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[nc][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[nc][ns], para, select_way)
if n == 0:
imgs_now.append(img0.copy())
titles.append('Original img')
if if_mark:
imgs_now.append(marked_img.copy())
else:
imgs_now.append(img1.copy())
# tmp = img1.reshape(-1, )
# tmp[order[:nums_features[n]]] = 0
# show_multiple_images_v1([tmp.reshape(img1.shape)])
ssim = compare_ssim(img0, img1)
title = 'N=' + str(nums_features[n]) + ', SSIM=' + str(ssim)
titles.append(title)
save_one_image(join_imgs_in_one_row(imgs_now), '0' + select_way)
imgs = imgs + imgs_now.copy()
save_exp = str(classes) + '_' + dataset
show_multiple_images_v1(imgs,
lxy=(num_s_tot, num_f + 1),
titles=titles,
save_name=save_exp)
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:
imgs_now.append(marked_img.copy())
else:
imgs_now.append(img1.copy())
ssim = compare_ssim(img0, img1)
title = 'N=' + str(nums_features[n]) + ', SSIM=' + str(ssim)
titles.append(title)
# save_one_image(join_imgs_in_one_row(imgs_now), '0'+select_way)
imgs = imgs + imgs_now.copy()
save_exp = str(chi) + '_' + dataset
show_multiple_images_v1(original_imgs + imgs,
lxy=(1, num_c + 1),
save_name=save_exp)
from library.BasicFunctions import show_multiple_images_v1 import numpy as np x = np.random.rand(28, 28) show_multiple_images_v1([x])