modified arguments of CvHaarClassifierCascade#detect_objects to set options more easily

ext/opencv/cvhaarclassifiercascade.cpp

@@ -43,7 +43,6 @@ void define_ruby_class()
   rb_define_alloc_func(rb_klass, rb_allocate);
   rb_define_singleton_method(rb_klass, "load", RUBY_METHOD_FUNC(rb_load), 1);
   rb_define_method(rb_klass, "detect_objects", RUBY_METHOD_FUNC(rb_detect_objects), -1);
-  rb_define_method(rb_klass, "detect_objects_with_pruning", RUBY_METHOD_FUNC(rb_detect_objects_with_pruning), -1);
 }
 
 VALUE
@@ -86,74 +85,72 @@ rb_load(VALUE klass, VALUE path)
 
 /*
  * call-seq:
- *   detect_objects(image[,scale_factor = 1.1, min_neighbor = 3, min_size = CvSize.new(0,0)]) -> cvseq(include CvAvgComp object)
+ *   detect_objects(image[, options]) -> cvseq(include CvAvgComp object)
- *   detect_objects(image[,scale_factor = 1.1, min_neighbor = 3, min_size = CvSize.new(0,0)]){|cmp| ... } -> cvseq(include CvAvgComp object)
+ *   detect_objects(image[, options]){|cmp| ... } -> cvseq(include CvAvgComp object)
  *
  * Detects objects in the image. This method finds rectangular regions in the
  * given image that are likely to contain objects the cascade has been trained
  * for and return those regions as a sequence of rectangles.
  *
- * * scale_factor (should be > 1.0)
+ * * <i>option</i> should be Hash include these keys.
- *     The factor by which the search window is scaled between the subsequent scans, for example, 1.1 mean increasing window by 10%.
+ *   :scale_factor (should be > 1.0)
- * * min_neighbors
+ *      The factor by which the search window is scaled between the subsequent scans,
+ *      1.1 mean increasing window by 10%.
+ *   :storage
+ *      Memory storage to store the resultant sequence of the object candidate rectangles
+ *   :flags
+ *      Mode of operation. Currently the only flag that may be specified is CV_HAAR_DO_CANNY_PRUNING .
+ *      If it is set, the function uses Canny edge detector to reject some image regions that contain
+ *      too few or too much edges and thus can not contain the searched object. The particular threshold
+ *      values are tuned for face detection and in this case the pruning speeds up the processing
+ *   :min_neighbors
  *      Minimum number (minus 1) of neighbor rectangles that makes up an object.
  *      All the groups of a smaller number of rectangles than min_neighbors - 1 are rejected.
  *      If min_neighbors is 0, the function does not any grouping at all and returns all the detected
  *      candidate rectangles, whitch many be useful if the user wants to apply a customized grouping procedure.
- * * min_size
+ *   :min_size
- *     Minimum window size. By default, it is set to size of samples the classifier has been trained on.
+ *      Minimum window size. By default, it is set to size of samples the classifier has been
+ *      trained on (~20x20 for face detection).
+ *   :max_size
+ *      aximum window size to use. By default, it is set to the size of the image.
  */
 VALUE
 rb_detect_objects(int argc, VALUE *argv, VALUE self)
 { 
-  VALUE image, storage, scale_factor, min_neighbors, min_size, result;
+  VALUE image, options;
-  rb_scan_args(argc, argv, "14", &image, &storage, &scale_factor, &min_neighbors, &min_size);
+  rb_scan_args(argc, argv, "11", &image, &options);
+
   if (!rb_obj_is_kind_of(image, cCvMat::rb_class()))
     rb_raise(rb_eTypeError, "argument 1(target-image) should be %s.", rb_class2name(cCvMat::rb_class()));
-  double scale = IF_DBL(scale_factor, 1.1);
+
-  if (!(scale > 1.0))
+  double scale_factor;
-    rb_raise(rb_eArgError, "argument 2 (scale factor) must > 1.0.");
+  int flags, min_neighbors;
-  storage = CHECK_CVMEMSTORAGE(storage);
+  CvSize min_size, max_size;
-  CvSeq *seq = cvHaarDetectObjects(CVMAT(image), CVHAARCLASSIFIERCASCADE(self), CVMEMSTORAGE(storage),
+  VALUE storage_val;
-                                   scale, IF_INT(min_neighbors, 3), 0, NIL_P(min_size) ? cvSize(0,0) : VALUE_TO_CVSIZE(min_size));
+  if (NIL_P(options)) {
-  result = cCvSeq::new_sequence(cCvSeq::rb_class(), seq, cCvAvgComp::rb_class(), storage);
+    scale_factor = 1.1;
-  if (rb_block_given_p()) {
+    flags = 0;
-    for(int i = 0; i < seq->total; i++)
+    min_neighbors = 3;
-      rb_yield(REFER_OBJECT(cCvAvgComp::rb_class(), cvGetSeqElem(seq, i), storage));      
+    min_size = max_size = cvSize(0, 0);
+    storage_val = cCvMemStorage::new_object();
   }
-  return result;
+  else {
+    scale_factor = IF_DBL(LOOKUP_CVMETHOD(options, "scale_factor"), 1.1);
+    flags = IF_INT(LOOKUP_CVMETHOD(options, "flags"), 0);
+    min_neighbors = IF_INT(LOOKUP_CVMETHOD(options, "min_neighbors"), 3);
+    VALUE min_size_val = LOOKUP_CVMETHOD(options, "min_size");
+    min_size = NIL_P(min_size_val) ? cvSize(0, 0) : VALUE_TO_CVSIZE(min_size_val);
+    VALUE max_size_val = LOOKUP_CVMETHOD(options, "max_size");
+    max_size = NIL_P(max_size_val) ? cvSize(0, 0) : VALUE_TO_CVSIZE(max_size_val);
+    storage_val = CHECK_CVMEMSTORAGE(LOOKUP_CVMETHOD(options, "storage"));
   }
 
