def dice_coefficient(ground_truth, prediction, threshold=127):
ground_truth = moil.getBinaryThreshold(ground_truth, threshold)
prediction = moil.getBinaryThreshold(prediction, threshold)
intersection = np.logical_and(ground_truth, prediction)
union = np.logical_or(ground_truth, prediction)
dice = (2 * np.sum(intersection)) / (np.sum(intersection) + np.sum(union))
print("Dice Sorensen: " + str(dice))
return dice
def jaccard_index(ground_truth, prediction, threshold=127):
ground_truth = moil.getBinaryThreshold(ground_truth, threshold)
prediction = moil.getBinaryThreshold(prediction, threshold)
intersection = np.logical_and(ground_truth, prediction)
union = np.logical_or(ground_truth, prediction)
jaccard = np.sum(intersection) / np.sum(union)
print("Jaccard Index: " + str(jaccard))
return jaccard
def binaryDiff(pred, true, threshold=127, globalCount=False):
thresh = moil.getBinaryThreshold(pred, threshold)
threshTrue = moil.getBinaryThreshold(true, threshold)
unique, counts = np.unique(threshTrue, return_counts=True)
trueC = dict(zip(unique, counts))
diff = np.int16(threshTrue) - np.int16(thresh)
unique, counts = np.unique(diff, return_counts=True)
diffC = dict(zip(unique, counts))
try:
Negatives = trueC[0]
except:
Negatives = 0
try:
Positives = trueC[255]
except:
Positives = 0
try:
FN = diffC[255]
except:
FN = 0
try:
FP = diffC[-255]
except:
FP = 0
TP = Positives - FN
TN = Negatives - FP
try:
Specifity = TN / Negatives
except:
Specifity = 1
try:
Sensitivity = TP / Positives
except:
Sensitivity = 1
Accuracy = (TP + TN) / (Positives + Negatives)
Jouden = Sensitivity + Specifity - 1
print("Youden Index: " + str(Jouden) + ", Sensivity: " + str(Sensitivity) + ", Specifity: " + str(
Specifity) + ", Accuracy: " + str(Accuracy) )
if globalCount:
return [TP, FN, FP, TN]
return Jouden
def atrophyRate(img, threshold = 127):
img = moil.getBinaryThreshold(img, threshold)
unique, counts = np.unique(img, return_counts=True)
try:
count = dict(zip(unique, counts))[255]
except:
count = 0
return count / 20576
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 draw(pred, toDraw, morph_iter=0, threshold=127, thickness=2):
thresh = moil.getBinaryThreshold(pred, threshold)
closed = moil.morphMultiClosing(thresh, morph_iter)
contour = moil.selectBiggerCircularContour(closed)
if toDraw is not None and contour is not None:
cv2.drawContours(toDraw, [contour], -1, (0, 0, 255), thickness)
def getCenter(pred, widthRef, heightRef, morph_iter=0, threshold=127):
thresh = moil.getBinaryThreshold(pred, threshold)
closed = moil.morphMultiClosing(thresh, morph_iter)
contour = moil.selectBiggerCircularContour(closed)
w, h, c = moil.getWidthHeightChannels(pred)
try:
M = cv2.moments(contour)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
except Exception as e:
print("No contour detected! Guessing for center...")
cx = int(w / 2)
cy = int(h / 2)
w_scale = widthRef / w
h_scale = heightRef / h
cx = cx * w_scale
cy = cy * h_scale
return cx, cy
def centerDiff(pred, true=None, x=None, y=None, width=None, height=None, r=None, morph_iter=0, threshold=127,
toDraw=None, retDist=False):
assert true is not None or (
x is not None and y is not None and width is not None and height is not None and r is not None)
thresh = moil.getBinaryThreshold(pred, threshold)
closed = moil.morphMultiClosing(thresh, morph_iter)
contour = moil.selectBiggerCircularContour(closed)
if toDraw is not None and contour is not None:
cv2.drawContours(toDraw, [contour], -1, (255, 255, 255), 1)
w, h, c = moil.getWidthHeightChannels(pred)
try:
M = cv2.moments(contour)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
except Exception as e:
print("No contour detected! Guessing for center...")
cx = int(w / 2)
cy = int(h / 2)
if (x is None or y is None or width is None or height is None or r is None):
width, height, chan = moil.getWidthHeightChannels(true)
r = width / 10
thresh = moil.getBinaryThreshold(true, threshold)
im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
try:
M = cv2.moments(contours[0])
x = int(M['m10'] / M['m00'])
y = int(M['m01'] / M['m00'])
except:
print("Bad Ground-truth! Mask in center...")
x = int(w / 2)
y = int(h / 2)
w_scale = width / w
h_scale = height / h
cx = cx * w_scale
cy = cy * h_scale
dist = np.linalg.norm(np.asarray([cx, cy]) - np.asarray([x, y]))
if retDist:
return dist
maxDist = np.linalg.norm(np.asarray([w / 2, h / 2]) - np.asarray([x, y]))
DistanceMetric = 1 - dist / maxDist
CrossLength = math.sqrt(width ** 2 + height ** 2)
DistanceToCross = dist / CrossLength
print("Distance Metric: " + str(DistanceMetric) + ", Relative Distance: " + str(
DistanceToCross) + ", Distance: " + str(dist))
return DistanceMetric
