mirror of
https://github.com/davisking/dlib.git
synced 2024-11-01 10:14:53 +08:00
55 lines
2.0 KiB
Python
Executable File
55 lines
2.0 KiB
Python
Executable File
#!/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 and boost-python installed. On Ubuntu, this can be done easily by
|
|
# running the command:
|
|
# sudo apt-get install libboost-python-dev 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()))
|