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86 lines
3.5 KiB
Python
Executable File
86 lines
3.5 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 shows how to run a CNN based face detector using dlib. The
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# example loads a pretrained model and uses it to find faces in images. The
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# CNN model is much more accurate than the HOG based model shown in the
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# face_detector.py example, but takes much more computational power to
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# run, and is meant to be executed on a GPU to attain reasonable speed.
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#
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# You can download the pre-trained model from:
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# http://dlib.net/files/mmod_human_face_detector.dat.bz2
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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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# ./cnn_face_detector.py mmod_human_face_detector.dat ../examples/faces/*.jpg
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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 --yes DLIB_USE_CUDA
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# if you have a CPU that supports AVX instructions, you have an Nvidia GPU
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# and you have CUDA installed since this makes things run *much* faster.
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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 scikit-image which can be installed
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# via the command:
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# pip install 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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if len(sys.argv) < 3:
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print(
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"Call this program like this:\n"
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" ./cnn_face_detector.py mmod_human_face_detector.dat ../examples/faces/*.jpg\n"
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"You can get the mmod_human_face_detector.dat file from:\n"
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" http://dlib.net/files/mmod_human_face_detector.dat.bz2")
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exit()
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cnn_face_detector = dlib.cnn_face_detection_model_v1(sys.argv[1])
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win = dlib.image_window()
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for f in sys.argv[2:]:
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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 = cnn_face_detector(img, 1)
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'''
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This detector returns a mmod_rectangles object. This object contains a list of mmod_rectangle objects.
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These objects can be accessed by simply iterating over the mmod_rectangles object
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The mmod_rectangle object has two member variables, a dlib.rectangle object, and a confidence score.
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It is also possible to pass a list of images to the detector.
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- like this: dets = cnn_face_detector([image list], upsample_num, batch_size = 128)
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In this case it will return a mmod_rectangless object.
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This object behaves just like a list of lists and can be iterated over.
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'''
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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: {} Confidence: {}".format(
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i, d.rect.left(), d.rect.top(), d.rect.right(), d.rect.bottom(), d.confidence))
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rects = dlib.rectangles()
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rects.extend([d.rect for d in dets])
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win.clear_overlay()
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win.set_image(img)
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win.add_overlay(rects)
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dlib.hit_enter_to_continue()
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