YaprakTest.py

__author__ = 'altug'

from RGBHistogram import RGBHistogram
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from TrainClassify import TrainClassify
import cv2
import glob

#****************************************************YAPRAK EGITIM***************************************************************
imagePaths = sorted(glob.glob("dataset/images" + "/*.png"))#YAPRAK Pathi verilecek.
maskPaths = sorted(glob.glob("dataset/masks" + "/*.png"))

trainObj = TrainClassify(imagePaths, maskPaths)

#****************************************************YAPRAK TEST***************************************************************
imagePath = 'testimg.jpg'
image = cv2.imread(imagePath)

rgbHistObj = RGBHistogram([8, 8, 8])
features = rgbHistObj.calculateHist(image, mask=None)

flower = trainObj.le.inverse_transform(trainObj.model.predict(features))[0] #Burada ilk feature i isim olarak aliyor bunu integerdan ceviriyor.

print imagePath

if flower == 'crocus' : flower = 'cigdem'
if flower == 'daisy' : flower = 'papatya'
if flower == 'pansy' : flower = 'menekse'
if flower == 'sunflower' : flower = 'aycicegi'

print "Bu cicek %s olabilir." % (flower.upper())