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):
import glob
import cv2
ap = argparse.ArgumentParser()
ap.add_argument(
"-d",
"--dataset",
required=True,
help="Path to the catalog that contains the images to be indexed")
ap.add_argument("-i", "--index", required=True, help="Path to index")
args = vars(ap.parse_args())
datasetDir = args["dataset"]
index = {}
desc = RGBHistogram([8, 8, 8])
INIT_SIZE = 800
step = 0
for imagePath in glob.glob(datasetDir + "/*.*g"):
photoName = imagePath[imagePath.rfind("/") + 1:]
image = cv2.imread(imagePath)
size = image.shape[:2]
coefficient = size[0] / size[1]
# Resize image
width = INIT_SIZE
height = int(INIT_SIZE * coefficient)
from RGBHistogram import RGBHistogram
from ZernikeMoments import ZernikeMoments
import cv2
from matplotlib import pyplot as plt
import glob
import pickle
from Searcher import Searcher
# desc = RGBHistogram([8 8, 8])
desc = RGBHistogram([4, 4, 4])
test_image_path = '../data/flowers/image_0030.jpg'
query_image = cv2.imread(test_image_path)
query_feature = desc.describe(query_image)
# print(query_feature)
# quit()
# load the index and initialize our searcher
index = pickle.load(open("Histogram_only_index_4bins.cpickle", "rb"))
print(index.items())
searcher = Searcher(index)
results = searcher.search(query_feature)
print(results)
for i in range(0, 5):
ret_path = '../data/' + results[i][1].replace('\\','/')
img = cv2.imread(ret_path)
cv2.imshow(str(i), img)
cv2.waitKey()
exit()
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
imagePaths = sorted(glob.glob("dataset/images/*.png"))
maskPaths = sorted(glob.glob("dataset/masks/*.png"))
imagePath = "TestImg/picTac.png"
data = []
target = []
desc = RGBHistogram([8, 8, 8])
counter = 0
for imagePath, maskPath in zip(imagePaths, maskPaths):
image = cv2.imread(imagePath)
mask = cv2.imread(maskPath)
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
features = desc.describe(image, mask)
data.append(features)
target.append(imagePath.split('_')[-2])
#print target, '\n'
targetNames = np.unique(target)
le = LabelEncoder()
imagePaths = sorted(glob.glob("dataset/imagesjpg" + "/*.jpg")) # Egitim path i.
maskPaths = sorted(glob.glob("dataset/masksjpg" + "/*.jpg"))
trainObj = TrainClassify(imagePaths, maskPaths)
# ****************************************************TAC TEST***************************************************************
# adaptiveObj = Adaptive()
testPaths = sorted(glob.glob("test" + "/*.jpg"))
for testPath in testPaths:
image = cv2.imread(testPath)
# threshImage = adaptiveObj.getThresh(imagePath)
# convertedThresh = cv2.cvtColor(threshImage, cv2.COLOR_GRAY2BGR)
# masked_img = cv2.bitwise_and(image,image,mask = threshImage)
# cv2.imshow("asdasd", masked_img)
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 testPath
if flower == "crocus":
flower = "cigdem"
if flower == "daisy":
flower = "papatya"
if flower == "pansy":
flower = "menekse"
if flower == "sunflower":