-/*
+  CvSeq *seq = cvHaarDetectObjects(CVMAT(image), CVHAARCLASSIFIERCASCADE(self), CVMEMSTORAGE(storage_val),
- * call-seq:
+                                   scale_factor, min_neighbors, flags, min_size, max_size);
- *   detect_objects_with_pruning(image[,scale_factor = 1.1, min_neighbor = 3, min_size = CvSize.new(0,0)]) -> cvseq(include CvAvgComp object)
+  VALUE result = cCvSeq::new_sequence(cCvSeq::rb_class(), seq, cCvAvgComp::rb_class(), storage_val);
- *   detect_objects_with_pruning(image[,scale_factor = 1.1, min_neighbor = 3, min_size = CvSize.new(0,0)]){|cmp| ... } -> cvseq(include CvAvgComp object)
- *
- * Almost same to #detect_objects (Return detected objects).
- * 
- * Before scanning to image, Canny edge detector to reject some image regions
- * that contain too few or too much edges, and thus can not contain the searched object.
- *
- *   note: The particular threshold values are tuned for face detection.
- *         And in this case the pruning speeds up the processing.
- */ 
-VALUE
-rb_detect_objects_with_pruning(int argc, VALUE *argv, VALUE self)
-{
-  VALUE image, storage, scale_factor, min_neighbors, min_size, result;
-  rb_scan_args(argc, argv, "14", &image, &storage, &scale_factor, &min_neighbors, &min_size);
-  if (!rb_obj_is_kind_of(image, cCvMat::rb_class()))
-    rb_raise(rb_eTypeError, "argument 1(target-image) should be %s.", rb_class2name(cCvMat::rb_class()));
-  double scale = IF_DBL(scale_factor, 1.1);
-  if (!(scale > 1.0))
-    rb_raise(rb_eArgError, "argument 2 (scale factor) must > 1.0.");
-  storage = CHECK_CVMEMSTORAGE(storage);
-  CvSeq *seq = cvHaarDetectObjects(CVMAT(image), CVHAARCLASSIFIERCASCADE(self), CVMEMSTORAGE(storage),
-                                   scale, IF_INT(min_neighbors, 3), CV_HAAR_DO_CANNY_PRUNING, NIL_P(min_size) ? cvSize(0,0) : VALUE_TO_CVSIZE(min_size));
-  result = cCvSeq::new_sequence(cCvSeq::rb_class(), seq, cCvAvgComp::rb_class(), storage);
   if (rb_block_given_p()) {
-    for(int i = 0; i < seq->total; i++)
+    for(int i = 0; i < seq->total; ++i)
-      rb_yield(REFER_OBJECT(cCvAvgComp::rb_class(), cvGetSeqElem(seq, i), storage));      
+      rb_yield(REFER_OBJECT(cCvAvgComp::rb_class(), cvGetSeqElem(seq, i), storage_val));
   }
   return result;
 }

