def solve_cs(ori_img, denoiser, reshape_img_fun, compress_ratio,
noise_mean=0, noise_std=0.,
alpha=0.3, lambda_l1=0.1, max_iter=100, solver_tol=1e-2):
import cs as problem
x_shape = ori_img.shape
(A_fun, AT_fun, A) = problem.setup(x_shape, compress_ratio)
y, noise = add_noise.exe(A_fun(ori_img), noise_mean=noise_mean, noise_std=noise_std)
info = {'ori_img': ori_img, 'y': y, 'noise': noise, 'compress_ratio': compress_ratio,
'noise_std': noise_std,
'alpha': alpha, 'max_iter': max_iter, 'solver_tol': solver_tol, 'lambda_l1': lambda_l1}
# save the problem
base_folder = '%s/cs_ratio%.2f_std%.2f' % (result_folder, compress_ratio, noise_std)
if not os.path.exists(base_folder):
os.makedirs(base_folder)
filename = '%s/settings.mat' % base_folder
sp.io.savemat(filename, info)
filename = '%s/ori_img.jpg' % base_folder
sp.misc.imsave(filename, sp.misc.imresize((reshape_img(np.clip(ori_img, 0.0, 1.0))*255).astype(np.uint8), 4.0, interp='nearest'))
if run_ours:
# ours
folder = '%s/ours_alpha%f' % (base_folder, alpha)
if not os.path.exists(folder):
os.makedirs(folder)
(x, z, u, infos) = solver.solve(y, A_fun, AT_fun, denoiser, reshape_img_fun, folder,
show_img_progress=show_img_progress, alpha=alpha,
max_iter=max_iter, solver_tol=solver_tol)
save_results(folder, infos, x, z, u)
if run_l1:
# wavelet l1
folder = '%s/l1_lambdal1%f_alpha%f' % (base_folder, lambda_l1, alpha_l1)
if not os.path.exists(folder):
os.makedirs(folder)
(x, z, u, infos) = solver_l1.solve(y, A_fun, AT_fun, lambda_l1, reshape_img_fun, folder,
show_img_progress=show_img_progress, alpha=alpha_l1,
max_iter=max_iter_l1, solver_tol=solver_tol_l1)
save_results(folder, infos, x, z, u)
z1 = reshape_img(np.clip(z, 0.0, 1.0))
ori_img1 = reshape_img(np.clip(ori_img, 0.0, 1.0))
psnr = 10*np.log10( 1.0 /np.linalg.norm(z1-ori_img1)**2*np.prod(z1.shape))
img = Image.fromarray( sp.misc.imresize(np.uint8(z1*255), 4.0, interp='nearest' ) )
draw = ImageDraw.Draw(img)
#font = ImageFont.truetype(font='tnr.ttf', size=50)
#draw.text((135, 200), "%.2f"%psnr, (255,255,255), font=font)
filename = '%s/z.jpg' % folder
img.save(filename)
def solve_denoising_dropping(ori_img, denoiser, reshape_img_fun, drop_prob=0.3,
noise_mean=0, noise_std=0.1,
alpha=0.3, lambda_l1=0.1, max_iter=100, solver_tol=1e-2):
import inpaint as problem
x_shape = ori_img.shape
(A_fun, AT_fun, mask) = problem.setup(x_shape, drop_prob=drop_prob)
y, noise = add_noise.exe(A_fun(ori_img), noise_mean=noise_mean, noise_std=noise_std)
if show_img_progress:
fig = plt.figure('denoise')
plt.gcf().clear()
fig.canvas.set_window_title('denoise')
plt.subplot(1,2,1)
plt.imshow(reshape_img_fun(ori_img), interpolation='nearest')
plt.title('ori_img')
plt.subplot(1,2,2)
plt.imshow(reshape_img_fun(y), interpolation='nearest')
plt.title('y')
plt.pause(0.00001)
info = {'ori_img': ori_img, 'y': y, 'noise': noise, 'mask': mask, 'drop_prob': drop_prob, 'noise_std': noise_std,
'alpha': alpha, 'max_iter': max_iter, 'solver_tol': solver_tol, 'lambda_l1': lambda_l1}
# save the problem
base_folder = '%s/denoise_ratio%.2f_std%.2f' % (result_folder, drop_prob, noise_std)
if not os.path.exists(base_folder):
os.makedirs(base_folder)
filename = '%s/settings.mat' % base_folder
sp.io.savemat(filename, info)
