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 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 registerImageCsv(repo_path, image_path, image_name, image, function):
patient, date, eye = morn.getPatientDateEye(image_path)
width, height, channels = moil.getWidthHeightChannels(image)
func_name = function.__name__
header = [
'patient', 'date', 'eye', 'name', 'width', 'height', 'channels',
'function'
]
row = [patient, date, eye, image_name, width, height, channels, func_name]
writeToCsv(repo_path + "imageData.csv", header, row)
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 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 circle_mask_on_random_image_in_path(self,
path,
target_size=None,
r=None,
extension=".jpg",
check_csv=True,
list=None):
numb = len([
i for i in os.listdir(path)
if os.path.isfile(os.path.join(path, i))
])
temp = ([
a for a in os.listdir(path)
if os.path.isfile(os.path.join(path, a))
])
try:
j = np.random.randint(numb)
except:
print(path + ", numb: " + str(numb))
return
ImName = random.choice(temp)
if not os.path.exists(path + '/mask'):
os.makedirs(path + '/mask')
tempName = path + '/mask/' + ImName
if os.path.exists(tempName):
print("Path exists (" + tempName + ")")
return
if check_csv:
paths = morn.getRepoPathAndImagePath(path)
row = paths[1].split("/")[:-1]
row.append(ImName)
if mocl.checkIfExistsInCSV(row, paths[0], list, False):
print("In CSV exists (" + tempName + ")")
return
if r is None and target_size is not None:
self.rr = int(target_size[0] / 10)
else:
self.rr = r
img = moil.read_and_size(ImName,
path=path,
target_size=target_size,
extension='')
w, h, c = moil.getWidthHeightChannels(img)
if r is None and target_size is None:
self.rr = int(w / 10)
target_size = (w, h)
moil.show(img)
accepted = False
while not accepted:
accepted = True
im2, contours, hierarchy = cv2.findContours(
self.mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
im2 = copy.deepcopy(img)
cv2.drawContours(im2, contours, 0, (0, 255, 255), 2)
moil.show(im2)
split_path = path.split("/")[:-1]
repo_path = reduce((lambda x, y: x + '/' + y),
split_path[:len(split_path) - 3])
if not os.path.isfile(repo_path + "/maskData.csv"):
csvFile = open(repo_path + '/maskData.csv', 'w', newline="")
writer = csv.writer(csvFile)
writer.writerow([
'patient', 'date', 'eye', 'name', 'width', 'height', 'x', 'y',
'r'
])
csvFile.close()
csvFile = open(repo_path + '/maskData.csv', 'a', newline="")
writer = csv.writer(csvFile)
ls = split_path[-3:]
ls.extend([
ImName, target_size[0], target_size[1], self.xx, self.yy, self.rr
])
writer.writerow(ls)
csvFile.close()
cv2.imwrite(path + '/mask/' + ImName, self.mask)
self.masks_done += 1
print("masks: " + str(self.masks_done))
cv2.destroyWindow('mask')
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 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
