Updated the Python API, train_simple_object_detector() so you can call it

directly on already loaded data rather than needing to use an XML file as
input.

python_examples/train_object_detector.py

@@ -99,3 +99,33 @@ for f in glob.glob(faces_folder+"/*.jpg"):
     win.add_overlay(dets)
     raw_input("Hit enter to continue")
 
+
+
+
+
+# Finally, note that you don't have to use the XML based input to
+# train_simple_object_detector().  If you have already loaded your training
+# images and bounding boxes for the objects then you can call it as shown
+# below.
+
+# You just need to put your images into a list.
+images = [io.imread(faces_folder + '/2008_002506.jpg'), io.imread(faces_folder + '/2009_004587.jpg') ]
+# Then for each image you make a list of rectangles which give the pixel
+# locations of the edges of the boxes.
+boxes_img1 = ([dlib.rectangle(left=329, top=78, right=437, bottom=186), 
+    dlib.rectangle(left=224, top=95, right=314, bottom=185), 
+    dlib.rectangle(left=125, top=65, right=214, bottom=155) ] )
+boxes_img2 = ([dlib.rectangle(left=154, top=46,  right=228, bottom=121 ),
+               dlib.rectangle(left=266, top=280, right=328, bottom=342) ] )
+# And then you aggregate those lists of boxes into one big list and then call
+# train_simple_object_detector().
+boxes = [boxes_img1, boxes_img2]
+
+dlib.train_simple_object_detector(images, boxes, "detector2.svm", options)
+
+# Now let's load the trained detector and look at its HOG filter!
+detector2 = dlib.simple_object_detector("detector2.svm")
+win_det.set_image(detector2)
+raw_input("Hit enter to continue")
+
+

tools/python/src/object_detection.cpp

@@ -220,6 +220,39 @@ string print_simple_test_results(const simple_test_results& r)
     return sout.str();
 }
 
+inline void train_simple_object_detector_on_images_py (
+    const object& pyimages,
+    const object& pyboxes,
+    const std::string& detector_output_filename,
+    const simple_object_detector_training_options& options 
+)
+{
+    const unsigned long num_images = len(pyimages);
+    if (num_images != len(pyboxes))
+        throw dlib::error("The length of the boxes list must match the length of the images list.");
+
+    // We never have any ignore boxes for this version of the API.
+    std::vector<std::vector<rectangle> > ignore(num_images), boxes(num_images);
+    dlib::array<array2d<rgb_pixel> > images(num_images);
+    // Now copy the data into dlib based objects so we can call the trainer.
+    for (unsigned long i = 0; i < num_images; ++i)
+    {
+        const unsigned long num_boxes = len(pyboxes[i]);
+        for (unsigned long j = 0; j < num_boxes; ++j)
+            boxes[i].push_back(extract<rectangle>(pyboxes[i][j]));
+
+        object img = pyimages[i];
+        if (is_gray_python_image(img))
+            assign_image(images[i], numpy_gray_image(img));
+        else if (is_rgb_python_image(img))
+            assign_image(images[i], numpy_rgb_image(img));
+        else
+            throw dlib::error("Unsupported image type, must be 8bit gray or RGB image.");
+    }
+
+    train_simple_object_detector_on_images("", images, boxes, ignore, detector_output_filename, options);
+}
+
 // ----------------------------------------------------------------------------------------
 
 void bind_object_detection()
@@ -340,6 +373,39 @@ ensures \n\
     !*/
         );
 
+    def("train_simple_object_detector", train_simple_object_detector_on_images_py,
+        (arg("images"), arg("boxes"), arg("detector_output_filename"), arg("options")),
+"requires \n\
+    - options.C > 0 \n\
+    - len(images) == len(boxes) \n\
+    - images should be a list of numpy matrices that represent images, either RGB or grayscale. \n\
+    - boxes should be a list of lists of dlib.rectangle object. \n\
+ensures \n\
+    - Uses the structural_object_detection_trainer to train a \n\
+      simple_object_detector based on the labeled images and bounding boxes.  \n\
+    - This function will apply a reasonable set of default parameters and \n\
+      preprocessing techniques to the training procedure for simple_object_detector \n\
+      objects.  So the point of this function is to provide you with a very easy \n\
+      way to train a basic object detector.   \n\
+    - The trained object detector is serialized to the file detector_output_filename." 
+    /*!
+        requires
+            - options.C > 0
+            - len(images) == len(boxes)
+            - images should be a list of numpy matrices that represent images, either RGB or grayscale.
+            - boxes should be a dlib.rectangles object (i.e. an array of rectangles).
+            - boxes should be a list of lists of dlib.rectangle object.
+        ensures
+            - Uses the structural_object_detection_trainer to train a
+              simple_object_detector based on the labeled images and bounding boxes. 
+            - This function will apply a reasonable set of default parameters and
+              preprocessing techniques to the training procedure for simple_object_detector
+              objects.  So the point of this function is to provide you with a very easy
+              way to train a basic object detector.  
+            - The trained object detector is serialized to the file detector_output_filename.
+    !*/
+        );
+
     def("test_simple_object_detector", test_simple_object_detector,
         (arg("dataset_filename"), arg("detector_filename")),
 "ensures \n\

