<jupyter_start><jupyter_text>Pretty Depth Maps<jupyter_code>import numpy as np
import re
import sys

from scipy import misc

import matplotlib.pyplot as plt

%matplotlib inline
DIR = "/home/barsana/datasets/kitti/odometry-dataset/sequences/05/precomputed-depth-dispnet-png/"

for i in range(1, 100):
    fpath = "{}{:06d}.png".format(DIR, i)
    fpath_pretty = "{}{:06d}-viridis.png".format(DIR, i)
    img = plt.imread(fpath)
    
    plt.imsave(fpath_pretty, img, cmap='viridis')
    


D_ELAS = "/home/barsana/datasets/kitti/odometry-dataset/sequences/06/image_2/"

elas = D_ELAS + "000028_disp.pgm"
elas_dispmap = plt.imread(elas)
plt.imshow(elas_dispmap, cmap='viridis')
plt.imsave(D_ELAS + "0000028_disp-viridis.png", elas_dispmap, cmap='viridis')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14431673052362706, 'fraction_numerical': 0.033205619412515965, 'mean_word_length': 5.030769230769231, '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': '624386', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 352, 'n_tokens_neox': 324, 'n_words': 51, 'path': 'notebooks/Pretty Depth Maps.ipynb', 'repo_name': 'Wayne-Mai/DynSLAM', 'size': '783'}