Changement avec parser et logging

Code/FeatExtraction/ExecClassifMV.py

+#!/usr/bin/env python
+
+""" Script to perform feature parameter optimisation """
+
+# Import built-in modules
+#import cv2                      # for OpenCV 
+#import cv                       # for OpenCV
+#import datetime                 # for TimeStamp in CSVFile
+#from scipy.cluster.vq import *  # for Clustering http://docs.scipy.org/doc/scipy/reference/cluster.vq.html
+#import numpy as np              # for arrays
+#import time                     # for time calculations
+from argparse import ArgumentParser # for acommand line arguments
+
+# Import 3rd party modules
+
+# Import own modules
+#import DBCrawl			# Functions to read Images from Database
+#import ExportResults            # Functions to render results
+#import FeatExtraction           # Functions to extract the features from Database   
+
+# Author-Info
+__author__ 	= "Nikolas Huelsmann"
+__status__ 	= "Development" #Production, Development, Prototype
+__date__	= 2016-03-10
+
+### Argument Parser
+
+parser = ArgumentParser(description='Perform feature parameter optimisation')
+
+parser.add_argument('-p', '--path', action='store', help='Path to the database', default='D:\\CaltechMini')
+parser.add_argument('-c', '--cores', action='store', type=int, help='Nb cores used for parallelization', default=1)
+
+args = parser.parse_args()
\ No newline at end of file

