Was able to do some feature mapping, and have preliminary success before image preprocessing

_readme.md

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+opencv scripts for pick-and-place
+
+includes research materials where relevant
+
+targeted at mac OSX mavericks python2.7
+
+opencv installed with homebrew.... each dependency was installed manually .. in this case, eigen, qt, and  a reinstall of cmake.  in some cases, numpy / scipy will need to be installed as well
+
+to do:
+___________________
+
+auto-range for thresholding based on expected values
+
+~~find contours~~
+
+mask object
+
+find  coordinates of masked object
+
+integrate the components into a single toolchain
+
+
+video
+--------------------
+<dl>
+<dt>3dcam</dt>
+<dd>supports the kinect through libfreenect, tweaked some fairly straightforward video feed example, powered from a desktop power supply</dd>
+<dt>cntr</dt>
+<dd>does single largest blob tracking in video, look for the blue dot </dd>
+using static images
+--------------------
+<dt> cell</dt>
+<dd>usage: `python cell.py <image.jpg>`</dd>
+<dd>the image should be within ~/projects/cv/images and should be already thresholded</dd>
+<dd>the bread-and-butter of this project: working on actually identifying the cell on a field</dd>
+<dt>thresholding</dt>
+<dd>the lynchpin of the rest of the software, performs thresholding operations to reduce noise</dd>
+<dd>usage: `python thresholding.py <img.jpg>` </dd>
+<dd>displays the thresholded image and saves it</dd>
+<dt>corner.py</dt>
+<dd>foundation functions for edge detection sloppy but reasonably quick</dd>
+<dt>good_corner</dt>
+<dd>just a demo from the tutorial... pretty accurate but savagely slow.</dd>
+<dt>contours</dt>
+<dd>an attempt to define the longest continuous rect</dd>
+<dt>square and sq2</dt>
+<dd>attempts to find black square</dd>

src/c++/SURF_Homography.cpp

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+/**
+ * @file SURF_Homography
+ * @brief SURF detector + descriptor + FLANN Matcher + FindHomography
+ * @author A. Huaman
+ */
+
+#include <stdio.h>
+#include <iostream>
+#include "opencv2/core/core.hpp"
+#include "opencv2/features2d/features2d.hpp"
+#include "opencv2/highgui/highgui.hpp"
+#include "opencv2/calib3d/calib3d.hpp"
+#include "opencv2/nonfree/features2d.hpp"
+
+using namespace std;
+using namespace cv;
+
+void readme();
+
+/**
+ * @function main
+ * @brief Main function
+ */
+int main( int argc, char** argv )
+{
+  if( argc != 3 )
+  { readme(); return -1; }
+
+  Mat img_object = imread( argv[1], IMREAD_GRAYSCALE );
+  Mat img_scene = imread( argv[2], IMREAD_GRAYSCALE );
+
+  if( !img_object.data || !img_scene.data )
+  { std::cout<< " --(!) Error reading images " << std::endl; return -1; }
+
+  //-- Step 1: Detect the keypoints using SURF Detector
+  int minHessian = 400;
+
+  SurfFeatureDetector detector( minHessian );
+
+  std::vector<KeyPoint> keypoints_object, keypoints_scene;
+
+  detector.detect( img_object, keypoints_object );
+  detector.detect( img_scene, keypoints_scene );
+
+  //-- Step 2: Calculate descriptors (feature vectors)
+  SurfDescriptorExtractor extractor;
+
+  Mat descriptors_object, descriptors_scene;
+
+  extractor.compute( img_object, keypoints_object, descriptors_object );
+  extractor.compute( img_scene, keypoints_scene, descriptors_scene );
+
+  //-- Step 3: Matching descriptor vectors using FLANN matcher
+  FlannBasedMatcher matcher;
+  std::vector< DMatch > matches;
+  matcher.match( descriptors_object, descriptors_scene, matches );
+
+  double max_dist = 0; double min_dist = 100;
+
+  //-- Quick calculation of max and min distances between keypoints
+  for( int i = 0; i < descriptors_object.rows; i++ )
+  { double dist = matches[i].distance;
+    if( dist < min_dist ) min_dist = dist;
+    if( dist > max_dist ) max_dist = dist;
+  }
+
+  printf("-- Max dist : %f \n", max_dist );
+  printf("-- Min dist : %f \n", min_dist );
+
+  //-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
+  std::vector< DMatch > good_matches;
+
+  for( int i = 0; i < descriptors_object.rows; i++ )
+  { if( matches[i].distance < 3*min_dist )
+    { good_matches.push_back( matches[i]); }
+  }
+
+  Mat img_matches;
+  drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
+               good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
+               vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
+
+
+  //-- Localize the object from img_1 in img_2
+  std::vector<Point2f> obj;
+  std::vector<Point2f> scene;
+
+  for( size_t i = 0; i < good_matches.size(); i++ )
+  {
+    //-- Get the keypoints from the good matches
+    obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
+    scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
+  }
+
+  Mat H = findHomography( obj, scene, RANSAC );
+
+  //-- Get the corners from the image_1 ( the object to be "detected" )
+  std::vector<Point2f> obj_corners(4);
+  obj_corners[0] = Point(0,0); obj_corners[1] = Point( img_object.cols, 0 );
+  obj_corners[2] = Point( img_object.cols, img_object.rows ); obj_corners[3] = Point( 0, img_object.rows );
+  std::vector<Point2f> scene_corners(4);
+
+  perspectiveTransform( obj_corners, scene_corners, H);
+
+
+  //-- Draw lines between the corners (the mapped object in the scene - image_2 )
+  Point2f offset( (float)img_object.cols, 0);
+  line( img_matches, scene_corners[0] + offset, scene_corners[1] + offset, Scalar(0, 255, 0), 4 );
+  line( img_matches, scene_corners[1] + offset, scene_corners[2] + offset, Scalar( 0, 255, 0), 4 );
+  line( img_matches, scene_corners[2] + offset, scene_corners[3] + offset, Scalar( 0, 255, 0), 4 );
+  line( img_matches, scene_corners[3] + offset, scene_corners[0] + offset, Scalar( 0, 255, 0), 4 );
+
+  //-- Show detected matches
+  imshow( "Good Matches & Object detection", img_matches );
+
+  waitKey(0);
+
+  return 0;
+}
+
+/**
+ * @function readme
+ */
+void readme()
+{ std::cout << " Usage: ./SURF_Homography <img1> <img2>" << std::endl; }

src/c++/makescript.sh

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+# Todo: make a real makefile
+  g++ -Wall -Wextra `pkg-config --cflags --libs opencv` -o SURF_Homography SURF_Homography.cpp
+
+