More robust detection of invalid particles

currently only fully implemented in legacy conversion, has to be adapted during particle detection as well!

analysis/analysisplots.py

@@ -83,7 +83,6 @@ class TypeHistogramView(QtWidgets.QScrollArea):
         self.widgets = []
         
     def updateTypeHistogram(self, types):
-#        print("Updating polymer type view", flush=True)
         for pi in self.widgets:
             self.indicatorbox.removeWidget(pi)
             pi.setParent(None)
@@ -97,7 +96,6 @@ class TypeHistogramView(QtWidgets.QScrollArea):
         
         for index, entry in enumerate(types):
             num, text, color = entry
-#            print("num, text, color:", num, text, color, flush=True)
             pi = ParticleIndicator(num, numtotal, color, text)
             self.indicatorbox.addWidget(pi)
             pi.clicked.connect(getIndexFunction(index))
@@ -129,7 +127,7 @@ class SpectraPlot(QtWidgets.QGroupBox):
         self.spectra = self.dataset.particleContainer.getSpectraFromDisk()
         self.canvas.draw()
         
-    def updateParticleSpectrum(self, specIndex, assignment, particleSize, hqi, color):
+    def updateParticleSpectrum(self, specIndex, assignment, hqi):
         #draw Sample Spectrum
         self.spec_axis.axis("on")
         self.spec_axis.clear()
@@ -138,9 +136,11 @@ class SpectraPlot(QtWidgets.QGroupBox):
         self.spec_axis.set_ylabel('Counts', fontsize = 15)
         
         if self.spectra is not None:
+            specInfo = f'ScanPoint Number {specIndex+1}, with assignment {assignment} (hqi = {hqi})'
             self.spec_axis.plot(self.spectra[:, 0], self.spectra[:, specIndex+1])
-            self.spec_axis.set_title('{}, ScanPoint Number {}, Size = {} µm, HQI = {}, color = {}'.format(assignment, specIndex+1, particleSize, hqi, color))
+            self.spec_axis.set_title(specInfo, fontsize=13)
             self.spec_axis.set_xbound(100, (3400 if self.spectra[-1, 0] > 3400 else self.spectra[-1, 0]))
+            
             wavenumber_diff = list(self.spectra[:, 0]-100)
             y_start = wavenumber_diff.index(min(wavenumber_diff))
             y_min = min(self.spectra[y_start:, specIndex+1])

analysis/analysisview.py

@@ -45,7 +45,7 @@ except:
 class ParticleAnalysis(QtWidgets.QMainWindow):
     def __init__(self, dataset, viewparent=None):
         super(ParticleAnalysis, self).__init__(viewparent)
-        self.resize(1680, 1050)
+#        self.resize(1680, 1050)
         self.setWindowTitle('Results of polymer analysis')
         self.layout = QtWidgets.QHBoxLayout()
         self.widget = QtWidgets.QWidget()
@@ -202,8 +202,6 @@ class ParticleAnalysis(QtWidgets.QMainWindow):
         
         self.layout.addLayout(self.menuLayout)
         self.layout.addLayout(viewLayout)
-        
-
          
         self.createActions()
         self.createMenus()
@@ -220,40 +218,45 @@ class ParticleAnalysis(QtWidgets.QMainWindow):
         self.loadSpectraAct = QtWidgets.QAction("Load &Spectra", self)
         self.loadSpectraAct.triggered.connect(self.initializeSpecPlot)
         
-        self.noOverlayAct = QtWidgets.QAction("&No Overlay", self)
-        self.selOverlayAct = QtWidgets.QAction("&Selected Overlay", self)
-        self.fullOverlayAct = QtWidgets.QAction("&Full Overlay", self)
+        self.databaseAct = QtWidgets.QAction("&ManageDatabase", self)
+        self.databaseAct.triggered.connect(self.launchDBManager)
+        
+        self.expExcelAct= QtWidgets.QAction("Export &Excel List", self)
+        self.expExcelAct.setDisabled(True)
+        self.expExcelAct.triggered.connect(self.exportToExcel)
+        
+        self.expSQLAct = QtWidgets.QAction("Export to &SQL Database", self)
+        self.expSQLAct.setDisabled(True)
+        self.expSQLAct.triggered.connect(self.exportToSQL)
+        
+        self.getAndActivateActionsFromGepardMain()
+    
+    def getAndActivateActionsFromGepardMain(self):
+        gepard = self.viewparent.imparent
+        
+        self.noOverlayAct = gepard.noOverlayAct
+        self.selOverlayAct = gepard.selOverlayAct
+        self.fullOverlayAct = gepard.fullOverlayAct
         
