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 laplas():
header, rgb = bmp.readBmp(orig_filename)
y_cb_cr = bmp.convertYCbCr(rgb)
y_cb_cr_res = BmpFilters.BmpFilters.laplas_operator(y_cb_cr, 0)
y_cb_cr_res += 128
np.putmask(y_cb_cr_res, y_cb_cr_res < 0, 0)
np.putmask(y_cb_cr_res, y_cb_cr_res > 255, 255)
y_cb_cr_res[..., 1] = y_cb_cr_res[..., 0]
y_cb_cr_res[..., 2] = y_cb_cr_res[..., 0]
y_cb_cr[..., 1] = y_cb_cr[..., 0]
y_cb_cr[..., 2] = y_cb_cr[..., 0]
bmp.writeBmp(laplas_folder + "/laplas.bmp", header, y_cb_cr_res)
bmp.writeBmp(laplas_folder + "/orig.bmp", header, y_cb_cr)
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")
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)