def validate_to_csv(self, metrics, values, name=''):
path = self.path.split('/')[:-1]
path = '/'.join(path)
epoch = (self.var_file(True) % 100) * 100
epoch = epoch + int(self.var_file(True) / 100) * 100
mocl.writeToCsv(path + '/scores' + name + '.csv', ['name'] + metrics,
[self.path.split('/')[-1] + '_' + str(epoch)] + values)
def save_to_csv(self):
if self.path is None:
return
if self.predicted == False:
return
mocl.writeToCsv("output.csv", mocl.getOutputHeader(), [
self.path, self.x, self.y, self.xOut, self.yOut, self.atrophyRate
])
def createImagesInRepoAfterFunctionOnPath(self,
path,
new_repo_path,
function,
target_size,
override=False,
extension='.jpg',
onlyMasked=False):
success = 0
fail = 0
repo_path, image_path = morn.getRepoPathAndImagePath(path)
base2, name2 = BuildRepo.getBaseRepoPathAndRepoName(new_repo_path)
BuildRepo.createImagesRepo(base2 + '/', name2)
new_path = new_repo_path + image_path
maskList = None
if onlyMasked:
maskList = mocl.getCsvList(repo_path, False)
for a in os.listdir(path):
if not os.path.isfile(os.path.join(path, a)):
continue
patient, date, eye = morn.getPatientDateEye(image_path)
if onlyMasked and not os.path.isfile(
os.path.join(path + 'mask/', a)
) and not mocl.checkIfExistsInCSV(
[patient, date, eye, a], list=maskList, image=False):
continue
name = a.split(".")[0]
base_image = moil.read_and_size(name,
path=path,
target_size=target_size)
image = function(base_image)
if morn.createImageInPath(new_path, name + extension, image,
override):
mocl.registerImageCsv(new_repo_path, image_path,
name + extension, image, function)
success += 1
else:
fail += 1
return success, fail
def CreateMasks(self):
cv2.destroyWindow(self.winname)
cv2.namedWindow(self.winname)
cv2.setMouseCallback(self.winname, self.draw_circle)
list = mocl.getCsvList(self.image_path, False)
while (True):
self.circle_mask_on_random_image_in_path(
morn.random_path(self.image_path),
target_size=self.targetSize,
check_csv=True,
list=list)
def createMaskFromCsv(self,
repo_path,
imageRow,
list=None,
override=False):
if list is None:
list = mocl.getCsvList(repo_path, image=False)
target = mocl.checkIfExistsInCSV(imageRow,
repo_path=repo_path,
list=list,
image=False,
returnTargetRow=True)
if target is None:
return False
imageW = int(imageRow[4])
imageH = int(imageRow[5])
maskW = int(target[4])
maskH = int(target[5])
maskX = int(target[6])
maskY = int(target[7])
maskR = int(target[8])
Wratio = imageW / maskW
Hratio = imageH / maskH
Rratio = math.sqrt((((Wratio**2) + (Hratio**2)) / 2))
outX = int(maskX * (Wratio))
outY = int(maskY * (Hratio))
outR = int(maskR * (Rratio))
mask = np.zeros((imageH, imageW), dtype=np.uint8)
mask = cv2.circle(mask, (outX, outY), outR, 255, -1)
path = repo_path + reduce(
(lambda x, y: x + '/' + y), imageRow[:3]) + '/mask/'
return morn.createImageInPath(path, imageRow[3], mask, override)
def splitCsv(maskDataPath, name, csv1, csv2, howManyToNewCsv):
list = mocl.getCsvList(maskDataPath, False, name=name)
i = 0
for row in list:
if len(list) - i > howManyToNewCsv:
if csv1 is not None:
mocl.writeToCsv(csv1, mocl.getMaskHeader(), row)
else:
if csv2 is not None:
mocl.writeToCsv(csv2, mocl.getMaskHeader(), row)
i += 1
def visualizeTable(columns=['Dystans', 'Youden', 'Jaccard', 'Dice']):
scores = mocl.getScoresList("../../weights/scores.csv")
names = []
data = []
for score in scores:
data.append([float(x) for x in score[1:]])
names.append(score[0][10:-4])
data = [
y for _, y in (
sorted(zip(names, data), key=lambda pair: int(pair[0][-3:])))
]
data = [['%.3f' % j for j in i] for i in data]
fig, axs = plt.subplots(2, 1)
axs[0].axis('tight')
axs[0].axis('off')
table = axs[0].table(colLabels=columns,
loc='center',
cellText=data,
rowLabels=names,
rowColours=['gray', 'red'] * 3,
rowLoc='right',
cellLoc='right',
cellColours=[['gray'] * 4, ['red'] * 4] * 3)
table.set_fontsize(12)
