mirror of
https://github.com/davisking/dlib.git
synced 2024-11-01 10:14:53 +08:00
117 lines
4.9 KiB
Python
Executable File
117 lines
4.9 KiB
Python
Executable File
#!/usr/bin/python
|
|
# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
|
|
#
|
|
# This example shows how to use dlib's face recognition tool. This tool maps
|
|
# an image of a human face to a 128 dimensional vector space where images of
|
|
# the same person are near to each other and images from different people are
|
|
# far apart. Therefore, you can perform face recognition by mapping faces to
|
|
# the 128D space and then checking if their Euclidean distance is small
|
|
# enough.
|
|
#
|
|
# When using a distance threshold of 0.6, the dlib model obtains an accuracy
|
|
# of 99.38% on the standard LFW face recognition benchmark, which is
|
|
# comparable to other state-of-the-art methods for face recognition as of
|
|
# February 2017. This accuracy means that, when presented with a pair of face
|
|
# images, the tool will correctly identify if the pair belongs to the same
|
|
# person or is from different people 99.38% of the time.
|
|
#
|
|
# Finally, for an in-depth discussion of how dlib's tool works you should
|
|
# refer to the C++ example program dnn_face_recognition_ex.cpp and the
|
|
# attendant documentation referenced therein.
|
|
#
|
|
#
|
|
#
|
|
#
|
|
# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
|
|
# You can install dlib using the command:
|
|
# pip install dlib
|
|
#
|
|
# Alternatively, if you want to compile dlib yourself then go into the dlib
|
|
# root folder and run:
|
|
# python setup.py install
|
|
#
|
|
# Compiling dlib should work on any operating system so long as you have
|
|
# CMake installed. On Ubuntu, this can be done easily by running the
|
|
# command:
|
|
# sudo apt-get install cmake
|
|
#
|
|
# Also note that this example requires Numpy which can be installed
|
|
# via the command:
|
|
# pip install numpy
|
|
|
|
import sys
|
|
import os
|
|
import dlib
|
|
import glob
|
|
|
|
if len(sys.argv) != 4:
|
|
print(
|
|
"Call this program like this:\n"
|
|
" ./face_recognition.py shape_predictor_5_face_landmarks.dat dlib_face_recognition_resnet_model_v1.dat ../examples/faces\n"
|
|
"You can download a trained facial shape predictor and recognition model from:\n"
|
|
" http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2\n"
|
|
" http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2")
|
|
exit()
|
|
|
|
predictor_path = sys.argv[1]
|
|
face_rec_model_path = sys.argv[2]
|
|
faces_folder_path = sys.argv[3]
|
|
|
|
# Load all the models we need: a detector to find the faces, a shape predictor
|
|
# to find face landmarks so we can precisely localize the face, and finally the
|
|
# face recognition model.
|
|
detector = dlib.get_frontal_face_detector()
|
|
sp = dlib.shape_predictor(predictor_path)
|
|
facerec = dlib.face_recognition_model_v1(face_rec_model_path)
|
|
|
|
win = dlib.image_window()
|
|
|
|
# Now process all the images
|
|
for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
|
|
print("Processing file: {}".format(f))
|
|
img = dlib.load_rgb_image(f)
|
|
|
|
win.clear_overlay()
|
|
win.set_image(img)
|
|
|
|
# Ask the detector to find the bounding boxes of each face. The 1 in the
|
|
# second argument indicates that we should upsample the image 1 time. This
|
|
# will make everything bigger and allow us to detect more faces.
|
|
dets = detector(img, 1)
|
|
print("Number of faces detected: {}".format(len(dets)))
|
|
|
|
# Now process each face we found.
|
|
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 = sp(img, d)
|
|
# Draw the face landmarks on the screen so we can see what face is currently being processed.
|
|
win.clear_overlay()
|
|
win.add_overlay(d)
|
|
win.add_overlay(shape)
|
|
|
|
# Compute the 128D vector that describes the face in img identified by
|
|
# shape. In general, if two face descriptor vectors have a Euclidean
|
|
# distance between them less than 0.6 then they are from the same
|
|
# person, otherwise they are from different people. Here we just print
|
|
# the vector to the screen.
|
|
face_descriptor = facerec.compute_face_descriptor(img, shape)
|
|
print(face_descriptor)
|
|
# It should also be noted that you can also call this function like this:
|
|
# face_descriptor = facerec.compute_face_descriptor(img, shape, 100)
|
|
# The version of the call without the 100 gets 99.13% accuracy on LFW
|
|
# while the version with 100 gets 99.38%. However, the 100 makes the
|
|
# call 100x slower to execute, so choose whatever version you like. To
|
|
# explain a little, the 3rd argument tells the code how many times to
|
|
# jitter/resample the image. When you set it to 100 it executes the
|
|
# face descriptor extraction 100 times on slightly modified versions of
|
|
# the face and returns the average result. You could also pick a more
|
|
# middle value, such as 10, which is only 10x slower but still gets an
|
|
# LFW accuracy of 99.3%.
|
|
|
|
|
|
dlib.hit_enter_to_continue()
|
|
|
|
|