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| * | |
| !README.md | |
| !setup.py | |
| !tedview.py | |
| !face_detection.py | |
| !data/ | |
| !data/data.txt |
| name: base | |
| channels: | |
| - defaults | |
| dependencies: | |
| - _ipyw_jlab_nb_ext_conf=0.1.0=py37_0 | |
| - alabaster=0.7.11=py37_0 | |
| - anaconda=5.3.0=py37_0 | |
| - anaconda-client=1.7.2=py37_0 | |
| - anaconda-navigator=1.9.2=py37_0 | |
| - anaconda-project=0.8.2=py37_0 |
| """ | |
| Strip the output from a jupyter notebook. | |
| A utility to shrink the deltas when versioning. | |
| """ | |
| import json | |
| import shutil | |
| import click | |
| def readnb(fname): |
| """ | |
| Download all the 3d Pose data from the CMU panoptic studio. | |
| """ | |
| from tqdm import tqdm | |
| import requests | |
| import os | |
| scenes = [ | |
| "171204_pose1", | |
| "171204_pose2", |
The MDD file format is very simple and here is a brief description for TD's and developers who are interested in adding tools to a pipeline built around MDD.
The first thing to note is that the MDD file format is Motorola Big Endian byte order as opposed to the Intel Little Endian standard. So however you implement the structure below, you must come up with an algorithm to flip the bytes during file IO.
The data structure is:
| from __future__ import print_function | |
| import numpy as np | |
| import cv2 | |
| import time | |
| np.set_printoptions(formatter={'float': '{: 0.3f}'.format}) | |
| def triangulate_nviews(P, ip): | |
| """ |
| # ================================================================== | |
| # module list | |
| # ------------------------------------------------------------------ | |
| # python 3.5 (apt) | |
| # tensorflow latest (pip) | |
| # opencv latest (git) | |
| # caffe latest (git) | |
| # openpose latest (git) | |
| # pyopenpose latest (git) | |
| # custom_dependencies latest (apt) |