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import cv2
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import os
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import gain_face
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from face_train import Model, Dataset
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def main():
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judge = False
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while True:
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print("是否录入人脸信息(Yes or No)?，请输入英文名")
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input_ = input()
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if input_ == 'Yes':
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# 员工姓名(要输入英文)
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new_user_name = input("请输入您的姓名：")
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print("请看摄像头！")
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judge = True
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# 采集员工图像的数量自己设定，越多识别准确度越高，但训练速度贼慢
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window_name = 'Information Collection' # 图像窗口
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camera_id = 0 # 相机的ID号
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images_num = 200 # 采集图片数量
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path = './FaceData/' + new_user_name # 图像保存位置
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gain_face.CatchPICFromVideo(window_name, camera_id, images_num, path, new_user_name)
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elif input_ == 'No':
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break
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else:
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print("错误输入，请输入Yes或者No")
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# 加载模型
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if judge == True:
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user_num = len(os.listdir('./FaceData/'))
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dataset = Dataset('./FaceData/')
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dataset.load()
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model = Model()
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# 先前添加的测试build_model()函数的代码
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model.build_model(dataset, nb_classes=user_num)
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# 测试训练函数的代码
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model.train(dataset)
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model.save_model(file_path='./model/aggregate.face.model.h5')
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else:
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model = Model()
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model.load_model(file_path='./model/aggregate.face.model.h5')
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# 框住人脸的矩形边框颜色
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color = (255, 255, 255)
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# 捕获指定摄像头的实时视频流
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cap = cv2.VideoCapture(0)
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# 人脸识别分类器本地存储路径
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cascade_path = "./haarcascade_frontalface_alt2.xml"
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# 循环检测识别人脸
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while True:
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ret, frame = cap.read() # 读取一帧视频
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if ret is True:
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# 图像灰化，降低计算复杂度
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frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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else:
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continue
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# 使用人脸识别分类器，读入分类器
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cascade = cv2.CascadeClassifier(cascade_path)
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# 利用分类器识别出哪个区域为人脸
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faceRects = cascade.detectMultiScale(frame_gray, scaleFactor=1.2, minNeighbors=2, minSize=(32, 32))
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for (x, y, w, h) in faceRects:
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# 截取脸部图像提交给模型识别这是谁
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image = frame[y: y + h, x: x + w]
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faceID = model.face_predict(image)
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print(faceID)
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cv2.rectangle(frame, (x - 10, y - 10), (x + w + 10, y + h + 10), color, thickness=1)
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for i in range(len(os.listdir('./FaceData/'))):
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if i == faceID:
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# 文字提示是谁
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cv2.putText(frame, os.listdir('./FaceData/')[i], (x + 30, y + 30), cv2.FONT_HERSHEY_SIMPLEX, 1,
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color, 2)
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cv2.imshow("recognition! press 'Q' to quit", frame)
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# 等待10毫秒看是否有按键输入
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k = cv2.waitKey(10)
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# 如果输入q则退出循环
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if k & 0xFF == ord('q'):
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break
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# 释放摄像头并销毁所有窗口
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cap.release()
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cv2.destroyAllWindows() |
