Refactoring

Code/Niko/ExecClassifMonoView.py

@@ -25,18 +25,18 @@ __date__	= 2016-03-25
 
 ### Argument Parser
 parser = argparse.ArgumentParser(
-description='This methods permits to execute a multiclass classification with one single view. At this point the used classifier is a RandomForest. The GridSearch permits to vary the number of trees and CrossValidation with k-folds.', 
+description='This methods permits to execute a multiclass classification with one single view. At this point the used classifier is a RandomForest. The GridSearch permits to vary the number of trees and CrossValidation with k-folds. The result will be a plot of the score per class and a CSV with the best classifier found by the GridSearch.', 
 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
 
-groupStandard = parser.add_argument_group('necessary arguments:')
+groupStandard = parser.add_argument_group('necessary arguments')
 groupStandard.add_argument('--name', metavar='STRING', action='store', help='Name of Database (default: %(default)s)', default='Caltech')
 groupStandard.add_argument('--feat', metavar='STRING', action='store', help='Name of Feature for Classification (default: %(default)s)', default='RGB')
-groupStandard.add_argument('--pathF', metavar='STRING', action='store', help='Path to the features (default: %(default)s)', default='Results-FeatExtr\\')
+groupStandard.add_argument('--pathF', metavar='STRING', action='store', help='Path to the features (default: %(default)s)', default='Results-FeatExtr/')
 groupStandard.add_argument('--fileCL', metavar='STRING', action='store', help='Name of classLabels CSV-file  (default: %(default)s)', default='classLabels.csv')
 groupStandard.add_argument('--fileFeat', metavar='STRING', action='store', help='Name of feature CSV-file  (default: %(default)s)', default='feature.csv')
 groupStandard.add_argument('-log', action='store_true', help='Use option to activate Logging to Console')
 
-groupClass = parser.add_argument_group('Classification arguments:')
+groupClass = parser.add_argument_group('Classification arguments')
 groupClass.add_argument('--CL_split', metavar='FLOAT', action='store', help='Determine the the train size', type=float, default=0.8)
 groupClass.add_argument('--CL_RF_trees', metavar='STRING', action='store', help='GridSearch: Determine the trees', default='50 100 150 200')
 groupClass.add_argument('--CL_RF_CV', metavar='INT', action='store', help='Number of k-folds for CV', type=int, default=8)
@@ -50,7 +50,7 @@ t_start = time.time()
 
 # Configure Logger
 dir = os.path.dirname(os.path.abspath(__file__)) + "/Results-ClassMonoView/"
-logfilename= datetime.datetime.now().strftime("%Y_%m_%d") + "-LOG-CMV-" + args.name + "-" + args.feat
+logfilename= datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + args.name + "-" + args.feat + "-LOG"
 logfile = dir + logfilename
 if os.path.isfile(logfile + ".log"):
         for i in range(1,20):

Code/Niko/ExecFeatExtraction.py

@@ -27,35 +27,35 @@ parser = argparse.ArgumentParser(
 description='This methods permits to export one or more features at the same time for a database of images (path, name). To extract one feature activate it by using the specific argument (e.g. -RGB). For each feature you can define the parameters by using the optional arguments (e.g. --RGB_Hist 32). The results will be exported to a CSV-File.', 
 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
 
-groupStandard = parser.add_argument_group('necessary arguments:')
+groupStandard = parser.add_argument_group('necessary arguments')
 groupStandard.add_argument('--name', metavar='STRING', action='store', help='Select a name of DB, e.g. Caltech (default: %(default)s)', default='DB')
 groupStandard.add_argument('--path', metavar='STRING', action='store', help='Path to the database (default: %(default)s)', default='D:\\CaltechMini')
 groupStandard.add_argument('-log', action='store_true', help='Use option to activate Logging to Console')
 
