---
title: "Reconstructed Slides: 120k players in a week: Lessons from the first viral CLIP app: Joseph Nelson"
category: "slides"
video_id: "OimPoLxioYg"
sourceLabels: ["Cropped public YouTube video frames", "Local OpenCV slide-region detection", "Local RapidOCR"]
---

# Reconstructed Slides: 120k players in a week: Lessons from the first viral CLIP app: Joseph Nelson

## Source Video
[120k players in a week: Lessons from the first viral CLIP app: Joseph Nelson](https://www.youtube.com/watch?v=OimPoLxioYg)

## Method
This deck is reconstructed from the existing video frame captures by detecting likely slide regions with OpenCV, cropping/upscaling those regions, deduplicating similar crops, and OCRing the cropped slide images locally. It is a cleaner companion to the full-stage frame deck.

## Reconstructed Slides
![[assets/reconstructed-slides/OimPoLxioYg/slide-001.jpg]]

- Source frame: `slide-001.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `176.38`
![[assets/reconstructed-slides/OimPoLxioYg/slide-002.jpg]]

- Source frame: `slide-002.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `176.36`
![[assets/reconstructed-slides/OimPoLxioYg/slide-003.jpg]]

- Source frame: `slide-003.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `171.43`
![[assets/reconstructed-slides/OimPoLxioYg/slide-004.jpg]]

- Source frame: `slide-004.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `177.03`
![[assets/reconstructed-slides/OimPoLxioYg/slide-005.jpg]]

- Source frame: `slide-005.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `176.32`
![[assets/reconstructed-slides/OimPoLxioYg/slide-006.jpg]]

- Source frame: `slide-006.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `172.46`
![[assets/reconstructed-slides/OimPoLxioYg/slide-007.jpg]]

- Source frame: `slide-007.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `166.07`
![[assets/reconstructed-slides/OimPoLxioYg/slide-008.jpg]]

- Source frame: `slide-008.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `165.98`
![[assets/reconstructed-slides/OimPoLxioYg/slide-009.jpg]]

- Source frame: `slide-009.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `163.76`
![[assets/reconstructed-slides/OimPoLxioYg/slide-010.jpg]]

- Source frame: `slide-010.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `166.88`
![[assets/reconstructed-slides/OimPoLxioYg/slide-011.jpg]]

- Source frame: `slide-011.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `167.74`
![[assets/reconstructed-slides/OimPoLxioYg/slide-012.jpg]]

- Source frame: `slide-012.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `177.14`
![[assets/reconstructed-slides/OimPoLxioYg/slide-013.jpg]]

- Source frame: `slide-013.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `157.7`
![[assets/reconstructed-slides/OimPoLxioYg/slide-014.jpg]]

- Source frame: `slide-014.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `176.71`
![[assets/reconstructed-slides/OimPoLxioYg/slide-015.jpg]]

- Source frame: `slide-015.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `176.34`
![[assets/reconstructed-slides/OimPoLxioYg/slide-016.jpg]]

- Source frame: `slide-016.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `161.9`
![[assets/reconstructed-slides/OimPoLxioYg/slide-017.jpg]]

- Source frame: `slide-017.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `156.59`
![[assets/reconstructed-slides/OimPoLxioYg/slide-018.jpg]]

- Source frame: `slide-018.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `166.26`
![[assets/reconstructed-slides/OimPoLxioYg/slide-019.jpg]]

- Source frame: `slide-019.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `165.72`
![[assets/reconstructed-slides/OimPoLxioYg/slide-020.jpg]]

- Source frame: `slide-020.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `174.02`
![[assets/reconstructed-slides/OimPoLxioYg/slide-021.jpg]]

- Source frame: `slide-021.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `176.44`
![[assets/reconstructed-slides/OimPoLxioYg/slide-022.jpg]]

- Source frame: `slide-022.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `177.46`
## Dense Scene-Detected Slide Candidates
- [[youtube-OimPoLxioYg-dense-slides]]