├── test_classifiers.h
├── kmeans_trainer.h
├── make_test_background_image.h
├── kmeans_trainer.cpp
├── train_bovw.h
├── train_SVM_alone.cpp
├── foodcam_predict.cpp
├── CMakeLists.txt
├── make_test_background_image.cpp
├── predict_common.h
├── train_bovw.cpp
├── README
├── test_classifiers.cpp
├── training_common.cpp
├── manual_classifier.cpp
├── main.cpp
├── predict_common.cpp
├── test.txt
├── test_one_class.txt
├── test_one_class_rects.txt
├── training.txt
└── FoodcamClassifier.xcodeproj
    └── project.pbxproj


/test_classifiers.h:
--------------------------------------------------------------------------------
 1 | /*
 2 |  *  test_classifiers.h
 3 |  *  FoodcamClassifier
 4 |  *
 5 |  *  Created by Roy Shilkrot on 8/20/11.
 6 |  *  Copyright 2011 MIT. All rights reserved.
 7 |  *
 8 |  */
 9 | 
10 | #include "predict_common.h"


--------------------------------------------------------------------------------
/kmeans_trainer.h:
--------------------------------------------------------------------------------
 1 | /*
 2 |  *  kmeans_trainer.h
 3 |  *  FoodcamClassifier
 4 |  *
 5 |  *  Created by Roy Shilkrot on 8/20/11.
 6 |  *  Copyright 2011 MIT. All rights reserved.
 7 |  *
 8 |  */
 9 | 
10 | #include <stdio.h>
11 | #include <stdlib.h>
12 | 
13 | #include <opencv2/opencv.hpp>
14 | #include <fstream>
15 | #include <iostream>
16 | #include <string>
17 | 
18 | #include <dirent.h>
19 | #include <unistd.h>
20 | #include <sys/stat.h>
21 | #include <sys/types.h>
22 | 
23 | 
24 | using namespace cv;
25 | using namespace std;


--------------------------------------------------------------------------------
/make_test_background_image.h:
--------------------------------------------------------------------------------
 1 | /*
 2 |  *  make_test_background_image.h
 3 |  *  FoodcamClassifier
 4 |  *
 5 |  *  Created by Roy Shilkrot on 8/21/11.
 6 |  *  Copyright 2011 MIT. All rights reserved.
 7 |  *
 8 |  */
 9 | 
10 | #include <stdio.h>
11 | #include <stdlib.h>
12 | 
13 | #include <opencv2/opencv.hpp>
14 | #include <fstream>
15 | #include <iostream>
16 | #include <string>
17 | 
18 | #include <dirent.h>
19 | #include <unistd.h>
20 | #include <sys/stat.h>
21 | #include <sys/types.h>
22 | 
23 | using namespace cv;
24 | using namespace std;


--------------------------------------------------------------------------------
/kmeans_trainer.cpp:
--------------------------------------------------------------------------------
 1 | /*
 2 |  *  kmeans_trainer.cpp
 3 |  *  FoodcamClassifier
 4 |  *
 5 |  *  Created by Roy Shilkrot on 8/20/11.
 6 |  *  Copyright 2011 MIT. All rights reserved.
 7 |  *
 8 |  */
 9 | 
10 | #include "kmeans_trainer.h"
11 | 
12 | int main(int argc, char** argv) {
13 | 
14 | 	FileStorage fs("training _descriptors.yml", FileStorage::READ);
15 | 	Mat training_descriptors;
16 | 	fs["training_descriptors"] >> training_descriptors;
17 | 	fs.release();
18 | 	
19 | 	BOWKMeansTrainer bowtrainer(1000); //num clusters
20 | 	bowtrainer.add(training_descriptors);
21 | 	cout << "cluster BOW features" << endl;
22 | 	Mat vocabulary = bowtrainer.cluster();
23 | 
24 | 	FileStorage fs1("vocabulary_1000.yml", FileStorage::WRITE);
25 | 	fs1 << "vocabulary" << vocabulary;
26 | 	fs1.release();
27 | 
28 | }


--------------------------------------------------------------------------------
/train_bovw.h:
--------------------------------------------------------------------------------
 1 | /*
 2 |  *  train_bovw.h
 3 |  *  FoodcamClassifier
 4 |  *
 5 |  *  Created by Roy Shilkrot on 8/19/11.
 6 |  *  Copyright 2011 MIT. All rights reserved.
 7 |  *
 8 |  */
 9 | 
10 | 
11 | #include <stdio.h>
12 | #include <stdlib.h>
13 | 
14 | #include <opencv2/opencv.hpp>
15 | #include <fstream>
16 | #include <iostream>
17 | #include <string>
18 | 
19 | #include <dirent.h>
20 | #include <unistd.h>
21 | #include <sys/stat.h>
22 | #include <sys/types.h>
23 | 
24 | #include <omp.h>
25 | 
26 | 
27 | using namespace cv;
28 | using namespace std;
29 | 
30 | void trainSVM(map<string,Mat>& classes_training_data, string& file_postfix, int response_cols, int response_type);
31 | void extract_training_samples(Ptr<FeatureDetector>& detector, BOWImgDescriptorExtractor& bowide, map<string,Mat>& classes_training_data);
32 | 


--------------------------------------------------------------------------------
/train_SVM_alone.cpp:
--------------------------------------------------------------------------------
 1 | /*
 2 |  *  train_SVM_alone.cpp
 3 |  *  
 4 |  *
 5 |  *  Created by Roy Shilkrot on 8/22/11.
 6 |  *  Copyright 2011 MIT. All rights reserved.
 7 |  *
 8 |  */
 9 | #include "train_bovw.h"
10 | 
11 | int main() {
12 | 	cout << "load from file.."<<endl;
13 | 	map<string,Mat> classes_training_data;
14 | 	FileStorage fs("training_samples.yml",FileStorage::READ);
15 | 	vector<string> classes_names;
16 | 	fs["classes"] >> classes_names;
17 | 	for (vector<string>::iterator it = classes_names.begin(); it != classes_names.end(); ++it) {
18 | 		fs[(*it)] >> classes_training_data[*it];
19 | 	}
20 | 	
21 | 	cout << "train SVM.." <<endl;
22 | 	string file_postfix = "with_colors";
23 | 	Mat& one_class = (*(classes_training_data.begin())).second;
24 | 	trainSVM(classes_training_data, file_postfix, one_class.cols, one_class.type());
25 | }


--------------------------------------------------------------------------------
/foodcam_predict.cpp:
--------------------------------------------------------------------------------
 1 | /*
 2 |  *  foodcam_predict.cpp
 3 |  *  FoodcamClassifier
 4 |  *
 5 |  *  Created by Roy Shilkrot on 8/23/11.
 6 |  *  Copyright 2011 MIT. All rights reserved.
 7 |  *
 8 |  */
 9 | 
10 | #include "predict_common.h"
11 | 
12 | int main(int argc, char* argv[]) {
13 | 	if (argc < 2) {
14 | 		cerr << "USAGE: ./foodcam-predict <image_file.png>" << endl;
15 | 		return 1;
16 | 	}
17 | 	
18 | 	FoodcamPredictor predictor;
19 | 	predictor.setDebug(false);
20 | 	
21 | 	Mat __img = imread(argv[1]),_img;
22 | 	if(__img.size() != Size(640,480)) {
23 | 		cerr << "Foodcam images are 640x480, you provided " << Point(__img.size()) << endl;
24 | 		return 1;
25 | 	}
26 | 	
27 | 	vector<string> max_class;
28 | 	predictor.evaluateOneImage(__img,max_class);	
29 | 	
30 | 	cout << max_class[0];
31 | 	if (max_class.size()>1) {
32 | 		cout << "," << max_class[1];
33 | 	}
34 | 	cout << endl;
35 | 	
36 | 	return 0;
37 | }


