def predict(self, im):
w, h, c = moil.getWidthHeightChannels(im)
if w != self.colDim or h != self.rowDim or c != self.channels:
im = cv2.resize(im, (self.colDim, self.rowDim))[:, :]
im = self.prepareImage(im)
im = self.model.predict(im)
im = moil.convertImageNetOutput(im)
return im
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 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