Code/FeatExtraction/ExecFeatExtraction.py

+#!/usr/bin/env python
+
+""" Script to perform feature parameter optimisation """
+
+# Import built-in modules
+import cv2                      # for OpenCV 
+import cv                       # for OpenCV
+import datetime                 # for TimeStamp in CSVFile
+from scipy.cluster.vq import *  # for Clustering http://docs.scipy.org/doc/scipy/reference/cluster.vq.html
+import numpy as np              # for arrays
+import time                     # for time calculations
+from argparse import ArgumentParser # for acommand line arguments
+
+# Import 3rd party modules
+
+# Import own modules
+import DBCrawl			# Functions to read Images from Database
+import ExportResults            # Functions to render results
+import FeatExtraction           # Functions to extract the features from Database   
+
+# Author-Info
+__author__ 	= "Nikolas Huelsmann"
+__status__ 	= "Development" #Production, Development, Prototype
+__date__	= 2016-03-10
+
+### Argument Parser
+
+parser = ArgumentParser(description='Export Features')
+
+parser.add_argument('--name', action='store', help='Name of DB, default DB', default='DB')
+parser.add_argument('--path', action='store', help='Path to the database e.g. D:\\Caltech', default='D:\\CaltechMini')
+parser.add_argument('--cores', action='store', help='Number of cores used for parallelization of HOG, default 1', type=int, default=1)
+
+parser.add_argument('--RGB', action='store_true', help='Use option to activate RGB')
+parser.add_argument('--RGB_Hist', action='store', help='RGB: Number of bins for histogram, default 16', type=int, default=16)
+parser.add_argument('--RGB_CI', action='store', help='RGB: Max Color Intensity [0 to VALUE], default 256', type=int, default=256)
+parser.add_argument('--RGB_NMinMax', action='store_true', help='RGB: Use option to actvate MinMax Norm, default distribtion')
+
+parser.add_argument('--HSV', action='store_true', help='Use option to activate HSV')
+parser.add_argument('--HSV_H', action='store', help='HSV: Number of bins for Hue, default 8', type=int, default=8)
+parser.add_argument('--HSV_S', action='store', help='HSV: Number of bins for Saturation, default 3', type=int, default=3)
+parser.add_argument('--HSV_V', action='store', help='HSV: Number of bins for Value, default 3', type=int, default=3)
+parser.add_argument('--HSV_NMinMax', action='store_true', help='HSV: Use option to actvate MinMax Norm, default distribtion')
+
+parser.add_argument('--SIFT', action='store_true', help='Use option to activate SIFT')
+parser.add_argument('--SIFT_Cluster', action='store', help='SIFT: Number of k-means cluster, default 50', type=int, default=50)
+parser.add_argument('--SIFT_NMinMax', action='store_true', help='SIFT: Use option to actvate MinMax Norm, default distribtion')
+        
+parser.add_argument('--SURF', action='store_true', help='Use option to activate SURF')
+parser.add_argument('--SURF_Cluster', action='store', help='SURF: Number of k-means cluster, default 50', type=int, default=50)
+parser.add_argument('--SURF_NMinMax', action='store_true', help='SURF: Use option to actvate MinMax Norm, default distribtion')
+
+parser.add_argument('--HOG', action='store_true', help='Use option to activate HOG')
+parser.add_argument('--HOG_CellD', action='store', help='HOG: CellDimension for local histograms, default 5', type=int, default=5)
+parser.add_argument('--HOG_Orient', action='store', help='HOG: Number of bins of local histograms , default 8', type=int, default=8)
+parser.add_argument('--HOG_Cluster', action='store', help='HOG: Number of k-means cluster, default 12', type=int, default=12)
+parser.add_argument('--HOG_Iter', action='store', help='HOG: Max. number of iterations for clustering, default 100', type=int, default=100)
+
+
+# CELL_DIMENSION is the dimension of the cells on which we will compute local histograms 
+# NB_ORIENTATIONS is the number of bins of this local histograms 
+# intuitively, if CELL_DIMENSION is small it's better to have a small NB_ORIENTATIONS in order to have meaningful local histograms
+# NB_CLUSTERS is the number of bins of the global histograms (the number of clusters in the KMEANS algorithm used for the bag of word)
+# MAXITER is the maximum number of iteration for the clustering algorithm
+
+args = parser.parse_args()
+path = args.path
+NB_CORES = args.cores
+nameDB = args.name
+
+### Helper
+
+# Function to transform the boolean deciscion of norm into a string
+def boolNormToStr(norm):
+        if(norm):
+                return "MinMax"
+        else:
+                return "Distr"
+
+### Main Programm
+
+print "### Main Programm for Feature Extraction ###"
+features = ""
+if(args.RGB):
+        features = features + "RGB "
+if(args.HSV):
+        features = features + "HSV "
+if(args.SIFT):
+        features = features + "SIFT "
+if(args.SURF):
+        features = features + "SURF "
+if(args.HOG):
+        features = features + "HOG"
+
+print "Infos:\t NameDB=" + nameDB + ", Path=" + path + ", Cores=" + str(NB_CORES) + ", Features=" + features
+
+################################ Read Images from Database
+# Determine the Database to extract features
+
+print "Start:\t Exportation of images from DB"
+
+t_db_start = time.time()
+
+# get dictionary to link classLabels Text to Integers
+sClassLabels = DBCrawl.getClassLabels(path)
+
+# Get all path from all images inclusive classLabel as Integer
+dfImages,nameDB = DBCrawl.imgCrawl(path, sClassLabels, nameDB)