filename = '%s/y.jpg' % base_folder
sp.misc.imsave(filename, sp.misc.imresize((reshape_img(np.clip(y, 0.0, 1.0))*255).astype(np.uint8), 4.0, interp='nearest'))
filename = '%s/ori_img.jpg' % base_folder
sp.misc.imsave(filename, sp.misc.imresize((reshape_img(np.clip(ori_img, 0.0, 1.0))*255).astype(np.uint8), 4.0, interp='nearest'))
if run_ours:
# ours
folder = '%s/ours_alpha%f' % (base_folder, alpha)
if not os.path.exists(folder):
os.makedirs(folder)
(x, z, u, infos) = solver.solve(y, A_fun, AT_fun, denoiser, reshape_img_fun, folder,
show_img_progress=show_img_progress, alpha=alpha,
max_iter=max_iter, solver_tol=solver_tol)
save_results(folder, infos, x, z, u)
if run_l1:
# wavelet l1
folder = '%s/l1_lambdal1%f_alpha%f' % (base_folder, lambda_l1, alpha_l1)
if not os.path.exists(folder):
os.makedirs(folder)
(x, z, u, infos) = solver_l1.solve(y, A_fun, AT_fun, lambda_l1, reshape_img_fun, folder,
show_img_progress=show_img_progress, alpha=alpha_l1,
max_iter=max_iter_l1, solver_tol=solver_tol_l1)
save_results(folder, infos, x, z, u)
z1 = reshape_img(np.clip(z, 0.0, 1.0))
ori_img1 = reshape_img(np.clip(ori_img, 0.0, 1.0))
psnr = 10*np.log10( 1.0 /np.linalg.norm(z1-ori_img1)**2*np.prod(z1.shape))
img = Image.fromarray( sp.misc.imresize(np.uint8(z1*255), 4.0, interp='nearest' ) )
draw = ImageDraw.Draw(img)
#font = ImageFont.truetype(font='tnr.ttf', size=50)
#draw.text((135, 200), "%.2f"%psnr, (255,255,255), font=font)
filename = '%s/z.jpg' % folder
img.save(filename)
def solve_superres(ori_img,
denoiser,
reshape_img_fun,
resize_ratio=0.5,
noise_mean=0,
noise_std=0.,
alpha=0.3,
lambda_l1=0.1,
max_iter=100,
solver_tol=1e-2):
import superres as problem
x_shape = ori_img.shape
(A_fun, AT_fun) = problem.setup(x_shape, resize_ratio=resize_ratio)
y, noise = add_noise.exe(A_fun(ori_img),
noise_mean=noise_mean,
noise_std=noise_std)
bicubic_img = cv2.resize(y[0],
dsize=(ori_img.shape[1], ori_img.shape[2]),
interpolation=cv2.INTER_CUBIC)
if show_img_progress:
fig = plt.figure('superres')
plt.gcf().clear()
fig.canvas.set_window_title('superres')
plt.subplot(1, 3, 1)
plt.imshow(reshape_img_fun(ori_img), interpolation='nearest')
plt.title('ori_img')
plt.subplot(1, 3, 2)
plt.imshow(reshape_img_fun(y), interpolation='nearest')
plt.title('y')
plt.subplot(1, 3, 3)
plt.imshow(np.clip(bicubic_img, 0, 255), interpolation='nearest')
plt.title('bicubic')
plt.pause(0.00001)
bicubic_img = bicubic_img.astype(float) / 255.0
l2_dis = np.mean(np.square(ori_img[0] - bicubic_img))
print('bicubic err = %f' % (l2_dis))
info = {
'ori_img': ori_img,
'y': y,
'bicubic': bicubic_img,
'noise': noise,
'resize_ratio': resize_ratio,
'noise_std': noise_std,
'alpha': alpha,
'max_iter': max_iter,
'solver_tol': solver_tol,
'lambda_l1': lambda_l1
}
# save the problem
base_folder = '%s/superres_ratio%.2f_std%.2f' % (
result_folder, resize_ratio, noise_std)
if not os.path.exists(base_folder):
os.makedirs(base_folder)
filename = '%s/settings.mat' % base_folder
sp.io.savemat(filename, info)
filename = '%s/y.jpg' % base_folder
cv2.imwrite(
filename,
cv2.resize(
(reshape_img(np.clip(y, 0.0, 1.0)) * 255).astype(np.uint8),
None,
fx=1.0,
fy=1.0,
interpolation=cv2.INTER_NEAREST))
filename = '%s/ori_img.jpg' % base_folder
