def AtrophyPrediction(self, roi):
img = self.ModAtrophy.predict(roi)
atrophyRate = met.atrophyRate(img)
w, h, c = moil.getWidthHeightChannels(self.currentImg)
img = cv2.resize(img,
(round(160 * w / 600), round(160 * (w * 0.75) / 450)))
img = moil.getBinaryThreshold(img)
return atrophyRate, img
def ExitPrediction(self, roi, xExitShift, yExitShift, xRef, yRef):
img = self.ModExit.predict(roi)
img = cv2.resize(img, (xExitShift * 2, yExitShift * 2))
x, y = met.getCenter(img, xExitShift * 2, yExitShift * 2)
x = int(x)
y = int(y)
return x + xRef - xExitShift, y + yRef - yExitShift
def OpticDiscPrediction(self):
img = self.Mod.predict(self.currentImg)
# img = moil.stackImageChannels(img)
# resizing prediction
w, h, c = moil.getWidthHeightChannels(self.currentImg)
img = cv2.resize(img, (w, h))
# getting coords
x, y = met.getCenter(img, w, h)
x = int(x)
y = int(y)
return x, y, img
def check_performance(self,
validate_generator,
times=1,
metrics=['distance', 'youden', 'jaccard', 'dice']):
for i in range(times):
pic = validate_generator.next()
true = pic[1][0]
pred = self.model.predict(pic[0][0].reshape(
1, self.rowDim, self.colDim, self.channels))
pred = moil.convertImageNetOutput(pred)
true = moil.convertImageNetOutput(true)
met.customMetric(pred, true, metrics=metrics)
x = []
x.append(pic[0][0].reshape(
(self.rowDim, self.colDim, self.channels)))
x.append(true)
x.append(pred)
if self.show_function != None:
self.show_function(x)
def make_prediction(self):
x, y, pred = self.OpticDiscPrediction()
self.x = x
self.y = y
copy = self.currentImg.copy()
drawCopy = self.currentImg.copy()
drawCopy = moil.stackImageChannels(drawCopy)
w, h, c = moil.getWidthHeightChannels(copy)
xShift = int(80 * w / 600)
yShift = int(80 * (w * 0.75) / 450)
xExitShift = int(40 * w / 600)
yExitShift = int(40 * (w * 0.75) / 450)
roi = moil.getRegionOfInterest(copy, x, y, xShift, yShift)
roiExit = moil.getRegionOfInterest(copy, x, y, xExitShift, yExitShift)
atrophyRate, atrophyMap = self.AtrophyPrediction(roi)
self.atrophyRate = atrophyRate
self.label.configure(
text="Stopień zaniku (tylko faza tętniczo-żylna): " +
str(atrophyRate))
xExit, yExit = self.ExitPrediction(roiExit, xExitShift, yExitShift, x,
y)
self.xOut = xExit
self.yOut = yExit
dist = np.linalg.norm(
np.asarray([xExit / w * 600, yExit / (w * 0.75) * 450]) -
np.asarray([x / w * 600, y / (w * 0.75) * 450]))
if dist > 16:
self.labelExit.configure(
text='Przesunięcie naczyń (faza tętniczo-żylna lub późna) : ' +
str(dist) + ', ZNACZNE!')
