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Diffstat (limited to 'ml/dlib/python_examples/find_candidate_object_locations.py')
| -rwxr-xr-x | ml/dlib/python_examples/find_candidate_object_locations.py | 54 |
1 files changed, 0 insertions, 54 deletions
diff --git a/ml/dlib/python_examples/find_candidate_object_locations.py b/ml/dlib/python_examples/find_candidate_object_locations.py deleted file mode 100755 index a5c386425..000000000 --- a/ml/dlib/python_examples/find_candidate_object_locations.py +++ /dev/null @@ -1,54 +0,0 @@ -#!/usr/bin/python -# -# This example shows how to use find_candidate_object_locations(). The -# function takes an input image and generates a set of candidate rectangles -# which are expected to bound any objects in the image. -# It is based on the paper: -# Segmentation as Selective Search for Object Recognition by Koen E. A. van de Sande, et al. -# -# Typically, you would use this as part of an object detection pipeline. -# find_candidate_object_locations() nominates boxes that might contain an -# object and you then run some expensive classifier on each one and throw away -# the false alarms. Since find_candidate_object_locations() will only generate -# a few thousand rectangles it is much faster than scanning all possible -# rectangles inside an image. -# -# -# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE -# You can install dlib using the command: -# pip install dlib -# -# Alternatively, if you want to compile dlib yourself then go into the dlib -# root folder and run: -# python setup.py install -# or -# python setup.py install --yes USE_AVX_INSTRUCTIONS -# if you have a CPU that supports AVX instructions, since this makes some -# things run faster. -# -# Compiling dlib should work on any operating system so long as you have -# CMake installed. On Ubuntu, this can be done easily by running the -# command: -# sudo apt-get install cmake -# -# Also note that this example requires scikit-image which can be installed -# via the command: -# pip install scikit-image -# Or downloaded from http://scikit-image.org/download.html. - - - -import dlib -from skimage import io - -image_file = '../examples/faces/2009_004587.jpg' -img = io.imread(image_file) - -# Locations of candidate objects will be saved into rects -rects = [] -dlib.find_candidate_object_locations(img, rects, min_size=500) - -print("number of rectangles found {}".format(len(rects))) -for k, d in enumerate(rects): - print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format( - k, d.left(), d.top(), d.right(), d.bottom())) |