TrainClassify.py

__author__ = 'altug'

from Adaptive import Adaptive
from RGBHistogram import RGBHistogram
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import train_test_split
from sklearn.metrics import classification_report
import numpy as np
import argparse
import glob
import cv2
#*************************************************************EGITIM BOLUMU**************************************************************
class TrainClassify:

    def __init__(self, imagePaths, maskPaths):

        '''ap = argparse.ArgumentParser()
        ap.add_argument("-i", "--images", required = True,
        help = "path to the image dataset")
        ap.add_argument("-m", "--masks", required = True,
        help = "path to the image masks")
        args = vars(ap.parse_args())'''

        data = []
        target = []

        rgbHistObj = RGBHistogram([8, 8, 8])
        adaptiveObj = Adaptive()
        i=1
        for imagePath in imagePaths:
            image = cv2.imread(imagePath)
            #mask = cv2.imread(maskPath)
            #mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
            #threshImage = adaptiveObj.getThresh(maskPath)
            print '->'+str(i)
            i=i+1
            features = rgbHistObj.calculateHist(image, mask=None)#Maskta size hatasi veriyor none yaptim.
            data.append(features)
            target.append(imagePath.split("_")[1])

        #Machine Learning
        targetNames = np.unique(target)#Tekrarlari at.
        self.le = LabelEncoder()
        target = self.le.fit_transform(target)#Tur isimlerini integer a cevir mach. lear. icin

        #Random Train ve Test datasi olustur elimizdeki verecegimiz egitim setinin overfittting olmamasi icin.
        #Datanin dogrulugunu kontrol icin AccuracyTest kodu yazildi.
        (trainData, testData, trainTarget, testTarget) = train_test_split(data, target, test_size=0.3, random_state=42)

        self.model = RandomForestClassifier(n_estimators=25, random_state=84)
        self.model.fit(trainData, trainTarget) # Egitim gerceklesiyor.

        print '*********************************************EGITIM VERLERI************************************************\n' +\
              classification_report(testTarget, self.model.predict(testData), target_names = targetNames)+\
            '************************************************************************************************************'


        '''for i in np.random.choice(np.arange(0, len(imagePaths)), 10):
                imagePath = imagePaths[i]
                maskPath = maskPaths[i]

                image = cv2.imread(imagePath)
                mask = cv2.imread(maskPath)
                mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)'''