《Python人脸识别实现指南》源码

# 人脸识别 # coding=utf-8 import cv2 import numpy from PIL import Image, ImageDraw, ImageFont # 语音说话 import pyttsx3 engine = pyttsx3.init() # 解决cv2.putText绘制中文乱码 def cv2ImgAddText(img2, text, left, top, textColor=(0, 0, 255), textSize=20): if isinstance(img2, numpy.ndarray): # 判断是否OpenCV图片类型 img2 = Image.fromarray(cv2.cvtColor(img2, cv2.COLOR_BGR2RGB)) # 创建一个可以在给定图像上绘图的对象 draw = ImageDraw.Draw(img2) # 字体的格式 fontStyle = ImageFont.truetype(r"C:\WINDOWS\FONTS\MSYH.TTC", textSize, encoding="utf-8") # 绘制文本 draw.text((left, top), text, textColor, font=fontStyle) # 转换回OpenCV格式 return cv2.cvtColor(numpy.asarray(img2), cv2.COLOR_RGB2BGR) recognizer = cv2.face.LBPHFaceRecognizer_create() recognizer.read('FaceTrainer/trainer.yml') cascadePath = "haarcascade_frontalface_default.xml" faceCascade = cv2.CascadeClassifier(cascadePath) font = cv2.FONT_HERSHEY_SIMPLEX num = 0 names = ['用户一', '用户二', '用户三'] 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) num, confidence = recognizer.predict(gray[y:y + h, x:x + w]) if confidence < 100: name = names[num] # confidence = "{0}%".format(round(100 - confidence)) # confidence = format(round(100 - confidence)) else: name = "unknown" # confidence = "{0}%".format(round(100 - confidence)) # confidence = format(round(100 - confidence)) # 解决cv2.putText绘制中文乱码 img = cv2ImgAddText(img, name, x + 5, y - 30) # cv2.putText(img, name, (x + 5, y - 5), font, 1, (0, 0, 255), 1) 无法显示中文 # cv2.putText(img, str(confidence.encode('utf-8')), (x+5, y+h-5), font, 1, (0, 0, 0), 1) if name == "unknown": engine.say('识别失败') engine.runAndWait() else: engine.say(name + '用户，识别成功') engine.runAndWait() cv2.imshow('camera', img) k = cv2.waitKey(5) if k == 27: break cam.release() cv2.destroyAllWindows()