-groupRGB = parser.add_argument_group('RGB arguments:')
+groupRGB = parser.add_argument_group('RGB arguments')
 groupRGB.add_argument('-RGB', action='store_true', help='Use option to activate RGB')
 groupRGB.add_argument('--RGB_Bins', metavar='INT', action='store', help='Number of bins for histogram', type=int, default=16)
 groupRGB.add_argument('--RGB_CI', metavar='INT', action='store', help='Max Color Intensity [0 to VALUE]', type=int, default=256)
 groupRGB.add_argument('-RGB_NMinMax', action='store_true', help='Use option to actvate MinMax Norm instead of Distribution')
 
-groupHSV = parser.add_argument_group('HSV arguments:')
+groupHSV = parser.add_argument_group('HSV arguments')
 groupHSV.add_argument('-HSV', action='store_true', help='Use option to activate HSV')
 groupHSV.add_argument('--HSV_H_Bins', metavar='INT', action='store', help='Number of bins for Hue', type=int, default=16)
 groupHSV.add_argument('--HSV_S_Bins', metavar='INT', action='store', help='Number of bins for Saturation', type=int, default=4)
 groupHSV.add_argument('--HSV_V_Bins', metavar='INT', action='store', help='Number of bins for Value', type=int, default=4)
 groupHSV.add_argument('-HSV_NMinMax', action='store_true', help='Use option to actvate MinMax Norm instead of Distribution')
 
-groupSIFT = parser.add_argument_group('SIFT arguments:')
+groupSIFT = parser.add_argument_group('SIFT arguments')
 groupSIFT.add_argument('-SIFT', action='store_true', help='Use option to activate SIFT')
 groupSIFT.add_argument('--SIFT_Cluster', metavar='INT', action='store', help='Number of k-means cluster', type=int, default=50)
 groupSIFT.add_argument('-SIFT_NMinMax', action='store_true', help='Use option to actvate MinMax Norm instead of Distribution')
         
-groupSURF = parser.add_argument_group('SURF arguments:')
+groupSURF = parser.add_argument_group('SURF arguments')
 groupSURF.add_argument('-SURF', action='store_true', help='Use option to activate SURF')
 groupSURF.add_argument('--SURF_Cluster', metavar='INT', action='store', help='Number of k-means cluster', type=int, default=50)
 groupSURF.add_argument('-SURF_NMinMax', action='store_true', help='Use option to actvate MinMax Norm instead of Distribution')
 
-groupHOG = parser.add_argument_group('HOG arguments:')
+groupHOG = parser.add_argument_group('HOG arguments')
 groupHOG.add_argument('-HOG', action='store_true', help='Use option to activate HOG')
 groupHOG.add_argument('--HOG_CellD', metavar='INT', action='store', help='CellDimension for local histograms', type=int, default=5)
 groupHOG.add_argument('--HOG_Orient', metavar='INT', action='store', help='Number of bins of local histograms', type=int, default=8)
@@ -93,7 +93,7 @@ if(args.HOG):
 
 # Configure Logger
 dir = os.path.dirname(os.path.abspath(__file__)) + "/Results-FeatExtr/"
-logfilename= datetime.datetime.now().strftime("%Y_%m_%d") + "-LOG-FE-" + args.name + "-" + features.replace(" ", "_").rstrip("_")
+logfilename= datetime.datetime.now().strftime("%Y_%m_%d") + "-FE-" + args.name + "-" + features.replace(" ", "_").rstrip("_") + "-LOG"
 logfile = dir + logfilename
 if os.path.isfile(logfile + ".log"):
         for i in range(1,20):

Code/Niko/ExecFeatParaOpt.py

@@ -27,45 +27,46 @@ parser = argparse.ArgumentParser(
 description='This methods permits to perform an optimisation of the parameter of one feature. Therefore you have so specify which feature to use (e.g. --feature RGB) and which of his parameters (the parameters depend on the feature chosen, e.g. for RGB: --parameter Bins). The method will calculate the results in your given range and export the results to a CSV-File.',
 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
 
-groupStandard = parser.add_argument_group('necessary arguments:')
+groupStandard = parser.add_argument_group('necessary arguments')
 groupStandard.add_argument('--name', metavar='STRING', action='store', help='Select a name of DB, e.g. Caltech (default: %(default)s)', default='DB')
 groupStandard.add_argument('--path', metavar='STRING', action='store', help='Path to the database (default: %(default)s)', default='D:\\CaltechMini')
 groupStandard.add_argument('-log', action='store_true', help='Use option to activate Logging to Console')
+groupStandard.add_argument('-show', action='store_true', help='Use option to show results instead of saving it')
 