+
+print "Done:\t Exportation of images from DB"
+
+t_db  = t_db_start - time.time()
+
+################################ Feature Extraction
+print "Start:\t Features Extraction"
+
+### Setup RGB
+if(args.RGB):
+        
+        print "RGB:\t Start"
+        t_rgb_start = time.time()
+        
+        numberOfBins = args.RGB_Hist
+        maxColorIntensity = args.RGB_CI
+        boolNormMinMax = args.RGB_NMinMax
+        
+        # Infos
+        print "RGB:\t NumberOfBins=" + str(numberOfBins) + ", MaxColorIntensity=" + str(maxColorIntensity) + ", Norm=" + boolNormToStr(boolNormMinMax)
+         
+        # Extract Feature from DB
+        rgb_feat_desc,rgb_f_extr_res = FeatExtraction.calcRGBColorHisto(nameDB, dfImages, numberOfBins, maxColorIntensity, boolNormMinMax)
+
+        t_rgb = time.time() - t_rgb_start
+        print "RGB:\t Done in: " + str(t_rgb) + "[s]"
+
+
+### Setup HSV
+if(args.HSV):
+        print "HSV:\t Start"
+        t_hsv_start = time.time()
+        
+        h_bins = args.HSV_H
+        s_bins = args.HSV_S
+        v_bins = args.HSV_V
+        histSize = [h_bins, s_bins, v_bins]
+        boolNormMinMax = args.HSV_NMinMax
+        
+        # Infos
+        print "HSV:\t HSVBins=[" + str(h_bins) + "," + str(s_bins) + "," + str(v_bins) + "], Norm=" + boolNormToStr(boolNormMinMax)
+
+        # Extract Feature from DB
+        hsv_feat_desc,hsv_f_extr_res = FeatExtraction.calcHSVColorHisto(nameDB, dfImages, histSize, boolNormMinMax)
+        t_hsv = time.time() - t_hsv_start
+        print "HSV:\t Done in: " + str(t_hsv) + "[s]"
+
+
+
+### Setup SIFT
+if(args.SIFT):
+        print "SIFT:\t Start"
+        t_sift_start = time.time()
+        
+        boolSIFT = True
+        cluster = args.SIFT_Cluster
+        boolNormMinMax = args.SIFT_NMinMax
+        
+        print "SIFT:\t Cluster=" + str(cluster) + ", Norm=" + boolNormToStr(boolNormMinMax)
+
+        sift_descriptors,sift_des_list = FeatExtraction.calcSURFSIFTDescriptors(dfImages, boolSIFT)
+        sift_feat_desc,sift_f_extr_res = FeatExtraction.calcSURFSIFTHisto(nameDB, dfImages, cluster, boolNormMinMax, sift_descriptors, sift_des_list, boolSIFT)
+        t_sift = time.time() - t_sift_start 
+        print "SIFT:\t Done in: " + str(t_sift) + "[s]"
+
+
+### Setup SURF
+if(args.SURF):
+        print "SURF:\t Start"
+        t_surf_start = time.time()
+        
+        boolSIFT = False
+        cluster = args.SURF_Cluster
+        boolNormMinMax = args.SURF_NMinMax
+        
+        print "SURF:\t Cluster=" + str(cluster) + ", Norm=" + boolNormToStr(boolNormMinMax)
+
+        # Extract Feature from DB
+        surf_descriptors,surf_des_list = FeatExtraction.calcSURFSIFTDescriptors(dfImages, boolSIFT)
+        surf_feat_desc,surf_f_extr_res = FeatExtraction.calcSURFSIFTHisto(nameDB, dfImages, cluster, boolNormMinMax, surf_descriptors, surf_des_list, boolSIFT)
+        t_surf = time.time() - t_surf_start 
+        print "SURF:\t Done in: " + str(t_surf) + "[s]"
+
+### Setup HOG
+if(args.HOG):
+        print "HOG:\t Start"
+        t_hog_start = time.time()
+        
+        CELL_DIMENSION = args.HOG_CellD
+        NB_ORIENTATIONS = args.HOG_Orient
+        NB_CLUSTERS = args.HOG_Cluster
+        MAXITER = args.HOG_Iter
+        
+        print "HOG:\t CellDim=" + str(CELL_DIMENSION) + ", NbOrientations=" + str(NB_ORIENTATIONS) +", Cluster=" + str(NB_CLUSTERS) + ", MaxIter=" + str(MAXITER)
+
+        # Extract Feature from DB
+        hog_feat_desc,hog_f_extr_res = FeatExtraction.calcHOGParallel(nameDB, dfImages.values, CELL_DIMENSION, NB_ORIENTATIONS, NB_CLUSTERS, MAXITER, NB_CORES)
+        #hog_feat_desc,hog_f_extr_res = FeatExtraction.calcHOG(nameDB, dfImages.values, CELL_DIMENSION, NB_ORIENTATIONS, NB_CLUSTERS, MAXITER)   
+        t_hog = time.time() - t_hog_start
+        print "HOG:\t Done in: " + str(t_hog) + "[s]"
+
+print "Done:\t Features Extraction"
+
+
+################################ SAVE TO FEATURES DATABASES
+print "Start:\t Save Features to CSV Databases"
+
+### Classlabels and Description
+OutputfileNameClassLabels = datetime.datetime.now().strftime("%Y_%m_%d") + "-" + nameDB + "-ClassLabels"
+ExportResults.exportNumpyToCSV(dfImages.classLabel, OutputfileNameClassLabels, '%i')
+
+fileNameClassLabels = datetime.datetime.now().strftime("%Y_%m_%d") + "-" + nameDB + "-ClassLabels-Description"
+ExportResults.exportPandasToCSV(sClassLabels, fileNameClassLabels)
+
+format = '%1.30f'
+### RGB
+if(args.RGB):
+        fileName = datetime.datetime.now().strftime("%Y_%m_%d") + "-" + rgb_feat_desc
+        ExportResults.exportNumpyToCSV(rgb_f_extr_res, fileName, format)
+        
+
+### HSV
+if(args.HSV):
+        fileName = datetime.datetime.now().strftime("%Y_%m_%d") + "-" + hsv_feat_desc
+        ExportResults.exportNumpyToCSV(hsv_f_extr_res, fileName, format)
+
+### SIFT
+if(args.SIFT):
+        fileName = datetime.datetime.now().strftime("%Y_%m_%d") + "-" + sift_feat_desc
+        ExportResults.exportNumpyToCSV(sift_f_extr_res, fileName, format)
+
+### SURF
+if(args.SURF):
+        fileName = datetime.datetime.now().strftime("%Y_%m_%d") + "-" + surf_feat_desc
+        ExportResults.exportNumpyToCSV(surf_f_extr_res, fileName, format)
+
+### HOG
+if(args.HOG):
+        fileName = datetime.datetime.now().strftime("%Y_%m_%d") + "-" + hog_feat_desc
+        ExportResults.exportNumpyToCSV(hog_f_extr_res, fileName, format)
+
+print "Done:\t Save Features to CSV Databases"
\ No newline at end of file