-        self.transpAct = QtWidgets.QAction("&Transparent Overlay", self)
+        self.transpAct = gepard.transpAct
         self.transpAct.triggered.connect(self.updateContourColors)
             
-        self.hideLabelAct = QtWidgets.QAction('&Hide Polymer Numbers', self)
+        self.hideLabelAct = gepard.hideLabelAct
         self.hideLabelAct.triggered.connect(self.show_hide_labels)
         
-        self.darkenAct = QtWidgets.QAction("&Darken Image", self)
+        self.darkenAct = gepard.darkenAct
         self.darkenAct.triggered.connect(self.darkenBackground)
         
         for act in [self.noOverlayAct, self.selOverlayAct, self.fullOverlayAct, self.hideLabelAct, self.transpAct, self.darkenAct]:
             act.setCheckable(True)
         self.fullOverlayAct.setChecked(True)
         
-        self.seedAct = QtWidgets.QAction("&Set Color Seed", self)
+        self.seedAct = gepard.seedAct
         self.seedAct.triggered.connect(self.updateColorSeed)
         
-        self.removeTinyParticlesAct = QtWidgets.QAction("&Remove not unknown Particles < 1 µm")
-        self.removeTinyParticlesAct.triggered.connect(self.removeTinyParticles)
+        for act in [self.noOverlayAct, self.selOverlayAct, self.fullOverlayAct, self.hideLabelAct, self.transpAct, self.darkenAct, self.seedAct]:
+            act.setDisabled(False)
 
-        self.databaseAct = QtWidgets.QAction("&ManageDatabase", self)
-        self.databaseAct.triggered.connect(self.launchDBManager)
-        
-        self.expExcelAct= QtWidgets.QAction("Export &Excel List", self)
-        self.expExcelAct.setDisabled(True)
-        self.expExcelAct.triggered.connect(self.exportToExcel)
-        
-        self.expSQLAct = QtWidgets.QAction("Export to &SQL Database", self)
-        self.expSQLAct.setDisabled(True)
-        self.expSQLAct.triggered.connect(self.exportToSQL)
-    
     def createMenus(self):
         self.importMenu = QtWidgets.QMenu("&Import Spectra and Results")
         self.importMenu.addActions([self.loadSpectraAct, self.loadTrueMatchAct])
@@ -272,7 +275,6 @@ class ParticleAnalysis(QtWidgets.QMainWindow):
         self.dispMenu.addActions([self.transpAct, self.hideLabelAct, self.darkenAct, self.seedAct])
         
         self.toolMenu = QtWidgets.QMenu("&Tools")
-        self.toolMenu.addAction(self.removeTinyParticlesAct)
         self.toolMenu.addAction(self.databaseAct)
         
         self.exportMenu = QtWidgets.QMenu("&Export", self)
@@ -308,7 +310,6 @@ class ParticleAnalysis(QtWidgets.QMainWindow):
             del self.importWindow
         
         self.importWindow = LoadTrueMatchResults(self.particleContainer, self)
-#        self.importWindow.exec()
         
     @QtCore.pyqtSlot()
     def applyHQIThresholdToResults(self):
@@ -452,11 +453,9 @@ class ParticleAnalysis(QtWidgets.QMainWindow):
             self.sizeHist_ax.figure.canvas.draw()
     
     def updateSpecPlot(self):
-        particleSize = np.round(self.particleContainer.getSizeOfParticleByIndex(self.currentParticleIndex))
         hqi = self.particleContainer.getHQIOfSpectrumIndex(self.currentSpectrumIndex)
         assignment = self.particleContainer.getParticleAssignmentByIndex(self.currentParticleIndex)
-        color = self.particleContainer.getParticleColorByIndex(self.currentParticleIndex)
-        self.specPlot.updateParticleSpectrum(self.currentSpectrumIndex, assignment, particleSize, hqi, color)
+        self.specPlot.updateParticleSpectrum(self.currentSpectrumIndex, assignment, hqi)
             
         if self.refSelector.isEnabled() and self.refSelector.currentText() != '':
             refID = self.dbWin.activeDatabase.spectraNames.index(self.refSelector.currentText())
@@ -574,39 +573,6 @@ class ParticleAnalysis(QtWidgets.QMainWindow):
             self.dataset.colorSeed = text
             self.updateHistogramsAndContours()
     