-groupOpt = parser.add_argument_group('Optimisation arguments:')
+groupOpt = parser.add_argument_group('Optimisation arguments')
 groupOpt.add_argument('--feature', choices=['RGB', 'HSV', 'SURF', 'SIFT', 'HOG'], help='Set feature from list (RGB, HSV, ..)', default='RGB')
 groupOpt.add_argument('--param', choices=['RGB_Bins', 'RGB_MaxCI', 'HSV_H_Bins', 'HSV_S_Bins', 'HSV_V_Bins', 'SIFT_Cluster', 'SURF_Cluster', 'HOG_Cluster'], help='Parameter to optimise (remember depends on feature)', default='RGB_Bins')
 groupOpt.add_argument('--valueStart', metavar='INT', action='store', help='Start-Value for optimisation range', type=int)
 groupOpt.add_argument('--valueEnd', metavar='INT', action='store', help='End-Value for optimisation range', type=int)
 groupOpt.add_argument('--nCalcs', metavar='INT', action='store', help='Number of calculations between Start and End-Value', type=int)
 
-groupRGB = parser.add_argument_group('RGB arguments:')
+groupRGB = parser.add_argument_group('RGB arguments')
 groupRGB.add_argument('--RGB_Bins', metavar='INT', action='store', help='Number of bins for histogram', type=int, default=16)
 groupRGB.add_argument('--RGB_CI', metavar='INT', action='store', help='Max Color Intensity [0 to VALUE]', type=int, default=256)
 groupRGB.add_argument('-RGB_NMinMax', action='store_true', help='Use option to actvate MinMax Norm instead of Distribution')
 
-groupHSV = parser.add_argument_group('HSV arguments:')
+groupHSV = parser.add_argument_group('HSV arguments')
 groupHSV.add_argument('--HSV_H_Bins', metavar='INT', action='store', help='Number of bins for Hue', type=int, default=16)
 groupHSV.add_argument('--HSV_S_Bins', metavar='INT', action='store', help='Number of bins for Saturation', type=int, default=4)
 groupHSV.add_argument('--HSV_V_Bins', metavar='INT', action='store', help='Number of bins for Value', type=int, default=4)
 groupHSV.add_argument('-HSV_NMinMax', action='store_true', help='Use option to actvate MinMax Norm instead of Distribution')
 
-groupSIFT = parser.add_argument_group('SIFT arguments:')
+groupSIFT = parser.add_argument_group('SIFT arguments')
 groupSIFT.add_argument('--SIFT_Cluster', metavar='INT', action='store', help='Number of k-means cluster', type=int, default=50)
 groupSIFT.add_argument('-SIFT_NMinMax', action='store_true', help='Use option to actvate MinMax Norm instead of Distribution')
         
-groupSURF = parser.add_argument_group('SURF arguments:')
+groupSURF = parser.add_argument_group('SURF arguments')
 groupSURF.add_argument('--SURF_Cluster', metavar='INT', action='store', help='Number of k-means cluster', type=int, default=50)
 groupSURF.add_argument('-SURF_NMinMax', action='store_true', help='Use option to actvate MinMax Norm instead of Distribution')
 
-groupHOG = parser.add_argument_group('HOG arguments:')
+groupHOG = parser.add_argument_group('HOG arguments')
 groupHOG.add_argument('--HOG_CellD', metavar='INT', action='store', help='CellDimension for local histograms', type=int, default=5)
 groupHOG.add_argument('--HOG_Orient', metavar='INT', action='store', help='Number of bins of local histograms', type=int, default=8)
 groupHOG.add_argument('--HOG_Cluster', metavar='INT', action='store', help='Number of k-means cluster', type=int, default=12)
 groupHOG.add_argument('--HOG_Iter', metavar='INT', action='store', help='Max. number of iterations for clustering', type=int, default=100)
 groupHOG.add_argument('--HOG_cores', metavar='INT', action='store', help='Number of cores for HOG', type=int, default=1)
 