tools/python/src/simple_object_detector.h

@@ -95,15 +95,22 @@ namespace dlib
             const simple_object_detector_training_options& options
         )
         {
-            image_dataset_metadata::dataset data;
-            load_image_dataset_metadata(data, dataset_filename);
 
             std::ostringstream sout;
+            // Note that the 1/16 factor is here because we will try to upsample the image
+            // 2 times to accommodate small boxes.  We also take the max because we want to
+            // lower bound the size of the smallest recommended box.  This is because the
+            // 8x8 HOG cells can't really deal with really small object boxes.
             sout << "Error!  An impossible set of object boxes was given for training. ";
             sout << "All the boxes need to have a similar aspect ratio and also not be ";
-            sout << "smaller than about " << options.detection_window_size/16 << " pixels in area. ";
-            sout << "The following images contain invalid boxes:\n";
+            sout << "smaller than about " << std::max<long>(20*20,options.detection_window_size/16) << " pixels in area. ";
+
             std::ostringstream sout2;
+            if (dataset_filename.size() != 0)
+            {
+                sout << "The following images contain invalid boxes:\n";
+                image_dataset_metadata::dataset data;
+                load_image_dataset_metadata(data, dataset_filename);
                 for (unsigned long i = 0; i < removed.size(); ++i)
                 {
                     if (removed[i].size() != 0)
@@ -112,14 +119,19 @@ namespace dlib
                         sout2 << "  " << imgname << "\n";
                     }
                 }
+            }
             throw error("\n"+wrap_string(sout.str()) + "\n" + sout2.str());
         }
     }
 
 // ----------------------------------------------------------------------------------------
 
-    inline void train_simple_object_detector (
-        const std::string& dataset_filename,
+    template <typename image_array>
+    inline void train_simple_object_detector_on_images (
+        const std::string& dataset_filename, // can be "" if it's not applicable
+        image_array& images,
+        std::vector<std::vector<rectangle> >& boxes,
+        std::vector<std::vector<rectangle> >& ignore,
         const std::string& detector_output_filename,
         const simple_object_detector_training_options& options 
     )
@@ -129,12 +141,13 @@ namespace dlib
         if (options.epsilon <= 0)
             throw error("Invalid epsilon value given to train_simple_object_detector(), epsilon must be > 0.");
 
-        dlib::array<array2d<rgb_pixel> > images;
-        std::vector<std::vector<rectangle> > boxes, ignore;
-        ignore = load_image_dataset(images, boxes, dataset_filename);
+        if (images.size() != boxes.size())
+            throw error("The list of images must have the same length as the list of boxes.");
+        if (images.size() != ignore.size())
+            throw error("The list of images must have the same length as the list of ignore boxes.");
 
         if (impl::contains_any_boxes(boxes) == false)
-            throw error("Error, the dataset in " + dataset_filename + " does not have any labeled object boxes in it.");
+            throw error("Error, the training dataset does not have any labeled object boxes in it.");
 
         typedef scan_fhog_pyramid<pyramid_down<6> > image_scanner_type; 
         image_scanner_type scanner;
@@ -215,6 +228,21 @@ namespace dlib
         }
     }
 
+// ----------------------------------------------------------------------------------------
+
+    inline void train_simple_object_detector (
+        const std::string& dataset_filename,
+        const std::string& detector_output_filename,
+        const simple_object_detector_training_options& options 
+    )
+    {
+        dlib::array<array2d<rgb_pixel> > images;
+        std::vector<std::vector<rectangle> > boxes, ignore;
+        ignore = load_image_dataset(images, boxes, dataset_filename);
+
+        train_simple_object_detector_on_images(dataset_filename, images, boxes, ignore, detector_output_filename, options);
+    }
+
 // ----------------------------------------------------------------------------------------
 
     struct simple_test_results