cv2.imwrite(
filename,
cv2.resize((reshape_img(np.clip(ori_img, 0.0, 1.0)) * 255).astype(
np.uint8),
None,
fx=1.0,
fy=1.0,
interpolation=cv2.INTER_NEAREST))
filename = '%s/bicubic_img.jpg' % base_folder
cv2.imwrite(
filename,
cv2.resize((bicubic_img * 255).astype(np.uint8),
None,
fx=1.0,
fy=1.0,
interpolation=cv2.INTER_NEAREST))
if run_ours:
# ours
folder = '%s/ours_alpha%f' % (base_folder, alpha)
if not os.path.exists(folder):
os.makedirs(folder)
(x, z, u,
infos) = solver.solve(y,
A_fun,
AT_fun,
denoiser,
reshape_img_fun,
folder,
show_img_progress=show_img_progress,
alpha=alpha,
max_iter=max_iter,
solver_tol=solver_tol)
save_results(folder, infos, x, z, u)
if run_l1:
# wavelet l1
folder = '%s/l1_lambdal1%f_alpha%f' % (base_folder, lambda_l1,
alpha_l1)
if not os.path.exists(folder):
os.makedirs(folder)
(x, z, u,
infos) = solver_l1.solve(y,
A_fun,
AT_fun,
lambda_l1,
reshape_img_fun,
folder,
show_img_progress=show_img_progress,
alpha=alpha_l1,
max_iter=max_iter_l1,
solver_tol=solver_tol_l1)
save_results(folder, infos, x, z, u)
z1 = reshape_img(np.clip(z, 0.0, 1.0))
ori_img1 = reshape_img(np.clip(ori_img, 0.0, 1.0))
psnr = 10 * np.log10(
1.0 / np.linalg.norm(z1 - ori_img1)**2 * np.prod(z1.shape))
img = Image.fromarray(
cv2.resize(np.uint8(z1 * 255),
None,
fx=1.0,
fy=1.0,
interpolation=cv2.INTER_NEAREST))
draw = ImageDraw.Draw(img)
#font = ImageFont.truetype(font='tnr.ttf', size=50)
#draw.text((135, 200), "%.2f"%psnr, (255,255,255), font=font)
filename = '%s/z.jpg' % folder
img.save(filename)
def solve_cs(ori_img,
denoiser,
reshape_img_fun,
compress_ratio,
noise_mean=0,
noise_std=0.,
alpha=0.3,
lambda_l1=0.1,
max_iter=100,
solver_tol=1e-2):
import cs as problem
x_shape = ori_img.shape
(A_fun, AT_fun, A) = problem.setup(x_shape, compress_ratio)
y, noise = add_noise.exe(A_fun(ori_img),
noise_mean=noise_mean,
noise_std=noise_std)
info = {
'ori_img': ori_img,
'y': y,
'noise': noise,
'compress_ratio': compress_ratio,
'noise_std': noise_std,
'alpha': alpha,
'max_iter': max_iter,
'solver_tol': solver_tol,
'lambda_l1': lambda_l1
}
# save the problem
base_folder = '%s/cs_ratio%.2f_std%.2f' % (result_folder,
compress_ratio, noise_std)
if not os.path.exists(base_folder):
os.makedirs(base_folder)
filename = '%s/settings.mat' % base_folder
sp.io.savemat(filename, info)
filename = '%s/ori_img.jpg' % base_folder
cv2.imwrite(
filename,
cv2.resize((reshape_img(np.clip(ori_img, 0.0, 1.0)) * 255).astype(
np.uint8),
None,
fx=1.0,
fy=1.0,
interpolation=cv2.INTER_NEAREST))
if run_ours:
# ours
folder = '%s/ours_alpha%f' % (base_folder, alpha)
if not os.path.exists(folder):
os.makedirs(folder)
(x, z, u,
infos) = solver.solve(y,
A_fun,
AT_fun,
denoiser,
reshape_img_fun,
folder,
show_img_progress=show_img_progress,
alpha=alpha,
max_iter=max_iter,
solver_tol=solver_tol)
save_results(folder, infos, x, z, u)
if run_l1:
# wavelet l1
folder = '%s/l1_lambdal1%f_alpha%f' % (base_folder, lambda_l1,
alpha_l1)
if not os.path.exists(folder):
os.makedirs(folder)
(x, z, u,
infos) = solver_l1.solve(y,
A_fun,
AT_fun,
lambda_l1,
reshape_img_fun,
folder,
show_img_progress=show_img_progress,
alpha=alpha_l1,
max_iter=max_iter_l1,
solver_tol=solver_tol_l1)
save_results(folder, infos, x, z, u)