-    def removeTinyParticles(self):
-        indices = []
-        for particle in self.particleContainer.particles:
-            if particle.getParticleAssignment() != 'unknown' and particle.getParticleSize() < 1:
-                indices.append(particle.index)
-        
-        indices = sorted(indices, reverse=True)
-        numIndices = len(indices)
-        
-        for index, partIndex in enumerate(indices):
-            self.setWidgetsToNewParticleIndex(partIndex)
-            assignment = self.particleContainer.getParticleAssignmentByIndex(partIndex)
-            specIndices = self.particleContainer.getSpectraIndicesOfParticle(partIndex)
-            self.viewparent.highLightContour(partIndex)
-            self.viewparent.centerOnRamanIndex(specIndices[0])
-            reply = QtWidgets.QMessageBox.question(self, f'Particle {index+1} of {numIndices}',
-                                f"Do you want to remove that particle? Type = {assignment}",
-                                QtWidgets.QMessageBox.Yes | 
-                                QtWidgets.QMessageBox.No | QtWidgets.QMessageBox.Cancel, QtWidgets.QMessageBox.No)
-            
-            if reply == QtWidgets.QMessageBox.Yes:
-                self.viewparent.removeParticleContour(partIndex)
-                self.particleContainer.removeParticles([partIndex])
-                
-            elif reply == QtWidgets.QMessageBox.Cancel:
-                self.particleContainer.resetParticleIndices()
-                self.viewparent.resetContourIndices()
-                return
-        
-        self.particleContainer.resetParticleIndices()
-        self.viewparent.resetContourIndices()
-        self.updateHistogramsAndContours()
-    
     def show_hide_labels(self):
         hidden = self.hideLabelAct.isChecked()
         for scanIndicator in self.viewparent.ramanscanitems:

analysis/loadresults.py

@@ -70,6 +70,7 @@ class LoadTrueMatchResults(QtWidgets.QWidget):
     
     def show3FlagsReview(self):
         self.editEntryWindow = ModifyManualEdits(self, self.manualPolymers, self.manualAdditives)        
+        self.setDisabled(True)
         self.editEntryWindow.show()
 
     def getImportFiles(self):

analysis/particleAndMeasurement.py

@@ -19,17 +19,14 @@ along with this program, see COPYING.
 If not, see <https://www.gnu.org/licenses/>.
 """
 import numpy as np
-from viewitems import SegmentationContour, RamanScanIndicator
-from .particleCharacterization import getParticleColor
 
 class Particle(object):
     def __init__(self):
         super(Particle, self).__init__()
         self.index = None
-        self.longSize_ellipse = np.nan
-        self.shortSize_ellipse = np.nan
-        self.longSize_box = np.nan
-        self.shortSize_box = np.nan
+        self.longSize = np.nan
+        self.shortSize = np.nan
+        self.height = None
         self.area = None
         self.contour = None
         self.measurements = []
@@ -78,26 +75,18 @@ class Particle(object):
         return assignments[indexOfHighestHQI]
     
     def getParticleSize(self):
-        if not np.isnan(self.longSize_ellipse):
-            size = self.longSize_ellipse
-        elif not np.isnan(self.longSize_box):
-            size = self.longSize_box
-        else:
+        if np.isnan(self.longSize):
             print(f'Error, particle size requested, but not yet set.\nParticle Index is {self.index}')
             raise ValueError
-        assert size is not None, f'Error, size of particle {self.index} is None'
-        return size
+        else:
+            return self.longSize
     
     def getShortParticleSize(self):
-        if not np.isnan(self.shortSize_ellipse):
-            size = self.shortSize_ellipse
-        elif not np.isnan(self.shortSize_box):
-            size = self.shortSize_box
-        else:
+        if np.isnan(self.shortSize):
             print(f'Error, particle size requested, but not yet set.\nParticle Index is {self.index}')
             raise ValueError
-        assert size is not None, f'Error, short size of particle {self.index} is None'
-        return size
+        else:
+            return self.shortSize
     
