def find_most_frequency(L):
d = {}
for i in L:
if i not in d.keys():
d[i] = L.count(i)
A = sorted(d.items(), key=operator.itemgetter(1), reverse=True)
if len(A) == 1:
max = A[0][0]
fre = A[0][1]
elif A[0][1] == A[1][1]:
max = fm.find_median(L)
fre = A[0][1]
else:
max = A[0][0]
fre = A[0][1]
# print(d.items())
# print(A)
# print(len(A))
# print(A[0])
# print(A[0][1])
# print(max)
return (max, fre)
def fitness_test(G, n, name):
data1 = op.read_pgm(name)
[row, column] = data1[1]
array1 = data1[0]
image1 = array1.reshape(row, column)
data2 = op.read_pgm(name)
[row, column] = data2[1]
array2 = data2[0]
image2 = array2.reshape(row, column)
after_smooth = np.zeros([row, column])
C = np.zeros(n * n)
c = int((n - 1) / 2)
for m in range(row - n):
for k in range(column - n):
q = 0
filter_array = np.zeros([n * n])
for i in range(m, m + n):
for j in range(k, k + n):
filter_array[q] = int(image2[i][j])
q += 1
filter_list = filter_array.tolist()
for x in range(len(filter_list)):
C[x] = G[x] * filter_list[x]
Q = C.tolist()
K = fmf.find_most_frequency(Q)
if K[0] == 0 and K[1] > (n * n - 1) / 2:
M = 0
else:
med = fd.find_median(C)
for y in range(n * n):
if C[y] == med:
M = filter_list[y]
after_smooth[m + c][k + c] = M
total = row * column
same = 0
for a in range(row):
for b in range(column):
if after_smooth[a][b] == image1[a][b]:
same += 1
fitness = float(same / total)
return (fitness)
def find_most_frequency(L):
d = {}
for i in L:
if i not in d.keys():
d[i] = L.count(i) # create a dictionary to store all the value
A = sorted(
d.items(), key=operator.itemgetter(1), reverse=True
) # sort the value from max to min according to the frequency of occurrence
if len(A) == 1:
max = A[0][0]
fre = A[0][1]
# if two maximum value's frequency of occurrence is equal, then use the median value to replace the maximum value
elif A[0][1] == A[1][1]:
max = fm.find_median(L)
fre = A[0][1]
else:
max = A[0][0]
fre = A[0][1]
return (max, fre)
def three_d_median_filter(a, b, t, n):
data = tda.three_d(a, b, t, n)
three_d_array = data[1]
[z, y, x] = data[0]
start = time.time()
filter_three_d_array = np.zeros([n * n * n])
c = int(((n - 1) / 2))
three_d_after_smooth = np.zeros([z, y, x])
for d in range(z - n):
for e in range(y - n):
for f in range(x - n):
q = 0
# build the filter
filter_three_d_array = np.zeros([n * n * n])
for m in range(d, d + n):
for j in range(e, e + n):
for i in range(f, f + n):
filter_three_d_array[q] = three_d_array[m][j][i]
q += 1
filter_three_d_list = filter_three_d_array.tolist()
Z = fmf.find_most_frequency(filter_three_d_list)
z = Z[1]
# if the more than half pixels have the same value, the median is equal to the highest frequency of occurrence value
if z > (n * n * n - 1) / 2:
med = Z[0]
else:
med = fd.find_median(filter_three_d_array)
med = int(med)
three_d_after_smooth[d + c - 1][e + c - 1][f + c - 1] = med
elapsed = (time.time() - start)
print('time used', elapsed)
return (three_d_after_smooth.shape, three_d_after_smooth)
def testFindMedianWithDuplicateAndNegativeValues(self):
numbers = ["-1"] * 100 + range(50)
self.create_input_file(numbers)
expected_median = -1
actual_median = find_median.find_median(self.test_filename)
self.assertEqual(expected_median, actual_median)
def testFindMedianForSmallSet(self):
self.create_input_file(range(50))
expected_median = 24
actual_median = find_median.find_median(self.test_filename)
self.assertEqual(expected_median, actual_median)
def testFindMedianForUnEqualBucketSet(self):
self.create_input_file(range(502))
expected_median = 250
actual_median = find_median.find_median(self.test_filename)
self.assertEqual(expected_median, actual_median)
def test_find_median(self): self.assertEqual(find_median.find_median([0, 1, 2, 4, 6, 5, 3]), 3)
def two_d_improve_median_filter(name):
t = int(input('the number of times to increase the width and height：'))
n = int(input('the size of filter:'))
k = int((n - 1) / 2)
# A=[9,1,2,3,4,5,6,7,8,5,6,7]
# array_A=np.array(A)
# image=array_A.reshape(4,3)
# print(image)
data = op.read_pgm(name)
[row, column] = data[1]
list = data[0]
array = np.array(list)
image = array.reshape(row, column)
# plt.imshow(image)
# plt.show()
new_image = bo.border(name, n, t)
# plt.imshow(new_image)
# plt.show()
# [row,column]=image.shape #get the original image shape
[new_row, new_column] = new_image.shape #get the newimage shape
c = int(((n - 1) / 2))
after_smooth = np.zeros([new_row - c, new_column - c
]) #the array to contain the after smooth array
filter_array = np.zeros([n * n])
# print(filter_array)
start = time.time()
for m in range(new_row - n):
for k in range(new_column - n):
q = 0
filter_array = np.zeros([n * n])
for i in range(m, m + n):
for j in range(k, k + n):
filter_array[q] = int(new_image[i][j])
q += 1
# # normal
# med = fd.find_median(filter_array)
# med = int(med)
# # print(med)
# after_smooth[m][k] = med
# use the find most frequency
filter_list = filter_array.tolist()
Z = fmf.find_most_frequency(filter_list)
z = Z[1]
if z > (n * n - 1) / 2:
med = Z[0]
else:
med = fd.find_median(filter_array)
med = int(med)
# print(med)
after_smooth[m][k] = med
# # use the counter
# filter_list = filter_array.tolist()
# Max=Counter(filter_list).most_common(1)
# z=Max[0][1]
# if z > (n * n - 1) / 2:
# med = Max[0][0]
# else:
# med = fd.find_median(filter_array)
# med = int(med)
# # print(med)
# after_smooth[m][k] = med
elapsed = (time.time() - start)
print('time used', elapsed)
# file = open('2Dcheck\\AfterimfCircleWithTriangle1.pgm', 'w')
# [m, n] = after_smooth.shape
# max = 2
# file.write('P2\n' + str(n) + ' ' + str(m) + '\n' + str(max) + '\n')
# for i in range(0, m):
# for j in range(0, n):
# file.write(str(int(after_smooth[i][j])) + '\n')
# file.close()
plt.imshow(after_smooth)
plt.show()
return (after_smooth)
