头歌平台-人脸识别系统——Dlib人脸识别

EduCoder平台：人脸识别系统——Dlib人脸识别

第1关：dlib人脸检测的基本原理

编程要求：

请在右侧编辑器中的BEGIN-END之间编写代码，使用Dlib识别人脸并输出识别结果：

• • 计算已知图片所有人脸特征向量；
• • 计算待识别图片与已知图片特征向量间的欧氏距离；
• • 打印识别结果。

代码如下：

import os
import dlib
import glob
import numpy as np
import cv2

predictor_path = 'step1/model/shape_predictor_5_face_landmarks.dat'
face_rec_model_path = 'step1/model/dlib_face_recognition_resnet_model_v1.dat'
known_image_path = 'step1/image/known_image'
test_image_path = "step1/image/test_image"

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)

'''****************BEGIN****************'''
# 加载人脸识别模型
facerec =dlib.face_recognition_model_v1(face_rec_model_path)
'''**************** END ****************'''

descriptors = []
names = ["TongDaWei","XiaYu","ZhangYiShan"]

# 计算已知图片的特征向量
for f in glob.glob(os.path.join(known_image_path, "*.jpg")):
    img = dlib.load_rgb_image(f)
    # 1.人脸检测
    dets = detector(img, 1)
    for k, d in enumerate(dets):
        '''****************BEGIN****************'''
        # 2.关键点检测
        shape = predictor(img, d)
        # 3.特征向量
        face_descriptor =facerec.compute_face_descriptor(img, shape)
        # 转换为numpy array
        v =np.array(face_descriptor)
        descriptors.append(v)
        '''**************** END ****************'''

for f in glob.glob(os.path.join(test_image_path, "*.jpg")):
    img = dlib.load_rgb_image(f)
    dets = detector(img, 1)
    for k, d in enumerate(dets):
        '''****************BEGIN****************'''
        shape = predictor(img, d)
        face_descriptor = facerec.compute_face_descriptor(img, shape)
        # 当前待识别的图片特征向量为current
        current =np.array(face_descriptor)
        '''**************** END ****************'''
        # 计算欧式距离，识别人脸
        tolerance = 0.4
        current_name = "Unknow"
        '''****************BEGIN****************'''
        # 输出识别的结果
        
        for i in range(len(descriptors)):
            distance = np.linalg.norm(descriptors[i]-current)
            if distance<tolerance:
               #names用于存放于已知人脸特征向量对应的名字
                current_name = names[i]
                break
        
        print("当前图片识别结果为："+current_name)
        '''**************** END ****************'''

第2关：绘制人脸识别结果

编程要求：

请在右侧编辑器中的BEGIN-END之间编写代码，使用OpenCV绘制人脸识别结果，并保存图片到指定路径：

• 绘制人脸区域，边框颜色为(0,0,255),边框粗度为2；
• 将文字内容放在(d.left(),d.bottom+13)处；
• 文字内容为识别的结果；
• 字体为cv2.FONT_HERSHEY_PLAIN；
• 字体颜色为(255,0,0)；
• 字体倍数为0.5；
• 字体厚度为1。

代码如下：

import os
import dlib
import glob
import numpy as np
import cv2

predictor_path = 'step1/model/shape_predictor_5_face_landmarks.dat'
face_rec_model_path = 'step1/model/dlib_face_recognition_resnet_model_v1.dat'
known_image_path = 'step1/image/known_image'
test_image_path = "step1/image/test_image"

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)
facerec = dlib.face_recognition_model_v1(face_rec_model_path)

descriptors = []
names = ["TongDaWei","XiaYu","ZhangYiShan"]

for f in sorted(glob.glob(os.path.join(known_image_path, "*.jpg"))):
    img = dlib.load_rgb_image(f)
    # 1.人脸检测
    dets = detector(img, 1)
    for k, d in enumerate(dets):
        # 2.关键点检测
        shape = predictor(img, d)
        # 3.描述子提取，128D向量
        face_descriptor = facerec.compute_face_descriptor(img, shape)
        # 转换为numpy array
        v = np.array(face_descriptor)
        descriptors.append(v)

count = 0
for f in sorted(glob.glob(os.path.join(test_image_path, "*.jpg"))):
    img = dlib.load_rgb_image(f)
    dets = detector(img, 1)
    for k, d in enumerate(dets):
        shape = predictor(img, d)
        face_descriptor = facerec.compute_face_descriptor(img, shape)
        # 当前待识别的图片特征向量为current
        current = np.array(face_descriptor)
        # 计算欧式距离，识别人脸
        tolerance = 0.4
        current_name = "Unknow"
        for i in range(len(descriptors)):
            distance = np.linalg.norm(descriptors[i]-current)
            if distance<tolerance:
                #names用于存放于已知人脸特征向量对应的名字
                current_name = names[i]
                break

        '''****************BEGIN****************'''
        # 绘制人脸区域
        cv2.rectangle(img,(d.left(), d.top()),(d.right(), d.bottom()),(0,0,255),2)
        # 在图片上添加文字
        font = cv2.FONT_HERSHEY_PLAIN
        cv2.putText(img, current_name, (d.left(), d.bottom()+13), font, 0.5, (255,0,0), 1)

        '''**************** END ****************'''

    # 保存图片
    count = count+1
    cv2.imwrite("step2/image/out/"+str(count)+".jpg",cv2.cvtColor(img,cv2.COLOR_RGB2BGR))