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47 lines
1.7 KiB
Python
Executable File
47 lines
1.7 KiB
Python
Executable File
#!/usr/bin/python
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#
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# This example shows how to use find_candidate_object_locations(). The
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# function takes an input image and generates a set of candidate rectangles
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# which are expected to bound any objects in the image.
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# It is based on the paper:
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# Segmentation as Selective Search for Object Recognition by Koen E. A. van de Sande, et al.
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#
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# Typically, you would use this as part of an object detection pipeline.
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# find_candidate_object_locations() nominates boxes that might contain an
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# object and you then run some expensive classifier on each one and throw away
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# the false alarms. Since find_candidate_object_locations() will only generate
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# a few thousand rectangles it is much faster than scanning all possible
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# rectangles inside an image.
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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 dlib
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image_file = '../examples/faces/2009_004587.jpg'
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img = dlib.load_rgb_image(image_file)
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# Locations of candidate objects will be saved into rects
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rects = []
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dlib.find_candidate_object_locations(img, rects, min_size=500)
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print("number of rectangles found {}".format(len(rects)))
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for k, d in enumerate(rects):
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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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