-groupClass = parser.add_argument_group('Classification arguments:')
+groupClass = parser.add_argument_group('Classification arguments')
 groupClass.add_argument('--CL_split', metavar='FLOAT', action='store', help='Determine the train size', type=float, default=0.8)
 groupClass.add_argument('--CL_RF_trees', metavar='STRING', action='store', help='GridSearch: Determine the trees', default='50 100 150 200')
 groupClass.add_argument('--CL_RF_CV', metavar='INT', action='store', help='Number of k-folds for CV', type=int, default=8)
@@ -92,7 +93,7 @@ para_Cl = [args.CL_split, map(int, args.CL_RF_trees.split()), args.CL_RF_CV, arg
 
 # Configure Logger
 dir = os.path.dirname(os.path.abspath(__file__)) + "/Results-FeatParaOpt/"
-logfilename= datetime.datetime.now().strftime("%Y_%m_%d") + "-LOG-FPO-" + args.name + "-" + args.feat + "-" + args.param
+logfilename= datetime.datetime.now().strftime("%Y_%m_%d") + "-FPO-" + args.name + "-" + args.feature + "-" + args.param + "-LOG"
 logfile = dir + logfilename
 if os.path.isfile(logfile + ".log"):
         for i in range(1,20):
@@ -134,7 +135,7 @@ logging.debug("### Done:\t Feautre Optimisation ")
 ################################ Render results
 logging.debug("### Start:\t Exporting to CSV ")
 dir = os.path.dirname(os.path.abspath(__file__)) + "/Results-FeatParaOpt/"
-filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-FeatParaOpt-" + args.feature
+filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-FPO-" + args.name + "-" + args.feature + "-" + args.param
 ExportResults.exportPandasToCSV(df_feat_res, dir, filename)
 logging.debug("### Done:\t Exporting to CSV ")
 
@@ -168,13 +169,18 @@ cl_desc = df_feat_res.c_cl_desc.values
 
 # Description of Feature
 feat_desc = df_feat_res.a_feat_desc.values
+
+# Store or Show plot
+if(args.show):
+        store = False
+else:
         store = True
 
-fileName = datetime.datetime.now().strftime("%Y_%m_%d") + "-" + "-FPO-" + args.name + "-" + args.feature + "-" + args.param
+fileName = datetime.datetime.now().strftime("%Y_%m_%d") + "-FPO-" + args.name + "-" + args.feature + "-" + args.param
 # Show Results for Calculation
-ExportResults.showScoreTime(dir, fileName + "-TotalTime.png", store, score, tot_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation \n DB: ' + args.name + 'Feat: ' + args.feature, 'Precision', 'Total Time (Feature Extraction+Classification)\n [s]')
-ExportResults.showScoreTime(dir, fileName + "-FeatExtTime.png", store, score, feat_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation \n DB: ' + args.name + 'Feat: ' + args.feature, 'Precision', 'Feature Extraction Time\n [s]')
-ExportResults.showScoreTime(dir, fileName + "-ClassTime.png", store, score, cl_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation \n DB: ' + args.name + 'Feat: ' + args.feature, 'Precision', 'Classification Time\n [s]')
+ExportResults.showScoreTime(dir, fileName + "-TotalTime", store, score, tot_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation - DB:' + args.name + ' Feat:' + args.feature, 'Precision', 'Total Time (Feature Extraction+Classification)\n [s]')
+ExportResults.showScoreTime(dir, fileName + "-FeatExtTime", store, score, feat_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation - DB:' + args.name + ' Feat:' + args.feature, 'Precision', 'Feature Extraction Time\n [s]')
+ExportResults.showScoreTime(dir, fileName + "-ClassTime", store, score, cl_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation - DB:' + args.name + ' Feat:' + args.feature, 'Precision', 'Classification Time\n [s]')
 
 logging.debug("### Done:\t Plot Result")
 

