def koponging(bw):
bwr = bw.copy()
# remove filler
rollup, rolldown, rollleft, rollright = nph.roll_all(bwr)
bwr -= bwr * rolldown * rollup * rollright * rollleft
# remove zigzag
rollup, rollright = nph.roll_up(bwr), nph.roll_right(bwr)
rolldownleft = nph.roll_left(nph.roll_down(bwr))
bwr -= bwr * rollup * rollright * (rolldownleft == False)
rollright, rolldown = nph.roll_right(bwr), nph.roll_down(bwr)
rollupleft = nph.roll_left(nph.roll_up(bwr))
bwr -= bwr * rollright * rolldown * (rollupleft == False)
rolldown, rollleft = nph.roll_down(bwr), nph.roll_left(bwr)
rollupright = nph.roll_right(nph.roll_up(bwr))
bwr -= bwr * rolldown * rollleft * (rollupright == False)
rollleft, rollup = nph.roll_left(bwr), nph.roll_up(bwr)
rolldownright = nph.roll_right(nph.roll_down(bwr))
bwr -= bwr * rollleft * rollup * (rolldownright == False)
return bwr
def gaussian(img):
# kernel = [1, 3, 5, 3, 1]
kernel = [1, 4, 7, 9, 7, 4, 1]
kernel_count = len(kernel)
kernel_half = (kernel_count-1)/2
rolls = range(0, len(kernel))
result = np.zeros(img.shape, dtype=np.float)
rolls[kernel_half] = img.astype(np.float)
for i in range(1, kernel_half+1):
rolls[kernel_half+i] = nph.roll_left(rolls[kernel_half+i-1], img[:,-1].copy())
rolls[kernel_half-i] = nph.roll_right(rolls[kernel_half-i+1], img[:,0].copy())
for i in range(0, kernel_count):
result += rolls[i] * kernel[i]
result /= sum(kernel)
rolls[kernel_half] = result.copy()
result = 0
for i in range(1, kernel_half+1):
rolls[kernel_half+i] = nph.roll_up(rolls[kernel_half+i-1], img[-1].copy())
rolls[kernel_half-i] = nph.roll_down(rolls[kernel_half-i+1], img[0].copy())
for i in range(0, kernel_count):
result += rolls[i] * kernel[i]
result /= sum(kernel)
return result.round().astype(np.uint8)
def create_rolls(gray, kernel):
mid = (len(kernel)-1)/2
rolls = []
for y in kernel:
roll = []
for x in y:
roll.append(gray.copy().astype(np.float))
rolls.append(roll)
for i in range(0, mid):
for j in range(0, i+1):
for k in range(0, len(rolls)):
rolls[j][k][:] = nph.roll_down(rolls[j][k], rolls[mid][j][0].copy())
for i in range(len(rolls)-1, mid, -1):
for j in range(len(rolls)-1, i-1, -1):
for k in range(0, len(rolls)):
rolls[j][k][:] = nph.roll_up(rolls[j][k], rolls[mid][j][-1].copy())
for i in range(0, mid):
for j in range(0, i+1):
for k in range(0, len(rolls)):
rolls[k][j][:] = nph.roll_right(rolls[k][j], rolls[k][mid][:, 0].copy())
for i in range(len(rolls)-1, mid, -1):
for j in range(len(rolls)-1, i-1, -1):
for k in range(0, len(rolls)):
rolls[k][j][:] = nph.roll_left(rolls[k][j], rolls[k][mid][:, -1].copy())
return rolls
def mulsum(gray, operator_baris, operator_kolom, y, x):
rolled = np.array(gray, dtype=np.int32)
if y == 0:
rolled[:] = nph.roll_down(rolled)
elif y == 2:
rolled[:] = nph.roll_up(rolled)
if x == 0:
rolled[:] = nph.roll_right(rolled)
elif x == 2:
rolled[:] = nph.roll_left(rolled)
return rolled * (operator_baris[y][x] + operator_kolom[y][x])