python_api | MarkLogic Python API | REST library

In python I would like to check that a given function is called within a with statement of a given type

I have a sequences of pngs and corresponding depth files (aligned to the corresponding images) from an external camera.

RGB: 1.png 2.png 3.png etc 150.png

Depth: 1.txt 2.txt 3.txt etc 150.txt

I also have the intrinsics and corresponding camera information in another file called camera.txt.

My goal is to convert these images and depth files to an mkv file in order to utilize the pykinect's body tracker (https://github.com/ibaiGorordo/pyKinectAzure)

So far, I've been able to convert the images and and depth files into an open3D RGBD object. See: http://www.open3d.org/docs/release/python_api/open3d.geometry.RGBDImage.html

I would think we need to run it through the azure kinect reader (https://github.com/isl-org/Open3D/blob/0ec3e5b24551eaffa3c7708aae8630fde9b00e6c/examples/python/reconstruction_system/sensors/azure_kinect_recorder.py#L34), but this seems to open up the camera for additional input.

How can I save this rgbd images to an mkv file format to read in to the pykinect reader? I

I'm constructing a library "mylib" that is C++ header-only and has a Python API using pybind11. I want to use "mylib" both as CMake target, containing compile instructions, and as name of the Python API. However, this leads to a name conflict.

Consider the following file structure:

I recently developed a fully-functioning random forest regression SW with scikit-learn RandomForestRegressor model and now I'm interested in comparing its performance with other libraries. So I found a scikit-learn API for XGBoost random forest regression and I made a little SW test with an X feature and Y datasets of all zeros.

On the documentation page for xgboost for python we see a feature_types https://xgboost.readthedocs.io/en/latest/python/python_api.html parameters but have no idea what are the possible values.

The documentation is really bad.

What are the possible values for feature_types?

I want to get the maximum matching of a graph.

Now, I use the algorithm in Networkx: nx.algorithms.bipartite.matching.hopcroft_karp_matching(G)

However, I didn't find a similar algorithm in SNAPenter link description here.

And for NetworKit, I found this page:enter link description here. But I don't know how to use it.

Any ideas? How to use NetworKit/SNAP to get the maximum matching of a graph?

I've created a Oriented Bounding Box from a clustered sub point cloud of a Velodyne Lidar (rotating laser sensor). I want to get the orientation of the Bounding Box (preferable as a quaternion).

The Python API doesn't give much more information other than that the seed= parameter is passed to numpy.random.seed:

seed (int) – Seed used to generate the folds (passed to numpy.random.seed).

But what features of xgboost use numpy.random.seed?

• Running xgboost with all default settings still produces the same performance even when altering the seed.
• I have already been able to verify colsample_bytree does so; different seeds yield different performance.
• I have been told it is also used by subsample and the other colsample_* features, which seems plausible since any form of sampling requires randomness.

What other features of xgboost rely on numpy.random.seed?

I'm a wee bit stuck.

I have a 3D point cloud (an array of (n,3) vertices), in which I am trying to generate a 3D triangular mesh from. So far I have had no luck.

The format my data comes in:

• (x,y) values in regularly spaced (z) intervals. Think of the data as closed loop planar contours stored slice by slice in the z direction.
• The vertices in my data must be absolute positions for the mesh triangles (i.e. I don't want them to be smoothed out such that the volume begins to change shape, but linear interpolation between the layers is fine).

Illustration: