Video datasets in Python

Question

I am a new to deep learning algorithms and Machine learning as well as working with data. I am currently trying to work with annotated video dataset, I tried to have a simple example on How I should get started. I am aware that to work with video dataset, we will first need to extract the images from videos and then do the image processing. However, as I am new it is still difficult for me to understand the steps. I came accross this link, it is great but the data is really large and it cannot be downloaded on my computer. https://www.analyticsvidhya.com/blog/2019/09/step-by-step-deep-learning-tutorial-video-classification-python/

Any suggestions to a walk through examples I can use to build my understanding and Know how to deal with these datasets

Answer 1

Here is a way to create synthetic video dataset quickly:

import numpy as np
import skvideo.io as sk

# creating sample video data (Here object is moving towards left)
num_vids = 5
num_imgs = 50
img_size = 50
min_object_size = 1
max_object_size = 5

for i_vid in range(num_vids):
    imgs = np.zeros((num_imgs, img_size, img_size))  # set background to 0
    vid_name = "vid" + str(i_vid) + ".mp4"
    w, h = np.random.randint(min_object_size, max_object_size, size=2)
    x = np.random.randint(0, img_size - w)
    y = np.random.randint(0, img_size - h)
    i_img = 0
    while x > 0:
        imgs[i_img, y : y + h, x : x + w] = 255  # set rectangle as foreground
        x = x - 1
        i_img = i_img + 1
    sk.vwrite(vid_name, imgs.astype(np.uint8))

from IPython.display import Video
Video("vid3.mp4")  # the script & video generated should be in same folder

Similarly you can create videos where, object(s) move(s) in other directions.