Overview
Private tool means I use it in my own editing workflow. It is not published as an app or a repository. I can show it working on a call, or adapt it to the way your videos are put together.
CapCut Automation is a Python tool by Haseeb Sagheer that places images, photographer credits and animations into CapCut projects automatically, for long-form YouTube videos.
Private tool means I use it in my own editing workflow. It is not published as an app or a repository. I can show it working on a call, or adapt it to the way your videos are put together.
CapCut Automation prepares a CapCut project before I start editing. It places the images, attaches the right photographer credit to each one, and applies the animations.
Long documentary-style videos are where it pays off. A single video can need dozens of images, and each one needs a credit on screen. Doing that by hand means dragging, typing and aligning the same thing over and over, and it is easy to attach a credit to the wrong image.
The output is an ordinary CapCut project. I open it, adjust what needs adjusting, and export. The tool handles the repetitive placement and leaves the editorial decisions to me.
A long documentary-style video uses dozens of images, and each one needs a photo credit and an animation. Dragging them into the timeline one at a time is slow and easy to get wrong.
Editors of long, image-heavy videos, where every image needs a credit and an animation.
I wrote the tool in Python and use it on my own long-form videos.
It places images, photo credits and animations into a CapCut project automatically, so the editor starts from a nearly finished timeline.
Yes. Video Maker renders a video from a script on its own. CapCut Automation prepares a CapCut project that is then finished in CapCut.
No. It removes the repetitive placement work. The final edit is still done by a person in CapCut.
Python.