<jupyter_start><jupyter_text>![SegmentLocal](gifs/texture_single_tshirt_vertex_0.001.gif "segment")<jupyter_code>(canvas_orig, 
 candidate_orig,
 subset_orig,
 heatmap_avg_orig,
 paf_avg_orig) = get_body_image_from_mesh(mesh, body_estimation, renderer)

(canvas_noise, 
 candidate_noise,
 subset_noise,
 heatmap_avg_noise,
 paf_avg_noise) = get_body_image_from_mesh(model.cur_mesh, body_estimation, renderer)
fig = plt.figure(figsize=(20, 10))
ax = fig.add_subplot(1, 2, 1)
ax.imshow(canvas_orig)
ax.set_title('Original')

ax = fig.add_subplot(1, 2, 2)
ax.imshow(canvas_noise)
ax.set_title('Adv')
filename_output = "./noise_single_tshirt.gif"
writer = imageio.get_writer(filename_output, mode='I', duration=0.30)
for rot in tqdm(range(0, 360, 4)):
    R, T = look_at_view_transform(10.0, 1, rot) 
    T[0, 1] = -1.0
    cur_image = renderer(model.cur_mesh,  R=R.cuda(), T=T.cuda())
    
    cur_image = cur_image[0, ..., :3].detach().squeeze().cpu().numpy()
    cur_image = np.clip(cur_image, -1, 1)
    cur_image = img_as_ubyte(cur_image)
    writer.append_data(cur_image)
    
writer.close()<jupyter_output>100%|██████████| 90/90 [00:05<00:00, 15.50it/s]
{'fraction_non_alphanumeric': 0.16176470588235295, 'fraction_numerical': 0.045847750865051905, 'mean_word_length': 5.726744186046512, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '110705', 'license': 'MIT', 'n_tokens_mistral': 530, 'n_tokens_neox': 484, 'n_words': 70, 'path': 'experiments/torch3d/061_optimize_single_tshirt_texture.ipynb', 'repo_name': 'alexus37/MasterThesisCode', 'size': '1156'}