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Diffstat (limited to 'ml/dlib/python_examples/face_recognition.py')
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diff --git a/ml/dlib/python_examples/face_recognition.py b/ml/dlib/python_examples/face_recognition.py deleted file mode 100755 index da2bdbc55..000000000 --- a/ml/dlib/python_examples/face_recognition.py +++ /dev/null @@ -1,123 +0,0 @@ -#!/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() - - |