VSUMM TOOLKIT

Identifying and visualising the main themes emerging from a video collection of videos.

RQ: Which are the main themes (based on number of scenes) in the Amazon Fires related YouTube videos?

VISUALIZATION

Query "Amazon Fires"

Query "Pray for Amazonia"

STEPS

WHAT’S IT FOR

TOOLS

DETAILS AND MATERIALS

SCRAPING

DATA EXPLORATION

DATA PREPARATION

URLS CREATION

RENAMING THE NEW COLUMN

DOWNLOAD VIDEOS

COLLECTING VIDEOS IN A NEW FOLDER

EXTRACTING FRAMESWITH REGULAR INTERVAL *

COLLECTING ALL THE FRAMES IN FOLDERS DIVIDED BY VIDEO

EXTRACTING CHARACTERS WITH RUNAWAY ML

CREATING THE VISUALISATION FOR EACH VIDEO

EXPORTING THE VISUALISATION

ANNOTATE THE VISUALISATION

GET A LIST OF VIDEOS FOR EACH CHOSEN QUERY AND SELECTED TIME-FRAME.

OPEN THE YOUTUBE DATA TOOL(YDT) CSV DOWNLOADED AND EXPLORE THE DATA.

FILTER THE LIST BY ORDER OF VIEWS TO TAKE THE FIRST 10 VIDEOS AS SAMPLES FOR EACH QUERY.

INSIDE THE YDT.CSV THERE IS ONLY THE VIDEOS ID, BUT YOU NEED THE URL TO DOWNLOAD THEM.

TO KEEP TRACK OF THE NEW COLUMN IN WHICH WE HAVE ALL THE VIDEO URLS.

DOWNLOAD THE VIDEO SAMPLE QUICKLY AND AUTOMATICALLY.

IT’S IMPORTANT FOR THE NEXT SCRIPT THAT THE FOLDER CONTAINS ONLY THE DOWNLOADED VIDEOS.

TO ANALYSE THE AMOUNT OF HUMAN FIGURES IT IS NECESSARY TO HAVE A REGULAR FRAME EXTRACTION TO COMPARE IT IN ALL VIDEOS. 3 OPTIONS

THE NEXT STEP REQUIRES HAVING THE FRAMES OF EACH VIDEO IN A SINGLE FOLDER.

INSERT THE FRAMES TO RUN THE MODEL AND INDICATE THE DIRECTORY WHERE THE NEW FRAMES WILL BE SAVED.

IMPORT ALL FRAMES AND ARRANGE THEM IN CHRONOLOGICAL ORDER.

SET TO FULL VIEW AND TAKE A SCHREENSHOT BY SELECTING THE VIEWING FRAME.

CREATE VIDEOS GRID FOR EACH QUERY.

Youtube Data Tools ⟶ [Video List]

Excel ⟶ [Import Data]

Excel ⟶ [Filter-Discending]

Excel ⟶ =CONCATENA(E2;F2)

Excel

Python3 ⟶ [PyTube3]

No tool needed

“Amazon Fires” - “Pray for Amazonia”

videoIdvideoTitlepublishedAtviewCountposition

E2 ⟩ http://www.youtube.com/watch?v=

F2 ⟶ videoId

videoUrl

LINK TO PYTHON3 DOCUMENTATION

LINK TO PYTUBE3 DOCUMENTATION

LINK TO REPOSITORY AND STEP-BY-STEP GUIDE

Rename the videos inside the folder like this:

vid1vid2vid3vid4

LINK TO PYSCENEDETECT DOCUMENTATION

LINK TO REPOSITORY AND STEP-BY-STEP GUIDE

Rename the frames inside the folder like this:

frame1frame2frame3frame4frame5

[SEARCH VIDEO-SCENE /

REPLACE WITH:VIDEO1..VIDEO2..]

LINK TO DOWNLOAD FIGMA

METODOLOGY

aim

This method aims to identify which are the main themes emerging within a collection of videos. Frame extraction for this purpose is based on scene change detection, so that the images to be analysed are only taken once and there are no duplicates due to scene length. The layout used to arrange the frames according to their visual similarity is offered by Pixplot, which uses UMAP projection, a dimensionality reduction algorithm, specifically designed for visualising complex data in low dimensions (2D or 3D).

output

The final visualisation is a clusterisation of frames sorted by visual similarity that allows the identification of predominant thematic clusters within the analysed video collection. The thematic annotations of the visualisation were drawn following the boundaries identified by the original Pixplot visualisation.