     def getNumberOfMeasurements(self):
         return len(self.measurements)
@@ -119,10 +108,7 @@ class Particle(object):
     def applyHQITresholdToMeasurements(self, minHQI):
         for measurement in self.measurements:
             measurement.applyHQIThreshold(minHQI)
-    
-    def recreateViewItem(self):
-        self.viewItem = SegmentationContour()
-            
+
     
 class Measurement(object):
     def __init__(self):

analysis/particleCharacterization.py

@@ -24,66 +24,44 @@ import numpy as np
 import cv2
 from copy import deepcopy
 
-def getContourStatsWithPixelScale(cnt, pixelscale):
-        long, short, longellipse, shortellipse, area = getContourStats(cnt)
-        return long*pixelscale, short*pixelscale, longellipse*pixelscale, shortellipse*pixelscale, area*pixelscale**2
-
-def getContourStats(cnt):
-    ##characterize particle
-        longellipse, shortellipse = np.nan, np.nan
-        
-        if cnt.shape[0] >= 5:       ##at least 5 points required for ellipse fitting...
-            ellipse = cv2.fitEllipse(cnt)
-            shortellipse, longellipse = ellipse[1]
+from .particleClassification.colorClassification import ColorClassifier
+from .particleClassification.shapeClassification import ShapeClassifier
+from segmentation import closeHolesOfSubImage
+from errors import NotConnectedContoursError, InvalidParticleError
+
+class ParticleStats(object):
+    longSize = None
+    shortSize = None
+    height = None
+    area = None
+    shape = None
+    color = None
+
+def particleIsValid(particle):
+    if particle.longSize == 0 or particle.shortSize == 0:
+        return False
+    
+    if cv2.contourArea(particle.contour) == 0:
+        return False
+    return True
+    
 
-        rect = cv2.minAreaRect(cnt)
-        long, short = rect[1]
-        if short>long:
-            long, short = short, long
-        
-        area = cv2.contourArea(cnt)
+def getParticleStatsWithPixelScale(cnt, pixelscale, fullimage, dataset):
+    newStats = ParticleStats()
     
-        return long, short, longellipse, shortellipse, area
-
-class ColorRangeHSV(object):
-    def __init__(self, name, hue, hue_tolerance, min_sat, max_sat):
-        self.name = name
-        self.minHue = hue-hue_tolerance/2
-        self.maxHue = hue+hue_tolerance/2
-        self.minSat = min_sat
-        self.maxSat = max_sat
+    newStats.longSize, newStats.shortSize, newStats.area = getContourStats(cnt)
+    newStats.longSize *= pixelscale
+    newStats.shortSize *= pixelscale
+    newStats.area *= (pixelscale**2)
+
+    newStats.height = getParticleHeight(cnt, dataset)    
+    print('newHeight =', newStats.height)
+    newStats.shape = getParticleShape(cnt, newStats.height)
     
-    def containsHSV(self, hsv):
-        hue = hsv[0]
-        sat = hsv[1]
-        
-        if self.minHue <= hue <= self.maxHue and self.minSat <= sat <= self.maxSat:
-            return True
-        else:
-            if self.name != 'white':
-                return False
-            else:
-                if sat < 128 and hsv[2] > 70:
-                    return True
-
-class ColorClassifier(object):
-    def __init__(self):
-        hue_tolerance = 30
-        self.colors = [ColorRangeHSV('yellow', 30, hue_tolerance, 30, 255),
-                  ColorRangeHSV('blue', 120, hue_tolerance, 80, 255),
-                  ColorRangeHSV('red', 180, hue_tolerance, 50, 255),
-                  ColorRangeHSV('red', 0, hue_tolerance, 50, 255),
-                  ColorRangeHSV('green', 70, hue_tolerance, 50, 255),
-                  ColorRangeHSV('white', 128, 256, 0, 50)]
+    partImg = getParticleImageFromFullimage(cnt, fullimage)
+    newStats.color = getParticleColor(partImg)
     
-    def classifyColor(self, meanHSV):
-        result = 'non-determinable'
-        for color in self.colors:
-            if color.containsHSV(meanHSV):
-                result = color.name
-                break
-        
-        return result
+    return newStats
 
 def getParticleColor(imgRGB, colorClassifier=None):
     img = cv2.cvtColor(imgRGB, cv2.COLOR_RGB2HSV_FULL)
@@ -93,6 +71,44 @@ def getParticleColor(imgRGB, colorClassifier=None):
     color = colorClassifier.classifyColor(meanHSV)
     return color
 
