def gauss_filter():
header, y_cb_cr = bmp.readBmp(gauss_noise_filename)
h, y_cb_cr_o = bmp.readBmp(original_file_y_cb_cr)
sigmaRange = np.arange(0.0001, 1.1, 0.1)
psnr = defaultdict(list)
rRange = range(1, 10)
legends = []
out_psnr = np.zeros((len(list(rRange)) + 1, (len(list(sigmaRange)) + 1)))
out_psnr[1:, 0] = list(rRange)
out_psnr[0, 1:] = list(sigmaRange)
print('orig ', bmp.psnr(y_cb_cr_o[..., 0], y_cb_cr[..., 0]))
for r in rRange:
psnr = []
for i in sigmaRange:
res = BmpFilters.BmpFilters.gauss_filter(y_cb_cr, r, i)
ps = bmp.psnr(y_cb_cr_o[..., 0], res[..., 0])
res[..., 1] = res[..., 0]
res[..., 2] = res[..., 0]
bmp.writeBmp(
gauss_filter_filename +
"/gauss r = {0:.2f} d = {1:.2f}.bmp".format(r, i), header, res)
psnr.append(ps)
p = np.array(psnr)
out_psnr[r, 1:] = p
l, = plt.plot(list(sigmaRange), psnr, label=f'R = {r}')
legends.append(l)
plt.legend(handles=legends)
plt.savefig(gauss_filter_filename + f"/psnr total.png")
np.savetxt(gauss_filter_filename + '/psnr.csv',
out_psnr,
delimiter=',',
fmt='%.2f')
def median_for_impulse():
header, y_orig = bmp.readBmp(original_file_y_cb_cr)
folder = 'impulse2/'
folder_res = 'impulse2/res/'
pa = [0.02, 0.05, 0.2, 0.3]
pb = [0.03, 0.05, 0.05, 0.2]
for a, b in zip(pa, pb):
percent = (a + b) * 100
h, y = bmp.readBmp(folder + f'impulse % = {percent}.bmp')
psnr = []
x = []
labels = []
print(f'original {percent}', bmp.psnr(y_orig, y))
x.append(0)
psnr.append(bmp.psnr(y_orig, y))
for i in range(1, 10):
res = BmpFilters.BmpFilters.median_filter(y, i)
p = bmp.psnr(y_orig[..., 0], res[..., 0])
x.append(i)
psnr.append(p)
res[..., 1] = res[..., 0]
res[..., 2] = res[..., 0]
bmp.writeBmp(folder_res + f' % {percent} r = {i}.bmp', header, res)
labels.append(plt.plot(x, psnr, label=f'r = {i}'))
plt.savefig(folder + 'psnr.png', handlers=labels)
def median():
header, y_cb_cr = bmp.readBmp(gauss_noise_filename)
header_o, y_cb_cr_o = bmp.readBmp(original_file_y_cb_cr)
psnr = []
print('orig', bmp.psnr(y_cb_cr_o[..., 0], y_cb_cr[..., 0]))
x = range(1, 10)
for r in x:
res = BmpFilters.BmpFilters.median_filter(y_cb_cr, r * 2 + 1)
res[..., 1] = res[..., 0]
res[..., 2] = res[..., 0]
psnr.append(bmp.psnr(y_cb_cr_o[..., 0], res[..., 0]))
bmp.writeBmp(median_folder + f"/median r = {r}.bmp", header, res)
np.savetxt(median_folder + "/psnr.csv",
np.array(psnr).T,
delimiter=',',
fmt='%.2f')
plt.plot(list(x), psnr)
plt.savefig(median_folder + "/psnr.png")
def avgDecimationYCbCr(rgb, n):
print(f'\n \n \n avg decimation {n}')
y = rgb.shape[0]
x = rgb.shape[1]
y_cb_cr = bmp.convertYCbCr(rgb)
resG = bmp.decimationByAverageValue(y_cb_cr[..., 1], n)
resR = bmp.decimationByAverageValue(y_cb_cr[..., 2], n)
resG2 = bmp.recoverDecimationByDeletingEven(resG, n)
resR2 = bmp.recoverDecimationByDeletingEven(resR, n)
rgb_recovered = np.zeros((y, x, 3), 'uint8')
rgb_recovered[..., 0] = y_cb_cr[..., 0]
rgb_recovered[..., 1] = resG2
rgb_recovered[..., 2] = resR2
print("psnr cb", bmp.psnr(y_cb_cr[..., 1], resG2))
print("psnr cr", bmp.psnr(y_cb_cr[..., 2], resR2))
rgb_recovered = bmp.inverseConvertYCbCr(rgb_recovered)
print("psnr blue", bmp.psnr(rgb[..., 0], rgb_recovered[..., 0]))
print("psnr green", bmp.psnr(rgb[..., 1], rgb_recovered[..., 1]))
print("psnr red", bmp.psnr(rgb[..., 2], rgb_recovered[..., 2]))
return rgb_recovered
def impulse_noise():
# hist = defaultdict(list)
# x = defaultdict(list)
# hist[0.1].append(1)
# hist[0.1].append(2)
# x[0.1].append(3)
# x[0.1].append(6)
# x[0.1].append(6)
# for (k, v), (k2, v2) in zip(hist.items(), x.items()):
# print(k, v, k2, v2)
# print('\n')
#
# return
small = 0.00001
