|
- import cv2
- from pylab import *
-
- Image = cv2.imread('02.jpg', 1) # 读入原图
- image = cv2.cvtColor(Image, cv2.COLOR_BGR2GRAY)
- img = np.array(image, dtype=np.float64) # 读入到np的array中,并转化浮点类型
-
- # 初始水平集函数
- IniLSF = np.ones((img.shape[0], img.shape[1]), img.dtype)
- IniLSF[300:320, 300:320] = -1
- IniLSF = -IniLSF
-
- # 画初始轮廓
- Image = cv2.cvtColor(Image, cv2.COLOR_BGR2RGB)
- plt.figure(1), plt.imshow(Image), plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis
- plt.contour(IniLSF, [0], color='b', linewidth=2) # 画LSF=0处的等高线
- plt.draw(), plt.show(block=False)
-
-
- def mat_math(intput, str):
- output = intput
- for i in range(img.shape[0]):
- for j in range(img.shape[1]):
- if str == "atan":
- output[i, j] = math.atan(intput[i, j])
- if str == "sqrt":
- output[i, j] = math.sqrt(intput[i, j])
- return output
-
-
- # CV函数
- def CV(LSF, img, mu, nu, epison, step):
- Drc = (epison / math.pi) / (epison * epison + LSF * LSF)
- Hea = 0.5 * (1 + (2 / math.pi) * mat_math(LSF / epison, "atan"))
- Iy, Ix = np.gradient(LSF)
- s = mat_math(Ix * Ix + Iy * Iy, "sqrt")
- Nx = Ix / (s + 0.000001)
- Ny = Iy / (s + 0.000001)
- Mxx, Nxx = np.gradient(Nx)
- Nyy, Myy = np.gradient(Ny)
- cur = Nxx + Nyy
- Length = nu * Drc * cur
-
- Lap = cv2.Laplacian(LSF, -1)
- Penalty = mu * (Lap - cur)
-
- s1 = Hea * img
- s2 = (1 - Hea) * img
- s3 = 1 - Hea
- C1 = s1.sum() / Hea.sum()
- C2 = s2.sum() / s3.sum()
- CVterm = Drc * (-1 * (img - C1) * (img - C1) + 1 * (img - C2) * (img - C2))
-
- LSF = LSF + step * (Length + Penalty + CVterm)
- # plt.imshow(s, cmap ='gray'),plt.show()
- return LSF
-
-
- # 模型参数
- mu = 1
- nu = 0.003 * 255 * 255
- num = 20
- epison = 1
- step = 0.1
- LSF = IniLSF
- for i in range(1, num):
- LSF = CV(LSF, img, mu, nu, epison, step) # 迭代
- if i % 1 == 0: # 显示分割轮廓
- plt.imshow(Image), plt.xticks([]), plt.yticks([])
- plt.contour(LSF, [0], colors='r', linewidth=2)
- plt.draw(), plt.show(block=False), plt.pause(0.01)
|