def _getNewYEstimateAtCurrentResolutionLevel(self, X, radiograph,
currentTooth,
currentResolutionLevel,
filter_settings):
# Pre process image
img = Filter.process_image(
deepcopy(radiograph.getImage(deepCopy=True)), filter_settings[0],
filter_settings[1], filter_settings[2])
derivate_img = Filter.laplacian(img)
# derivate_img = Filter.histogramEql(Filter.process_image(deepcopy(img), filter_settings[0], filter_settings[1], filter_settings[2]))
# derivate_img = Filter.process_image(deepcopy(derivate_img), median_kernel=3, bilateral_kernel=5)
#cv2.imshow("Test", derivate_img)
#cv2.waitKey(0)
# Get the correct model
multiResModel = self.completeStatisticalModel.getGrayLevelMultiResolutionModel(
deepCopy=True)
singleResModel = multiResModel.getGrayLevelSignleResModelByResolutionLevelIndex(
currentResolutionLevel)
toothModel = singleResModel.getGrayLevelToothModelByToothIndex(
currentTooth, currentResolutionLevel)
# Init X and Y
Y = np.zeros((40, 2))
X = X.getLandmarks()
counter = 0
for i in range(40):
Y[i], is_close = self._getYPointEstimateFromGivenModelPoint(
toothModel, i, X, currentResolutionLevel, currentTooth, img,
derivate_img)
if is_close:
counter += 1
return Y, counter / 40
def filterTest(self):
radiograph = self.dataHandler.getRadiographs(deepCopy=True)[0]
blurred_img = Filter.process_image(deepcopy(radiograph.getImage()),
median_kernel=3,
bilateral_kernel=10)
cv2.imshow("BlurredImage", blurred_img)
clahe = cv2.equalizeHist(blurred_img)
cv2.imshow("Clahe", clahe)
img_1 = Filter.laplacian(blurred_img)
cv2.imshow("Img1", img_1)
def trainGrayLevelModelForAllPointsAllExamples(self, k, resolutionLevel, filter_settings):
"""
calcualtes the gray-level vectors for all point of all teeth in all provided examples.
"""
radiographs = self.completeDataHandler.getRadiographs(deepCopy=True)
g_all = list()
for radiograph in radiographs:
# Scale image to current resolution level
for i in range(resolutionLevel):
radiograph.downScale()
# Pre process image
img = Filter.process_image(deepcopy(radiograph.getImage(deepCopy=True)), filter_settings[0], filter_settings[1], filter_settings[2])
derivate_img = Filter.laplacian(img)
# derivate_img = Filter.histogramEql(Filter.process_image(deepcopy(img), filter_settings[0], filter_settings[1], filter_settings[2]))
# derivate_img = Filter.process_image(deepcopy(derivate_img), median_kernel=3, bilateral_kernel=5)
g_ex = self.trainGrayLevelModelForAllPointsOneExample(img, derivate_img, radiograph, k)
g_all.append(g_ex)
return np.array(g_all)