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95 lines
3.9 KiB
Python
Executable File
95 lines
3.9 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 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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# The face detector we use 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 (see
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# https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/):
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# C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic.
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# 300 faces In-the-wild challenge: Database and results.
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# Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation "In-The-Wild". 2016.
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# You can get the trained model file from:
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# http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2.
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# Note that the license for the iBUG 300-W dataset excludes commercial use.
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# So you should contact Imperial College London to find out if it's OK for
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# you to use this model file in a commercial product.
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#
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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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#
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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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#
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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) != 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://dlib.net/files/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 = 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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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 = predictor(img, d)
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print("Part 0: {}, Part 1: {} ...".format(shape.part(0),
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shape.part(1)))
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# Draw the face landmarks on the screen.
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win.add_overlay(shape)
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win.add_overlay(dets)
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dlib.hit_enter_to_continue()
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