Code/Niko/ExportResults.py

@@ -12,6 +12,7 @@ import numpy as np                      # for Numpy Arrays
 import matplotlib.pyplot as plt                                         # for Plots
 from scipy.interpolate import interp1d                                  # to Interpolate Data
 from matplotlib.offsetbox import AnchoredOffsetbox, TextArea, HPacker   # to generate the Annotations in plot
+from pylab import rcParams                                              # to change size of plot
 
 # Import own modules
 
@@ -57,9 +58,13 @@ def showScoreTime(dir, filename, store, resScore, resTime, rangeX, parameter, fe
         f_score_interp = interp1d(rangeX, resScore, kind='quadratic')
         f_time_interp  = interp1d(rangeX, resTime, kind='quadratic')
         
+        # Change size of plot
+        rcParams['figure.figsize'] =20, 10
+        
         # Figure1 with subplot
         fig, ax1 = plt.subplots()
         
+        
         #plt.plot(x, y, type of line)
         # Generating X-Axis
         xnew = np.linspace(0, max(rangeX), num=100, endpoint=True)
@@ -86,7 +91,7 @@ def showScoreTime(dir, filename, store, resScore, resTime, rangeX, parameter, fe
                 ax1.annotate(letter, xy=(act_x, act_score), xytext=(act_x, act_score))
                 ax2.annotate(letter, xy=(act_x, act_time), xytext=(act_x, act_time))
                 # Creates a legend with description of feature and classificator of each datapoint
-                legend = legend + letter + ": " + act_feat_desc + " Classf: " + act_cl_desc + "\n"
+                legend = legend + letter + ") Feature: " + act_feat_desc + "; Classifier: " + act_cl_desc + "\n"
                 
         # Remove last \n
         legend = legend[:-1]
@@ -99,9 +104,9 @@ def showScoreTime(dir, filename, store, resScore, resTime, rangeX, parameter, fe
         anchored_box = AnchoredOffsetbox(loc=3,
                                          child=box, pad=0.2,
                                          frameon=True,
-                                         bbox_to_anchor=(0., 1.02),
+                                         bbox_to_anchor=(0, 1.04), #to change the place of the legend (text above of figure)
                                          bbox_transform=ax1.transAxes,
-                                         borderpad=1.5,
+                                         borderpad=1.0,
                                          )
         ax1.add_artist(anchored_box)
         fig.subplots_adjust(top=0.7)

Code/Niko/FeatExtraction.py

@@ -193,9 +193,9 @@ def calcSURFSIFTDescriptors(dfImages, boolSIFT):
         npImages = dfImages.values
     
         if(boolSIFT==True):
-                feat = "SIFT:\t "
+                feat = "### SIFT:\t "
         else:
-                feat = "SURF:\t "
+                feat = "### SURF:\t "
         
         # List where all the descriptors are stored
         des_list = []
@@ -316,9 +316,9 @@ def calcSURFSIFTHisto(nameDB, dfImages, cluster, boolNormMinMax, descriptors,des
 	
         param = "Cluster_" +  str(cluster) + "-" + "Norm_" + norm
         if(boolSIFT==True):
-                feat="SIFT"
+                feat="### SIFT"
         else:
-                feat="SURF"
+                feat="### SURF"
         description = nameDB + "-" + feat + "-" + param
         
