Get started with some basics

1. Read a video, given its path

# Import
from mydia import Videos

# Initialize video path
video_path = r"./sample_video/bigbuckbunny.mp4"

# Create a reader object
reader = Videos()

# Call the 'read()' function to get the video tensor
video = reader.read(video_path)

# a tensor of shape (1, 132, 720, 1280, 3)
print("The shape of the tensor:", video.shape)

Out:

The shape of the tensor: (1, 132, 720, 1280, 3)

The tensor can be interpreted as -

• 1 video
• Having 132 frames,
• Dimension (width x height) of each frame: 1280x720 pixels
• 3 denotes that the video is RGB

2. Read multiple videos

from mydia import Videos

video_paths = [
    "path/to/video_1",
    "path/to/video_2",
    "path/to/video_3",
    ...,
]          # list of path of videos

reader = Videos()
video = reader.read(video_paths)

3. Use multiple workers for reading the videos in parallel

from mydia import Videos

video_paths = [
    "path/to/video_1",
    "path/to/video_2",
    "path/to/video_3",
    ...,
]          # list of path of videos

reader = Videos()
video = reader.read(video_paths, workers=4)

The code above will use 4 CPUs to read the videos in parallel, which could result in a significant speed up, depending on the videos to be read and the performance of the CPU.

4. Use a python generator for multiple videos

from mydia import Videos

video_paths = [
    "path/to/video_1",
    "path/to/video_2",
    "path/to/video_3",
    ...,
]          # list of path of videos

reader = Videos()

def generate_video():
    for path in video_paths:
        video = reader.read(path)
        yield video

for i in range(len(video_paths)):
    vid = next(generate_video())
    # Do something

For information on the parameters available, read the examples ahead and also refer to the documentation of the class mydia.Videos.

Saving the loaded video tensor

Important

Once the videos have been processed, they could be saved as numpy.ndarray (in .npz or .npy format). For further details, view the documentation of:

• numpy.save: for saving in .npy format
• numpy.savez: for saving in .npz format
• numpy.load: for loading back the saved numpy tensors

Since the whole reading process is time consuming, this could turn out to be a useful way to store and reload the video tensors.

Total running time of the script: ( 0 minutes 1.035 seconds)