结合OpenCV摄像头使用Dlib库进行人脸检测及标注特征点和提取人脸特征-Python

小灰灰 2022-05-29 05:05 209阅读 0赞

**注意：需要安装opencv和dlib库，安装可见此**[教程][Link 1]**。同时需要下载dlib库封装好的代码shape\_predictor\_68\_face\_landmarks.dat(用于人脸68维特征点标注)及dlib\_face\_recognition\_resnet\_model\_v1.dat(用于提取人脸128维特征)。**  
**本文所有代码及封装库可从[https://github.com/TimeIvyace/Dlib-OpenCV\_face.git][https_github.com_TimeIvyace_Dlib-OpenCV_face.git]中下载。**

#### 使用OpenCV调用摄像头并用Dlib人脸检测和标注特征点 ####

import cv2
    import dlib
    
    PREDICTOR_PATH = "shape_predictor_68_face_landmarks.dat"
    
    # 使用dlib自带的frontal_face_detector作为人脸提取器
    detector = dlib.get_frontal_face_detector()
    
    # 使用官方模型构建特征提取器
    predictor = dlib.shape_predictor(PREDICTOR_PATH)
    
    if __name__ == '__main__':
    
        #框住人脸的矩形边框颜色
        color = (0, 255, 0)
    
        #捕获指定摄像头的实时视频流
        cap = cv2.VideoCapture(0)
    
        #循环检测识别人脸
        while True:
            _, frame = cap.read()   #读取一帧视频
    
            # 图像灰化，降低计算复杂度
            frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    
            # 使用detector进行人脸检测 rects为返回的结果
            rects = detector(frame_gray, 1)
    
            if len(rects) > 0:
                for k, d in enumerate(rects):
                    cv2.rectangle(frame, (d.left(), d.top()), (d.right(), d.bottom()), (255, 255, 255))
                    shape = predictor(frame, d)
                    for i in range(68):
                        cv2.circle(frame, (shape.part(i).x, shape.part(i).y), 5, (0, 255, 0), -1, 8)
                        cv2.putText(frame, str(i), (shape.part(i).x, shape.part(i).y), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
                                    (255, 255, 255))
    
            cv2.imshow("find me", frame)
    
            #等待10毫秒看是否有按键输入
            k = cv2.waitKey(10)
            #如果输入q则退出循环
            if k & 0xFF == ord('q'):
                break
    
        #释放摄像头并销毁所有窗口
        cap.release()
        cv2.destroyAllWindows()

#### 使用Dlib中的残差网络封装库提取人脸128维特征 ####

import dlib
    import cv2
    import numpy as np
    
    
    def dlib_get_face(frame_path, face_rec_model_path, predictor_path):
        # 使用dlib自带的frontal_face_detector作为人脸提取器
        detector = dlib.get_frontal_face_detector()
        shape_predictor = dlib.shape_predictor(predictor_path)
        face_rec_model = dlib.face_recognition_model_v1(face_rec_model_path)
    
        # opencv读取图片，并显示
        frame = cv2.imread(frame_path, cv2.IMREAD_COLOR)
    
        # opencv的bgr格式图片转换成rgb格式
        b, g, r = cv2.split(frame)
        frame2 = cv2.merge([r, g, b])
    
        # 使用detector进行人脸检测dets为返回的结果
        dets = detector(frame, 1)
    
        face_descriptor = []
        for index, face in enumerate(dets):
            shape = shape_predictor(frame, face)  # 提取68个特征点
            # 计算人脸的128维的向量
            face_descriptor.append(face_rec_model.compute_face_descriptor(frame2, shape))
        return np.array(face_descriptor)
    
    
    frame_path = "a.jpeg" # 需要检测的图像，可结合上式代码使用摄像头
    face_rec_model_path = "dlib_face_recognition_resnet_model_v1.dat"
    predictor_path = "shape_predictor_68_face_landmarks.dat"
    face = dlib_get_face(frame_path, face_rec_model_path, predictor_path)
    print(face.shape)

[Link 1]: http://blog.csdn.net/gaoyueace/article/details/79198023
[https_github.com_TimeIvyace_Dlib-OpenCV_face.git]: https://github.com/TimeIvyace/Dlib-OpenCV_face.git