def show(self, flag='image'):
(_, name) = os.path.split(self.image_list[self.current_image_mark])
if flag == 'image':
path = self.path_image
plotwindow = self.image
elif flag == 'GT':
path = self.path_mask_GT
plotwindow = self.mask_GT
elif flag == 'pred':
path = self.path_mask_pred
plotwindow = self.mask_pred
try:
img = Iopen(os.path.join(path, name))
self.data[flag] = array(img)
if (flag == 'GT' and self.data['pred'] is not None) or (
flag == 'pred' and self.data['GT'] is not None):
matrix = evaluate.Matrix(self.data['pred'], self.data['GT'])
_, miou = matrix.miou()
_, acc = matrix.accuracy()
kappa = matrix.kappa()
self.miou.setText(f'{miou}')
self.accuracy.setText(f'{acc}')
self.kappa.setText(f'{kappa}')
self.progressBar.setValue(self.current_image_mark + 1)
img = img.resize((341, 251))
self.file_path.setText(self.image_list[self.current_image_mark])
plotwindow.setPixmap(img.toqpixmap())
self.check_LastAndNext()
except FileNotFoundError as e:
raise (e)
def show_merge(self, flag='GT'):
if flag == 'GT':
path = self.path_mask_GT
plotwindow = self.merge
elif flag == 'pred':
path = self.path_mask_pred
plotwindow = self.merge_pred
(_, name) = os.path.split(self.image_list[self.current_image_mark])
try:
mask = Iopen(os.path.join(path, name))
image = Iopen(self.image_list[self.current_image_mark])
mask = mask.resize((511, 431))
image = image.resize((511, 431))
merge = array(image)
mask_arr = array(mask)
merge[mask_arr == 0] = [0, 0, 0]
merge = fromarray(merge)
plotwindow.setPixmap(merge.toqpixmap())
except FileNotFoundError as e:
pass
class FVRT:
# 初始化各系数
# 变换后的宽
WIDTH = 75
# 变换后的高
HEIGHT = 170
# 灰度放大系数
THREFACTOR = 4
# 隶属度分割界限
THREGRAY = 0.3
# 迭代次数
ITER = 3
# Niblack模板边长
SIDELENGTH = 11
# 补偿权值
THREK = 0.05
# 开运算模板边长
OPENSIDE = 3
# 闭运算模板边长
CLOSEDSIDE = 3
# 中值滤波模板边长
BLURSIDE = 5
# 圈定面积
MINSIZE = 20
# 输入原始图像
def __init__(self, addr):
# if addr!='':
self.__im = ImOpen(addr).convert('L')
# else:
# self.__im=image
# 尺寸归一化
def SizeNorm(self, width, height):
self.__im = self.__im.resize((width, height), BILINEAR)
# 灰度归一化
def GrayNorm(self):
pix = array(self.__im)
norm = Normalize(vmin=pix.min(), vmax=pix.max())
pix = norm(pix) * 255
pix = pix.astype(int32)
self.__im = fromarray(pix)
# 基于方向的谷型检测
def DirectValley(self, ThreFactor):
pix = array(self.__im)
row = array([0, 2, 4, 6, 8])
value = array([3, -1, -4, -1, 3])
A = {}
for i in range(4):
if i == 0:
col = row
elif i == 1:
col = array([2, 3, 4, 5, 6])
elif i == 2:
col = array([4, 4, 4, 4, 4])
else:
col = array([6, 5, 4, 3, 2])
A[i] = csc_matrix((value, (row, col)), shape=(9, 9)).toarray()
A[i + 4] = rot90(A[i])
pixdict = {}
for i in range(8):
pixdict[i] = convolve2d(pix, A[i], 'same')
m, n = pix.shape
for x in range(m):
for y in range(n):
valmax = -1000