+def getParticleShape(contour, particleHeight, shapeClassifier=None):
+    if shapeClassifier is None:
+        shapeClassifier = ShapeClassifier()
+    try:
+        shape = shapeClassifier.classifyShape(contour, particleHeight)
+    except InvalidParticleError:
+        raise
+    return shape
+
+def getParticleHeight(contour, dataset):
+    zimg = getParticleImageFromFullimage(contour, dataset.getZvalImg())
+    if zimg.shape[0] == 0 or zimg.shape[1] == 0:
+        raise InvalidParticleError
+        
+    zimg = cv2.medianBlur(zimg, 5)
+    avg_ZValue = np.mean(zimg[zimg > 0])
+    if np.isnan(avg_ZValue):  #i.e., only zeros in zimg
+        avg_ZValue = 0
+    z0, z1 = dataset.zpositions.min(), dataset.zpositions.max()
+    height = avg_ZValue/255.*(z1-z0) + z0
+    return height
+
+def getContourStats(cnt):
+    ##characterize particle
+        if cnt.shape[0] >= 5:       ##at least 5 points required for ellipse fitting...
+            ellipse = cv2.fitEllipse(cnt)
+            short, long = ellipse[1]
+        else:
+            rect = cv2.minAreaRect(cnt)
+            long, short = rect[1]
+            
+        if short>long:
+            long, short = short, long
+        
+        area = cv2.contourArea(cnt)
+    
+        return long, short, area
+
 def mergeContours(contours):
     img, xmin, ymin, padding = contoursToImg(contours)
     return imgToCnt(img, xmin, ymin, padding)
@@ -115,13 +131,15 @@ def getParticleImageFromFullimage(contour, fullimage):
     img = np.array(img, dtype = np.uint8)
     return img
     
-def contoursToImg(contours, padding=2):
+def contoursToImg(contours, padding=0):
     contourCopy = deepcopy(contours)
     xmin, xmax, ymin, ymax = getContourExtrema(contourCopy)
     
     padding = padding   #pixel in each direction
     rangex = int(np.round((xmax-xmin)+2*padding))
     rangey = int(np.round((ymax-ymin)+2*padding))
+    if rangex == 0 or rangey == 0:
+        raise InvalidParticleError
 
     img = np.zeros((rangey, rangex))
     for curCnt in contourCopy:
@@ -130,42 +148,44 @@ def contoursToImg(contours, padding=2):
             curCnt[i][0][1] -= ymin-padding
         cv2.drawContours(img, [curCnt], -1, 255, -1)
         cv2.drawContours(img, [curCnt], -1, 255, 1)
-    
     img = np.uint8(cv2.morphologyEx(img, cv2.MORPH_CLOSE, np.ones((3, 3))))
     return img, xmin, ymin, padding
 
-def imgToCnt(img, xmin, ymin, padding):
-    def getSimpleContour(img):
+def imgToCnt(img, xmin, ymin, padding=0):
+    def getContour(img, flag):
         if cv2.__version__ > '3.5':
-            contour, hierarchy = cv2.findContours(img, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
+            contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, flag)
         else:
-            temp, contour, hierarchy = cv2.findContours(img, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
+            temp, contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, flag)
         
-        if len(contour)>1:
-            raise NotConnectedContoursError
+        if len(contours) == 0:   #i.e., no contour found
+            raise InvalidParticleError
+        elif len(contours) == 1:   #i.e., exactly one contour found
+            contour = contours[0]
+        else:       #i.e., multiple contours found
+            contour = getLargestContour(contours)
+            
         return contour
     
-    def getFullContour(img):
-        if cv2.__version__ > '3.5':
-            contour, hierarchy = cv2.findContours(img, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
-        else:
-            temp, contour, hierarchy = cv2.findContours(img, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
-        
-        if len(contour)>1:
-            raise NotConnectedContoursError
-        return contour
+    def getLargestContour(contours):
+        areas = []
+        for contour in contours:
+            areas.append(cv2.contourArea(contour))
+        maxIndex = areas.index(max(areas))
+        print(f'{len(contours)} contours found, getting the largest one. Areas are: {areas}, taking contour at index {maxIndex}')
+        return contours[maxIndex]
+
+    img = closeHolesOfSubImage(img)
+    contour = getContour(img, flag=cv2.CHAIN_APPROX_SIMPLE)
+
+    if len(contour) < 5:
+        contour = getContour(img, flag=cv2.CHAIN_APPROX_NONE)
     
