dlib的人脸地标检测程序中如何获取点坐标位置？

Question

dlib 中有一个示例 python 程序来检测面部标志位置。 face_landmark_detection.py

该程序检测人脸特征，并在原始照片中用点和线表示地标。

我想知道是否可以获取每个点的坐标位置。就像 (10, 25)。 'a' 表示嘴角。

在稍微修改程序一次处理一张图片后，我尝试打印出dets和shape的值，但没有成功。

>>>print(dets)
<dlib.dlib.rectangles object at 0x7f3eb74bf950>
>>>print(dets[0])
[(1005, 563) (1129, 687)]

表示面部标志点的参数和参数的数据类型仍然未知。 这是简化的代码

import dlib
from skimage import io

#shape_predictor_68_face_landmarks.dat is the train dataset in the same directory
predictor_path = "shape_predictor_68_face_landmarks.dat"

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

#FDT.jpg is the picture file to be processed in the same directory
img = io.imread("FDT.jpg")

win.set_image(img)

dets = detector(img)

print("Number of faces detected: {}".format(len(dets)))
for k, d in enumerate(dets):
    print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
        k, d.left(), d.top(), d.right(), d.bottom()))
    # Get the landmarks/parts for the face in box d.
    shape = predictor(img, d)
    #print(shape)
    print("Part 0: {}, Part 1: {} ...".format(shape.part(0),
                                              shape.part(1)))
# Draw the face landmarks on the screen.
win.add_overlay(shape)

win.add_overlay(dets)
dlib.hit_enter_to_continue()

---------------2016 年 3 月 10 日更新--------------- ------------

今天想起python中的help()方法，试了一下。

>>>help(predictor)

Help on shape_predictor in module dlib.dlib object:

class shape_predictor(Boost.Python.instance)
 |  This object is a tool that takes in an image region containing 
some object and outputs a set of point locations that define the pose 
of the object. The classic example of this is human face pose 
prediction, where you take an image of a human face as input and are
expected to identify the locations of important facial landmarks such
as the corners of the mouth and eyes, tip of the nose, and so forth.

在原代码中，变量shape是预测器方法的输出。

>>>help(shape)

形状描述

class full_object_detection(Boost.Python.instance)
 |  This object represents the location of an object in an image along 
with the positions of each of its constituent parts.
----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  num_parts
 |      The number of parts of the object.
 |  
 |  rect
 |      The bounding box of the parts.
 |  
 |  ----------------------------------------------------------------------

变量shape似乎与点坐标位置有关。

>>>print(shape.num_parts)
68
>>>print(shape.rect)
[(1005, 563) (1129, 687)]

我假设有 68 个表示人脸标志点。

>>> print(shape.part(68))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
IndexError: Index out of range
>>> print(shape.part(65))
(1072, 645)
>>> print(shape.part(66))
(1065, 647)
>>> print(shape.part(67))
(1059, 646)

如果是真的。剩下的问题是哪个部分在响应哪个面部标志点。

Answer 1

我稍微修改了代码。

import dlib
import numpy as np
from skimage import io

predictor_path = "shape_predictor_68_face_landmarks.dat"

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

img = io.imread("FDT.jpg")

dets = detector(img)

#output face landmark points inside retangle
#shape is points datatype
#http://dlib.net/python/#dlib.point
for k, d in enumerate(dets):
    shape = predictor(img, d)

vec = np.empty([68, 2], dtype = int)
for b in range(68):
    vec[b][0] = shape.part(b).x
    vec[b][1] = shape.part(b).y

print(vec)

这是输出

[[1003  575]
 [1005  593]
 [1009  611]
 [1014  627]
 [1021  642]
 [1030  655]
 [1041  667]
 [1054  675]
 [1069  677]
 [1083  673]
 [1095  664]
 [1105  651]
 [1113  636]
 [1120  621]
 [1123  604]
 [1124  585]
 [1124  567]
 [1010  574]
 [1020  570]
 [1031  571]
 [1042  574]
 [1053  578]
 [1070  577]
 [1081  572]
 [1092  568]
 [1104  566]
 [1114  569]
 [1063  589]
 [1063  601]
 [1063  613]
 [1063  624]
 [1050  628]
 [1056  630]
 [1064  632]
 [1071  630]
 [1077  627]
 [1024  587]
 [1032  587]
 [1040  586]
 [1048  588]
 [1040  590]
 [1031  590]
 [1078  587]
 [1085  585]
 [1093  584]
 [1101  584]
 [1094  588]
 [1086  588]
 [1045  644]
 [1052  641]
 [1058  640]
 [1064  641]
 [1070  639]
 [1078  640]
 [1086  641]
 [1080  651]
 [1073  655]
 [1066  656]
 [1059  656]
 [1052  652]
 [1048  645]
 [1059  645]
 [1065  646]
 [1071  644]
 [1083  642]
 [1072  645]
 [1065  647]
 [1059  646]]

还有一个开源项目OpenFace，它基于dlib，描述了face中每个点的相关部分。

The url of describing image