file_name = orig_filename
header, rgb = bmp.readBmp(file_name)
y_cb_cr = bmp.convertYCbCr(rgb)
hist = defaultdict(list)
x = defaultdict(list)
rng = np.arange(0, 1.1, 0.1)
for i in rng:
for j in rng:
if i + j >= 1:
continue
if i == j == 0:
i = small
j = small
res = BmpFilters.BmpFilters.add_impulse_noise(y_cb_cr, i, j)
p = bmp.psnr(y_cb_cr[..., 0], res[..., 0])
if i == j == small:
i = 0
j = 0
x[i].append(j)
hist[i].append(p)
res[..., 1] = res[..., 0]
res[..., 2] = res[..., 0]
bmp.writeBmp(
'impulse/impulse pa = {0:.1f} pb = {1:.1f}.png'.format(
float(i), float(j)), header, res)
l = []
for (k, v), (k1, v1) in zip(x.items(), hist.items()):
leg, = plt.plot(list(v),
list(v1),
label='pa = {0:.1}'.format(float(k)))
l.append(leg)
plt.legend(handles=l)
plt.savefig('impulse/psnr.png')
def impulse_with_persent():
folder = 'impulse2/'
header, y_cb_cr = bmp.readBmp(original_file_y_cb_cr)
pa = [0.02, 0.05, 0.2, 0.3]
pb = [0.03, 0.05, 0.05, 0.2]
psnr = []
x = []
for a, b in zip(pa, pb):
res = BmpFilters.BmpFilters.add_impulse_noise(y_cb_cr, a, b)
psnr.append(bmp.psnr(y_cb_cr[..., 0], res[..., 0]))
x.append((a + b) * 100)
res[..., 1] = res[..., 0]
res[..., 2] = res[..., 0]
bmp.writeBmp(folder + f'impulse % = {(a + b) * 100}.bmp', header, res)
plt.plot(x, psnr)
plt.savefig(folder + 'psnr.png')
def gauss_noise():
header, rgb = bmp.readBmp(orig_filename)
y_cb_cr = bmp.convertYCbCr(rgb)
psnr = []
rng = range(0, 250, 10)
for i in rng:
if i == 0:
i = 0.0000001
res = BmpFilters.BmpFilters.add_gauss_noise(y_cb_cr, d=i, m=0)
res[..., 1] = res[..., 0]
res[..., 2] = res[..., 0]
p = bmp.psnr(y_cb_cr[..., 0], res[..., 0])
psnr.append(p)
if i == 0.0000001:
i = 0
bmp.writeBmp(f"gauss/gauss d ={i}.bmp", header, res)
plt.plot(list(rng), psnr)
plt.savefig(gauss_noise_psnr_filename)
def avg():
file_name = gauss_noise_filename
header, rgb = bmp.readBmp(file_name)
psnr = []
rng = range(1, 25)
y_cb_cr = bmp.convertYCbCr(rgb)
orinal_y_cb_cr = bmp.convertYCbCr(orig_pic)
for i in rng:
res = BmpFilters.BmpFilters.avg(y_cb_cr, i)
psnr.append(bmp.psnr(orinal_y_cb_cr[..., 0], res[..., 0]))
res[..., 1] = res[..., 0]
res[..., 2] = res[..., 0]
# bmp.writeBmp(moving_average_folder + f"/avg {i}.bmp", header, res)
x = np.array(list(rng), dtype='uint')
y = np.array(psnr)
np.savetxt(moving_average_folder + "/psnr.csv", (x, y),
delimiter=',',
fmt='%.2f')
plt.plot(list(rng), psnr)
plt.savefig(moving_average_folder + "/psnr.png")
header, rgb = bmp.readBmp(in_file)
a = rgb[...][...][0] - rgb[...][...][1]
print(np.max(a))
sys.exit(0)
y_cb_cr = bmp.convertYCbCr(rgb)
cbDecimated = bmp.decimationByDeletingEven(y_cb_cr[..., 1], 4)
crDecimated = bmp.decimationByDeletingEven(y_cb_cr[..., 2], 4)
cbRecovered = bmp.recoverDecimationByDeletingEven(cbDecimated, 4)
crRecovered = bmp.recoverDecimationByDeletingEven(crDecimated, 4)
y_cb_cr[..., 1] = cbRecovered
y_cb_cr[..., 2] = crRecovered
rgb2 = bmp.inverseConvertYCbCr(y_cb_cr)
bmp.writeBmp(res_file_path + "testRec.bmp", header, rgb2)
print(bmp.psnr(rgb[..., 0], rgb2[..., 0]))
print(bmp.psnr(rgb[..., 1], rgb2[..., 1]))
print(bmp.psnr(rgb[..., 2], rgb2[..., 2]))
sys.stdout = open('console.txt', 'w')
b = rgb[..., 0]
g = rgb[..., 1]
r = rgb[..., 2]
# [4] b
getAutoCor(r, "red")
getAutoCor(g, "green")
getAutoCor(b, "blue")
# 7 [y_cb_cr]
y_cb_cr = bmp.convertYCbCr(rgb)
rgbRec = bmp.inverseConvertYCbCr(y_cb_cr)