z1 = reshape_img(np.clip(z, 0.0, 1.0))
ori_img1 = reshape_img(np.clip(ori_img, 0.0, 1.0))
psnr = 10 * np.log10(
1.0 / np.linalg.norm(z1 - ori_img1)**2 * np.prod(z1.shape))
img = Image.fromarray(
cv2.resize(np.uint8(z1 * 255),
None,
fx=1.0,
fy=1.0,
interpolation=cv2.INTER_NEAREST))
draw = ImageDraw.Draw(img)
#font = ImageFont.truetype(font='tnr.ttf', size=50)
#draw.text((135, 200), "%.2f"%psnr, (255,255,255), font=font)
filename = '%s/z.jpg' % folder
img.save(filename)
def solve_inpaint_block(ori_img,
denoiser,
reshape_img_fun,
box_size=1,
total_box=1,
noise_mean=0,
noise_std=0.,
alpha=0.3,
lambda_l1=0.1,
max_iter=100,
solver_tol=1e-2):
import inpaint_block as problem
x_shape = ori_img.shape
(A_fun, AT_fun, mask) = problem.setup(x_shape,
box_size=box_size,
total_box=total_box)
y, noise = add_noise.exe(A_fun(ori_img),
noise_mean=noise_mean,
noise_std=noise_std)
if show_img_progress:
fig = plt.figure('inpaint')
plt.gcf().clear()
fig.canvas.set_window_title('inpaint')
plt.subplot(1, 2, 1)
plt.imshow(reshape_img_fun(ori_img), interpolation='nearest')
plt.title('ori_img')
plt.subplot(1, 2, 2)
plt.imshow(reshape_img_fun(y), interpolation='nearest')
plt.title('y')
plt.pause(0.00001)
info = {
'ori_img': ori_img,
'y': y,
'noise': noise,
'mask': mask,
'box_size': box_size,
'total_box': total_box,
'noise_std': noise_std,
'alpha': alpha,
'max_iter': max_iter,
'solver_tol': solver_tol,
'lambda_l1': lambda_l1
}
# save the problem
base_folder = '%s/inpaint_bs%d_tb%d_std%.2f' % (
result_folder, box_size, total_box, noise_std)
if not os.path.exists(base_folder):
os.makedirs(base_folder)
filename = '%s/settings.mat' % base_folder
sp.io.savemat(filename, info)
filename = '%s/y.jpg' % base_folder
cv2.imwrite(
filename,
cv2.resize(
(reshape_img(np.clip(y, 0.0, 1.0)) * 255).astype(np.uint8),
None,
fx=1.0,
fy=1.0,
interpolation=cv2.INTER_NEAREST))
filename = '%s/ori_img.jpg' % base_folder
cv2.imwrite(
filename,
cv2.resize((reshape_img(np.clip(ori_img, 0.0, 1.0)) * 255).astype(
np.uint8),
None,
fx=1.0,
fy=1.0,
interpolation=cv2.INTER_NEAREST))
if run_ours:
# ours
folder = '%s/ours_alpha%f' % (base_folder, alpha)
if not os.path.exists(folder):
os.makedirs(folder)
(x, z, u,
infos) = solver.solve(y,
A_fun,
AT_fun,
denoiser,
reshape_img_fun,
folder,
show_img_progress=show_img_progress,
alpha=alpha,
max_iter=max_iter,
solver_tol=solver_tol)
save_results(folder, infos, x, z, u)
if run_l1:
# wavelet l1
folder = '%s/l1_lambdal1%f_alpha%f' % (base_folder, lambda_l1,
alpha_l1)
if not os.path.exists(folder):
os.makedirs(folder)
(x, z, u,
infos) = solver_l1.solve(y,
A_fun,
AT_fun,
lambda_l1,
reshape_img_fun,
folder,
show_img_progress=show_img_progress,
alpha=alpha_l1,
max_iter=max_iter_l1,
solver_tol=solver_tol_l1)
save_results(folder, infos, x, z, u)
z1 = reshape_img(np.clip(z, 0.0, 1.0))
ori_img1 = reshape_img(np.clip(ori_img, 0.0, 1.0))
psnr = 10 * np.log10(
1.0 / np.linalg.norm(z1 - ori_img1)**2 * np.prod(z1.shape))
img = Image.fromarray(
cv2.resize(np.uint8(z1 * 255),
None,
fx=1.0,
fy=1.0,
interpolation=cv2.INTER_NEAREST))
draw = ImageDraw.Draw(img)
#font = ImageFont.truetype(font='tnr.ttf', size=50)
#draw.text((135, 200), "%.2f"%psnr, (255,255,255), font=font)
filename = '%s/z.jpg' % folder
img.save(filename)