How to get points coordinate position in the face landmark detection program of dlib? - feature-detection

Question

There is one example python program in dlib to detect the face landmark position.
face_landmark_detection.py
This program detect the face feature and denote the landmarks with dots and lines in original photo.
I wonder if it is possible to obtain each points' coordinate position. Like a(10, 25). 'a' denotes corner of the mouth.
After slightly modifying the program to process one picture at one time, I try to print out the value of dets and shape without success.
>>>print(dets)
<dlib.dlib.rectangles object at 0x7f3eb74bf950>
>>>print(dets[0])
[(1005, 563) (1129, 687)]
The arguments to denote face landmark points and the datatype of arguments still remain unknown.
And here is the simplified code
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()
---------------------------update on 3.10.2016---------------------------
Today, I remember the help() method in python and have a trial with it.
>>>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.
In the original code, variable shape is the output of predictor method.
>>>help(shape)
The description of shape
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.
|
| ----------------------------------------------------------------------
It seems that variable shape is related with points coordinate position.
>>>print(shape.num_parts)
68
>>>print(shape.rect)
[(1005, 563) (1129, 687)]
I assume that there are 68 denoted face landmark points.
>>> 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)
If it is true. The remained problem is that which part is responding to which face landmark point.

Answer 1

I slightly modified the code.
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)
Here is the output
[[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]]
And there is another open source project OpenFace, which is based on dlib and describes each point's correlating part in face.
The url of describing image