--------------------------------------------------------------------------------
/CMakeLists.txt:
--------------------------------------------------------------------------------
 1 | PROJECT( foodcam_classifier )
 2 | cmake_minimum_required(VERSION 2.8)
 3 | 
 4 | FIND_PACKAGE( OpenCV 2.3 REQUIRED )
 5 | message(${OpenCV_DIR})
 6 | include_directories(${OpenCV_INCLUDE_DIRS})
 7 | 
 8 | set (CMAKE_CXX_FLAGS -fopenmp )
 9 | 
10 | ADD_EXECUTABLE( manual-classifier manual_classifier.cpp )
11 | TARGET_LINK_LIBRARIES( manual-classifier ${OpenCV_LIBS} )
12 | 
13 | ADD_EXECUTABLE( train-bovw train_bovw.cpp training_common.cpp )
14 | TARGET_LINK_LIBRARIES( train-bovw ${OpenCV_LIBS} )
15 | 
16 | ADD_EXECUTABLE( build-vocabulary build_vocabolary.cpp )
17 | TARGET_LINK_LIBRARIES( build-vocabulary ${OpenCV_LIBS} )
18 | 
19 | ADD_EXECUTABLE( kmeans-trainer kmeans_trainer.cpp )
20 | TARGET_LINK_LIBRARIES( kmeans-trainer ${OpenCV_LIBS} )
21 | 
22 | ADD_EXECUTABLE( make-test-background make_test_background_image.cpp )
23 | TARGET_LINK_LIBRARIES( make-test-background ${OpenCV_LIBS} )
24 | 
25 | ADD_EXECUTABLE( test-classifiers test_classifiers.cpp predict_common.cpp )
26 | TARGET_LINK_LIBRARIES( test-classifiers ${OpenCV_LIBS} )
27 | 
28 | ADD_EXECUTABLE( train-SVM-alone train_SVM_alone.cpp training_common.cpp )
29 | TARGET_LINK_LIBRARIES( train-SVM-alone ${OpenCV_LIBS} )
30 | 
31 | ADD_EXECUTABLE( foodcam-predict foodcam_predict.cpp predict_common.cpp )
32 | TARGET_LINK_LIBRARIES( foodcam-predict ${OpenCV_LIBS} )
33 | 


--------------------------------------------------------------------------------
/make_test_background_image.cpp:
--------------------------------------------------------------------------------
 1 | /*
 2 |  
 3 |  *  make_test_background_image.cpp
 4 |  *  FoodcamClassifier
 5 |  *
 6 |  *  Created by Roy Shilkrot on 8/21/11.
 7 |  *  Copyright 2011 MIT. All rights reserved.
 8 |  *
 9 |  */
10 | 
11 | #include "make_test_background_image.h"
12 | 
13 | int main(int argc, char** argv) {
14 | 	string dir, filepath;
15 | 	DIR *dp;
16 | 	struct dirent *dirp;
17 | 	struct stat filestat;
18 | 	
19 | 	//get images
20 | 	dir = "foodcamimages/TEST";
21 | 	dp = opendir( dir.c_str() );
22 | 	int count = 0;
23 | 	Mat accum;
24 | 	while ((dirp = readdir( dp )))
25 |     {
26 | 		count++;
27 | 		
28 | 		filepath = dir + "/" + dirp->d_name;
29 | 		
30 | 		// If the file is a directory (or is in some way invalid) we'll skip it 
31 | 		if (stat( filepath.c_str(), &filestat )) continue;
32 | 		if (S_ISDIR( filestat.st_mode ))         continue;
33 | 		if (dirp->d_name[0] == '.')					 continue; //hidden file!
34 | 		
35 | 		cout << "eval file " << filepath << endl;
36 | 
37 | 		Mat img = imread(filepath),img64;
38 | 		img.convertTo(img64, CV_64FC3);
39 | 		
40 | 		if (!accum.data) {
41 | 			accum.create(img.size(), CV_64FC3);
42 | 		}
43 | 		if (img64.size() == accum.size()) {
44 | 			accum += img64;
45 | 		}
46 | 	}
47 | 	
48 | 	accum /= count;
49 | 	Mat accum_8UC3; accum.convertTo(accum_8UC3, CV_8UC3);
50 | 	
51 | 	imwrite("background.png", accum_8UC3);
52 | 	
53 | 	imshow("accum", accum_8UC3);
54 | 	waitKey(0);
55 | }


--------------------------------------------------------------------------------
/predict_common.h:
--------------------------------------------------------------------------------
 1 | /*
 2 |  *  predict_common.h
 3 |  *  FoodcamClassifier
 4 |  *
 5 |  *  Created by Roy Shilkrot on 8/23/11.
 6 |  *  Copyright 2011 MIT. All rights reserved.
 7 |  *
 8 |  */
 9 | 
10 | 
11 | #include <stdio.h>
12 | #include <stdlib.h>
13 | 
14 | #include <opencv2/opencv.hpp>
15 | #include <fstream>
16 | #include <iostream>
17 | #include <string>
18 | #include <set>
19 | 
20 | #include <dirent.h>
21 | #include <unistd.h>
22 | #include <sys/stat.h>
23 | #include <sys/types.h>
24 | 
25 | #include <omp.h>
26 | 
27 | using namespace cv;
28 | using namespace std;
29 | 
30 | class FoodcamPredictor {
31 | public:	
32 | 	FoodcamPredictor();
33 | 	void evaluateOneImage(Mat& __img, vector<string>& out_classes);
34 | 	map<string,CvSVM>& getClassesClassifiers() { return classes_classifiers; }
35 | 	void normalizeClassname(string& max_class) { 
36 | 		if(max_class.compare("cake")==0) max_class = "cookies";
37 | 		if(max_class.compare("fruit")==0) max_class = "fruit_veggie";
38 | 	}
39 | 	void setDebug(bool _b) { debug = _b;}
40 | 	
41 | private:
42 | 	void initColors();
43 | 	void initSVMs();
44 | 	void initVocabulary();
45 | 	
46 | 	bool debug;
47 | 	
48 | 	Ptr<FeatureDetector > detector;
49 | 	Ptr<BOWImgDescriptorExtractor > bowide;
50 | 	Ptr<DescriptorMatcher > matcher;
51 | 	Ptr<DescriptorExtractor > extractor;
52 | 	map<string,CvSVM> classes_classifiers;
53 | 	map<string,Scalar> classes_colors;
54 | 	Mat background;
55 | 	Mat vocabulary;
56 | };


