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人脸关键点数据集WFLW数据预处理详解

阅读数 3

参考：

由6,14以及68点人脸关键点计算头部姿态

dlib https://www.zhihu.com/question/34524316

模型：https://astuteinternet.dl.sourceforge.net/project/dclib/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2

https://blog.csdn.net/ChuiGeDaQiQiu/article/details/88623267

3D数据

https://github.com/yinguobing/head-pose-estimation

https://www.learnopencv.com/facial-landmark-detection/

重磅！头部姿态估计「原理详解 + 实战代码」来啦！:

https://zhuanlan.zhihu.com/p/51208197

使用opencv和dlib进行人脸姿态估计( python )

https://blog.csdn.net/yuanlulu/article/details/82763170

head-pose-estimation--提供3D模型

https://github.com/YadiraF/face3d

https://github.com/lincolnhard/head-pose-estimation

其中关于可视化的一些问题：参考：https://github.com/lincolnhard/head-pose-estimation

比如

还有这种：

原始的：

import osimport cv2import numpy as np # self.img = cv2.imread(self.line[0])# self.landmark = np.asarray(self.line[1:197], dtype=np.float32)# self.attribute = np.asarray(self.line[197:203], dtype=np.int32)# self.euler_angle = np.asarray(self.line[203:206], dtype=np.float32) def groberect(points, ww, hh):    x1 = points[0]    y1 = points[1]    x2 = points[2]    y2 = points[3]     w = x2 - x1 + 1    h = y2 - y1 + 1    px = float(x1 + x2) / 2    py = float(y1 + y2) / 2     w = w * 1.3    h = h * 1.3     l = max(0, px - w / 2)    r = min(ww - 1, px + w / 2)    t = max(0, py - h / 2)    b = min(hh - 1, py + h / 2)     # x1 y1 x2 y2    return [int(l), int(t), int(r), int(b)] file_ynh = open("./WFLW_annotations/list_98pt_rect_attr_train_test/list_98pt_rect_attr_train.txt",'r')lines = file_ynh.readlines()count = len(lines)num = 1for line in lines:    print(num, "/", count)    num += 1    line = line.strip().split()    landmark = line[0:196]    detection = line[196:200]    attributes = line[200:206]    name = line[206:207]    img = cv2.imread("./WFLW_images/" + str(name[0]))    if img is None:        exit()        continue    h,w = img.shape[0:2]     # cv2.rectangle(img, (int(detection[0]),int(detection[1])), (int(detection[2]),int(detection[3])), (0, 255, 0), 2, 8)    # for index in range(0, len(landmark), 2):    # cv2.circle(img, (int(float(landmark[index])), int(float(landmark[index+1]))), 1, (255, 0, 0), -1)    # cv2.imshow("img.jpg", img)    # cv2.waitKey(0)     detection = list(map(int, detection))    rect = groberect(detection, w, h)    rectimg = img[rect[1]:rect[3],rect[0]:rect[2],:]     landmark = list(map(float, landmark))    for index in range(0, len(landmark), 2):        if( (landmark[index]-rect[0])<0 or (landmark[index+1]-rect[1])<0 ):            print("特征点超出扩展框，应该改变策略，使用特征点和原始框的最小外接矩形做扩展框")            print(landmark[index]-rect[0],"\n")            print(landmark[index+1]-rect[1], "\n")        cv2.circle(rectimg, (int(landmark[index]-rect[0]), int(landmark[index+1]-rect[1])), 1, (255, 0, 0), -1)    cv2.imwrite("./result/"+"img_%s.jpg"%(str(num)), rectimg)    # cv2.imshow("rectimg.jpg", rectimg)    # cv2.waitKey(0) file_ynh.close()

写完自己的代码才发现，官方有，我，，，，，，内心羊驼在奔跑。。。。。。。。官方的放在了最后

最小外接矩形改进：

import osimport cv2import numpy as np # self.img = cv2.imread(self.line[0])# self.landmark = np.asarray(self.line[1:197], dtype=np.float32)# self.attribute = np.asarray(self.line[197:203], dtype=np.int32)# self.euler_angle = np.asarray(self.line[203:206], dtype=np.float32) def groberect(points, ww, hh):    x1 = points[0]    y1 = points[1]    x2 = points[2]    y2 = points[3]     w = x2 - x1 + 1    h = y2 - y1 + 1    px = float(x1 + x2) / 2    py = float(y1 + y2) / 2     w = w * 1.2    h = h * 1.2     l = max(0, px - w / 2)    r = min(ww - 1, px + w / 2)    t = max(0, py - h / 2)    b = min(hh - 1, py + h / 2)     # x1 y1 x2 y2    return [int(l), int(t), int(r), int(b)] file_ynh = open("./WFLW_annotations/list_98pt_rect_attr_train_test/list_98pt_rect_attr_train.txt",'r')lines = file_ynh.readlines()count = len(lines)num = 1for line in lines:    print(num, "/", count)    num += 1    line = line.strip().split()    landmark = line[0:196]    detection = line[196:200]    attributes = line[200:206]    name = line[206:207]    img = cv2.imread("./WFLW_images/" + str(name[0]))    if img is None:        exit()        continue    h,w = img.shape[0:2]     # cv2.rectangle(img, (int(detection[0]),int(detection[1])), (int(detection[2]),int(detection[3])), (0, 255, 0), 2, 8)    # for index in range(0, len(landmark), 2):    # cv2.circle(img, (int(float(landmark[index])), int(float(landmark[index+1]))), 1, (255, 0, 0), -1)    # cv2.imshow("img.jpg", img)    # cv2.waitKey(0)     landmark = list(map(float, landmark))    new_landmark = []    for index in range(0, len(landmark), 2):        new_landmark.append([landmark[index], landmark[index+1]])     landmark_array = np.asarray(new_landmark)    xmax, ymax = landmark_array.max(axis=0)    xmin
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