ext/opencv/cvhaarclassifiercascade.h

@@ -26,7 +26,6 @@ VALUE rb_allocate(VALUE klass);
 
 VALUE rb_load(VALUE klass, VALUE path);
 VALUE rb_detect_objects(int argc, VALUE *argv, VALUE self);
-VALUE rb_detect_objects_with_pruning(int argc, VALUE *argv, VALUE self);
 
 __NAMESPACE_END_CVHAARCLASSIFERCASCADE
 inline CvHaarClassifierCascade *CVHAARCLASSIFIERCASCADE(VALUE object) {

ext/opencv/opencv.cpp

@@ -413,6 +413,9 @@ define_ruby_module()
   /* Flags of window */
   rb_define_const(rb_module, "CV_WINDOW_AUTOSIZE", INT2FIX(CV_WINDOW_AUTOSIZE));
 
+  /* Object detection mode */
+  rb_define_const(rb_module, "CV_HAAR_DO_CANNY_PRUNING", INT2FIX(CV_HAAR_DO_CANNY_PRUNING));
+  
   VALUE inversion_method = rb_hash_new();
   /* {:lu, :svd, :svd_sym(:svd_symmetric)}: Inversion method */
   rb_define_const(rb_module, "INVERSION_METHOD", inversion_method);

ext/opencv/opencv.h

@@ -145,6 +145,7 @@ extern "C"{
 #define IF_DEPTH(val, ifnone) NIL_P(val) ? ifnone : FIX2INT(val)
 
 #define RESIST_CVMETHOD(hash, str, value) rb_hash_aset(hash, ID2SYM(rb_intern(str)), INT2FIX(value))
+#define LOOKUP_CVMETHOD(hash, key_as_cstr) (rb_hash_lookup(hash, ID2SYM(rb_intern(key_as_cstr))))
 
 #define maxint(a,b) ({int _a = (a), _b = (b); _a > _b ? _a : _b; })
 

test/test_cvhaarclassifiercascade.rb

@@ -42,6 +42,22 @@ class TestCvHaarClassifierCascade < OpenCVTestCase
     assert_equal(CvSeq, detected.class)
     assert_equal(1, detected.size)
     assert_equal(CvAvgComp, detected[0].class)
+
+    detected = @cascade.detect_objects(img, :scale_factor => 2.0, :flags => CV_HAAR_DO_CANNY_PRUNING,
+                                       :min_neighbors => 5, :min_size => CvSize.new(10, 10),
+                                       :max_size => CvSize.new(100, 100))
+    assert_equal(CvSeq, detected.class)
+    assert_equal(1, detected.size)
+    assert_equal(CvAvgComp, detected[0].class)
+    assert_equal(109, detected[0].x)
+    assert_equal(102, detected[0].y)
+    assert_equal(80, detected[0].width)
+    assert_equal(80, detected[0].height)
+    assert_equal(7, detected[0].neighbors)
+
+    assert_raise(TypeError) {
+      @cascade.detect_objects('foo')
+    }
   end
 end
 

test/test_opencv.rb

@@ -102,6 +102,9 @@ class TestOpenCV < OpenCVTestCase
 
     # Flags of window
     assert_equal(1, CV_WINDOW_AUTOSIZE)
+
+    # Object detection mode
+    assert_equal(1, CV_HAAR_DO_CANNY_PRUNING)
   end
 
   def test_symbols