marked_img = imgs_test.mark_pixels_on_full_image(img_new, order_now)
return imgs_test.images[:, sample].reshape(
imgs_test.img_size), img_new, marked_img, order
original_imgs = list()
num_c = list(chi).__len__()
num_f = list(nums_features).__len__()
imgs = list()
titles = list()
num_s_tot = 0
for nc in range(num_c):
num_s = list(samples).__len__()
num_s_tot += num_s
para['chi'] = chi[nc]
a, para = gtn_one_class(para)
b = TNmachineLearning.MachineLearningFeatureMap(para['d'], para['dataset'])
b.load_data(data_path=b.data_path,
file_sample='t10k-images.idx3-ubyte',
file_label='t10k-labels.idx1-ubyte')
b.select_samples([para['class']])
for ns in range(num_s):
imgs_now = list()
print('Processing the image ' + str(samples[ns]) + ' in class ' +
str(para['class']))
for n in range(num_f):
img0, img1, marked_img, order = get_marked_imgs(
a, b, nums_features[n], samples[ns], para, select_way)
if n == 0 and nc == 0:
original_imgs.append(img0.copy())
if if_mark:
if var_name is not 'which_class':
b.load_data(data_path=os.path.join(b.project_path, '..\\..\\MNIST\\' + para['dataset'] + '\\'),
file_sample='t10k-images.idx3-ubyte',
file_label='t10k-labels.idx1-ubyte', is_normalize=True)
b.select_samples([para['class']])
is_order_calculated = False
for t in range(var_values.size):
# Modify parameter
print('For ' + var_name + ' = ' + str(var_values[t]))
exec(var_name + ' = ' + str(var_values[t]))
if var_name in para:
exec('para[\'' + var_name + '\'] = ' + str(var_values[t]))
print('Train the generative TN')
a, para_gtn = gtn_one_class(para)
if var_name is 'which_class':
b.load_data(data_path='..\\..\\..\\MNIST\\' + para['dataset'] + '\\',
file_sample='t10k-images.idx3-ubyte',
file_label='t10k-labels.idx1-ubyte', is_normalize=True)
b.select_samples([para['class']])
if select_way is 'SequencedMeasure':
print('Calculate the sequence of the measurements')
order_file = os.path.join(para['data_path'], 'Order_'+para['save_exp'])
if (not is_order_calculated) and os.path.isfile(order_file):
order = load_pr(order_file, 'order')
else:
order = a.mps.markov_measurement(if_restore=True)[0]
save_pr(para['data_path'], 'Order_'+para['save_exp'], [order], ['order'])
order_now = copy.copy(order.reshape(-1, )[:num_features])
def quantum_jpeg(para_tot=None):
if para_tot is None:
para_tot = parameters_qjpg()
print('Preparing image')
exp = expression_save(para_tot)
exp = os.path.join(para_tot['data_path'], exp)
if os.path.isfile(exp) and para_tot['if_load']:
a, generator = load_pr(exp, ['a', 'mps'])
else:
a = qjpg(file=para_tot['file'],
b_size=para_tot['block_size'],
is_grey=True)
a.cut2blocks()
a.dct_blocks()
a.pre_process_data_before_ml(which=2)
para = parameters_gtn_one_class()
para['chi'] = para_tot['chi']
para['dataset'] = 'custom'
para['if_save'] = False
para['if_load'] = False
para['dct'] = False
generator = None
if 'real' in para_tot['tasks']:
print('Train in the real space')
for n in range(para_tot['reorder_time']):
generator = gtn_one_class(para=para,
images=a.blocks.squeeze())[0]
if n != (para_tot['reorder_time'] - 1):
order = generator.mps.markov_measurement(
if_restore=False)[0]
# ent = generator.mps.calculate_single_entropy()[0]
# order = np.argsort(ent)[::-1]
a.reorder_features(order, which=0)
if 'freq' in para_tot['tasks']:
for n in range(para_tot['reorder_time']):
print('Train in the frequency space： reorder time = ' + str(n))
generator = gtn_one_class(para=para,
images=a.blocks_dct.squeeze())[0]
if n != (para_tot['reorder_time'] - 1):
order = generator.mps.markov_measurement(
if_restore=False)[0]
# ent = generator.mps.calculate_single_entropy()[0]
# order = np.argsort(ent)[::-1]
a.reorder_features(order, which=1)
save_pr(para_tot['data_path'], exp, [a, generator, para_tot],
['a', 'mps', 'para_tot'])
# a.show_image()
if 'recover' in para_tot['tasks']:
blocks = a.encode_with_cutoff_dct(para_tot['pixel_cutoff'])
image_gtn1 = decode_with_generative_mps(blocks, generator)
image_cutoff = decode_with_cutoff_dct(blocks)
blocks_jpg = a.encode_standard_jpeg(para_tot['pixel_cutoff'])
image_jpg = decode_jpeg(blocks_jpg)
io.imsave('../data_QJPG/before.jpg', a.image.squeeze())
io.imsave('../data_QJPG/0cut.jpg', image_cutoff.squeeze())
io.imsave('../data_QJPG/1JPGway.jpg', image_jpg.squeeze())
io.imsave('../data_QJPG/2GTNway.jpg', image_gtn1.squeeze())
p1 = psnr(a.image0, image_jpg)
p2 = psnr(a.image0, image_cutoff)
p3 = psnr(a.image0, image_gtn1)
print('The PSNRs for jpg, cut-off, and gtn are %g, %g, and %g' %
(p1, p2, p3))