Dlib and OPENCV Installation

On the internet to see a lot of saying that the installation of dlib before the need to install CMake and boost, but also said that need to install VS2015 support the latest C compiler

It seems that it doesn't need to be so complicated to test it yourself.

The computer is configured with 64-bit WIN7, installed python3.6 (64bit), also installed Pycharm

1, installation OpenCV relatively simple, failure to try more than a few times

Pip Install--upgrade Setuptools

Pip Install NumPy matplotlib

Pip Install Opencv-python

This OPENCV even if installed, if the use of Face_cascade = Cv2. Cascadeclassifier (' Xxx/haarcascade_frontalface_default.xml ')

XXX in parentheses is the store path

2. Installation Dlib

Online also said that the first installation of Anaconda is simpler, this step I also omitted, the following to say my installation steps

First use CMD query to support which version of the current system environment dlib

Enter DOS separately input:

Python

Import pip

Print (pip.pep425tags.get_supported ())

Return:

[(' Cp36 ', ' cp36m ', ' win_amd64 '), (' cp36 ', ' none ', ' win_amd64 '), (' Py3 ', ' none ',
' Win_amd64 '), (' cp36 ', ' none ', ' any '), (' CP3 ', ' none ', ' any '), (' py36 ', ' none '),
' Any ', [' py3 ', ' none ', ' any '], (' py35 ', ' none ', ' any '), (' py34 ', ' none ', ' any ')
, (' py33 ', ' none ', ' any '), (' py32 ', ' none ', ' no '), (' py31 ', ' none ', ' any '), (' P

Y30 ', ' none ', ' any ')]

Indicates that the current environment supports the above format installation package

Use pip install dlib, install dlib19.9 failure, do not know is not the speed of the network or the environment does not support, on their own online under a

DLIB-19.7.0-CP36-CP36M-WIN_AMD64.WHL, this format meets the above requirements

Then pip install to store the path/DLIB-19.7.0-CP36-CP36M-WIN_AMD64.WHL

Success, and then run an example

Import Cv2
Import Dlib


detector = Dlib.get_frontal_face_detector ()
Landmark_predictor = Dlib.shape_predictor (' d:/pytion code/shape_predictor_68_face_landmarks.dat ')
img = cv2.imread (' 1.jpg ')
faces = detector (img,1)
if (Len (faces) > 0):
For k,d in Enumerate (faces):
Cv2.rectangle (IMG, (D.left (), D.top ()), (D.right (), D.bottom ()), (255,255,255))
Shape = Landmark_predictor (img,d)
For I in range (68):
Cv2.circle (IMG, (Shape.part (i). x, Shape.part (i). Y), 5, (0,255,0),-1, 8)
Cv2.puttext (Img,str ("), (Shape.part (i). X,shape.part (i). Y), Cv2. font_hershey_simplex,0.5, (255,2555,255))
Else
Print ("No face")
Pass


Print (' {} faces detected '. Format (len (faces))

Cv2.imshow (' Frame ', IMG)
Cv2.imwrite (' faces.jpg ', IMG)

Cv2.waitkey (0)

The directory where the program is stored can be identified with a human face map.

What needs to be explained is that Landmark_predictor = Dlib.shape_predictor (' d:/pytion code/shape_predictor_68_face_landmarks.dat ') contains a DAT database , this is the dlib relies on the face detection of 68 feature detection points, can be downloaded online, can also be downloaded to https://download.csdn.net/download/stephen_yu/10287390

Here is the code execution result