def getImageFeatures(imagePath, modelType):
if not isinstance(modelType, ModelType):
raise ValueError("Invalid modelType")
if modelType == ModelType.SIFT:
return SIFT(getGrayScaleImage(imagePath)).getFeatures()
if modelType == ModelType.CM:
return ColorMoments(getYUVImage(imagePath), 100, 100).getFeatures()
if modelType == ModelType.HOG:
return HOG(cv2.imread(imagePath, cv2.IMREAD_COLOR), 9, 8,
2).getFeatures()
if modelType == ModelType.LBP:
return LBP(getGrayScaleImage(imagePath),
blockSize=100,
numPoints=24,
radius=3).getFeatures()
def getDataMatrixForSIFT(imagePaths, dataMatrix):
imagesCount = len(imagePaths)
for index, imagePath in enumerate(imagePaths):
if not os.path.exists(imagePath): continue
print("Data matrix creation | Processed {} out of {} images".format(
index, imagesCount - 1))
dataMatrix.append(
getClusters(SIFT(
getGrayScaleImage(imagePath)).getFeatures()).flatten())
return dataMatrix
def getDataMatrixForLBPForLDA(imagePaths, dataMatrix):
imagesCount = len(imagePaths)
for index, imagePath in enumerate(imagePaths):
if not os.path.exists(imagePath): continue
print("Data matrix creation | Processed {} out of {} images".format(
index, imagesCount - 1))
lbp = LBP(getGrayScaleImage(imagePath),
blockSize=100,
numPoints=24,
radius=3)
features = lbp.getFeatureWithDim()
dataMatrix.append(features)
return dataMatrix