Dlib库实现人脸关键点检测（Opencv实现）

文章目录

1.dlib实现人脸实时检测2.dlib采用检测人脸的68个关键点3.相关文件的下载4.代码实战（1）导入库（2）人脸关键点的整合（3）加载dlib库的人脸检测和人脸关键点检测文件（4）对人脸画框（5）对检测得到的人脸关键点坐标重新整理（6）对人脸的关键点绘制点（7）整体代码（9）单张图片的人脸关键点检测

1.dlib实现人脸实时检测

https://mydreamambitious./article/details/124851743

2.dlib采用检测人脸的68个关键点

3.相关文件的下载

下载地址：/files/

4.代码实战

（1）导入库

import osimport cv2import dlibimport numpy as npfrom collections import OrderedDict

（2）人脸关键点的整合

#对于68个检测点，将人脸的几个关键点排列成有序，便于后面的遍历shape_predictor_68_face_landmark=OrderedDict([('mouth',(48,68)),('right_eyebrow',(17,22)),('left_eye_brow',(22,27)),('right_eye',(36,42)),('left_eye',(42,48)),('nose',(27,36)),('jaw',(0,17))])

（3）加载dlib库的人脸检测和人脸关键点检测文件

# 加载人脸检测与关键点定位#/python/index.html#dlib_pybind11.get_frontal_face_detectordetector = dlib.get_frontal_face_detector()#/python/index.html#dlib_pybind11.shape_predictorcriticPoints = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

（4）对人脸画框

#绘制人脸画矩形框def drawRectangle(detected,frame):margin = 0.2img_h,img_w,_=np.shape(frame)if len(detected) > 0:for i, locate in enumerate(detected):x1, y1, x2, y2, w, h = locate.left(), locate.top(), locate.right() + 1, locate.bottom() + 1, locate.width(), locate.height()xw1 = max(int(x1 - margin * w), 0)yw1 = max(int(y1 - margin * h), 0)xw2 = min(int(x2 + margin * w), img_w - 1)yw2 = min(int(y2 + margin * h), img_h - 1)cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)face = frame[yw1:yw2 + 1, xw1:xw2 + 1, :]cv2.putText(frame, 'Person', (locate.left(), locate.top() - 10),cv2.FONT_HERSHEY_SIMPLEX, 1.2, (255, 0, 0), 3)return frame

（5）对检测得到的人脸关键点坐标重新整理

#对检测之后获取的人脸关键点坐标进行转换def predict2Np(predict):# 创建68*2关键点的二维空数组[(x1,y1),(x2,y2)……]dims=np.zeros(shape=(predict.num_parts,2),dtype=np.int)#遍历人脸的每个关键点获取二维坐标length=predict.num_partsfor i in range(0,length):dims[i]=(predict.part(i).x,predict.part(i).y)return dims

（6）对人脸的关键点绘制点

#遍历预测框，进行人脸的关键点绘制def drawCriticPoints(detected,frame):for (step,locate) in enumerate(detected):#对获取的人脸框再进行人脸关键点检测#获取68个关键点的坐标值dims=criticPoints(frame,locate)#将得到的坐标值转换为二维dims=predict2Np(dims)#通过得到的关键点坐标进行关键点绘制# 从i->j这个范围内的都是同一个区域：比如上面的鼻子就是从27->36for (name,(i,j)) in shape_predictor_68_face_landmark.items():#对每个部位进行绘点for (x,y) in dims[i:j]:cv2.circle(img=frame,center=(x,y),radius=2,color=(0,255,0),thickness=-1)return frame

（7）整体代码

import osimport cv2import dlibimport numpy as npfrom collections import OrderedDict#https://mydreamambitious./article/details/123535760#对于68个检测点，将人脸的几个关键点排列成有序，便于后面的遍历shape_predictor_68_face_landmark=OrderedDict([('mouth',(48,68)),('right_eyebrow',(17,22)),('left_eye_brow',(22,27)),('right_eye',(36,42)),('left_eye',(42,48)),('nose',(27,36)),('jaw',(0,17))])#绘制人脸画矩形框def drawRectangle(detected,frame):margin = 0.2img_h,img_w,_=np.shape(frame)if len(detected) > 0:for i, locate in enumerate(detected):x1, y1, x2, y2, w, h = locate.left(), locate.top(), locate.right() + 1, locate.bottom() + 1, locate.width(), locate.height()xw1 = max(int(x1 - margin * w), 0)yw1 = max(int(y1 - margin * h), 0)xw2 = min(int(x2 + margin * w), img_w - 1)yw2 = min(int(y2 + margin * h), img_h - 1)cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)face = frame[yw1:yw2 + 1, xw1:xw2 + 1, :]cv2.putText(frame, 'Person', (locate.left(), locate.top() - 10),cv2.FONT_HERSHEY_SIMPLEX, 1.2, (255, 0, 0), 3)return frame#对检测之后获取的人脸关键点坐标进行转换def predict2Np(predict):# 创建68*2关键点的二维空数组[(x1,y1),(x2,y2)……]dims=np.zeros(shape=(predict.num_parts,2),dtype=np.int)#遍历人脸的每个关键点获取二维坐标length=predict.num_partsfor i in range(0,length):dims[i]=(predict.part(i).x,predict.part(i).y)return dims# 加载人脸检测与关键点定位#/python/index.html#dlib_pybind11.get_frontal_face_detectordetector = dlib.get_frontal_face_detector()#/python/index.html#dlib_pybind11.shape_predictorcriticPoints = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")#遍历预测框，进行人脸的关键点绘制def drawCriticPoints(detected,frame):for (step,locate) in enumerate(detected):#对获取的人脸框再进行人脸关键点检测#获取68个关键点的坐标值dims=criticPoints(frame,locate)#将得到的坐标值转换为二维dims=predict2Np(dims)#通过得到的关键点坐标进行关键点绘制# 从i->j这个范围内的都是同一个区域：比如上面的鼻子就是从27->36for (name,(i,j)) in shape_predictor_68_face_landmark.items():#对每个部位进行绘点for (x,y) in dims[i:j]:cv2.circle(img=frame,center=(x,y),radius=2,color=(0,255,0),thickness=-1)return frame#单张图片的人脸关键点检测def signal_detect(img_path='images/face1.jpg'):img=cv2.imread(img_path)detected=detector(img)frame=drawRectangle(detected,img)frame = drawCriticPoints(detected, img)cv2.imshow('frame',frame)cv2.waitKey(0)cv2.destroyAllWindows()#实时的人脸关键点检测def detect_time():cap=cv2.VideoCapture(0)while cap.isOpened():ret,frame=cap.read()detected = detector(frame)frame = drawRectangle(detected, frame)frame=drawCriticPoints(detected,frame)cv2.imshow('frame', frame)key=cv2.waitKey(1)if key==27:breakcap.release()cv2.destroyAllWindows()if __name__ == '__main__':print('Pycharm')signal_detect()# detect_time()

（9）单张图片的人脸关键点检测

注：实时检测部分读者可以自己测试。