Raw Code/FeatExtraction/ExecFeatParaOpt.py → Code/FeatExtraction/ExecFeatParaOpt.py

@@ -8,6 +8,7 @@ import cv                       # for OpenCV
 import datetime                         # for TimeStamp in CSVFile
 from scipy.cluster.vq import *          # for Clustering http://docs.scipy.org/doc/scipy/reference/cluster.vq.html
 import numpy as np                      # for arrays
+from argparse import ArgumentParser     # for acommand line arguments
 
 # Import sci-kit learn
 from sklearn.ensemble import RandomForestClassifier

Raw Code/FeatExtraction/ExportResults.py → Code/FeatExtraction/ExportResults.py

@@ -35,18 +35,18 @@ def exportPandasToCSV(pandasSorDF, filename):
                 pandasSorDF.to_csv(filename + ".csv", sep=';', decimal=',')
 
 
-def exportNumpyToCSV(numpyArray, filename):
+def exportNumpyToCSV(numpyArray, filename, format):
         path = os.getcwdu() + "\\" + filename
     
         if os.path.isfile(path + ".csv"):
                 for i in range(1,20):
                         testFileName = filename  + "-" + str(i) + ".csv"
                         if os.path.isfile(os.getcwdu() + "\\" +  testFileName)!=True:
-                                np.savetxt(testFileName, numpyArray, delimiter=",", fmt='%1.30f')
+                                np.savetxt(testFileName, numpyArray, delimiter=";", fmt=format)
                                 break
 
         else:
-                np.savetxt(filename + ".csv", numpyArray, delimiter=";", fmt='%1.30f')
+                np.savetxt(filename + ".csv", numpyArray, delimiter=";", fmt=format)
 		