else:
self.labelExit.configure(
text='Przesunięcie naczyń (faza tętniczo-żylna lub późna) : ' +
str(dist))
wA, hA, cA = moil.getWidthHeightChannels(atrophyMap)
mask = np.zeros((h, w), drawCopy.dtype)
mask = moil.addToRegionOfInterest(mask, x, y, round(wA / 2 + 0.00001),
round(hA / 2 + 0.00001), atrophyMap)
# mask[y-round(hA/2+0.00001):y+round(hA/2+0.00001), x-round(wA/2+0.00001):x+round(wA/2+0.00001)] = atrophyMap
redImg = np.zeros(drawCopy.shape, drawCopy.dtype)
redImg[:, :] = (255, 0, 0)
redMask = cv2.bitwise_and(redImg, redImg, mask=mask)
drawCopy = cv2.addWeighted(redMask, 1, drawCopy, 1, 0)
# moil.show(atrophyMap)
# drawCopy[mask] = (255, 0, 0)
cv2.rectangle(drawCopy, (x - xShift, y - yShift),
(x + xShift, y + yShift), (127, 0, 127),
int(5 / 1387 * w))
cv2.circle(drawCopy, (x, y),
int(12 / 1387 * w), (127, 0, 127),
thickness=int(5 / 1387 * w))
met.draw(pred, drawCopy, thickness=int(4 / 1387 * w))
cv2.circle(drawCopy, (xExit, yExit),
int(12 / 1387 * w), (0, 127, 0),
thickness=int(5 / 1387 * w))
self.updateGuiImage(drawCopy)
self.predicted = True
def centerDiffMetric(pred, x, y):
return met.centerDiff(pred, x, y)
def validate(self,
pathForce=None,
validateMode=0,
preprocessFunc=lambda x: x,
draw=True,
onlyWithMetric=False,
onlyWithoutMetric=False,
sumTimes=None,
metrics=['distance', 'youden', 'jaccard', 'dice'],
validTimes=1,
weightsTimesValids=None,
validName=''):
avgs, globals = (0, 0)
for i in range(validTimes):
if weightsTimesValids is not None:
self.constantVar = i * weightsTimesValids
self.load_weights()
sum = [0] * len(metrics)
confusion_matrix = [0] * 4
globalCount = False
for metr in metrics:
if 'global' in metr:
globalCount = True
times = 0
visited_path = {}
while True:
if pathForce is None:
path = self.validate_path_provider_func(
self.validate_start_path, visited_path)
visited_path[path] = times
else:
path = pathForce
if path is None:
break
if not os.path.exists(path):
continue
images = os.listdir(path)
for imp in images: # len(os.listdir(path)) - 2):
true_path = path + 'mask/'
if not os.path.exists(os.path.join(path, imp)):
continue
if onlyWithMetric and not os.path.exists(
os.path.join(true_path, imp)):
continue
else:
if onlyWithoutMetric and os.path.exists(
os.path.join(true_path, imp)):
continue
im = self.read_func(name=imp,
extension='',
path=path,
target_size=(self.colDim, self.rowDim),
mode=0)
imgX, img = self.prepareImage(im, retboth=True)
pred = self.model.predict(imgX)
pred = moil.convertImageNetOutput(pred)
toDraw = im if draw else None
x = [im, pred, img]
if os.path.exists(os.path.join(true_path, imp)):
true = self.read_func(name=imp,
extension='',
path=true_path,
target_size=(self.colDim,
self.rowDim))
true = true.reshape(
(self.rowDim, self.colDim, self.out_channels))
x.append(true)
results = met.customMetric(pred,
true,
toDraw=toDraw,
metrics=metrics,
globalCount=globalCount)
sum = list(map(add, sum, results[0]))
confusion_matrix = list(
map(add, confusion_matrix, results[1]))
times += 1
if sumTimes is not None and times >= sumTimes:
break
else:
met.draw(pred, toDraw)
if sumTimes is None:
self.show_function(x)
avgs = [x / times for x in sum]
strgSum = ''
strgAvgs = ''
for val in sum:
strgSum += str(val) + ', '
for val in avgs:
strgAvgs += str(val) + ', '
globals = []
if globalCount:
globals = met.globals(confusion_matrix)
print("Global Jaccard: " + str(globals[0]) +
", Global Dice: " + str(globals[1]))
print("Times: " + str(times) + ", sums: " + strgSum +
"Average metrics: " + strgAvgs)
self.validate_to_csv(metrics, avgs + globals, validName)
return avgs + globals