def main(args):
images = "textures"
featureMethod = "cu"
classMethod = "perc"
ifDisplayImages = False
ifClassify = True
radiusSize = 7
imagesToClassify = PP.ImagesToClassify(images)
featureExtactionMethod = PP.OtherImagesFeaturesType(featureMethod)
classificationMethod = PP.ClassificationMethod(classMethod)
if not ifClassify:
if(imagesToClassify == PP.ImagesToClassify.NumberImages):
print("Loading number images..")
trainImages, testImages = ImRead.LoadNumbersImages()
elif(imagesToClassify == PP.ImagesToClassify.OtherImages):
print("Loading other images..")
trainImages, testImages = ImRead.LoadOtherImages()
print("Processing images..")
imSeg.ExtractImages(trainImages)
imSeg.ExtractImages(testImages)
print("Computing shape descriptors..")
Features.ComputeFeatures(trainImages, featureExtactionMethod)
Features.ComputeFeatures(testImages, featureExtactionMethod)
trainData = "OtherImages\\trainData.txt"
trainLabels = "OtherImages\\trainLabels.txt"
testData = "OtherImages\\testData.txt"
testLabels = "OtherImages\\testLabels.txt"
Features.SaveFeaturesToFile(trainImages, trainLabels, trainData)
elif(imagesToClassify == PP.ImagesToClassify.Textures):
print("Loading textures..")
trainImages, testImages = ImRead.LoadTextures()
print("Processing images..")
imSeg.ExtractImages(trainImages, onlyConvertToGreyScale=True)
imSeg.ExtractImages(testImages, onlyConvertToGreyScale=True)
print("Computing image features..")
trainThread = threading.Thread(target=Features.ComputeFeatures, args=(trainImages, radiusSize, featureExtactionMethod))
# Features.ComputeFeatures(trainImages, featureExtactionMethod)
print("{0} Train feature extraction started!".format(datetime.datetime.now()))
trainThread.start()
Features.ComputeTexturePartFeatures(testImages, radiusSize, featureExtactionMethod)
trainThread.join()
print("{0} Train feature extraction finished!".format(datetime.datetime.now()))
trainData = "Textures\\trainData.txt"
trainLabels = "Textures\\trainLabels.txt"
print("Saving features to file")
Features.SaveFeaturesToFile(trainImages, trainLabels, trainData)
Features.SaveImagePartsFeatures(testImages, radiusSize)
elif ifClassify:
print("Classification started!")
featureFiles = ["test1.txt", "test2.txt", "test3.txt"]
textureObjects = []
for featureFile in featureFiles:
textureObjects.append(Features.ReadFeaturesOfImage(featureFile))
trainData = "Textures\\trainData.txt"
trainLabels = "Textures\\trainLabels.txt"
predictions = []
if(classificationMethod == PP.ClassificationMethod.OtoczenieKuliste):
OtKul = OK.OtoczKul(trainData, trainLabels)
for textureObj in textureObjects:
predictions.append([])
for imageFeatures in textureObj.imageFeatures:
predictions[-1].append(OtKul.PredictLabel(imageFeatures))
textureObj.CreateTexture(predictions[-1], radiusSize)
Features.SaveImage(textureObj)
elif(classificationMethod == PP.ClassificationMethod.KNN):
knn = KNN.KNN(trainData, trainLabels, 3)
for textureObj in textureObjects:
predictions.append([])
for imageFeatures in textureObj.imageFeatures:
predictions[-1].append(knn.predict(imageFeatures))
textureObj.CreateTexture(predictions[-1], radiusSize)
Features.SaveImage(textureObj)
elif(classificationMethod == PP.ClassificationMethod.Percepton):
trainDataD, trainLabelsD = readTrainData("Output\\" + trainData, "Output\\" + trainLabels)
hidden_layers = [10]
myDNN = Percepton(trainDataD.shape[1], 4, hidden_layers, ifBias=True, batch_size=0)
print("[%s] Training model.." %(datetime.datetime.now()))
myDNN.train(trainDataD, trainLabelsD, l_rate=0.1, n_epochs=10000, epsilon=10e-12)
print("[%s] Model has been trained!" %(datetime.datetime.now()))
for textureObj in textureObjects:
predictions = []
print("[%s] Predicting.." %(datetime.datetime.now()))
predictions.append(myDNN.predict(np.array(textureObj.imageFeatures)))
print("[%s] Saving.." %(datetime.datetime.now()))
textureObj.CreateTexture(predictions[-1], radiusSize)
Features.SaveImage(textureObj)
if(ifDisplayImages):
imDisp = ImDisp.ImageDisplayer(6)
imDisp.DisplayOtherImagesAnimation(trainImages)
