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
$ 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 testing
imagefolderpath = "Images/"
### Model for face detection
face_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.
