changes in common part
This commit is contained in:
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9e608db67c
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@ -1,231 +1,232 @@
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#Creating training and test set
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import os
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import numpy as np
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import shutil
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#Feature descriptors
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import cv2
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import mahotas
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#saving data
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import h5py
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from sklearn.preprocessing import LabelEncoder
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from sklearn.preprocessing import MinMaxScaler
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#classifier
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from sklearn.ensemble import RandomForestClassifier
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#other
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import random
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from models.Garbagetruck import GarbageTruck
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from models.garbageDump import Dump
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#https://www.kaggle.com/asdasdasasdas/garbage-classification - dataset
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def createSets():
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rootDir = 'ClassificationGarbage'
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typesDir = ['/cardboard', '/glass', '/metal', '/paper', '/plastic']
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testRatio = 0.2
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for cls in typesDir:
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os.makedirs(rootDir + '/trainSet' + cls)
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os.makedirs(rootDir + '/testSet' + cls)
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sourceDir = rootDir + cls
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allFileNames = os.listdir(sourceDir)
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np.random.shuffle(allFileNames)
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trainingFileNames, testFileNames = np.split(np.array(allFileNames), [int(len(allFileNames) * (1 - testRatio))])
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trainingFileNames = [sourceDir +'/' + name for name in trainingFileNames.tolist()]
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testFileNames = [sourceDir +'/' + name for name in testFileNames.tolist()]
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print(cls + ':')
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print('Total images: ', len(allFileNames))
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print('Training: ', len(trainingFileNames))
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print('Testing: ', len(testFileNames))
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for name in trainingFileNames:
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shutil.copy(name, rootDir +'/trainSet' + cls)
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for name in testFileNames:
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shutil.copy(name, rootDir +'/testSet' + cls)
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print("Images copied.")
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def processTrainData():
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trainTypes = os.listdir('ClassificationGarbage/trainSet')
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trainTypes.sort()
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features = []
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types = []
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trainDir = 'ClassificationGarbage/trainSet/'
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size = tuple((500, 500))
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#process data
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for type in trainTypes:
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dir = os.path.join(trainDir, type)
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currentType = type
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print("Processing " + type + "...")
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for imagename in os.listdir(dir):
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file = dir + "/" + imagename
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image = cv2.imread(file)
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image = cv2.resize(image, size)
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#Global features
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huMoments = hu_moments(image)
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hara = haralick(image)
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histo = histogram(image)
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allFeatures = np.hstack([histo, hara, huMoments])
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types.append(currentType)
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features.append(allFeatures)
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print("Done.")
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print("All processed.")
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print("Training...")
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#save data
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h5Data = 'output/data.h5'
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h5Types = 'output/types.h5'
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targetNames = np.unique(types)
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le = LabelEncoder()
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target = le.fit_transform(types)
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scaler = MinMaxScaler(feature_range=(0, 1))
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rescaledFeatures = scaler.fit_transform(features)
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fileData = h5py.File(h5Data, 'w')
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fileData.create_dataset('dataset_1', data=np.array(rescaledFeatures))
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fileTypes = h5py.File(h5Types, 'w')
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fileTypes.create_dataset('dataset_1', data=np.array(target))
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fileData.close()
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fileTypes.close()
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def processTestData():
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trainTypes = os.listdir('ClassificationGarbage/trainSet')
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trainTypes.sort()
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testDir = 'ClassificationGarbage/testSet/'
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size = tuple((500, 500))
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testTypes = []
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testFeatures = []
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print("Testing...")
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#process data
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for type in trainTypes:
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dir = os.path.join(testDir, type)
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currentType = type
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for imagename in os.listdir(dir):
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file = dir + "/" + imagename
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image = cv2.imread(file)
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image = cv2.resize(image, size)
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#Global features
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huMoments = hu_moments(image)
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hara = haralick(image)
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histo = histogram(image)
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allFeatures = np.hstack([histo, hara, huMoments])
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testTypes.append(currentType)
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testFeatures.append(allFeatures)
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#save data
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h5TestData = 'output/testdata.h5'
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h5TestTypes = 'output/testtypes.h5'
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targetNames = np.unique(testTypes)
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le = LabelEncoder()
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target = le.fit_transform(testTypes)
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scaler = MinMaxScaler(feature_range=(0, 1))
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rescaledFeatures = scaler.fit_transform(testFeatures)
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fileTestData = h5py.File(h5TestData, 'w')
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fileTestData.create_dataset('dataset_1', data=np.array(rescaledFeatures))
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fileTestTypes = h5py.File(h5TestTypes, 'w')
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fileTestTypes.create_dataset('dataset_1', data=np.array(target))
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fileTestData.close()
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fileTestTypes.close()
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def trainAndTest():
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h5Data = 'output/data.h5'
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h5Types = 'output/types.h5'
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h5TestData = 'output/testdata.h5'
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h5TestTypes = 'output/testtypes.h5'
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#import train data
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fileData = h5py.File(h5Data, 'r')
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fileTypes = h5py.File(h5Types, 'r')
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features = fileData['dataset_1']
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types = fileTypes['dataset_1']
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allFeatures = np.array(features)
