1 files changed, 54 insertions, 0 deletions
diff --git a/ml/dlib/python_examples/find_candidate_object_locations.py b/ml/dlib/python_examples/find_candidate_object_locations.py
new file mode 100755
index 000000000..a5c386425
--- /dev/null
+++ b/ml/dlib/python_examples/find_candidate_object_locations.py
@@ -0,0 +1,54 @@
+#!/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()))