Exercises_5 modifications

Exercises_5.cpp

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 //Exercises at end of Chapter 5
 // 1-6
 #include <opencv2/opencv.hpp>
@@ -211,8 +149,4 @@ int main( int argc, const char** argv )
 	cout << "6" << endl;
 	waitKey(-1); //Wait here until any key pressed
 	return 0;
-
 }
-Exercises_5.cpp
-Open with
-Displaying CMakeLists.txt.

README.md

@@ -3,26 +3,22 @@
 
 This is the example code that accompanies Learning OpenCV 3 by Adrian Kaehler and Gary Bradski (9781491937990). 
 
-**In progress May 24, 2017**
+--In progress May 24, 2017--
+**In progress June 13, 2017**
 
-This code is in progress, currently at Chapter 15, bear with us, the code is coming, both Authors are busy, as always, building startups. If you want to help, request to join the project. Keep pull requests to one function at a time. It doesn't have to be just examples from the book, it can be other code snippets from the book, exercises, new functionality or even a "how to use" opencv_contrib functions.
+This code is in progress, currently at --Chapter 15-- Chapter 21 (but missing 19, want to make 18 also read from disk, and add in a few other functions), bear with us, the code is coming, both Authors are busy, as always, building startups. If you want to help, request to join the project. Keep pull requests to one function at a time. It doesn't have to be just examples from the book, it can be other code snippets from the book, **exercises**, new functionality or even a "how to use" opencv_contrib functions.
 
 Note, for your interest, included here is a _Docker_ file that
+* Shares a directory with the host operating system and shares the camera
 * Loads Ubuntu 16.04 
 * Loads all dependencies for OpenCV 3.2 and opencv_contrib
 * Loads and builds OpenCV 3.2 and opencv_contrib into a build directory 
   * executable files end up in opencv-3.2.0/build/bin
 * Git clones the code (and Docker file) for Learning OpenCV 3 and builds it
   * executable files end up in Learning_OpenCV-3_examples/build
 * To get to the top level directory, just type: `cd`
-* If you just want to do this "by hand" in your home, replace the "RUN"s below with "sudo"
 
 
-New code is added regularly. We apologize that all of the code from the
-book is not up yet, but it will be uploaded as soon as possible. Please
-bear with us, the authors and the staff are very busy people and we are
-doing our best.
-
 Click the Download Zip button to the right to download example code.
 
 Visit the catalog page [here](http://shop.oreilly.com/product/0636920044765.do).

example_18-01.cpp

@@ -2,7 +2,7 @@
 // the
 // requested number of views, and calibrating the camera
 //
-// **GARY WILL MODIFY  THIS TO ALSO READ FROM DISK**
+// **GARY WILL MODIFY THIS TO ALSO READ FROM DISK**
 //
 #include <opencv2/opencv.hpp>
 #include <iostream>

