Lab
A Cloud Guru

Detecting Shot Changes with the Video Intelligence API

Detecting when a shot changes in a video is a foundational step when it comes to video indexing. Google Cloud Video Intelligence API is capable of processing digital video to output the beginning and ending time code, demarking individual moments taken by a single camera and representing a continuous time and space. In this lab, you'll use a Python script to process a digital video with the Video Intelligence API's annotate function to output a series of individual shots and then compare the timestamps to the original video.

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Path Info

Level

Intermediate

Duration

30m

Published

Jan 27, 2023

Challenge

Enable API

Enable the Video Intelligence API.
Challenge

Retrieve the Working Files
1. Activate the Cloud Shell.
2. Clone the desired repository.
3. Change directory to the working files.
Challenge

Set Up Service Account
1. Launch the Editor.
2. Expand the content-google-cloud-certified-professional-machine-learning-engineer folder.
3. Name the file key.json.
4. From this hands-on lab credentials, copy the Service Account Credentials.
5. Save the file.
6. Switch to the Terminal to export the key.json file.
Challenge

Send Request to Video Intelligence API
1. Execute the Python command to call the Video Intelligence API and pass in the digital video file path as an argument:
```
python shotchange.py https://storage.googleapis.com/cloud-samples-data/video/JaneGoodall.mp4
```
Challenge

Verify Output

Compare output shot change time codes to actual video for validtion of process.

Author

A Cloud Guru

The Cloud Content team comprises subject matter experts hyper focused on services offered by the leading cloud vendors (AWS, GCP, and Azure), as well as cloud-related technologies such as Linux and DevOps. The team is thrilled to share their knowledge to help you build modern tech solutions from the ground up, secure and optimize your environments, and so much more!

What's a lab?

Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.