Python Version License: MIT tests PyPI - Version Conda Version GitHub last commit

Speed-optimized, frame-accurate video and data IO tools for Python.

Links: API documentation, GitHub repository

• Efficient Video Reading: Use VideoReader for fast frame-accurate video reading with support for array-like indexing and iteration.
• Video Writing: Write video files easily with VideoWriter, supporting multiple codecs and pixel formats.

• Lazy Loading: Access HDF5 files with a dictionary-like interface using lazyh5, loading data only when accessed.
• Nested Data Support: Save and load nested dictionaries, including arrays, strings, and JSON-serializable objects.
• Interactive Exploration: Explore HDF5 file structures interactively in Jupyter notebooks.

• via conda or mamba: conda install conda-forge::daio
• if you prefer pip: pip install daio

Write video:

from daio.video import VideoReader, VideoWriter
writer = VideoWriter('/path/to/video.mp4', fps=25)
for i in range(20):
 frame = np.random.randint(0,255,size=(720,1280), dtype='uint8')
 writer.write(frame)
writer.close()

Read video using speed-optimized array-like indexing or iteration:

reader = VideoReader('/path/to/video.mp4')
frame_7 = reader[7]
first10_frames = reader[:10]
for frame in reader:
 process_frame(frame)
reader.close()

You can also use with statements to handle file closure:

with VideoWriter('/path/to/video.mp4', fps=25) as writer:
 for i in range(20):
 frame = np.random.randint(0,255,size=(720,1280), dtype='uint8')
 writer.write(frame)
#or
with VideoReader('/path/to/video.mp4') as reader:
 frame_7 = reader[7]

Lazily load HDF5 with a dict-like interface (contents are only loaded when accessed):

from daio.h5 import lazyh5
h5 = lazyh5('/path/to/datafile.h5')
b_loaded = h5['b']
e_loaded = h5['c']['e']
h5.keys()

Create a new HDF5 file (or add items to existing file by setting argument readonly=False):

h5 = lazyh5('test.h5')
h5['a'] = 1
h5['b'] = 'hello'
h5['c'] = {} # create subgroup
h5['c']['e'] = [2,3,4]

Load entire HDF5-file to dict, or save dict to HDF5-file:

# save dict to HDF5 file:
some_dict = dict(a = 1, b = np.random.randn(3,4,5), c = dict(g='nested'), d = 'some_string')
lazyh5('/path/to/datafile.h5').from_dict(some_dict)
# load dict from HDF5 file:
loaded = lazyh5('/path/to/datafile.h5').to_dict()

In Jupyter, you can interactively explore the file structure:

from daio.h5 import save_to_h5, load_from_h5
# save dict to HDF5 file:
some_dict = dict(a = 1, b = np.random.randn(3,4,5), c = dict(g='nested'), d = 'some_string')
save_to_h5('/path/to/datafile.h5', some_dict)
# load dict from HDF5 file:
dict_loaded = load_from_h5('/path/to/datafile.h5')