table.scale(0.8, 1.4)
for i in range(1, len(columns)):
tempDat = []
tempDat2 = []
for j in range(int(len(data) / 2)):
tempDat.append(float(data[j * 2 + 1][i]))
tempDat2.append(float(data[j * 2][i]))
if i == 1:
axs[1].plot([300, 500, 700], tempDat, 'r-', label='SAB')
axs[1].plot([300, 500, 700], tempDat2, 'k-', label='Gray')
else:
axs[1].plot([300, 500, 700], tempDat, 'r-')
axs[1].plot([300, 500, 700], tempDat2, 'k-')
plt.title("Youden, Dice, Jaccard (kolejno od góry)")
plt.legend()
plot_margin = 0.1
x0, x1, y0, y1 = plt.axis()
axs[1].axis((x0 - plot_margin, x1 + plot_margin, y0, y1))
plt.text(x=400,
y=0.58,
s='Liczebność zbioru treningowego',
fontdict={'size': 10})
plt.show()
def createAllMasksForImagesCsv(self, repo_path):
success = 0
fail = 0
list = mocl.getCsvList(repo_path, image=False)
iter = 0
with open(repo_path + "imageData.csv", 'r') as file:
reader = csv.reader(file)
next(reader, None)
for row in reader:
if self.createMaskFromCsv(repo_path, row, list, True):
success += 1
else:
fail += 1
iter += 1
print("Images looped: " + str(iter))
file.close()
print("Masks created: " + str(success) + ", failed to create: " +
str(fail))
def visualizeBar(scoreIndex, title):
scores = mocl.getScoresList("../../weights/scores.csv")
vals = []
names = []
for score in scores:
vals.append(float(score[scoreIndex]))
names.append(score[0][10:-4])
l = (sorted(zip(names, vals), key=lambda pair: int(pair[0][-3:])))
names, vals = [list(t) for t in zip(*l)]
plt.bar(names, vals, color=['gray', 'red'])
plt.title(title)
# plt.text(x=-1.5, y=-1.3, s='500 epok, redukcja kroku uczenia', fontdict={'size':8})
# plt.text(x=4, y=13.3, s='Gray vs SAB + zbiór treningowy', fontdict={'size':8})
# plt.xticks(y_pos, names)
plt.show()
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 visualizePlot2(scoreIndex, averaging=False):
scores = mocl.getScoresList("../../weights/scoreswn.csv")
names = []
metr = ['Youden', 'Jaccard', 'Dice']
data = []
base_name = scores[0][0][9:16]
for score in scores:
if score[0][9:] not in names:
data.append([float(x) for x in score[1:]])
tempName = score[0][15:-5]
#tempName = tempName[:5] + tempName[-1]
names.append(tempName)
l = (sorted(zip(names, data), key=lambda pair: pair[0]))
names, data = [list(t) for t in zip(*l)]
#names, data = [y for x, y in (sorted(zip(names, data), key=lambda pair: pair[0]))]
data = [['%.3f' % j for j in i] for i in data]
chunks = 21
data = [data[i:i + chunks] for i in range(0, len(data), chunks)]
names = [names[i:i + chunks] for i in range(0, len(names), chunks)]
color = ['b', 'm', 'g', 'r', 'c', 'k']
sums = [[0, 0, 0, 0] for x in range(chunks)]
for i in range(len(data)):
print(i)
name = names[i][0]
if name == '':
name = base_name
named = False
for k in scoreIndex:
temp = []
for j in range(len(data[i])):
if j == 0:
continue
temp.append(float(data[i][j][k]))
sums[j][k] = sums[j][k] + float(data[i][j][k])
l = list(np.arange(chunks - 1) * 100 + 100)
if not named:
plt.plot(l, temp, label=names[i][0], color=color[k])
named = True
else:
plt.plot(l, temp, color=color[i])
if averaging:
for k in scoreIndex:
temp = []
for j in range(len(sums)):
if j == 0:
continue
sums[j][k] /= len(data)
sums[j][k] = float('%.3f' % sums[j][k])
temp.append(sums[j][k])
l = list(np.arange(chunks - 1) * 100 + 100)
plt.plot(l, temp, label='Average', color='k')
sums = sums[1:]
plt.legend()
plt.title("Dystans wykrytego środka od prawidłowego")
plt.text(x=950, y=3.460, s='Ilość epok', fontdict={'size': 10})
fig, axs = plt.subplots(2, 1)
axs[0].axis('tight')
axs[0].axis('off')
table = axs[0].table(
colLabels=['Youden', 'G. Youden', 'G. Jaccard', 'G. Dice'],
loc='center',
cellText=sums,
rowLabels=[str(x) for x in np.arange(20) * 100 + 100],
rowLoc='right',
cellLoc='right',
cellColours=[['w'] * 4] * 20,
rowColours=['w', 'w', 'g', 'w', 'w'] * 4)
table.set_fontsize(12)
table.scale(0.8, 1.4)
plt.show()