Add detection threshold adjustment to object detection python interface (#140)

* Add cmake option to use external libjpeg on Mac OS

* Add adjust_threshold to python object detector

* Add cmake option to use external libjpeg on Mac OS

* Add adjust_threshold to python object detector

* Revert "Add cmake option to use external libjpeg on Mac OS"

This reverts commit 01f7fd13ea3f2b519312900333f9b68847fd1633.

* Update detector example to set adjust_threshold

python_examples/face_detector.py

@@ -71,11 +71,13 @@ for f in sys.argv[1:]:
 
 # Finally, if you really want to you can ask the detector to tell you the score
 # for each detection.  The score is bigger for more confident detections.
+# The third argument to run is an optional adjustment to the detection threshold,
+# where a negative value will return more detections and a positive value fewer.
 # Also, the idx tells you which of the face sub-detectors matched.  This can be
 # used to broadly identify faces in different orientations.
 if (len(sys.argv[1:]) > 0):
     img = io.imread(sys.argv[1])
-    dets, scores, idx = detector.run(img, 1)
+    dets, scores, idx = detector.run(img, 1, -1)
     for i, d in enumerate(dets):
         print("Detection {}, score: {}, face_type:{}".format(
             d, scores[i], idx[i]))

tools/python/src/object_detection.cpp

@@ -328,7 +328,7 @@ ensures \n\
       detector.  If you don't know how many times you want to upsample then \n\
       don't provide a value for upsample_num_times and an appropriate \n\
       default will be used.")
-        .def("run", run_rect_detector, (arg("image"), arg("upsample_num_times")=0),
+        .def("run", run_rect_detector, (arg("image"), arg("upsample_num_times")=0, arg("adjust_threshold")=0.0),
 "requires \n\
     - image is a numpy ndarray containing either an 8bit grayscale or RGB \n\
       image. \n\
@@ -350,7 +350,7 @@ ensures \n\
         .def("__init__", make_constructor(&load_object_from_file<type>),
 "Loads a simple_object_detector from a file that contains the output of the \n\
 train_simple_object_detector() routine.")
-        .def("__call__", &type::run_detector1, (arg("image"), arg("upsample_num_times")),
+        .def("__call__", &type::run_detector1, (arg("image"), arg("upsample_num_times"), arg("adjust_threshold")=0.0),
 "requires \n\
     - image is a numpy ndarray containing either an 8bit grayscale or RGB \n\
       image. \n\

tools/python/src/simple_object_detector_py.h

@@ -37,6 +37,7 @@ namespace dlib
         dlib::simple_object_detector& detector,
         boost::python::object img,
         const unsigned int upsampling_amount,
+        const double adjust_threshold,
         std::vector<double>& detection_confidences,
         std::vector<double>& weight_indices
     )
@@ -51,7 +52,7 @@ namespace dlib
             array2d<unsigned char> temp;
             if (upsampling_amount == 0)
             {
-                detector(numpy_gray_image(img), rect_detections, 0.0);
+                detector(numpy_gray_image(img), rect_detections, adjust_threshold);
                 split_rect_detections(rect_detections, rectangles,
                                       detection_confidences, weight_indices);
                 return rectangles;
@@ -66,7 +67,7 @@ namespace dlib
                     pyramid_up(temp);
                 }
 
-                detector(temp, rect_detections, 0.0);
+                detector(temp, rect_detections, adjust_threshold);
                 for (unsigned long i = 0; i < rect_detections.size(); ++i)
                     rect_detections[i].rect = pyr.rect_down(rect_detections[i].rect,
                                                             upsampling_amount);
@@ -81,7 +82,7 @@ namespace dlib
             array2d<rgb_pixel> temp;
             if (upsampling_amount == 0)
             {
-                detector(numpy_rgb_image(img), rect_detections, 0.0);
+                detector(numpy_rgb_image(img), rect_detections, adjust_threshold);
                 split_rect_detections(rect_detections, rectangles,
                                       detection_confidences, weight_indices);
                 return rectangles;
@@ -96,7 +97,7 @@ namespace dlib
                     pyramid_up(temp);
                 }
 
-                detector(temp, rect_detections, 0.0);
+                detector(temp, rect_detections, adjust_threshold);
                 for (unsigned long i = 0; i < rect_detections.size(); ++i)
                     rect_detections[i].rect = pyr.rect_down(rect_detections[i].rect,
                                                             upsampling_amount);
@@ -116,19 +117,23 @@ namespace dlib
         dlib::simple_object_detector& detector,
         boost::python::object img,
         const unsigned int upsampling_amount
+
     )
     {
         std::vector<double> detection_confidences;
         std::vector<double> weight_indices;
+        const double adjust_threshold = 0.0;
 
         return run_detector_with_upscale1(detector, img, upsampling_amount,
+                                          adjust_threshold,
                                           detection_confidences, weight_indices);
     }
 
     inline boost::python::tuple run_rect_detector (
         dlib::simple_object_detector& detector,
         boost::python::object img,
-        const unsigned int upsampling_amount)
+        const unsigned int upsampling_amount,
+        const double adjust_threshold)
     {
         boost::python::tuple t;
 
@@ -137,6 +142,7 @@ namespace dlib
         std::vector<rectangle> rectangles;
 
         rectangles = run_detector_with_upscale1(detector, img, upsampling_amount,
+                                                adjust_threshold,
                                                 detection_confidences, weight_indices);
 
         return boost::python::make_tuple(rectangles,