
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') # 加载人脸分类器
# 遍历列表中的图片
for Path in imagesPath:
pil_image = Image.open(Path).convert('L') # 转成灰度
gray_np_image = np.array(pil_image) # 灰度特征数组
# 获取人脸特征
faces = face_cascade.detectMultiScale(gray_np_image,1.1,5)
id = int(Path.split('_')[-1].split('.')[0]) # 获取到每张图片的ID
for x,y,w,h in faces:
ids.append(id) # 添加ID
facesSamplies.append(gray_np_image[y:y+h,x:x+w]) # 添加ROI特征
return facesSamplies,ids
person_fodler = './face_roi/Wh/'
# 加载人脸数据集和标签
faces, ids = get_ImgaesLabels(person_fodler)
# 创建LBPH人脸识别器
recognizer = cv2.face.LBPHFaceRecognizer_create()
# 训练人脸识别器
recognizer.train(faces, np.array(ids))
# 保存训练好的模型
recognizer.save("face_recognizer_model.yml")
识别人脸
# 加载已经训练好的LBPH人脸识别器模型
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read("face_recognizer_model.yml")
# 加载人脸级联分类器（用于检测人脸）
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
# 打开摄像头
cap = cv2.VideoCapture(0)
while True:
# 读取每帧
bool,frame = cap.read()
# 灰度
gray_image = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
# 通过分类器加载人脸的特征
faces = face_cascade.detectMultiScale(gray_image,1.1,5)
for (x,y,w,h) in faces:
# 绘制人脸矩形框
cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)
# 根据识别器识别人脸 confidence为预测值（0~100）
label, confidence = recognizer.predict(gray_image[y:y+h, x:x+w])
# 获取识别结果并显示
if confidence < 100:
text = "WH"
else:
text = "Unknown"
# 加入文本
cv2.putText(frame, text, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
# 显示结果帧
cv2.imshow("Face Recognition", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 释放摄像头并关闭窗口
cap.release()
cv2.destroyAllWindows()
完整流程
录入人脸特征（储存训练后的数据）
import cv2
import numpy as np
# 输入人脸信息
person_name = input("请输入姓名：")
# 加载人脸级联分类器（用于检测人脸）
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
# 打开摄像头
cap = cv2.VideoCapture(0)
# 创建LBPH人脸识别器
recognizer = cv2.face.LBPHFaceRecognizer_create()
# 判断是否存在特征信息,有的话就加载特征
yml_name = f"face_{ person_name }_model.yml"
try:
recognizer.read(yml_name)
except:
pass
# 初始化人脸数据和标签
faces = []
lable_ids = []
count = 0
while True:
count = count + 1
# 读取摄像头帧
ret, frame = cap.read()
# 将彩色帧转换为灰度图像
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 检测人脸
faces_rect = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5,)
for (x, y, w, h) in faces_rect:
# 绘制人脸矩形框
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
# 将人脸数据和标签添加到列表中
faces.append(gray[y:y+h, x:x+w])
lable_ids.append(count)
# 显示结果帧
cv2.imshow("Face Capture", frame)
# 按下'q'键退出循环
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 训练人脸识别器
recognizer.train(faces, np.array(lable_ids))
print("yml_name",yml_name)
# 保存训练好的模型
recognizer.save(yml_name)
# 释放摄像头并关闭窗口
cap.release()
cv2.destroyAllWindows()
识别人脸
# 加载已经训练好的LBPH人脸识别器模型
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read("face_xxxxx_model.yml")
# 加载人脸级联分类器（用于检测人脸）
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
# 打开摄像头
cap = cv2.VideoCapture(0)
while True:
bool,frame = cap.read()
# 灰度
gray_image = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
# 通过分类器加载人脸的特征
faces = face_cascade.detectMultiScale(gray_image,1.1,5)
for (x,y,w,h) in faces:
# 绘制人脸矩形框
cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)
# 根据识别器识别人脸 confidence:预测结果的可信度(0~100)
label, confidence = recognizer.predict(gray_image[y:y+h, x:x+w])
print("预测值=> ",confidence)
# 获取识别结果并显示
if confidence < 100:
text = "WH"
else:
text = "Unknown"
# 写入文本
cv2.putText(frame, text, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
# 显示结果帧
cv2.imshow("Face Recognition", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 释放摄像头并关闭窗口
cap.release()
cv2.destroyAllWindows()