Dlib Installation 9d1214a0cba5484e8cb9c2037995142d.md

Dlib Installation

sudo apt-get install libpng-dev

sudo apt-get install python3-pil

sudo apt-get install python3-dev

pip3 install --upgrade --no-cache-dir dlib

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For Jetson

sudo apt-get install python3-pil

Install dlib(from source, gpu) on Jetson NX did not work correctly

My environment: Xavier NX + JetPack 4.6

I solved this issue following the help here: https://forums.developer.nvidia.com/t/simple-accelerated-face-recognition/142679/19

Most importantly, make sure you use dlib19.21, neither dlib19.23 nor dlib 19.17.

Simple accelerated face recognition

Hi,

The fix is adding the cuDNNv8 new API support.And it is available in the newest dlib-19.21 release in Aug 8.

So you can build it from source directly:

Install dependencies

$ sudo apt-get install python3-pip
$ sudo apt-get install libjpeg-dev

Build dlib from source

https://drive.google.com/file/d/1Bck3tXiV2Gtok-R8R0rNS40hGmsrVPo_/view?usp=share_link

$ wget http://dlib.net/files/dlib-19.21.tar.bz2
$ tar jxvf dlib-19.21.tar.bz2
$ cd dlib-19.21/
$ mkdir build
$ cd build/
$ cmake ..
$ cmake --build .
$ cd ../
$ sudo python3 setup.py install
$ sudo pip3 install face_recognition

Testing

Please create Images folder and store some testing images in the folder.

test.py

import cv2
import os
import numpy as np
import dlib

face_locations = []
face_encodings = []

### Path where images are present for testingimagefolderpath = "Images/"

### Model for face detectionface_detector = dlib.get_frontal_face_detector()

for image in os.listdir(imagefolderpath):
    image = cv2.imread(os.path.join(imagefolderpath,image),1)

    t = time.time()
    faces = face_detector(image,0)
    for face in faces:
        x,y,w,h = face.left(),face.top(),face.right(),face.bottom()
        face_locations.append((x,y,h,w))
    face_encodings = face_recognition.face_encodings(image, known_face_locations = face_locations, num_jitters = 1)

    for (left, top, bottom, right) in face_locations:
        cv2.rectangle(image, (left,top), (right, bottom), (0, 0, 255), 2)
        cv2.imshow('Image', image)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

python3 test.py

You should be able to see CUDA and cuDNN are both enabled in the dlib-19.21.

...
-- Found CUDA: /usr/local/cuda (found suitable version "10.2", minimum required is "7.5")-- Looking for cuDNN install...-- Found cuDNN: /usr/lib/aarch64-linux-gnu/libcudnn.so-- Building a CUDA test project to see if your compiler is compatible with CUDA...-- Building a cuDNN test project to check if you have the right version of cuDNN installed...-- Enabling CUDA support for dlib.  DLIB WILL USE CUDA-- C++11 activated.-- Configuring done-- Generating done-- Build files have been written to: /home/nvidia/dlib-19.21/build

Thanks.