for i in range(8):
if pixdict[i][x, y] >= valmax:
valmax = pixdict[i][x, y]
if valmax >= 0:
pix[x, y] = valmax
else:
pix[x, y] = 0
nozero_num = count_nonzero(pix)
nozero_avg = sum(pix) / nozero_num
for x in range(m):
for y in range(n):
if pix[x, y] >= (ThreFactor * nozero_avg):
pix[x, y] = ThreFactor * nozero_avg
self.__im = fromarray(pix)
# 图像模糊增强
def BlurEnhance(self, ThreGray, Iter):
pix = array(self.__im)
pix = sin(pix * pi / (2 * 255))
for i in arange(Iter):
pix = (sin(pi * (pix - ThreGray)) + 1) / 2
pix = 255 * arcsin(pix**sqrt(Iter)) * 2 / pi
self.__im = fromarray(pix)
# 图像阈值分割
def ThreSegment(self, SideLength, ThreK):
pix = array(self.__im)
thresh_pix = threshold_niblack(pix,
window_size=SideLength * SideLength,
k=ThreK)
binary_pix = pix > thresh_pix
pix = binary_pix.astype(int) * 255
self.__im = fromarray(pix)
# 图像去噪
def Denoise(self, OpenSide, ClosedSide, BlurSide):
im = self.__im
im = cv2.cvtColor(asarray(im).astype(uint8), cv2.COLOR_GRAY2BGR)
openkernel = cv2.getStructuringElement(cv2.MORPH_RECT,
(OpenSide, OpenSide))
opening = cv2.morphologyEx(array(im), cv2.MORPH_OPEN, openkernel)
closedkernel = cv2.getStructuringElement(cv2.MORPH_RECT,
(ClosedSide, ClosedSide))
closed = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, closedkernel)
result = cv2.medianBlur(closed, BlurSide)
pix = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)
self.__im = fromarray(pix)
# 图像细化
def Thin(self):
pix = array(self.__im)
pix = pix / 255
pix = morphology.skeletonize(pix)
pix = pix.astype(int) * 255
self.__im = fromarray(pix)
# 二值化图像去除残余斑点
def CleanSmallObject(self, MinSize):
pix = array(self.__im) / 255
pix = pix.astype(bool)
pix = morphology.remove_small_objects(pix,
min_size=MinSize,
connectivity=2)
pix = pix.astype(int) * 255
self.__im = fromarray(pix)
# 获得图像数组
def GetArray(self):
return array(self.__im)
# 获得图像
def GetImage(self):
return self.__im
# 显示图像
def ShowImage(self):
self.__im.show()
# 设置各系数
@staticmethod
def SetFactor(width=75,
height=170,
ThreFactor=4,
ThreGray=0.3,
Iter=3,
SideLength=11,
ThreK=0.05,
OpenSide=3,
ClosedSide=3,
BlurSide=5,
MinSize=20):
FVRT.WIDTH = width
FVRT.HEIGHT = height
FVRT.THREFACTOR = ThreFactor
FVRT.THREGRAY = ThreGray
FVRT.ITER = Iter
FVRT.SIDELENGTH = SideLength
FVRT.THREK = ThreK
FVRT.OPENSIDE = OpenSide
FVRT.CLOSEDSIDE = ClosedSide
FVRT.BLURSIDE = BlurSide
FVRT.MINSIZE = MinSize
# 一键操作图像
@staticmethod
def OneKey(addr):
image = FVRT(addr)
image.SizeNorm(FVRT.WIDTH, FVRT.HEIGHT)
image.GrayNorm()
image.DirectValley(FVRT.THREFACTOR)
image.BlurEnhance(FVRT.THREGRAY, FVRT.ITER)
image.ThreSegment(FVRT.SIDELENGTH, FVRT.THREK)
image.Denoise(FVRT.OPENSIDE, FVRT.CLOSEDSIDE, FVRT.BLURSIDE)
image.Thin()
image.CleanSmallObject(FVRT.MINSIZE)
return image.GetImage()
