mirror of https://github.com/JDAI-CV/fast-reid.git
56 lines
2.1 KiB
Markdown
56 lines
2.1 KiB
Markdown
|
# Purpose:
|
||
|
|
|
||
|
|
NumPy offers the `save` method for easy saving of arrays into .npy and `savez` for zipping multiple .npy arrays together into a .npz file.
|
||
|
|
|
||
|
|
`cnpy` lets you read and write to these formats in C++.
|
||
|
|
|
||
|
|
The motivation comes from scientific programming where large amounts of data are generated in C++ and analyzed in Python.
|
||
|
|
|
||
|
|
Writing to .npy has the advantage of using low-level C++ I/O (fread and fwrite) for speed and binary format for size.
|
||
|
|
The .npy file header takes care of specifying the size, shape, and data type of the array, so specifying the format of the data is unnecessary.
|
||
|
|
|
||
|
|
Loading data written in numpy formats into C++ is equally simple, but requires you to type-cast the loaded data to the type of your choice.
|
||
|
|
|
||
|
|
# Installation:
|
||
|
|
|
||
|
|
Default installation directory is /usr/local.
|
||
|
|
To specify a different directory, add `-DCMAKE_INSTALL_PREFIX=/path/to/install/dir` to the cmake invocation in step 4.
|
||
|
|
|
||
|
|
1. get [cmake](www.cmake.org)
|
||
|
|
2. create a build directory, say $HOME/build
|
||
|
|
3. cd $HOME/build
|
||
|
|
4. cmake /path/to/cnpy
|
||
|
|
5. make
|
||
|
|
6. make install
|
||
|
|
|
||
|
|
# Using:
|
||
|
|
|
||
|
|
To use, `#include"cnpy.h"` in your source code. Compile the source code mycode.cpp as
|
||
|
|
|
||
|
|
```bash
|
||
|
|
g++ -o mycode mycode.cpp -L/path/to/install/dir -lcnpy -lz --std=c++11
|
||
|
|
```
|
||
|
|
|
||
|
|
# Description:
|
||
|
|
|
||
|
|
There are two functions for writing data: `npy_save` and `npz_save`.
|
||
|
|
|
||
|
|
There are 3 functions for reading:
|
||
|
|
- `npy_load` will load a .npy file.
|
||
|
|
- `npz_load(fname)` will load a .npz and return a dictionary of NpyArray structues.
|
||
|
|
- `npz_load(fname,varname)` will load and return the NpyArray for data varname from the specified .npz file.
|
||
|
|
|
||
|
|
The data structure for loaded data is below.
|
||
|
|
Data is accessed via the `data<T>()`-method, which returns a pointer of the specified type (which must match the underlying datatype of the data).
|
||
|
|
The array shape and word size are read from the npy header.
|
||
|
|
|
||
|
|
```c++
|
||
|
|
struct NpyArray {
|
||
|
|
std::vector<size_t> shape;
|
||
|
|
size_t word_size;
|
||
|
|
template<typename T> T* data();
|
||
|
|
};
|
||
|
|
```
|
||
|
|
|
||
|
|
See [example1.cpp](example1.cpp) for examples of how to use the library. example1 will also be build during cmake installation.
|