--------------------------------------------------------------------------------
/train_bovw.cpp:
--------------------------------------------------------------------------------
 1 | /*
 2 |  *  train_bovw.cpp
 3 |  *  FoodcamClassifier
 4 |  *
 5 |  *  Created by Roy Shilkrot on 8/19/11.
 6 |  *  Copyright 2011 MIT. All rights reserved.
 7 |  *
 8 |  */
 9 | 
10 | #include "train_bovw.h"
11 | 
12 | int main(int argc, char** argv_) {
13 | 	vector<string> argv;for (int i = 0; i < argc; i++) {argv.push_back(argv_[i]);}
14 | 	
15 | 	if (argv.size() < 3) {
16 | 		cout << "USAGE: train_bovw <vocabulary_file.yml> <postfix_for_output>"<<endl;
17 | 		return 1;
18 | 	}
19 | 	
20 | 	cout << "-------- train BOVW SVMs -----------" << endl;
21 | 	cout << "read vocabulary form file"<<endl;
22 | 	Mat vocabulary;
23 | 	FileStorage fs(argv[1], FileStorage::READ);
24 | 	fs["vocabulary"] >> vocabulary;
25 | 	fs.release();	
26 | 	
27 | 	Ptr<FeatureDetector > detector(new SurfFeatureDetector()); //detector
28 | 	//Ptr<DescriptorExtractor > extractor(new SurfDescriptorExtractor());//  extractor;
29 | 	Ptr<DescriptorExtractor > extractor(
30 | 		new OpponentColorDescriptorExtractor(
31 | 			 Ptr<DescriptorExtractor>(new SurfDescriptorExtractor())
32 | 			 )
33 | 		);
34 | 	Ptr<DescriptorMatcher > matcher(new BruteForceMatcher<L2<float> >());
35 | 	BOWImgDescriptorExtractor bowide(extractor,matcher);
36 | 	bowide.setVocabulary(vocabulary);
37 | 	
38 | 	//setup training data for classifiers
39 | 	map<string,Mat> classes_training_data; classes_training_data.clear();
40 | 	
41 | 	cout << "extract_training_samples.." << endl;
42 | 	extract_training_samples(detector, bowide, classes_training_data);
43 | 	
44 | 	cout << "got " << classes_training_data.size() << " classes." <<endl;
45 | 	for (map<string,Mat>::iterator it = classes_training_data.begin(); it != classes_training_data.end(); ++it) {
46 | 		cout << " class " << (*it).first << " has " << (*it).second.rows << " samples"<<endl;
47 | 	}
48 | 	
49 | 	cout << "train SVMs\n";
50 | 	string postfix = argv[2];
51 | 	trainSVM(classes_training_data, postfix, bowide.descriptorSize(), bowide.descriptorType());
52 | 
53 | 	return 0;
54 | }
55 | 


--------------------------------------------------------------------------------
/README:
--------------------------------------------------------------------------------
 1 | Foodcam Classifier
 2 | ==================
 3 | A Bag Of Visual Words classifier for MIT Media Lab's Foodcam.
 4 | Built using OpenCV 2.3
 5 | 
 6 | Implementing BOVW method: Visual Categorization with Bags of Keypoints by Gabriella Csurka, Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004.
 7 | Using Opponent-Color SURF features (192 floats): Van de Sande et al., CGIV 2008 Color Descriptors for Object Category Recognition. (they used SIFT)
 8 | Radial Basis Function SVMs for classification.
 9 | 
10 | This is basically a primer to BOVW methods using OpenCV, which are dead simple.
11 | I tweaked the basic method by:
12 | 	- using background subtraction (since these are pictures from a still camera).
13 | 	- using a sliding window. This enables better multi-class decisions, and actually paves the path to image segmentation. 
14 | 
15 | 
16 | Compiling
17 | ---------
18 | Basically, just run:
19 | cmake -D CMAKE_CXX_FLAGS=-fopenmp . ; make -j4
20 | 
21 | CMake should take you all the way, but I also have an .xcodeproj/ included.
22 | 
23 | Working it
24 | ----------
25 | Get the dataset:
26 | http://www.media.mit.edu/~roys/shared/foodcamimages.zip
27 | Assume the dataset is now in the same directory, under foodcamimages/.
28 | 
29 | 
30 | Manually classify training and test:
31 | ./manual-classifier foodcamimages/TRAIN/ train.txt
32 | ./manual-classifier foodcamimages/TEST/ test.txt
33 | (or you can use my manual classification included... lazy)
34 | 
35 | 
36 | Create a background image:
37 | ./make-test-background
38 | 
39 | 
40 | Build the vocabulary: (this will take a very very long time, KMeans on 1.3Million 192-long vectors to find 1000 cluster-centers...)
41 | ./build-vocabulary
42 | 
43 | (you may also like to use ./kmeans-trainer, if you break the operation before KMeans finished, which may take more than 10 hours)
44 | 
45 | 
46 | Train the classifiers:
47 | ./train-bovw vocabulary_color_1000.yml with_color
48 | 
49 | (you may also make use of './train-SVM-alone', if you just wanna tweak the SVM parameters)
50 | 
51 | 
52 | Test your work:
53 | ./test-classifiers 
54 | (it will output a whole lotta things, and then the confusion matrix in neighbors list form)
55 | 
56 | 
57 | Put it to work!:
58 | ./foodcam-predict some_640x480_image_of_food.png
59 | Will output either one or two found classes of food, based on how close the prediction is.
60 | 
61 | 
62 | Notes
63 | -----
64 | Some of the computation is sped up on multi-core machines by using OpenMP, so it's recommended to use it.
65 | 
66 | 
67 | Results
68 | -------
69 | This is the confusion matrix:
70 | classified ->	cookies	indian	italian	pizza	veggie+fruit	sandwiches
71 | cookies		56.3%	0.0%	4.3%	0.0%	13.5%		15.4%
72 | indian		0.0%	71.4%	6.4%	3.8%	3.4%		7.7%
73 | italian		0.0%	0.0%	63.8%	0.0%	14.6%		0.0%
74 | pizza		6.3%	0.0%	12.8%	92.3%	5.6%		15.4%
75 | veggie+fruit	0.0%	0.0%	2.1%	3.8%	49.4%		0.0%
76 | sandwiches	25.0%	14.3%	6.4%	0.0%	7.9%		61.5%
77 | 
78 | Quick scan shows that it can classify Pizzas pretty good!
79 | The downfall are the veggie+fruit class, with less than 50% accuracy.
80 | Rest are lukewarm, but this can probably be attribued to the very small training set.
81 | 


--------------------------------------------------------------------------------
/test_classifiers.cpp:
--------------------------------------------------------------------------------
  1 | /*
  2 |  *  test_classifiers.cpp
  3 |  *  FoodcamClassifier
  4 |  *
  5 |  *  Created by Roy Shilkrot on 8/20/11.
  6 |  *  Copyright 2011 MIT. All rights reserved.
  7 |  *
  8 |  */
  9 | 
 10 | #include "test_classifiers.h"
 11 | 
 12 | int main(int argc, char** argv) {
 13 | 	string filepath;
 14 | 	
 15 | 	cout << "------- test ---------\n";
 16 | 		
 17 | 	ifstream ifs("test.txt",ifstream::in);
 18 | 	char buf[255];
 19 | 	vector<string> lines; 
 20 | 	while(!ifs.eof()) {// && count++ < 30) {
 21 | 		ifs.getline(buf, 255);
 22 | 		lines.push_back(buf);
 23 | 	}	
 24 | 	ifs.close();
 25 | 	cout << "total " << lines.size() << " samples to scan" <<endl;
 26 | 	
 27 | 	FoodcamPredictor predictor;
 28 | 	predictor.setDebug(true);
 29 | 	
 30 | 	map<string,CvSVM>& classes_classifiers = predictor.getClassesClassifiers();
 31 | 	map<string,map<string,int> > confusion_matrix;
 32 | 	for (map<string,CvSVM>::iterator it = classes_classifiers.begin(); it != classes_classifiers.end(); ++it) {
 33 | 		for (map<string,CvSVM>::iterator it1 = classes_classifiers.begin(); it1 != classes_classifiers.end(); ++it1) {
 34 | 			string class1 = ((*it).first.compare("cake")==0) ? "cookies" : ((*it).first.compare("fruit")==0) ? "fruit_veggie" : (*it).first;
 35 | 			string class2 = ((*it1).first.compare("cake")==0) ? "cookies" : ((*it1).first.compare("fruit")==0) ? "fruit_veggie" : (*it1).first;
 36 | 			confusion_matrix[class1][class2] = 0;
 37 | 		}
 38 | 	}
 39 | 	
 40 | 	for (int i = 0; i < lines.size(); i++) {
 41 | 		string line(lines[i]);
 42 | 		cout << line << endl;
 43 | 		istringstream iss(line);
 44 | 		
 45 | 		iss >> filepath;
 46 | //		Rect r; char delim; iss >> r.x >> delim >> r.y >> delim >> r.width >> delim >> r.height;
 47 | 		vector<string> classes_; 
 48 | 		while (!iss.eof()) {
 49 | 			string class_; iss >> class_;
 50 | 			classes_.push_back(class_);
 51 | 		}
 52 | 		
 53 | 		if(classes_.size() == 0) continue;
 54 | 		
 55 | 		cout << "eval file " << filepath << " (" << i << "/" << lines.size() << ")" << endl;
 56 | 		
 57 | 		Mat __img = imread(filepath),_img;
 58 | 		if(__img.size() != Size(640,480)) continue;
 59 | 		vector<string> max_class;
 60 | 		predictor.evaluateOneImage(__img,max_class);
 61 | 		cout << "manual class: "; for(int j_=0;j_<classes_.size();j_++) cout << classes_[j_] << ",";
 62 | 		cout << endl;
 63 | 		
 64 | 		int j_=0;
 65 | 		for(;j_<classes_.size();j_++) {
 66 | 			if(classes_[j_].compare(max_class[0])==0) //got a hit
 67 | 			{
 68 | 				confusion_matrix[max_class[0]][classes_[j_]]++;
 69 | 				break;
 70 | 			}
 71 | 		}
 72 | 		if(j_==classes_.size()) //no hit was found, just use any class
 73 | 			confusion_matrix[max_class[0]][classes_[0]]++;
 74 | 		
 75 | //		cvtColor(copy, copy, CV_HSV2BGR);
 76 | //		cvtColor(seg, seg, CV_HSV2BGR);
 77 | //		addWeighted(seg, 0.2, copy, 0.8, 1.0, seg);
 78 | //		imshow("seg", seg);
 79 | 		
 80 | 		Mat out; __img.copyTo(out);
 81 | 		putText(out, max_class[0] + "!", Point(out.cols/2-100,out.rows/2-30), CV_FONT_HERSHEY_PLAIN, 3.0, Scalar(255), 2);
 82 | 		if(max_class.size()>1) {
 83 | 			putText(out, max_class[1] + "?", Point(out.cols/2-100,out.rows/2+30), CV_FONT_HERSHEY_PLAIN, 3.0, Scalar(255), 2);
 84 | 		}
 85 | 		imshow("out",out);
 86 | 		waitKey(0);
 87 | 		imwrite("output/"+filepath, out);
 88 |     }
 89 | 	
 90 | 	for(map<string,map<string,int> >::iterator it = confusion_matrix.begin(); it != confusion_matrix.end(); ++it) {
 91 | 		cout << (*it).first << " -> ";
 92 | 		for(map<string,int>::iterator it1 = (*it).second.begin(); it1 != (*it).second.end(); ++it1) {
 93 | 			cout << (*it1).first << ":" << (*it1).second << endl;
 94 | 		}
 95 | //		cout << endl;
 96 | 	}
 97 | 	
 98 | 	cout << endl;
 99 | 
100 | }


--------------------------------------------------------------------------------
/training_common.cpp:
--------------------------------------------------------------------------------
  1 | /*
  2 |  *  training_common.cpp
  3 |  *  FoodcamClassifier
  4 |  *
  5 |  *  Created by Roy Shilkrot on 8/22/11.
  6 |  *  Copyright 2011 MIT. All rights reserved.
  7 |  *
  8 |  */
  9 | 
 10 | #include "train_bovw.h"
 11 | 
 12 | void trainSVM(map<string,Mat>& classes_training_data, string& file_postfix, int response_cols, int response_type) {
 13 | 
 14 | 	//train 1-vs-all SVMs
 15 | 	vector<string> classes_names;
 16 | 	for (map<string,Mat>::iterator it = classes_training_data.begin(); it != classes_training_data.end(); ++it) {
 17 | 		classes_names.push_back((*it).first);
 18 | 	}
 19 | 	
 20 | 	string use_postfix = file_postfix;
 21 | 	#pragma omp parallel for schedule(dynamic) private(use_postfix)
 22 | 	for (int i=0;i<classes_names.size();i++) {
 23 | 		string class_ = classes_names[i];
 24 | 		cout << omp_get_thread_num() << " training class: " << class_ << ".." << endl;
 25 | 		
 26 | 		Mat samples(0,response_cols,response_type);
 27 | 		Mat labels(0,1,CV_32FC1);
 28 | 		
 29 | 		//copy class samples and label
 30 | 		cout << "adding " << classes_training_data[class_].rows << " positive" << endl;
 31 | 		samples.push_back(classes_training_data[class_]);
 32 | 		Mat class_label = Mat::ones(classes_training_data[class_].rows, 1, CV_32FC1);
 33 | 		labels.push_back(class_label);
 34 | 		
 35 | 		//copy rest samples and label
 36 | 		for (map<string,Mat>::iterator it1 = classes_training_data.begin(); it1 != classes_training_data.end(); ++it1) {
 37 | 			string not_class_ = (*it1).first;
 38 | 			if(not_class_.compare(class_)==0) continue;
 39 | 			samples.push_back(classes_training_data[not_class_]);
 40 | 			class_label = Mat::zeros(classes_training_data[not_class_].rows, 1, CV_32FC1);
 41 | 			labels.push_back(class_label);
 42 | 		}
 43 | 		
 44 | 		cout << "Train.." << endl;
 45 | 		Mat samples_32f; samples.convertTo(samples_32f, CV_32F);
 46 | 		if(samples.rows == 0) continue; //phantom class?!
 47 | 		CvSVM classifier; 
 48 | 		classifier.train(samples_32f,labels);
 49 | 
 50 | 		{
 51 | 			stringstream ss; 
 52 | 			ss << "SVM_classifier_"; 
 53 | 			if(file_postfix.size() > 0) ss << file_postfix << "_";
 54 | 			ss << class_ << ".yml";
 55 | 			cout << "Save.." << endl;
 56 | 			classifier.save(ss.str().c_str());
 57 | 		}
 58 | 	}
 59 | }
 60 | 
 61 | void extract_training_samples(Ptr<FeatureDetector>& detector, BOWImgDescriptorExtractor& bowide, map<string,Mat>& classes_training_data) {
 62 | 	cout << "look in train data"<<endl;
 63 | 	Ptr<ifstream> ifs(new ifstream("training.txt"));
 64 | 	int total_samples = 0;
 65 | 	vector<string> classes_names;
 66 | 	
 67 | 	char buf[255]; int count = 0;
 68 | 	vector<string> lines; 
 69 | 	while(!ifs->eof()) {// && count++ < 30) {
 70 | 		ifs->getline(buf, 255);
 71 | 		lines.push_back(buf);
 72 | 	}
 73 | 	
 74 | 	//try some multithreading
 75 | 	#pragma omp parallel for schedule(dynamic,3)
 76 | 	for(int i=0;i<lines.size();i++) {
 77 | //		printf("Hello from thread %d, nthreads %d\n", omp_get_thread_num(), omp_get_num_threads());
 78 | //		if(ifs->eof()) break;
 79 | 
 80 | 		vector<KeyPoint> keypoints;
 81 | 		Mat response_hist;
 82 | 		Mat img;
 83 | 		string filepath;
 84 | 
 85 | 		string line(lines[i]);
 86 | 		istringstream iss(line);
 87 | 
 88 | 		iss >> filepath;
 89 | 		Rect r; char delim; iss >> r.x >> delim >> r.y >> delim >> r.width >> delim >> r.height;
 90 | 		string class_; iss >> class_; class_ = "class_" + class_;
 91 | 		if(class_.size() == 0) continue;
 92 | 		
 93 | 		img = imread(filepath);
 94 | 		r &= Rect(0,0,img.cols,img.rows);
 95 | 		if(r.width != 0) {
 96 | 			img = img(r); //crop to interesting region
 97 | 		}
 98 | 		//		char c__[] = {(char)atoi(class_.c_str()),'\0'};
 99 | 		//		string c_(c__);
100 | 		//		cout << c_; cout.flush();
101 | 		//		putText(img, c_, Point(20,20), CV_FONT_HERSHEY_PLAIN, 2.0, Scalar(255), 2);
102 | 		//		imshow("pic",img);
103 | 		
104 | 		detector->detect(img,keypoints);
105 | 		bowide.compute(img, keypoints, response_hist);
106 | 
107 | 		cout << "."; cout.flush();
108 | 
109 | 		#pragma omp critical
110 | 		{
111 | 			if(classes_training_data.count(class_) == 0) { //not yet created...
112 | 				classes_training_data[class_].create(0,response_hist.cols,response_hist.type());
113 | 				classes_names.push_back(class_);
114 | 			}
115 | 			classes_training_data[class_].push_back(response_hist);
116 | 		}
117 | 		total_samples++;
118 | 
119 | 		//		waitKey(0);
120 | 	}
121 | 	cout << endl;
122 | 
123 | 	cout << "save to file.."<<endl;
124 | 	{
125 | 		FileStorage fs("training_samples.yml",FileStorage::WRITE);
126 | 		for (map<string,Mat>::iterator it = classes_training_data.begin(); it != classes_training_data.end(); ++it) {
127 | 			cout << "save " << (*it).first << endl;
128 | 			fs << (*it).first << (*it).second;
129 | 		}
130 | 	}
131 | }


--------------------------------------------------------------------------------
/manual_classifier.cpp:
--------------------------------------------------------------------------------
  1 | /*
  2 |  *  manual_classifier.cpp
  3 |  *  FoodcamClassifier
  4 |  *
  5 |  *  Created by Roy Shilkrot on 8/19/11.
  6 |  *  Copyright 2011 MIT. All rights reserved.
  7 |  *
  8 |  */
  9 | 
 10 | 
 11 | #include <iostream>
 12 | #include <opencv2/opencv.hpp>
 13 | #include <fstream>
 14 | #include <iostream>
 15 | #include <string>
 16 | #include <set>
 17 | 
 18 | #include <dirent.h>
 19 | #include <unistd.h>
 20 | #include <sys/stat.h>
 21 | #include <sys/types.h>
 22 | 
 23 | using namespace cv;
 24 | using namespace std;
 25 | 
 26 | Point origin;
 27 | Rect selection;
 28 | Mat image;
 29 | bool selectObject;
 30 | 
 31 | void onMouse( int event, int x, int y, int, void* )
 32 | {
 33 |     if( selectObject )
 34 |     {
 35 |         selection.x = MIN(x, origin.x);
 36 |         selection.y = MIN(y, origin.y);
 37 |         selection.width = std::abs(x - origin.x);
 38 |         selection.height = std::abs(y - origin.y);
 39 | 		
 40 |         selection &= Rect(0, 0, image.cols, image.rows);
 41 |     }
 42 | 	
 43 |     switch( event )
 44 |     {
 45 | 		case CV_EVENT_LBUTTONDOWN:
 46 | 			origin = Point(x,y);
 47 | 			selection = Rect(x,y,0,0);
 48 | 			selectObject = true;
 49 | 			break;
 50 | 		case CV_EVENT_LBUTTONUP:
 51 | 			selectObject = false;
 52 | 			break;
 53 |     }
 54 | }
 55 | 
 56 | string char_to_class(char c) {
 57 | 		switch (c) {
 58 | 			case 'h':
 59 | 			case 'H':
 60 | 				return "chinese";
 61 | 				break;
 62 | 			case 'p':
 63 | 			case 'P':
 64 | 				return "pizza";
 65 | 				break;
 66 | 			case 'i':
 67 | 			case 'I':
 68 | 				return "indian";
 69 | 				break;
 70 | 			case 'w':
 71 | 			case 'W':
 72 | 				return "wraps";
 73 | 				break;
 74 | 			case 's':
 75 | 			case 'S':
 76 | 				return "sandwiches";
 77 | 				break;
 78 | 			case 'a':
 79 | 			case 'A':
 80 | 				return "salad";
 81 | 				break;
 82 | 			case 'c':
 83 | 			case 'C':
 84 | 				return "cookies";
 85 | 				break;
 86 | 			case 'm':
 87 | 			case 'M':
 88 | 				return "mexican";
 89 | 				break;
 90 | 			case 'f':
 91 | 			case 'F':
 92 | 				return "fruit_veggie";
 93 | 				break;
 94 | 			case 'l':
 95 | 			case 'L':
 96 | 				return "misc";
 97 | 				break;
 98 | 			case 't':
 99 | 			case 'T':
100 | 				return "italian";
101 | 				break;
102 | 			default:
103 | 				return "misc";
104 | 		}
105 | }
106 | 
107 | int main(int argc, char * const argv[]) {
108 | 
109 | 	if(argc < 3) {
110 | 		cerr << "USAGE: manual_classifier <input_directory/> <output_file.txt>"<<endl;
111 | 		return 1;
112 | 	}
113 | 	string dir(argv[1]), filepath;
114 | 	DIR *dp;
115 | 	struct dirent *dirp;
116 | 	struct stat filestat;
117 | 	
118 | 	cout << "C'h'inease\nI't'alian\n'P'izza\n'I'ndian\n'W'raps\n'S'andwiches\nS'a'lad\n'C'ookies/Cake\n'M'exican\n'F'ruit/Veggies\nMisce'l'laneous" << endl;
119 | 	
120 | 	vector<pair<string, string> > classified;
121 | 
122 | 	namedWindow("pic");
123 | 	setMouseCallback( "pic", onMouse, 0 );
124 | 	
125 | 	ifstream ifs(argv[2],ifstream::in);
126 | 	
127 | 	set<string> files_already_listed;
128 | 	if (ifs.is_open() && !ifs.eof()) {
129 | 		//something in here, get everything already listed
130 | 		char buf[255];
131 | 		while (!ifs.eof()) {
132 | 			ifs.getline(buf, 255);
133 | 			string line(buf);
134 | 			files_already_listed.insert(line.substr(0, line.find(" ")));
135 | 		}
136 | 	}
137 | 	ifs.close();
138 | 	
139 | 	ofstream ofs(argv[2], fstream::app);
140 | 	
141 | 	int count = 0;
142 | 	Mat img;
143 | 	bool running = true;
144 | 	dp = opendir( dir.c_str() );
145 | 	while (count++ < 1000 && (dirp = readdir( dp )) && running)
146 |     {
147 | 		filepath = dir + "/" + dirp->d_name;
148 | 		
149 | 		// If the file is a directory (or is in some way invalid) we'll skip it 
150 | 		if (stat( filepath.c_str(), &filestat )) continue; //can't be opened...
151 | 		if (S_ISDIR( filestat.st_mode ))         continue; //a directory
152 | 		if (dirp->d_name[0] == '.')					 continue; //hidden file!
153 | 		if (files_already_listed.count(filepath)>0) continue; //already did that one
154 | 		
155 | 		ofs << filepath;
156 | 		img = imread(filepath);
157 | 		Point text_place(20,40);
158 | 		while (true) {
159 | 			img.copyTo(image);
160 | 			if( selection.width > 0 && selection.height > 0 )
161 | 			{
162 | 				Mat roi(image, selection);
163 | 				bitwise_not(roi, roi);
164 | 			}
165 | 			imshow("pic", image);
166 | 			int c = waitKey(10);
167 | 			
168 | 			if (c == ' ') {
169 | 				ofs << endl;
170 | 				break;
171 | 			} else if (c == 27) {
172 | 				running = false;
173 | 				break;
174 | 			} else if (c != -1) {
175 | 				if(selection.width != 0)
176 | 					ofs << " " << selection.x << "," << selection.y << "," << selection.width << "," << selection.height;
177 | 				ofs << " " << char_to_class(c);
178 | 				putText(img, char_to_class(c), text_place, CV_FONT_HERSHEY_PLAIN, 3.0, Scalar(255), 2);
179 | 				text_place += Point(0,40);
180 | 				selection = Rect();
181 | 			}
182 | 		}
183 |     }
184 | 	closedir(dp);
185 | 	
186 | 	
187 | 	for (int i=0; i<classified.size(); i++) {
188 | 		ofs << classified[i].first << " " << classified[i].second << endl;
189 | 	}
190 | 	ofs.close();
191 | }


--------------------------------------------------------------------------------
/main.cpp:
--------------------------------------------------------------------------------
  1 | #include <stdio.h>
  2 | #include <stdlib.h>
  3 | 
  4 | #include <opencv2/opencv.hpp>
  5 | #include <fstream>
  6 | #include <iostream>
  7 | #include <string>
  8 | 
  9 | #include <dirent.h>
 10 | #include <unistd.h>
 11 | #include <sys/stat.h>
 12 | #include <sys/types.h>
 13 | 
 14 | using namespace cv;
 15 | using namespace std;
 16 | 
 17 | int main (int argc, char * const argv[]) {
 18 | 	string dir = "/Users/royshilkrot/Downloads/foodcamimages/TRAIN", filepath;
 19 | 	DIR *dp;
 20 | 	struct dirent *dirp;
 21 | 	struct stat filestat;
 22 | 
 23 | 	dp = opendir( dir.c_str() );
 24 | 	
 25 | 	// detecting keypoints
 26 | 	SurfFeatureDetector detector(1000);
 27 | 	vector<KeyPoint> keypoints;	
 28 | 	
 29 | 	// computing descriptors
 30 | 	Ptr<DescriptorExtractor > extractor(new SurfDescriptorExtractor());//  extractor;
 31 | 	Mat descriptors;
 32 | 	Mat training_descriptors(1,extractor->descriptorSize(),extractor->descriptorType());
 33 | 	Mat img;
 34 | 	
 35 | 	cout << "------- build vocabulary ---------\n";
 36 | 
 37 | 	cout << "extract descriptors.."<<endl;
 38 | 	int count = 0;
 39 | 	while (count++ < 15 && (dirp = readdir( dp )))
 40 |     {
 41 | 		filepath = dir + "/" + dirp->d_name;
 42 | 		
 43 | 		// If the file is a directory (or is in some way invalid) we'll skip it 
 44 | 		if (stat( filepath.c_str(), &filestat )) continue;
 45 | 		if (S_ISDIR( filestat.st_mode ))         continue;
 46 | 		
 47 | 		img = imread(filepath);
 48 | 		detector.detect(img, keypoints);
 49 | 		extractor->compute(img, keypoints, descriptors);
 50 | 		
 51 | 		training_descriptors.push_back(descriptors);
 52 | 		cout << ".";
 53 |     }
 54 | 	cout << endl;
 55 | 	closedir( dp );
 56 | 
 57 | 	cout << "Total descriptors: " << training_descriptors.rows << endl;
 58 |     
 59 | 	BOWKMeansTrainer bowtrainer(150); //num clusters
 60 | 	bowtrainer.add(training_descriptors);
 61 | 	cout << "cluster BOW features" << endl;
 62 | 	Mat vocabulary = bowtrainer.cluster();
 63 | 	
 64 | 	Ptr<DescriptorMatcher > matcher(new BruteForceMatcher<L2<float> >());
 65 | 	BOWImgDescriptorExtractor bowide(extractor,matcher);
 66 | 	bowide.setVocabulary(vocabulary);
 67 | 	
 68 | 	//setup training data for classifiers
 69 | 	map<string,Mat> classes_training_data; classes_training_data.clear();
 70 | 	
 71 | 	cout << "------- train SVMs ---------\n";
 72 | 
 73 | 	Mat response_hist;
 74 | 	cout << "look in train data"<<endl;
 75 | 	count = 0;
 76 | 	char buf[255];
 77 | 	ifstream ifs("training.txt");
 78 | 	int total_samples = 0;
 79 | 	do
 80 |     {
 81 | 		ifs.getline(buf, 255);
 82 | 		string line(buf);
 83 | 		istringstream iss(line);
 84 | //		cout << line << endl;
 85 | 		iss >> filepath;
 86 | 		Rect r; char delim;
 87 | 		iss >> r.x >> delim;
 88 | 		iss >> r.y >> delim;
 89 | 		iss >> r.width >> delim;
 90 | 		iss >> r.height;
 91 | //		cout << r.x << "," << r.y << endl;
 92 | 		string class_;
 93 | 		iss >> class_;
 94 | 				
 95 | 		img = imread(filepath);
 96 | 		r &= Rect(0,0,img.cols,img.rows);
 97 | 		if(r.width != 0) {
 98 | 			img = img(r); //crop to interesting region
 99 | 		}
100 | 		char c__[] = {(char)atoi(class_.c_str()),'\0'};
101 | 		string c_(c__);
102 | 		cout << ".";
103 | //		putText(img, c_, Point(20,20), CV_FONT_HERSHEY_PLAIN, 2.0, Scalar(255), 2);
104 | //		imshow("pic",img);
105 | 		bowide.compute(img, keypoints, response_hist);
106 | 		
107 | 		if(classes_training_data.count(c_) == 0) { //not yet created...
108 | 			classes_training_data[c_].create(0,response_hist.cols,response_hist.type());
109 | 		}
110 | 		classes_training_data[c_].push_back(response_hist);
111 | 		total_samples++;
112 | //		waitKey(0);
113 | 	} while (!ifs.eof());
114 | 	cout << endl;
115 | 	
116 | 	//train 1-vs-all SVMs
117 | 	map<string,CvSVM> classes_classifiers;
118 | 	for (map<string,Mat>::iterator it = classes_training_data.begin(); it != classes_training_data.end(); ++it) {
119 | 		string class_ = (*it).first;
120 | 		cout << "training class: " << class_ << ".." << endl;
121 | 		
122 | 		Mat samples(0,response_hist.cols,response_hist.type());
123 | 		Mat labels(0,1,CV_32FC1);
124 | 		
125 | 		//copy class samples and label
126 | 		samples.push_back(classes_training_data[class_]);
127 | 		Mat class_label = Mat::ones(classes_training_data[class_].rows, 1, CV_32FC1);
128 | 		labels.push_back(class_label);
129 | 
130 | 		//copy rest samples and label
131 | 		for (map<string,Mat>::iterator it1 = classes_training_data.begin(); it1 != classes_training_data.end(); ++it1) {
132 | 			string not_class_ = (*it1).first;
133 | 			if(not_class_[0] == class_[0]) continue;
134 | 			samples.push_back(classes_training_data[not_class_]);
135 | 			class_label = Mat::zeros(classes_training_data[not_class_].rows, 1, CV_32FC1);
136 | 			labels.push_back(class_label);
137 | 		}
138 | 		
139 | 		Mat samples_32f; samples.convertTo(samples_32f, CV_32F);
140 | 		classes_classifiers[class_].train(samples_32f,labels);
141 | 	}
142 | 	
143 | 	cout << "------- test ---------\n";
144 | 
145 | 	//evaluate
146 | 	dir = "/Users/royshilkrot/Downloads/foodcamimages/TEST";
147 | 	dp = opendir( dir.c_str() );
148 | 	count = 0;
149 | 	while (count++ < 5 && (dirp = readdir( dp )))
150 |     {
151 | 		filepath = dir + "/" + dirp->d_name;
152 | 		
153 | 		// If the file is a directory (or is in some way invalid) we'll skip it 
154 | 		if (stat( filepath.c_str(), &filestat )) continue;
155 | 		if (S_ISDIR( filestat.st_mode ))         continue;
156 | 		if (dirp->d_name[0] == '.')					 continue; //hidden file!
157 | 		
158 | 		cout << "eval file " << filepath << endl;
159 | 		
160 | 		img = imread(filepath);
161 | 		bowide.compute(img, keypoints, response_hist);
162 | 		
163 | 		//test vs. SVMs
164 | 		for (map<string,CvSVM>::iterator it = classes_classifiers.begin(); it != classes_classifiers.end(); ++it) {
165 | 			float res = (*it).second.predict(response_hist,false);
166 | 			cout << "class: " << (*it).first << ", response: " << res << endl;
167 | 		}
168 | 		
169 | //		cout << ".";
170 |     }
171 | 	cout << endl;
172 | 	closedir( dp );
173 | 	
174 | 	
175 | 	cout <<"done"<<endl;
176 |     return 0;
177 | }
178 | 


--------------------------------------------------------------------------------
/predict_common.cpp:
--------------------------------------------------------------------------------
  1 | /*
  2 | *  predict_common.cpp
  3 | *  FoodcamClassifier
  4 | *
  5 | *  Created by Roy Shilkrot on 8/23/11.
  6 | *  Copyright 2011 MIT. All rights reserved.
  7 | *
  8 | */
  9 | 
 10 | #include "predict_common.h"
 11 | 
 12 | FoodcamPredictor::FoodcamPredictor() {
 13 | 	debug = false;
 14 | 	initSVMs();
 15 | 	initColors();
 16 | 	initVocabulary();
 17 | 	Ptr<FeatureDetector > _detector(new SurfFeatureDetector());
 18 | 	Ptr<DescriptorMatcher > _matcher(new BruteForceMatcher<L2<float> >());
 19 | 	Ptr<DescriptorExtractor > _extractor(new OpponentColorDescriptorExtractor(Ptr<DescriptorExtractor>(new SurfDescriptorExtractor())));
 20 | 	matcher = _matcher;
 21 | 	detector = _detector;
 22 | 	extractor = _extractor;
 23 | 	bowide = Ptr<BOWImgDescriptorExtractor>(new BOWImgDescriptorExtractor(extractor,matcher));
 24 | 	bowide->setVocabulary(vocabulary);
 25 | 	background = imread("background.png");
 26 | }
 27 | 
 28 | void FoodcamPredictor::initColors() {
 29 | 	int ccount = 0;
 30 | 	for (map<string,CvSVM>::iterator it = classes_classifiers.begin(); it != classes_classifiers.end(); ++it) {
 31 | 		classes_colors[(*it).first] = Scalar((float)(ccount++)/(float)(classes_classifiers.size())*180.0f,255,255);
 32 | 		if(debug) cout << "class " << (*it).first << " color " << classes_colors[(*it).first].val[0] << endl;
 33 | 	}
 34 | }	
 35 | 
 36 | void FoodcamPredictor::initSVMs() {
 37 | 	string dir, filepath;
 38 | 	DIR *dp;
 39 | 	struct dirent *dirp;
 40 | 	struct stat filestat;
 41 | 	
 42 | 	if(debug) cout << "load SVM classifiers" << endl;
 43 | 	dir = ".";
 44 | 	dp = opendir( dir.c_str() );
 45 | 	
 46 | 	while ((dirp = readdir( dp )))
 47 |     {
 48 | 		filepath = dir + "/" + dirp->d_name;
 49 | 		
 50 | 		// If the file is a directory (or is in some way invalid) we'll skip it 
 51 | 		if (stat( filepath.c_str(), &filestat )) continue;
 52 | 		if (S_ISDIR( filestat.st_mode ))         continue;
 53 | 		if (filepath.find("SVM_classifier_with_color") != string::npos)
 54 | 		{
 55 | 			string class_ = filepath.substr(filepath.rfind('_')+1,filepath.rfind('.')-filepath.rfind('_')-1);
 56 | 			if (debug) cout << "load " << filepath << ", class: " << class_ << endl;
 57 | 			classes_classifiers.insert(pair<string,CvSVM>(class_,CvSVM()));
 58 | 			classes_classifiers[class_].load(filepath.c_str());
 59 | 		}
 60 | 	}
 61 | 	closedir(dp);
 62 | }
 63 | 
 64 | void FoodcamPredictor::initVocabulary() {
 65 | 	if (debug) cout << "read vocabulary form file"<<endl;
 66 | 	FileStorage fs("vocabulary_color_1000.yml", FileStorage::READ);
 67 | 	fs["vocabulary"] >> vocabulary;
 68 | 	fs.release();	
 69 | }	
 70 | 
 71 | void FoodcamPredictor::evaluateOneImage(Mat& __img, vector<string>& out_classes) {
 72 | 	Mat diff = (__img - background), diff_8UC1;
 73 | 	
 74 | 	cvtColor(diff, diff_8UC1, CV_BGR2GRAY);
 75 | 	//		imshow("img no back", diff_8UC1);
 76 | 	Mat fg_mask = (diff_8UC1 > 5);
 77 | 	GaussianBlur(fg_mask, fg_mask, Size(11,11), 5.0);
 78 | 	fg_mask = fg_mask > 50;
 79 | 	
 80 | 	//		{
 81 | 	//			Mat _out; __img.copyTo(_out, fg_mask);
 82 | 	//			imshow("foregroung", _out);
 83 | 	//			imshow("to scan",__img);
 84 | 	//			waitKey(0);
 85 | 	//		}
 86 | 	
 87 | 	Rect crop_rect(0,100,640,480-100);
 88 | 	__img = __img(crop_rect);		//crop off top section
 89 | 	fg_mask = fg_mask(crop_rect);
 90 | 	
 91 | 	//_img.create(__img.size(), __img.type());
 92 | 	//		cvtColor(__img, _img, CV_BGR2GRAY);
 93 | 	//		equalizeHist(__img, _img);
 94 | 	Mat copy; cvtColor(__img, copy, CV_BGR2HSV);
 95 | 	
 96 | 	vector<Point> check_points;
 97 | 	//Sliding window approach.. (creating a vector here to ease the OMP parallel for-loop)
 98 | 	int winsize = 200;
 99 | 	map<string,pair<int,float> > found_classes;
100 | 	for (int x=0; x<__img.cols; x+=winsize/4) {
101 | 		for (int y=0; y<__img.rows; y+=winsize/4) {
102 | 			if (fg_mask.at<uchar>(y,x) == 0) {
103 | 				continue;
104 | 			}
105 | 			check_points.push_back(Point(x,y));
106 | 		}
107 | 	}
108 | 	
109 | 	if (debug) cout << "to check: " << check_points.size() << " points"<<endl;
110 | 	
111 | 	Mat seg = Mat::zeros(copy.size(),CV_8UC3);
112 | 	
113 | #pragma omp parallel for
114 | 	for (int i = 0; i < check_points.size(); i++) {
115 | 		int x = check_points[i].x;
116 | 		int y = check_points[i].y;
117 | 		//			if (debug) cout << omp_get_thread_num() << " scan " << check_points[i] << endl;
118 | 		Mat img,response_hist;
119 | 		__img(Rect(x-winsize/2,y-winsize/2,winsize,winsize)&Rect(0,0,__img.cols,__img.rows)).copyTo(img);
120 | 		
121 | 		vector<KeyPoint> keypoints;
122 | 		detector->detect(img,keypoints);
123 | 		//				vector<vector<int> > pointIdxsOfClusters;
124 | 		bowide->compute(img, keypoints, response_hist); //, &pointIdxsOfClusters);
125 | 		if (response_hist.cols == 0 || response_hist.rows == 0) {
126 | 			continue;
127 | 		}
128 | 		
129 | 		//		drawKeypoints(img, keypoints, img, Scalar(0,0,255));
130 | 		//		for (int i = 0; i < pointIdxsOfClusters.size(); i++) {
131 | 		//			if(pointIdxsOfClusters[i].size()>0) {
132 | 		//				Scalar clr(i/1000.0*255.0,0,0);
133 | 		//				for (int j = 0; j < pointIdxsOfClusters[i].size(); j++) {
134 | 		//					circle(img, keypoints[pointIdxsOfClusters[i][j]].pt, 1, clr, 2);
135 | 		//				}
136 | 		//			}
137 | 		//		}
138 | 		//		imshow("pic",img);
139 | 		
140 | 		//test vs. SVMs
141 | 		try {
142 | 			float minf = FLT_MAX; string minclass;
143 | 			for (map<string,CvSVM>::iterator it = classes_classifiers.begin(); it != classes_classifiers.end(); ++it) {
144 | 				float res = (*it).second.predict(response_hist,true);
145 | 				if ((*it).first == "misc" && res > 0.9) {
146 | 					continue;
147 | 				}
148 | 				if(res > 1.0) continue;
149 | 				if (res < minf) {
150 | 					minf = res;
151 | 					minclass = (*it).first;
152 | 				}
153 | 			}
154 | 			//				if (debug) cout << "best class: " << minclass << " ("<<minf<<")"<<endl;
155 | 			if (debug) cout << "."; if (debug) cout.flush();
156 | 			//circle(copy, Point(x,y), 5, classes_colors[minclass], CV_FILLED);
157 | 			float dim = MAX(MIN(minf - 0.8f,0.3f),0.0f) / 0.3f; //dimming the color: [0.8,1.1] -> [0.0,1.0]
158 | 			Scalar color_(classes_colors[minclass].val[0], classes_colors[minclass].val[1], classes_colors[minclass].val[2] * dim); 
159 | 			
160 | #pragma omp critical
161 | 			{
162 | 				putText(copy, minclass.substr(0, 4), Point(x-35,y+10), CV_FONT_HERSHEY_PLAIN, 2.0, Scalar(0,0,255), 2);
163 | 				circle(seg, check_points[i], winsize/5, color_, CV_FILLED);
164 | 				found_classes[minclass].first++;
165 | 				found_classes[minclass].second += minf;
166 | 			}
167 | 		}
168 | 		catch (cv::Exception) {
169 | 			continue;
170 | 		}
171 | 	}
172 | 	
173 | 	if (debug) cout << endl << "found classes: ";
174 | 	float max_class_f = FLT_MIN, max_class_f1 = FLT_MIN; string max_class, max_class1;
175 | 	vector<float> scores;
176 | 	for (map<string,pair<int,float> >::iterator it=found_classes.begin(); it != found_classes.end(); ++it) {
177 | 		float score = sqrtf((float)((*it).second.first) * (*it).second.second);
178 | 		if (score > 1e+10) {
179 | 			continue;	//an impossible score
180 | 		}
181 | 		scores.push_back(score);
182 | 		if (debug) cout << (*it).first << "(" << score << "),"; //<< (*it).second.first << "," << (*it).second.second / (float)(*it).second.first << "), ";
183 | 		if(score > max_class_f) { //1st place thrown off
184 | 			max_class_f1 = max_class_f;
185 | 			max_class1 = max_class;
186 | 			
187 | 			max_class_f = score;
188 | 			max_class = (*it).first;
189 | 		} else if (score >  max_class_f1) {	//2nd place thrown off
190 | 			max_class_f1 = score;
191 | 			max_class1 = (*it).first;
192 | 		}
193 | 	}
194 | 	if (debug) cout << endl;
195 | 
196 | 	normalizeClassname(max_class);
197 | 	normalizeClassname(max_class1);
198 | 
199 | 	Scalar mean_,stddev_;
200 | //	meanStdDev(Mat(scores), mean_, stddev_);
201 | 	out_classes.clear();
202 | 	out_classes.push_back(max_class);
203 | 	if(max_class_f - max_class_f1 < 10) {
204 | 		//Forget about it: variance is low (~10), so result is undecicive, we should take both max-classes.
205 | 		out_classes.push_back(max_class1);
206 | 	}	
207 | 	
208 | 	if (debug) cout << "chosen class: " << max_class << ", (" << max_class1 << "?)" << endl;
209 | }


--------------------------------------------------------------------------------
/test.txt:
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  2 | foodcamimages/TEST/20101202131934.jpg italian
  3 | foodcamimages/TEST/20101202144812.jpg italian salad
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