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78 lines
3.0 KiB
Python
Executable File
78 lines
3.0 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/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 dlib
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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 = dlib.load_rgb_image(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 i, d in enumerate(dets):
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print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
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i, 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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# Finally, if you really want to you can ask the detector to tell you the score
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# for each detection. The score is bigger for more confident detections.
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# The third argument to run is an optional adjustment to the detection threshold,
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# where a negative value will return more detections and a positive value fewer.
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# Also, the idx tells you which of the face sub-detectors matched. This can be
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# used to broadly identify faces in different orientations.
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if (len(sys.argv[1:]) > 0):
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img = dlib.load_rgb_image(sys.argv[1])
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dets, scores, idx = detector.run(img, 1, -1)
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for i, d in enumerate(dets):
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print("Detection {}, score: {}, face_type:{}".format(
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d, scores[i], idx[i]))
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