mirror of https://github.com/davisking/dlib.git
62 lines
2.5 KiB
Python
Executable File
62 lines
2.5 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. In
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# particular, it shows how you can take a list of images from the command
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# line and display each on the screen with red boxes overlaid on each human
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# face.
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#
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# The examples/faces folder contains some jpg images of people. You can run
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# this program on them and see the detections by executing the
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# following command:
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# ./face_detector.py ../examples/faces/*.jpg
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#
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# This face detector is made using the now classic Histogram of Oriented
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# Gradients (HOG) feature combined with a linear classifier, an image
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# pyramid, and sliding window detection scheme. This type of object detector
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# is fairly general and capable of detecting many types of semi-rigid objects
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# in addition to human faces. Therefore, if you are interested in making
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# your own object detectors then read the train_object_detector.py example
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# program.
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#
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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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#
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# Also note that this example requires scikit-image which can be installed
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# via the command:
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# pip install -U scikit-image
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# Or downloaded from http://scikit-image.org/download.html.
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import sys
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import dlib
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from skimage import io
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detector = dlib.get_frontal_face_detector()
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win = dlib.image_window()
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for f in sys.argv[1:]:
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print("Processing file: {}".format(f))
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img = io.imread(f)
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# The 1 in the second argument indicates that we should upsample the image
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# 1 time. This will make everything bigger and allow us to detect more
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# 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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win.clear_overlay()
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win.set_image(img)
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win.add_overlay(dets)
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dlib.hit_enter_to_continue()
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