         #### Bag of Words Approach

Code/Niko/Results-ClassMonoView/2016_03_22-CMV-CTMini-RGB-LOG.log

+2016-03-22 16:40:43,005 DEBUG:### Main Programm for Classification MonoView
+2016-03-22 16:40:43,005 DEBUG:### Info: Database:CTMini Feature:RGB train_size:0.8, GridSearch of Trees:50 100 150 200, CrossValidation k-folds:8, cores:1
+2016-03-22 16:40:43,006 DEBUG:### Start:	 Read CSV Files
+2016-03-22 16:40:43,016 DEBUG:### Info:	 Shape of Feature:(97L, 48L), Length of classLabels vector:(97L,)
+2016-03-22 16:40:43,016 DEBUG:### Done:	 Read CSV Files
+2016-03-22 16:40:43,016 DEBUG:### Start:	 Determine Train/Test split
+2016-03-22 16:40:43,016 DEBUG:### Info:	 Shape X_train:(77L, 48L), Length of y_train:77
+2016-03-22 16:40:43,017 DEBUG:### Info:	 Shape X_test:(20L, 48L), Length of y_test:20
+2016-03-22 16:40:43,017 DEBUG:### Done:	 Determine Train/Test split
+2016-03-22 16:40:43,017 DEBUG:### Start:	 Classification
+2016-03-22 16:40:45,684 DEBUG:### Info:	 Time for Classification: 2.67600011826[s]
+2016-03-22 16:40:45,684 DEBUG:### End:	 Classification
+2016-03-22 16:40:45,684 DEBUG:### Start:	 Exporting to CSV
+2016-03-22 16:40:45,690 DEBUG:### Done:	 Exporting to CSV
+2016-03-22 16:40:45,690 DEBUG:### Start:	 Plot Result
+2016-03-22 16:40:45,839 DEBUG:### Done:	 Plot Result

Code/Niko/Results-ClassMonoView/2016_03_22-CMV-CTMini-RGB.csv

+;a_class_time;b_cl_desc;c_cl_res;d_cl_score
+0;2.67600011826;Classif_RF-CV_8-Trees_50;"GridSearchCV(cv=8, error_score='raise',
+       estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
+            max_depth=None, max_features='auto', max_leaf_nodes=None,
+            min_samples_leaf=1, min_samples_split=2,
+            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
+            oob_score=False, random_state=None, verbose=0,
+            warm_start=False))]),
+       fit_params={}, iid=True, loss_func=None, n_jobs=1,
+       param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+       pre_dispatch='2*n_jobs', refit=True, score_func=None,
+       scoring='accuracy', verbose=0)";0.948051948052

Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-ClassLabels-Description.csv

+0;accordion
+1;anchor

Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-ClassLabels.csv

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Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-RGB_HSV_SIFT_SURF_HOG-LOG.log