example_21-01.cpp

@@ -0,0 +1,119 @@
+#include <opencv2/opencv.hpp>
+#include <stdio.h>
+#include <iostream>
+
+using namespace std;
+using namespace cv;
+
+void help(char **argv) {
+  cout << "\n\n"
+       << "Using binary decision trees to learn to recognize poisonous\n"
+       << "    from edible mushrooms based on visible attributes.\n" 
+       << "    This program demonstrates how to create and a train a \n"
+       << "    decision tree using ml library in OpenCV.\n"
+       << "Call:\n" << argv[0] << " <csv-file-path>\n\n"
+       << endl;
+}
+
+int main(int argc, char *argv[]) {
+  // If the caller gave a filename, great. Otherwise, use a default.
+  //
+  const char *csv_file_name = argc >= 2 ? argv[1] : "../mushroom/agaricus-lepiota.data";
+  cout << "OpenCV Version: " << CV_VERSION << endl;
+  help(argv);
+
+  // Read in the CSV file that we were given.
+  //
+  cv::Ptr<cv::ml::TrainData> data_set =
+      cv::ml::TrainData::loadFromCSV(csv_file_name, // Input file name
+                                     0, // Header lines (ignore this many)
+                                     0, // Responses are (start) at thie column
+                                     1, // Inputs start at this column
+                                     "cat[0-22]" // All 23 columns are categorical
+                                     );
+  // Use defaults for delimeter (',') and missch ('?')
+  // Verify that we read in what we think.
+  //
+  int n_samples = data_set->getNSamples();
+  if (n_samples == 0) {
+    cerr << "Could not read file: " << csv_file_name << endl;
+    exit(-1);
+  } else {
+    cout << "Read " << n_samples << " samples from " << csv_file_name << endl;
+  }
+
+  // Split the data, so that 90% is train data
+  //
+  data_set->setTrainTestSplitRatio(0.90, false);
+  int n_train_samples = data_set->getNTrainSamples();
+  int n_test_samples = data_set->getNTestSamples();
+  cout << "Found " << n_train_samples << " Train Samples, and "
+       << n_test_samples << " Test Samples" << endl;
+
+  // Create a DTrees classifier.
+  //
+  cv::Ptr<cv::ml::RTrees> dtree = cv::ml::RTrees::create();
+  // set parameters
+  //
+  // These are the parameters from the old mushrooms.cpp code
+  // Set up priors to penalize "poisonous" 10x as much as "edible"
+  //
+  float _priors[] = {1.0, 10.0};
+  cv::Mat priors(1, 2, CV_32F, _priors);
+  dtree->setMaxDepth(8);
+  dtree->setMinSampleCount(10);
+  dtree->setRegressionAccuracy(0.01f);
+  dtree->setUseSurrogates(false /* true */);
+  dtree->setMaxCategories(15);
+  dtree->setCVFolds(0 /*10*/); // nonzero causes core dump
+  dtree->setUse1SERule(true);
+  dtree->setTruncatePrunedTree(true);
+  // dtree->setPriors( priors );
+  dtree->setPriors(cv::Mat()); // ignore priors for now...
+  // Now train the model
+  // NB: we are only using the "train" part of the data set
+  //
+  dtree->train(data_set);
+
+  // Having successfully trained the data, we should be able
+  // to calculate the error on both the training data, as well
+  // as the test data that we held out.
+  //
+  cv::Mat results;
+  float train_performance = dtree->calcError(data_set,
+                                             false, // use train data
+                                             results // cv::noArray()
+                                             );
+  std::vector<cv::String> names;
+  data_set->getNames(names);
+  Mat flags = data_set->getVarSymbolFlags();
+
+  // Compute some statistics on our own:
+  //
+  {
+    cv::Mat expected_responses = data_set->getResponses();
+    int good = 0, bad = 0, total = 0;
+    for (int i = 0; i < data_set->getNTrainSamples(); ++i) {
+      float received = results.at<float>(i, 0);
+      float expected = expected_responses.at<float>(i, 0);
+      cv::String r_str = names[(int)received];
+      cv::String e_str = names[(int)expected];
+      cout << "Expected: " << e_str << ", got: " << r_str << endl;
+      if (received == expected)
+        good++;
+      else
+        bad++;
+      total++;
+    }
+    cout << "Correct answers: " <<(float(good)/total) <<" % " << endl;
+                cout << "Incorrect answers: " << (float(bad) / total) << "%"
+         << endl;
+  }
+  float test_performance = dtree->calcError(data_set,
+                                            true, // use test data
+                                            results // cv::noArray()
+                                            );
+  cout << "Performance on training data: " << train_performance << "%" << endl;
+  cout << "Performance on test data: " <<test_performance <<" % " <<endl;
+  return 0;
+}

mushroom/Index

@@ -0,0 +1,7 @@
+Index of mushroom
+
+02 Dec 1996      193 Index
+25 Jun 1990   111577 expanded.Z
+26 Feb 1990     4167 agaricus-lepiota.names
+30 May 1989      853 README
+30 May 1989   373704 agaricus-lepiota.data