在本文中，我们探索 OpenCV 和 Python 在创建强大的人脸识别系统方面的巨大潜力。从而逐步实现识别图像和视频中的人脸的整个过程。凭借清晰的解释和实际示例，希望您深入了解计算机视觉和人脸识别。
下载 OpenCV 库
访问官网进行下载并安装： https://opencv.org/
(资料图片仅供参考)
pip install opencv-python安装完成之后我们可以在文件夹下看到这些文件sources/data/haarcascades
包含了强大的识别算法，如眼睛识别和面部识别
人脸检测报警系统（可用于：家庭监控、人脸门禁、人脸考勤等）
抓拍人脸功能模块。
首先导入cv库。
import cv2然后调用我们的摄像头（0代表本地摄像头，其他数字代表外置摄像头）。
cap = cv2.VideoCapture(0)使用 while 循环来确定它是否处于打开状态。
while(cap.isOpened()):获取每一帧的图像。
ret_flag,Vshow = cap.read()调用控制键盘函数来控制和确定按键。
k = cv2.waitKey(1) & 0xFF使用 imshow 函数显示捕获的图像。
cv2.imshow("ceshi",Vshow)监听键盘输入，按“s”键保存。
if k == ord("s"):保存捕获图像的格式并打印提示文本。
cv2.imwrite("/tmp/"+str(num)+".name"+".jpg",Vshow) print("save "+str(num)+".jpg") print("-------------------------") num += 1释放摄像头和内存。
cap.release()cv2.destroyAllWindows()完整的代码片段
import cv2cap = cv2.VideoCapture(0) falg = 1num = 1 while(cap.isOpened()): ret_flag,Vshow = cap.read() k = cv2.waitKey(1) & 0xFF cv2.imshow("ceshi",Vshow) if k == ord("s"): cv2.imwrite("/tmp/"+str(num)+".name"+".jpg",Vshow) print("save "+str(num)+".jpg") print("-------------------------") num += 1 elif k == ord(" "): break cap.release()cv2.destroyAllWindows()录人脸功能模块
在编写这个功能模块之前，我们需要在Pycharm或者cmd中在这个文件的根目录下使用如下命令安装人脸模块
pip install opencv-contrib-python导入依赖
import osimport cv2from PIL import Imageimport numpy as np存储人脸数据
facesSamples=[]存储名称数据
ids=[]存储图片的信息
imagePaths=[os.path.join(path,f) for f in os.listdir(path)]加载分类器
face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")遍历图像
for imagePath in imagePaths:将打开的图像转换为灰度
PIL_img = Image.open(imagePath).convert("L")将图像转换为数组
img_numpy = np.array(PIL_img,"uint8")获取图片中的人脸特征
faces = face_detector.detectMultiScale(img_numpy)获取每张抓拍图片的id和name
id = int(os.path.split(imagePath)[1].split(".")[0])防止无脸拍照
for x,y,w,h in faces: ids.append(id) facesSamples.append(img_numpy[y:y+h,x:x+w])打印面部特征和ID，并返回数据
print("id",id) print("fs:",facesSamples) return facesSamples,ids调用图片路径（步骤1抓脸后按s键保存到"/tmp/"路径下的图片）
path = "/tmp/"检索图像数组和 ID 标签数组和名称
faces,ids=getImageAndLabels(path)加载识别器
recognizer = cv2.face.LBPHFaceRecognizer_create()训练数据
recognizer.train(faces,np.array(ids))创建一个目录存放读取到的人脸信息
recognizer.write("tmp/tupian.yml")完整的代码片段
import osimport cv2from PIL import Imageimport numpy as np def getImageAndLabels(path): facesSamples=[] ids=[] imagePaths=[os.path.join(path,f) for f in os.listdir(path)] face_detector = cv2.CascadeClassifier("<open-cv-installed-path>/sources/data/haarcascades/haarcascade_frontalface_default.xml") for imagePath in imagePaths: PIL_img = Image.open(imagePath).convert("L") img_numpy = np.array(PIL_img,"uint8") faces = face_detector.detectMultiScale(img_numpy) id = int(os.path.split(imagePath)[1].split(".")[0]) for x,y,w,h in faces: ids.append(id) facesSamples.append(img_numpy[y:y+h,x:x+w]) print("id",id) print("fs:",facesSamples) return facesSamples,ids if __name__ == "__main__": path = "/tmp/" faces,ids=getImageAndLabels(path) recognizer = cv2.face.LBPHFaceRecognizer_create() recognizer.train(faces,np.array(ids)) recognizer.write("tmp/tupian.yml")人脸识别功能模块
导入依赖
import cv2import os加载训练好的数据文件
recogizer = cv2.face.LBPHFaceRecognizer_create()加载保存的面部信息
recogizer.read("tmp/tupian.yml")定义名称数组
names=[]定义用于识别的全局变量
warningtime = 0视频模块中的人脸识别
def face_detect_demo(img): gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) face_detector=cv2.CascadeClassifier("<open-cv-installed-path>/sources/data/haarcascades/haarcascade_frontalface_default.xml") face=face_detector.detectMultiScale(gray) for x,y,w,h in face: cv2.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2) cv2.circle(img,center=(x+w//2,y+h//2),radius=w//2,color=(0,255,0),thickness=1) ids, confidence = recogizer.predict(gray[y:y + h, x:x + w]) if confidence > 80: global warningtime warningtime += 1 if warningtime > 100: # warning() warningtime = 0 cv2.putText(img, "unknown", (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1) else: cv2.putText(img,str(names[ids-1]), (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1) cv2.imshow("result",img)导入存储的图像名称标签
def name(): path = "/tmp/" # names = [] imagePaths=[os.path.join(path,f) for f in os.listdir(path)] for imagePath in imagePaths: name = str(os.path.split(imagePath)[1].split(".",2)[1]) names.append(name)加载视频
cap=cv2.VideoCapture("1.mp4")name()while True: flag,frame=cap.read() if not flag: break face_detect_demo(frame) if ord(" ") == cv2.waitKey(10): break释放内存和视频
cv2.destroyAllWindows()cap.release()完整的代码片段
import cv2import osrecogizer = cv2.face.LBPHFaceRecognizer_create()recogizer.read("tupian/tupian.yml")names=[]warningtime = 0def face_detect_demo(img): gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) face_detector=cv2.CascadeClassifier("<open-cv-installed-path>/sources/data/haarcascades/haarcascade_frontalface_default.xml") face=face_detector.detectMultiScale(gray) for x,y,w,h in face: cv2.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2) cv2.circle(img,center=(x+w//2,y+h//2),radius=w//2,color=(0,255,0),thickness=1) ids, confidence = recogizer.predict(gray[y:y + h, x:x + w]) if confidence > 80: global warningtime warningtime += 1 if warningtime > 100: # warning() warningtime = 0 cv2.putText(img, "unkonw", (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1) else: cv2.putText(img,str(names[ids-1]), (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1) cv2.imshow("result",img) def name(): path = "/tmp/" # names = [] imagePaths=[os.path.join(path,f) for f in os.listdir(path)] for imagePath in imagePaths: name = str(os.path.split(imagePath)[1].split(".",2)[1]) names.append(name)cap=cv2.VideoCapture("1.mp4")name()while True: flag,frame=cap.read() if not flag: break face_detect_demo(frame) if ord(" ") == cv2.waitKey(10): breakcv2.destroyAllWindows()cap.release()最后，希望这些示例代码对您的项目有所帮助！
