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