-    contour = getSimpleContour(img)
-    
-    if len(contour[0]) < 5:
-        contour = getFullContour(img)
-    
-    newContour = contour[0]
-    for i in range(len(newContour )):
-        newContour [i][0][0] += xmin-padding
-        newContour [i][0][1] += ymin-padding
+    for i in range(len(contour)):
+        contour [i][0][0] += xmin-padding
+        contour [i][0][1] += ymin-padding
         
-    return newContour 
+    return contour
 
 def getContourExtrema(contours):
     try:
@@ -174,22 +194,19 @@ def getContourExtrema(contours):
         ymin, ymax = cnt[:,0,:][:, 1].min(), cnt[:,0,:][:, 1].max()        
     except IndexError: #i.e., not a list of contours was passed, but an individual contour. Hence, the above indexing does not work
         xmin, xmax = cnt[:, 0].min(), cnt[:, 0].max()
-        ymin, ymax = cnt[:, 1].min(), cnt[:, 1].max()        
+        ymin, ymax = cnt[:, 1].min(), cnt[:, 1].max()
+        
+    xmin, xmax = int(round(xmin)), int(round(xmax))
+    ymin, ymax = int(round(ymin)), int(round(ymax))
+    
     return xmin, xmax, ymin, ymax
 
-class NotConnectedContoursError(Exception):
-    pass
-
-if __name__ == '__main__':
-    colors = {'white': (41, 25, 66),
-              "red": (128, 121, 57),
-              "red2": (23, 88, 49), 
-              "yellow": (25, 121, 91),
-              "pink": (11, 79, 51),
-              "brown": (32, 38, 64),
-              "green": (54, 99, 53)}
-    classifier= ColorClassifier()
-#    print(classifier.hsv)
-    for name, mean in colors.items():
-        print(name, classifier.classifyColor(mean))
-              
\ No newline at end of file
+def getParticleCenterPoint(contour):
+    img, xmin, ymin, padding = contoursToImg(contour)
+    dist = cv2.distanceTransform(img, cv2.DIST_L2, 3)
+    ind = np.argmax(dist)
+    y = ind//dist.shape[1]-1
+    x = ind%dist.shape[1]-1
+    x += xmin
+    y += ymin
+    return x, y

analysis/particleClassification/colorClassification.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+GEPARD - Gepard-Enabled PARticle Detection
+Copyright (C) 2018  Lars Bittrich and Josef Brandt, Leibniz-Institut für 
+Polymerforschung Dresden e. V. <bittrich-lars@ipfdd.de>    
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program, see COPYING.  
+If not, see <https://www.gnu.org/licenses/>.
+"""
+
+class ColorRangeHSV(object):
+    def __init__(self, name, hue, hue_tolerance, min_sat, max_sat):
+        self.name = name
+        self.minHue = hue-hue_tolerance/2
+        self.maxHue = hue+hue_tolerance/2
+        self.minSat = min_sat
+        self.maxSat = max_sat
+    
+    def containsHSV(self, hsv):
+        hue = hsv[0]
+        sat = hsv[1]
+        
+        if self.minHue <= hue <= self.maxHue and self.minSat <= sat <= self.maxSat:
+            return True
+        else:
+            if self.name != 'white':
+                return False
+            else:
+                if sat < 128 and hsv[2] > 70:
+                    return True
+
+class ColorClassifier(object):
+    def __init__(self):
+        hue_tolerance = 50
+        self.colors = [ColorRangeHSV('yellow', 30, hue_tolerance, 40, 255),
+                  ColorRangeHSV('blue', 120, hue_tolerance, 40, 255),
+                  ColorRangeHSV('red', 180, hue_tolerance, 40, 255),
+                  ColorRangeHSV('red', 0, hue_tolerance, 40, 255),
+                  ColorRangeHSV('green', 70, hue_tolerance, 40, 255),
+                  ColorRangeHSV('white', 128, 256, 0, 40)]
+