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allTypes = np.array(types)
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fileData.close()
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fileTypes.close()
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# create model
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clf = RandomForestClassifier(n_estimators=100, random_state=9)
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clf.fit(allFeatures, allTypes)
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#import test data
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fileTestData = h5py.File(h5TestData, 'r')
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fileTestTypes = h5py.File(h5TestTypes, 'r')
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features = fileTestData['dataset_1']
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types = fileTestTypes['dataset_1']
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allFeatures = np.array(features)
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allTypes = np.array(types)
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fileTestData.close()
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fileTestTypes.close()
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#Rfc score
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print("Random Forest Classifier score:")
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print(clf.score(allFeatures, allTypes))
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return clf
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#global features
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def hu_moments(image):
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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moments = cv2.moments(gray)
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huMoments = cv2.HuMoments(moments).flatten()
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return huMoments
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def haralick(image):
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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haralick = mahotas.features.haralick(gray).mean(axis=0)
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return haralick
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def histogram(image, mask=None):
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image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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hist = cv2.calcHist([image], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
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cv2.normalize(hist, hist)
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histogram = hist.flatten()
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return histogram
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#Test one image
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def classifyImage(file, clf):
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size = tuple((500, 500))
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types = os.listdir('ClassificationGarbage/testSet')
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types.sort()
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image = cv2.imread(file)
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image = cv2.resize(image, size)
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#Global features
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huMoments = hu_moments(image)
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hara = haralick(image)
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histo = histogram(image)
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allFeatures = np.hstack([histo, hara, huMoments])
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features = allFeatures.reshape(1,-1)
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prediction = clf.predict(features)[0]
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return types[prediction]
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#At the garbage dump
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def sortDump(cardboard, glass, metal, paper, plastic, clf, GT, dump):
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testDir = 'ClassificationGarbage/testSet'
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testTypes = os.listdir(testDir)
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testTypes.sort()
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noGarbage = [cardboard, glass, metal, paper, plastic]
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for i in range(len(testTypes)):
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print("Among " + testTypes[i] + ", we found: ")
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for j in range(0, noGarbage[i]):
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image = random.choice(os.listdir(testDir + '/' + testTypes[i]))
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file = testDir + '/' + testTypes[i] + "/"+ image
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prediction = classifyImage(file, clf)
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print(prediction)
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GT.empty(testTypes[i])
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dump.addGarbage(prediction)
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#Creating training and test set
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import os
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import numpy as np
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import shutil
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#Feature descriptors
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import cv2
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import mahotas
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#saving data
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import h5py
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from sklearn.preprocessing import LabelEncoder
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from sklearn.preprocessing import MinMaxScaler
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#classifier
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from sklearn.ensemble import RandomForestClassifier
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#other
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import random
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from models.Garbagetruck import GarbageTruck
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from models.garbageDump import Dump
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#https://www.kaggle.com/asdasdasasdas/garbage-classification - dataset
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def createSets():
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if not path.exists('ClassificationGarbage/trainSet'):
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rootDir = 'ClassificationGarbage'
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typesDir = ['/cardboard', '/glass', '/metal', '/paper', '/plastic']
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testRatio = 0.2
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for cls in typesDir:
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os.makedirs(rootDir + '/trainSet' + cls)
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os.makedirs(rootDir + '/testSet' + cls)
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sourceDir = rootDir + cls
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allFileNames = os.listdir(sourceDir)
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np.random.shuffle(allFileNames)
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trainingFileNames, testFileNames = np.split(np.array(allFileNames), [int(len(allFileNames) * (1 - testRatio))])
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trainingFileNames = [sourceDir +'/' + name for name in trainingFileNames.tolist()]
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testFileNames = [sourceDir +'/' + name for name in testFileNames.tolist()]
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print(cls + ':')
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print('Total images: ', len(allFileNames))
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print('Training: ', len(trainingFileNames))
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print('Testing: ', len(testFileNames))
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for name in trainingFileNames:
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shutil.copy(name, rootDir +'/trainSet' + cls)
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for name in testFileNames:
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shutil.copy(name, rootDir +'/testSet' + cls)
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print("Images copied.")
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def processTrainData():
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trainTypes = os.listdir('ClassificationGarbage/trainSet')
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trainTypes.sort()
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features = []
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types = []
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trainDir = 'ClassificationGarbage/trainSet/'
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size = tuple((500, 500))
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#process data
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for type in trainTypes:
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dir = os.path.join(trainDir, type)
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currentType = type
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print("Processing " + type + "...")
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for imagename in os.listdir(dir):
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file = dir + "/" + imagename
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image = cv2.imread(file)
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image = cv2.resize(image, size)
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#Global features
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huMoments = hu_moments(image)
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hara = haralick(image)
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histo = histogram(image)
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allFeatures = np.hstack([histo, hara, huMoments])
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types.append(currentType)
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features.append(allFeatures)
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print("Done.")