+2016-03-22 15:16:37,407 DEBUG:### Main Programm for Feature Extraction ###
+2016-03-22 15:16:37,408 DEBUG:Infos:	 NameDB=CTMini, Path=D:\CaltechMini, Features=RGB HSV SIFT SURF HOG
+2016-03-22 15:16:37,408 DEBUG:Start:	 Exportation of images from DB
+2016-03-22 15:16:37,667 DEBUG:Done:	 Exportation of images from DB in:0.259000062943[s]
+2016-03-22 15:16:37,668 DEBUG:Start:	 Features Extraction
+2016-03-22 15:16:37,668 DEBUG:RGB:	 Start
+2016-03-22 15:16:37,668 DEBUG:RGB:	 NumberOfBins=16, MaxColorIntensity=256, Norm=Distr
+2016-03-22 15:16:37,844 DEBUG:RGB:	 Done in: 0.175000190735[s]
+2016-03-22 15:16:37,844 DEBUG:HSV:	 Start
+2016-03-22 15:16:37,844 DEBUG:HSV:	 HSVBins=[16,4,4], Norm=Distr
+2016-03-22 15:16:38,188 DEBUG:HSV:	 Done in: 0.344999790192[s]
+2016-03-22 15:16:38,190 DEBUG:SIFT:	 Start
+2016-03-22 15:16:38,190 DEBUG:SIFT:	 Cluster=50, Norm=Distr
+2016-03-22 15:16:38,190 DEBUG:SIFT:	 Keypoints Calculation
+2016-03-22 15:16:40,349 DEBUG:SIFT:	 25% of images processed (Keypoints)
+2016-03-22 15:16:42,005 DEBUG:SIFT:	 50% of images processed (Keypoints)
+2016-03-22 15:16:43,447 DEBUG:SIFT:	 75% of images processed (Keypoints)
+2016-03-22 15:16:44,878 DEBUG:SIFT:	 Calculation of Dictonary with 50 Clusters
+2016-03-22 15:16:48,861 DEBUG:SIFT:	 Assign words from Dictonary to each Image
+2016-03-22 15:16:48,951 DEBUG:SIFT:	 25% of images processed (Assignments)
+2016-03-22 15:16:49,023 DEBUG:SIFT:	 50% of images processed (Assignments)
+2016-03-22 15:16:49,079 DEBUG:SIFT:	 75% of images processed (Assignments)
+2016-03-22 15:16:49,135 DEBUG:SIFT:	 Done in: 10.9459998608[s]
+2016-03-22 15:16:49,135 DEBUG:SURF:	 Start
+2016-03-22 15:16:49,137 DEBUG:SURF:	 Cluster=50, Norm=Distr
+2016-03-22 15:16:49,138 DEBUG:SURF:	 Keypoints Calculation
+2016-03-22 15:16:51,694 DEBUG:SURF:	 25% of images processed (Keypoints)
+2016-03-22 15:16:53,861 DEBUG:SURF:	 50% of images processed (Keypoints)
+2016-03-22 15:16:56,025 DEBUG:SURF:	 75% of images processed (Keypoints)
+2016-03-22 15:16:58,243 DEBUG:SURF:	 Calculation of Dictonary with 50 Clusters
+2016-03-22 15:17:00,667 DEBUG:SURF:	 Assign words from Dictonary to each Image
+2016-03-22 15:17:00,757 DEBUG:SURF:	 25% of images processed (Assignments)
+2016-03-22 15:17:00,829 DEBUG:SURF:	 50% of images processed (Assignments)
+2016-03-22 15:17:00,897 DEBUG:SURF:	 75% of images processed (Assignments)
+2016-03-22 15:17:00,986 DEBUG:SURF:	 Done in: 11.8489999771[s]
+2016-03-22 15:17:00,986 DEBUG:HOG:	 Start
+2016-03-22 15:17:00,986 DEBUG:HOG:	 CellDim=5, NbOrientations=8, Cluster=12, MaxIter=100, NbCores=1
+2016-03-22 15:19:21,826 DEBUG:HOG:	 Done in: 140.839999914[s]
+2016-03-22 15:19:21,828 DEBUG:Done:	 Features Extraction
+2016-03-22 15:19:21,828 DEBUG:Start:	 Save Features to CSV Databases
+2016-03-22 15:19:21,924 DEBUG:Done:	 Save Features to CSV Databases

