<jupyter_start><jupyter_text>colormap<jupyter_code>for i in color_b:
    b = np.where(b == i, color_b[i], b)

for i in color_g:
    g = np.where(g == i, color_g[i], g)

for i in color_r:
    r = np.where(r == i, color_r[i], r)
b = b.reshape(shape)
g = g.reshape(shape)
r = r.reshape(shape)
new_img = cv2.merge((b,g,r))
trans(img)
trans(new_img)
b
b = np.where(b == 128, 0, b)
b<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18427518427518427, 'fraction_numerical': 0.012285012285012284, 'mean_word_length': 4.230769230769231, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '497477', 'license': 'MIT', 'n_tokens_mistral': 205, 'n_tokens_neox': 200, 'n_words': 44, 'path': 'data/CityScapes/.ipynb_checkpoints/convert Image Mask-checkpoint.ipynb', 'repo_name': 'Genevievekim/segformer', 'size': '407'}