 		
 #### Rendering of results

Raw Code/FeatExtraction/ExecFeatExtraction.py

-#!/usr/bin/env python
-
-""" Script to perform feature parameter optimisation """
-
-# Import built-in modules
-import cv2                      # for OpenCV 
-import cv                       # for OpenCV
-import datetime                 # for TimeStamp in CSVFile
-from scipy.cluster.vq import *  # for Clustering http://docs.scipy.org/doc/scipy/reference/cluster.vq.html
-import numpy as np              # for arrays
-import time                     # for time calculations
-from argparse import ArgumentParser # for acommand line arguments
-
-# Import 3rd party modules
-
-# Import own modules
-import DBCrawl			# Functions to read Images from Database
-import ExportResults            # Functions to render results
-import FeatExtraction           # Functions to extract the features from Database   
-
-# Author-Info
-__author__ 	= "Nikolas Huelsmann"
-__status__ 	= "Development" #Production, Development, Prototype
-__date__	= 2016-02-04
-
-### Argument Parser
-
-parser = ArgumentParser(description='Perform feature parameter optimisation')
-
-parser.add_argument('-p', '--path', action='store', help='Path to the database', default='D:\\CaltechMini')
-parser.add_argument('-c', '--cores', action='store', type=int, help='Nb cores used for parallelization', default=1)
-
-args = parser.parse_args()
-
-path = args.path
-NB_CORES = args.cores
-
-### Main Programm
-
-################################ Read Images from Database
-# Determine the Database to extract features
-
-print "### Main Programm for Feature Extraction ###"
-# path ="D:\\CaltechMini"
-path = args.path
-nameDB = "CT-Mini"
-
-print "Start:\t Exportation of images from DB"
-
-t_db_start = time.time()
-
-# get dictionary to link classLabels Text to Integers
-sClassLabels = DBCrawl.getClassLabels(path)
-
-# Get all path from all images inclusive classLabel as Integer
-dfImages,nameDB = DBCrawl.imgCrawl(path, sClassLabels, nameDB)
-
-print "Done:\t Exportation of images from DB"
-
-t_db  = t_db_start - time.time()
-
-################################ Feature Extraction
-print "Start:\t Features Extraction"
-
-### Setup RGB
-t_rgb_start = time.time()
-print "RGB:\t Start"
-
-numberOfBins = 16
-maxColorIntensity = 256
-boolNormMinMax = False
- 
-# Extract Feature from DB
-rgb_feat_desc,rgb_f_extr_res = FeatExtraction.calcRGBColorHisto(nameDB, dfImages, numberOfBins, maxColorIntensity, boolNormMinMax)
-
-t_rgb = time.time() - t_rgb_start
-print "RGB:\t Done in: " + str(t_rgb) + "[s]"
-
-
-### Setup HSV
-t_hsv_start = time.time()
-print "HSV:\t Start"
-h_bins = 8 
-s_bins = 3
-v_bins = 3
-histSize = [h_bins, s_bins, v_bins]
-boolNormMinMax = False
-
-# Extract Feature from DB
-hsv_feat_desc,hsv_f_extr_res = FeatExtraction.calcHSVColorHisto(nameDB, dfImages, histSize, boolNormMinMax)
-t_hsv = time.time() - t_hsv_start
-print "HSV:\t Done in: " + str(t_hsv) + "[s]"
-
-
-
-### Setup SIFT
-t_sift_start = time.time()
-print "SIFT:\t Start"
-boolSIFT = True
-cluster = 50
-boolNormMinMax = False
-
-sift_descriptors,sift_des_list = FeatExtraction.calcSURFSIFTDescriptors(dfImages, boolSIFT)
-sift_feat_desc,sift_f_extr_res = FeatExtraction.calcSURFSIFTHisto(nameDB, dfImages, cluster, boolNormMinMax, sift_descriptors, sift_des_list, boolSIFT)
-t_sift = time.time() - t_sift_start 
-print "SIFT:\t Done in: " + str(t_sift) + "[s]"
-
-
-### Setup SURF
-t_surf_start = time.time()
-print "SURF:\t Start"
-boolSIFT = False
-cluster = 50
-boolNormMinMax = False
-
-# Extract Feature from DB
-surf_descriptors,surf_des_list = FeatExtraction.calcSURFSIFTDescriptors(dfImages, boolSIFT)
-surf_feat_desc,surf_f_extr_res = FeatExtraction.calcSURFSIFTHisto(nameDB, dfImages, cluster, boolNormMinMax, surf_descriptors, surf_des_list, boolSIFT)
-t_surf = time.time() - t_surf_start 
-print "SURF:\t Done in: " + str(t_surf) + "[s]"
-
-### Setup HOG
-t_hog_start = time.time()
-print "HOG:\t Start"
-CELL_DIMENSION = 5
-NB_ORIENTATIONS = 8
-NB_CLUSTERS = 12
-MAXITER = 100
-
-# Extract Feature from DB
-hog_feat_desc,hog_f_extr_res = FeatExtraction.calcHOGParallel(nameDB, dfImages.values, CELL_DIMENSION, NB_ORIENTATIONS, NB_CLUSTERS, MAXITER, NB_CORES)
-#hog_feat_desc,hog_f_extr_res = FeatExtraction.calcHOG(nameDB, dfImages.values, CELL_DIMENSION, NB_ORIENTATIONS, NB_CLUSTERS, MAXITER)   
-t_hog = time.time() - t_hog_start
-print "HOG:\t Done in: " + str(t_hog) + "[s]"
-
-print "Done:\t Features Extraction"
-
-
-################################ SAVE TO FEATURES DATABASES
-print "Start:\t Save Features to CSV Databases"
-
-### Classlabels and Description
-OutputfileNameClassLabels = datetime.datetime.now().strftime("%Y_%m_%d") + "-" + nameDB + "-ClassLabels"
-ExportResults.exportNumpyToCSV(dfImages.classLabel, OutputfileNameClassLabels)
-
-fileNameClassLabels = datetime.datetime.now().strftime("%Y_%m_%d") + "-" + nameDB + "-ClassLabels-Description"
-ExportResults.exportPandasToCSV(sClassLabels, fileNameClassLabels)
-
-### RGB
-fileName = datetime.datetime.now().strftime("%Y_%m_%d") + "-" + rgb_feat_desc
-ExportResults.exportNumpyToCSV(rgb_f_extr_res, fileName)
-
-### HSV
-fileName = datetime.datetime.now().strftime("%Y_%m_%d") + "-" + hsv_feat_desc
-ExportResults.exportNumpyToCSV(hsv_f_extr_res, fileName)
-
-### SIFT
-fileName = datetime.datetime.now().strftime("%Y_%m_%d") + "-" + sift_feat_desc
-ExportResults.exportNumpyToCSV(sift_f_extr_res, fileName)
-
-### SURF
-fileName = datetime.datetime.now().strftime("%Y_%m_%d") + "-" + surf_feat_desc
-ExportResults.exportNumpyToCSV(surf_f_extr_res, fileName)
-
-### HOG
-fileName = datetime.datetime.now().strftime("%Y_%m_%d") + "-" + hog_feat_desc
-ExportResults.exportNumpyToCSV(hog_f_extr_res, fileName)
-
-print "Done:\t Save Features to CSV Databases"
\ No newline at end of file