Initial commit, with some minor changes from orig (date, exec bit, line endings, /usr/bin/env)

ext/cvhaarclassifiercascade.cpp

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+/************************************************************
+
+  cvhaarclassifercascade.cpp -
+
+  $Author: lsxi $
+
+  Copyright (C) 2005-2007 Masakazu Yonekura
+
+************************************************************/
+#include "cvhaarclassifiercascade.h"
+/*
+ * Document-class: OpenCV::CvHaarClassifierCascade
+ *
+ * CvHaarClassifierCascade object is "fast-object-detector".
+ * This detector can discover object (e.g. human's face) from image.
+ *
+ * Find face-area from picture "lena"...
+ * link:../images/face_detect_from_lena.jpg
+ */
+__NAMESPACE_BEGIN_OPENCV
+__NAMESPACE_BEGIN_CVHAARCLASSIFERCASCADE
+
+VALUE rb_klass;
+
+VALUE
+rb_class()
+{
+  return rb_klass;
+}
+
+void define_ruby_class()
+{
+  if (rb_klass)
+    return;
+  /* 
+   * opencv = rb_define_module("OpenCV");
+   * 
+   * note: this comment is used by rdoc.
+   */
+  VALUE opencv = rb_module_opencv();
+  
+  rb_klass = rb_define_class_under(opencv, "CvHaarClassifierCascade", rb_cObject);
+  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
+rb_allocate(VALUE klass)
+{
+  return OPENCV_OBJECT(klass, 0);
+}
+
+/*
+ * call-seq:
+ *   CvHaarClassiferCascade.load(<i>path</i>) -> object-detector
+ * 
+ * Load trained cascade of haar classifers from file.
+ * Object detection classifiers are stored in XML or YAML files.
+ * sample of object detection classifier files is included by OpenCV.
+ *
+ * You can found these at
+ *    C:\Program Files\OpenCV\data\haarcascades\*.xml (Windows, default install path)
+ *
+ * e.g. you want to try to detect human's face.
+ *    detector = CvHaarClassiferCascade.load("haarcascade_frontalface_alt.xml")
+ */
+VALUE
+rb_load(VALUE klass, VALUE path)
+{
+  CvHaarClassifierCascade *cascade = (CvHaarClassifierCascade*)cvLoad(StringValueCStr(path), 0, 0, 0);
+  if(!CV_IS_HAAR_CLASSIFIER(cascade))
+    rb_raise(rb_eTypeError, "invalid format haar classifier cascade file.");
+  return OPENCV_OBJECT(rb_klass, cascade);
+}
+
+VALUE
+rb_save(VALUE self, VALUE path)
+{
+  rb_raise(rb_eNotImpError, "");
+}
+
+/*
+ * 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[,scale_factor = 1.1, min_neighbor = 3, min_size = CvSize.new(0,0)]){|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)
+ *     The factor by which the search window is scaled between the subsequent scans, for example, 1.1 mean increasing window by 10%.
+ * * 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
+ *     Minimum window size. By default, it is set to size of samples the classifier has been trained on.
+ */
+VALUE 
+rb_detect_objects(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), 0, 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++)
+      rb_yield(REFER_OBJECT(cCvAvgComp::rb_class(), cvGetSeqElem(seq, i), storage));      
+  }
+  return result;
+}
+
+/*
+ * call-seq:
+ *   detect_objects_with_pruning(image[,scale_factor = 1.1, min_neighbor = 3, min_size = CvSize.new(0,0)]) -> cvseq(include CvAvgComp object)
+ *   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++)
+      rb_yield(REFER_OBJECT(cCvAvgComp::rb_class(), cvGetSeqElem(seq, i), storage));      
+  }
+  return result;
+}
+
+__NAMESPACE_END_CVHAARCLASSIFERCASCADE
+__NAMESPACE_END_OPENCV