Code/Niko/Results-FeatParaOpt/2016_03_22-FPO-CTMini-RGB-RGB_Bins-LOG.log

+2016-03-22 16:32:30,171 DEBUG:### Main Programm for Feature Parameter Optimisation 
+2016-03-22 16:32:30,171 DEBUG:### Optimisation - Feature:RGB Parameter:RGB_Bins from:2 to:30 in #calc:10
+2016-03-22 16:32:30,173 DEBUG:### Start:	 Exportation of images from DB
+2016-03-22 16:32:30,434 DEBUG:### Done:	 Exportation of Images from DB 
+2016-03-22 16:32:30,434 DEBUG:### Start:	 Feautre Optimisation
+2016-03-22 16:32:30,434 DEBUG:### Start:	 FeatureExtraction Nr:1 from:10 with RGB_Bins=2 ###
+2016-03-22 16:32:30,605 DEBUG:### Done:	 FeatureExtraction Nr:1 from:10 ###
+2016-03-22 16:32:30,607 DEBUG:### Start:	 Classification Nr:1 from:10
+2016-03-22 16:32:33,263 DEBUG:### Done:	 Classification Nr:1 from:10
+2016-03-22 16:32:33,269 DEBUG:### Start:	 FeatureExtraction Nr:2 from:10 with RGB_Bins=5 ###
+2016-03-22 16:32:33,440 DEBUG:### Done:	 FeatureExtraction Nr:2 from:10 ###
+2016-03-22 16:32:33,441 DEBUG:### Start:	 Classification Nr:2 from:10
+2016-03-22 16:32:36,063 DEBUG:### Done:	 Classification Nr:2 from:10
+2016-03-22 16:32:36,068 DEBUG:### Start:	 FeatureExtraction Nr:3 from:10 with RGB_Bins=8 ###
+2016-03-22 16:32:36,236 DEBUG:### Done:	 FeatureExtraction Nr:3 from:10 ###
+2016-03-22 16:32:36,237 DEBUG:### Start:	 Classification Nr:3 from:10
+2016-03-22 16:32:38,921 DEBUG:### Done:	 Classification Nr:3 from:10
+2016-03-22 16:32:38,926 DEBUG:### Start:	 FeatureExtraction Nr:4 from:10 with RGB_Bins=11 ###
+2016-03-22 16:32:39,098 DEBUG:### Done:	 FeatureExtraction Nr:4 from:10 ###
+2016-03-22 16:32:39,099 DEBUG:### Start:	 Classification Nr:4 from:10
+2016-03-22 16:32:41,841 DEBUG:### Done:	 Classification Nr:4 from:10
+2016-03-22 16:32:41,845 DEBUG:### Start:	 FeatureExtraction Nr:5 from:10 with RGB_Bins=14 ###
+2016-03-22 16:32:42,023 DEBUG:### Done:	 FeatureExtraction Nr:5 from:10 ###
+2016-03-22 16:32:42,023 DEBUG:### Start:	 Classification Nr:5 from:10
+2016-03-22 16:32:44,762 DEBUG:### Done:	 Classification Nr:5 from:10
+2016-03-22 16:32:44,769 DEBUG:### Start:	 FeatureExtraction Nr:6 from:10 with RGB_Bins=18 ###
+2016-03-22 16:32:44,950 DEBUG:### Done:	 FeatureExtraction Nr:6 from:10 ###
+2016-03-22 16:32:44,950 DEBUG:### Start:	 Classification Nr:6 from:10
+2016-03-22 16:32:47,818 DEBUG:### Done:	 Classification Nr:6 from:10
+2016-03-22 16:32:47,822 DEBUG:### Start:	 FeatureExtraction Nr:7 from:10 with RGB_Bins=21 ###
+2016-03-22 16:32:48,043 DEBUG:### Done:	 FeatureExtraction Nr:7 from:10 ###
+2016-03-22 16:32:48,045 DEBUG:### Start:	 Classification Nr:7 from:10
+2016-03-22 16:32:51,453 DEBUG:### Done:	 Classification Nr:7 from:10
+2016-03-22 16:32:51,459 DEBUG:### Start:	 FeatureExtraction Nr:8 from:10 with RGB_Bins=24 ###
+2016-03-22 16:32:51,677 DEBUG:### Done:	 FeatureExtraction Nr:8 from:10 ###
+2016-03-22 16:32:51,677 DEBUG:### Start:	 Classification Nr:8 from:10
+2016-03-22 16:32:54,657 DEBUG:### Done:	 Classification Nr:8 from:10
+2016-03-22 16:32:54,661 DEBUG:### Start:	 FeatureExtraction Nr:9 from:10 with RGB_Bins=27 ###
+2016-03-22 16:32:54,859 DEBUG:### Done:	 FeatureExtraction Nr:9 from:10 ###
+2016-03-22 16:32:54,861 DEBUG:### Start:	 Classification Nr:9 from:10
+2016-03-22 16:32:57,914 DEBUG:### Done:	 Classification Nr:9 from:10
+2016-03-22 16:32:57,920 DEBUG:### Start:	 FeatureExtraction Nr:10 from:10 with RGB_Bins=30 ###
+2016-03-22 16:32:58,135 DEBUG:### Done:	 FeatureExtraction Nr:10 from:10 ###
+2016-03-22 16:32:58,138 DEBUG:### Start:	 Classification Nr:10 from:10
+2016-03-22 16:33:01,158 DEBUG:### Done:	 Classification Nr:10 from:10
+2016-03-22 16:33:01,164 DEBUG:### Done:	 Feautre Optimisation 
+2016-03-22 16:33:01,164 DEBUG:### Start:	 Exporting to CSV 
+2016-03-22 16:33:01,173 DEBUG:### Done:	 Exporting to CSV 
+2016-03-22 16:33:01,173 DEBUG:### Start:	 Plot Result
+2016-03-22 16:33:03,180 DEBUG:### Done:	 Plot Result