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])