def calculateOrientation21(image, w):
# smooth image with gaussian blur
kernel = getgauss(3, 5)
smoothed_im = applyfil(image, kernel)
# calculates gradients with sobel filter
dx, dy = gradientSobel(smoothed_im)
# smooth gradients
Gx = applyfil(dx, kernel)
Gy = applyfil(dy, kernel)
# compute gradient magnitude
vx = 2 * Gx * Gy
vy = (Gx**2) - (Gy**2)
#G = np.sqrt(Gxx + Gyy)
# calculate theta
theta = 0.5 * np.arctan2(vx, vy)
# smoothens the angles
tabx = np.zeros((w, w), np.uint8)
taby = np.zeros((w, w), np.uint8)
for p in range(0, w - 1):
for q in range(0, w - 1):
tabx[p, q] = math.cos(2 * theta[p, q])
taby[p, q] = math.sin(2 * theta[p, q])
theta[p, q] = np.pi + math.atan2(tabx[p, q], taby[p, q])
return theta[w / 2, w / 2]
def calculateOrientation3(image, w):
"""
:param image: image segment
:param w: blocksize
:return: block orientation
"""
# smooth image with gaussian blur
kernel = getgauss(5, 5)
smoothed_im = applyfil(image, kernel)
# calculates gradients with sobel filter
dx, dy = gradientSobel(smoothed_im)
# smooth gradients
Gx = applyfil(dx, kernel)
Gy = applyfil(dy, kernel)
vx = vy = 0
# compute gradient magnitude
for p in range(0, w - 1):
for q in range(0, w - 1):
if Gx[p, q] == 0:
Gx[p, q] = 1
if Gy[p, q] == 0:
Gy[p, q] = 1
if Gx[p, q] == 0 and Gy[p, q] == 0:
Gx[p, q] = Gy[p, q] = 1
vx += 2 * Gx[p, q] * Gy[p, q]
vy += (Gx[p, q]**2) - (Gy[p, q]**2)
# G = np.sqrt(Gxx + Gyy)
# calculate theta
theta = 0.5 * np.arctan2(vx, vy)
# print(theta)
# smoothens the angles
if (theta < 0 and vx < 0) or (vx > 0 and theta > 0):
k = 0.5
if (theta < 0 and vx > 0):
k = 1
if (theta > 0 and vx < 0):
k = 0
tabx = math.cos(2 * theta)
taby = math.sin(2 * theta)
theta = k * np.pi + math.atan2(tabx, taby)
return theta * 180 / np.pi
def binarize(img):
gray = sharpen(img)
kernal = getgauss(3, -20)
img2 = applyfil(img, kernal)
img3 = backtoim(img2)
th3 = cv2.adaptiveThreshold(img3, 1, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 11, 2) #best
return th3
def binarize3(img):
img = sharpen(img)
kernal = getgauss(3, -20)
img = applyfil(img, kernal)
img = backtoim(img)
#th3 = cv2.adaptiveThreshold(img3, 254, cv2.ADAPTIVE_THRESH_GAUSSIAN_C ,cv2.THRESH_BINARY,11,2 ) #best
th3 = cv2.adaptiveThreshold(img, 1, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 255, 0)
return th3
def binarize2(img):
gray = sharpen(img)
#print(type(gray))
kernal = getgauss(3, -20)
img2 = applyfil(img, kernal)
#img2 = cv2.GaussianBlur(gray, (3, 3), -20)
img3 = backtoim(img2)
#th3 = cv2.adaptiveThreshold(img3, 1, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 255, 0) #original
th3 = cv2.adaptiveThreshold(img3, 1, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 11, 2) #best
#blur = cv2.GaussianBlur(img, (5, 5), 0)
#ret3, th3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
#ret, th3 = cv2.threshold(img, 10, 255, cv2.THRESH_BINARY)
#th3 = cv2.adaptiveThreshold(img3, 1, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 255, 0)
#print(img2.shape)
return th3