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print("All processed.")
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print("Training...")
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#save data
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h5Data = 'output/data.h5'
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h5Types = 'output/types.h5'
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targetNames = np.unique(types)
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le = LabelEncoder()
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target = le.fit_transform(types)
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scaler = MinMaxScaler(feature_range=(0, 1))
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rescaledFeatures = scaler.fit_transform(features)
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fileData = h5py.File(h5Data, 'w')
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fileData.create_dataset('dataset_1', data=np.array(rescaledFeatures))
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fileTypes = h5py.File(h5Types, 'w')
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fileTypes.create_dataset('dataset_1', data=np.array(target))
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fileData.close()
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fileTypes.close()
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def processTestData():
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trainTypes = os.listdir('ClassificationGarbage/trainSet')
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trainTypes.sort()
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testDir = 'ClassificationGarbage/testSet/'
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size = tuple((500, 500))
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testTypes = []
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testFeatures = []
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print("Testing...")
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#process data
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for type in trainTypes:
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dir = os.path.join(testDir, type)
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currentType = type
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for imagename in os.listdir(dir):
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file = dir + "/" + imagename
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image = cv2.imread(file)
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image = cv2.resize(image, size)
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#Global features
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huMoments = hu_moments(image)
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hara = haralick(image)
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histo = histogram(image)
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allFeatures = np.hstack([histo, hara, huMoments])
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testTypes.append(currentType)
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testFeatures.append(allFeatures)
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#save data
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h5TestData = 'output/testdata.h5'
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h5TestTypes = 'output/testtypes.h5'
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targetNames = np.unique(testTypes)
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le = LabelEncoder()
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target = le.fit_transform(testTypes)
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scaler = MinMaxScaler(feature_range=(0, 1))
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rescaledFeatures = scaler.fit_transform(testFeatures)
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fileTestData = h5py.File(h5TestData, 'w')
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fileTestData.create_dataset('dataset_1', data=np.array(rescaledFeatures))
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fileTestTypes = h5py.File(h5TestTypes, 'w')
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fileTestTypes.create_dataset('dataset_1', data=np.array(target))
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fileTestData.close()
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fileTestTypes.close()
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def trainAndTest():
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h5Data = 'output/data.h5'
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h5Types = 'output/types.h5'
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h5TestData = 'output/testdata.h5'
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h5TestTypes = 'output/testtypes.h5'
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#import train data
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fileData = h5py.File(h5Data, 'r')
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fileTypes = h5py.File(h5Types, 'r')
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features = fileData['dataset_1']
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types = fileTypes['dataset_1']
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allFeatures = np.array(features)
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allTypes = np.array(types)
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fileData.close()
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fileTypes.close()
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# create model
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clf = RandomForestClassifier(n_estimators=100, random_state=9)
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clf.fit(allFeatures, allTypes)
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#import test data
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fileTestData = h5py.File(h5TestData, 'r')
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fileTestTypes = h5py.File(h5TestTypes, 'r')
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features = fileTestData['dataset_1']
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types = fileTestTypes['dataset_1']
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allFeatures = np.array(features)
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allTypes = np.array(types)
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fileTestData.close()
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fileTestTypes.close()
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#Rfc score
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print("Random Forest Classifier score:")
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print(clf.score(allFeatures, allTypes))
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return clf
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#global features
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def hu_moments(image):
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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moments = cv2.moments(gray)
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huMoments = cv2.HuMoments(moments).flatten()
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return huMoments
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def haralick(image):
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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haralick = mahotas.features.haralick(gray).mean(axis=0)
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return haralick
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def histogram(image, mask=None):
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image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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hist = cv2.calcHist([image], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
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cv2.normalize(hist, hist)
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histogram = hist.flatten()
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return histogram
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#Test one image
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def classifyImage(file, clf):
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size = tuple((500, 500))
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types = os.listdir('ClassificationGarbage/testSet')
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types.sort()
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image = cv2.imread(file)
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image = cv2.resize(image, size)
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#Global features
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huMoments = hu_moments(image)
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hara = haralick(image)
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histo = histogram(image)
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allFeatures = np.hstack([histo, hara, huMoments])
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features = allFeatures.reshape(1,-1)
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prediction = clf.predict(features)[0]
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return types[prediction]
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#At the garbage dump
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def sortDump(cardboard, glass, metal, paper, plastic, clf, GT, dump):
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testDir = 'ClassificationGarbage/testSet'
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testTypes = os.listdir(testDir)
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testTypes.sort()
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noGarbage = [cardboard, glass, metal, paper, plastic]
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for i in range(len(testTypes)):
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print("Among " + testTypes[i] + ", we found: ")
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for j in range(0, noGarbage[i]):
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image = random.choice(os.listdir(testDir + '/' + testTypes[i]))
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file = testDir + '/' + testTypes[i] + "/"+ image
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prediction = classifyImage(file, clf)
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print(prediction)
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GT.empty(testTypes[i])
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dump.addGarbage(prediction)
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