def filterR4Crisp(img, k, tc, mu, beta, img_A=None, img_B=None, img_C=None):
""" Apply filter R4-Crsip on img given pre-computed tc, mu and beta """
l = len(img)
result = np.zeros((l, l))
if img_A is None:
img_A = filterA(img, k, mu, beta)
if img_B is None:
img_B = filterB(img, k, beta)
if img_C is None:
img_C = filterC(img, k, mu, beta)
for i in range(l):
for j in range(l):
c1 = small(tc[i][j])
c2 = medium(tc[i][j])
c3 = large(tc[i][j])
if c1 == max(c1, c2, c3):
result[i][j] = img_B[i][j]
elif c2 == max(c1, c2, c3):
result[i][j] = img_A[i][j]
else:
result[i][j] = img_C[i][j]
return result
def plot(tc, imagepath):
l = len(tc)
sm_img = np.zeros((l, l))
med_img = np.zeros((l, l))
large_img = np.zeros((l, l))
for i in range(l):
for j in range(l):
sm_img[i][j] = small(tc[i][j])
med_img[i][j] = medium(tc[i][j])
large_img[i][j] = large(tc[i][j])
op_imagepath = os.path.join(
'images', 'tc',
os.path.basename(imagepath)[:-4] + '_Small.png')
normalize_and_save(op_imagepath, sm_img)
op_imagepath = os.path.join(
'images', 'tc',
os.path.basename(imagepath)[:-4] + '_Medium.png')
normalize_and_save(op_imagepath, med_img)
op_imagepath = os.path.join(
'images', 'tc',
os.path.basename(imagepath)[:-4] + '_Large.png')
normalize_and_save(op_imagepath, large_img)
def filterR4(img, k, tc, mu, beta, img_A=None, img_B=None, img_C=None):
""" Apply filter R4 on img given pre-computed tc, mu and beta """
l = len(img)
result = np.zeros((l, l))
if img_A is None:
img_A = filterA(img, k, mu, beta)
if img_B is None:
img_B = filterB(img, k, beta)
if img_C is None:
img_C = filterC(img, k, mu, beta)
for i in range(l):
for j in range(l):
c1 = small(tc[i][j])
c2 = medium(tc[i][j])
c3 = large(tc[i][j])
result[i][j] = (c1 * img_B[i][j] + c2 * img_A[i]
[j] + c3 * img_C[i][j]) / (c1 + c2 + c3)
return result