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75 lines
3.2 KiB
Python
75 lines
3.2 KiB
Python
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#!/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 program shows how to find frontal human faces in an image and
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# estimate their pose. The pose takes the form of 68 landmarks. These are
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# points on the face such as the corners of the mouth, along the eyebrows, on
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# the eyes, and so forth.
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#
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# This face detector is made using the classic Histogram of Oriented
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# Gradients (HOG) feature combined with a linear classifier, an image pyramid,
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# and sliding window detection scheme. The pose estimator was created by
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# using dlib's implementation of the paper:
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# One Millisecond Face Alignment with an Ensemble of Regression Trees by
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# Vahid Kazemi and Josephine Sullivan, CVPR 2014
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# and was trained on the iBUG 300-W face landmark dataset.
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#
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# Also, note that you can train your own models using dlib's machine learning
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# tools. See train_shape_predictor.py to see an example.
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#
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# You can get the shape_predictor_68_face_landmarks.dat file from:
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# http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2
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#
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# COMPILING THE DLIB PYTHON INTERFACE
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# Dlib comes with a compiled python interface for python 2.7 on MS Windows. If
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# you are using another python version or operating system then you need to
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# compile the dlib python interface before you can use this file. To do this,
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# run compile_dlib_python_module.bat. This should work on any operating
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# system so long as you have CMake and boost-python installed.
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# On Ubuntu, this can be done easily by running the command:
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# sudo apt-get install libboost-python-dev cmake
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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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from skimage import io
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if len(sys.argv) != 3:
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print(
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"Give the path to the trained shape predictor model as the first "
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"argument and then the directory containing the facial images.\n"
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"For example, if you are in the python_examples folder then "
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"execute this program by running:\n"
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" ./face_landmark_detection.py shape_predictor_68_face_landmarks.dat ../examples/faces\n"
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"You can download a trained facial shape predictor from:\n"
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" http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2")
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exit()
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predictor_path = sys.argv[1]
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faces_folder_path = sys.argv[2]
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detector = dlib.get_frontal_face_detector()
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predictor = dlib.shape_predictor(predictor_path)
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win = dlib.image_window()
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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 = io.imread(f)
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win.clear_overlay()
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win.set_image(img)
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dets = detector(img, 1)
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print("Number of faces detected: {}".format(len(dets)))
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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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shapes = predictor(img, d)
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print("Part 0: {}, Part 1: {} ...".format(shapes.part(0),
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shapes.part(1)))
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# Add all facial landmarks one at a time
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win.add_overlay(shapes)
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win.add_overlay(dets)
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raw_input("Hit enter to continue")
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