コード例 #1
0
    def populateData(self):

        path = "./training3/"

        fs = listdir(path)

        #print fs

        for f in fs:
            s = f.split(".")
            if len(s) < 2:
                continue

            s = f.split("_")
            if len(s) < 2:
                continue

            c = const.getConstantFromString(s[0])
            c = cc.getColorFromId(c)

            if c is not None:
                self.loadTrainingImage(path + f, c)
                print "trained: " + path + f + " as " + str(c)

        return
コード例 #2
0
    def populateData(self):

        path = "./training3/"

        fs = listdir(path) 

        #print fs

        for f in fs:
            s = f.split(".")
            if len(s) < 2:
                continue

            s = f.split("_")
            if len(s) < 2:
                continue

            c = const.getConstantFromString(s[0])
            c = cc.getColorFromId(c)

            if c is not None:
                self.loadTrainingImage(path+f, c)
                print "trained: "+path+f+ " as "+str(c)
             
        return 
コード例 #3
0
    def train(self, readFromFile=True, path="./training3/"):
        """
        train the image processor
        :return: True if the training succeeds, false otherwise.
        """
        self.knn = ColorKnnClassifier()
        self.ratioKnns = []

        for i in range(7):
            self.ratioKnns.append(LetterKnnClassifier())

        if readFromFile:
            f = open(path, 'r')

            for l in f:
                l = l.split(",")
                x = [float(i) for i in l[1:]]
                self.ratioKnns[cc.getColorFromId(int(l[0]))].train.append((int(l[0]), x))

            f.close()

            f = open("color_data.csv", 'r')

            for l in f:
                l = l.split(',')
                x = [float(i) for i in l[1:]]
                self.knn.train.append((int(l[0]), x))

            f.close()

        else:
            fs = listdir(path)

            for f in fs:
                s = f.split(".")
                if len(s) < 2:
                    continue

                s = f.split("_")
                if len(s) < 2:
                    continue

                c1 = const.getConstantFromString(s[0])

                c2 = cc.getColorFromId(c1)

                if c2 is not None:
                    self.ratioKnns[c2].loadTrainingImage(path + f, c1)
                    print "LetterClass. " + str(c2) + ":" + str(
                        cc.getColorFromNumber(c2)) + " trained: " + f + " as " + str(const.getStringFromNumber(c1))

        self.knn.trainModel()

        for r in self.ratioKnns:
            r.trainModel()

        print "Completed training"
        print "------------------"
コード例 #4
0

path = "./training3/"

fs = listdir(path)

for f in fs:
    s = f.split(".")
    if len(s) < 2:
        continue

    s = f.split("_")
    if len(s) < 2:
        continue

    c1 = const.getConstantFromString(s[0])

    c2 = cc.getColorFromId(c1)

    if c2 is not None:
        ratioKnns[c2].loadTrainingImage(path+f, c1)
        print "LetterClass. "+str(c2)+":"+str(cc.getColorFromNumber(c2))+" trained: "+f+ " as "+str(const.getStringFromNumber(c1))

knn.populateData()
knn.trainModel()

for r in ratioKnns:
    r.trainModel()

print "done training"
コード例 #5
0
ファイル: driver.py プロジェクト: sdiemert/CodeMagnets

path = "./training3/"

fs = listdir(path)

for f in fs:
    s = f.split(".")
    if len(s) < 2:
        continue

    s = f.split("_")
    if len(s) < 2:
        continue

    c1 = const.getConstantFromString(s[0])

    c2 = cc.getColorFromId(c1)

    if c2 is not None:
        ratioKnns[c2].loadTrainingImage(path + f, c1)
        print "LetterClass. " + str(c2) + ":" + str(cc.getColorFromNumber(c2)) + " trained: " + f + " as " + str(
            const.getStringFromNumber(c1)
        )

knn.populateData()
knn.trainModel()

for r in ratioKnns:
    r.trainModel()
コード例 #6
0
    def train(self, readFromFile=True, path="./training3/"):
        """
        train the image processor
        :return: True if the training succeeds, false otherwise.
        """
        self.knn = ColorKnnClassifier()
        self.ratioKnns = []

        for i in range(7):
            self.ratioKnns.append(LetterKnnClassifier())

        if readFromFile:
            f = open(path, 'r')

            for l in f:
                l = l.split(",")
                x = [float(i) for i in l[1:]]
                self.ratioKnns[cc.getColorFromId(int(l[0]))].train.append(
                    (int(l[0]), x))

            f.close()

            f = open("color_data.csv", 'r')

            for l in f:
                l = l.split(',')
                x = [float(i) for i in l[1:]]
                self.knn.train.append((int(l[0]), x))

            f.close()

        else:
            fs = listdir(path)

            for f in fs:
                s = f.split(".")
                if len(s) < 2:
                    continue

                s = f.split("_")
                if len(s) < 2:
                    continue

                c1 = const.getConstantFromString(s[0])

                c2 = cc.getColorFromId(c1)

                if c2 is not None:
                    self.ratioKnns[c2].loadTrainingImage(path + f, c1)
                    print "LetterClass. " + str(c2) + ":" + str(
                        cc.getColorFromNumber(
                            c2)) + " trained: " + f + " as " + str(
                                const.getStringFromNumber(c1))

        self.knn.trainModel()

        for r in self.ratioKnns:
            r.trainModel()

        print "Completed training"
        print "------------------"