diff options
Diffstat (limited to 'ml/dlib/python_examples/face_recognition.py')
| -rwxr-xr-x | ml/dlib/python_examples/face_recognition.py | 123 |
1 files changed, 123 insertions, 0 deletions
diff --git a/ml/dlib/python_examples/face_recognition.py b/ml/dlib/python_examples/face_recognition.py new file mode 100755 index 000000000..da2bdbc55 --- /dev/null +++ b/ml/dlib/python_examples/face_recognition.py @@ -0,0 +1,123 @@ +#!/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 +# or +# python setup.py install --yes USE_AVX_INSTRUCTIONS +# if you have a CPU that supports AVX instructions, since this makes some +# things run faster. This code will also use CUDA if you have CUDA and cuDNN +# installed. +# +# 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 scikit-image which can be installed +# via the command: +# pip install scikit-image +# Or downloaded from http://scikit-image.org/download.html. + +import sys +import os +import dlib +import glob +from skimage import io + +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 = io.imread(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() + + |