def spEdges(self, Kx) :
self.I2 = self.Ilast
if self.Ilast.ndim == 3 :
self.onGryscl()
self.I2 = self.Ilast
tempX = myFunc.conv2(self.I2, Kx)
tempY = myFunc.conv2(self.I2, np.transpose(Kx))
temp = np.abs(tempX) + np.abs(tempY) # calculate the new image
self.onPanel2(temp)
def spKer(self, kernel,offset = 0):
self.I2 = self.Ilast
kernel = np.float32(kernel)
self.I2= np.float32(self.I2)
if self.Ilast.ndim == 3 :
temp = np.zeros(self.I2.shape, dtype = self.I2.dtype)
temp[:,:,0] = myFunc.conv2(self.I2[:,:,0], kernel)
temp[:,:,1] = myFunc.conv2(self.I2[:,:,1], kernel)
temp[:,:,2] = myFunc.conv2(self.I2[:,:,2], kernel)
else:
temp = myFunc.conv2(self.I2, kernel)
np.putmask(temp, temp > 255.0, 255.0) # overfloe check
np.putmask(temp, temp < 0.0, 0.0) # check underflow
self.onPanel2(np.uint8(temp))
def canny(im):
#blurring the image
# with median filter of 5*5
temp = myFunc.medFilterSimple(im, 2)
# High pass derivative based filters
# with sobel operator
Kx = [[1, 0, -1], [2, 0, -2], [1, 0, -1]]
Gx = myFunc.conv2(temp, Kx)
Gy = myFunc.conv2(temp, np.transpose(Kx))
Grad = np.hypot(Gx, Gy)
theta = np.arctan2(Gx, Gy)
edge_map = non_maximal_edge_suppresion(Grad,theta)
edge_map = np.logical_and(edge_map, Grad > 50)
m, n = im.shape
gnh = np.zeros_like(edge_map)
gnl = np.zeros_like(edge_map)
th, tl = 0.2 * np.max(Grad), 0.1 * np.max(Grad)
for x in range(1,m-1):
for y in range(1,n-1):
if Grad[x][y]>=th:
gnh[x][y]=edge_map[x][y]
if Grad[x][y]>=tl:
gnl[x][y]=edge_map[x][y]
gnl = gnl-gnh
def traverse(i, j):
x = [-1, 0, 1, -1, 1, -1, 0, 1]
y = [-1, -1, -1, 0, 0, 1, 1, 1]
for k in range(8):
if gnh[i+x[k]][j+y[k]]==0 and gnl[i+x[k]][j+y[k]]!=0:
gnh[i+x[k]][j+y[k]]=1
traverse(i+x[k], j+y[k])
for i in range(1, m-1):
for j in range(1, n-1):
if gnh[i][j]:
gnh[i][j]=1
traverse(i, j)
return np.uint8(gnh) * 255
