Command line method for digitizing photos of whiteboards

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About a month ago, after searching for an existing solution, I built a little script to take photo's of whiteboards and clean them up/make them into near perfect digital representations. Here's the script: #!/bin/bash convert $1 -morphology Convolve DoG:15,100,0 -negate -normalize -blur 0x1 -channel RBG -level 60%,91%,0.1 $2 Example Input: Input 1: http://i.imgur.com/CTSIPwm.jpg Input 2: http://i.imgur.com/37GXrwo.jpg Example Output: Output 1: http://i.imgur.com/Qf3jkBW.png Output 2: http://i.imgur.com/oNueb0L.png The basic procedure was outlined in this excellent post for cleaning up whiteboard photos using GIMP: http://matthew.mceachen.us/blog/how-to-clean-up-photos-of-whiteboards-with-gimp-403.html I just replicated the procedure using Imagemagick. Since I do a ton of work on whiteboards nearly every day, I found this really helpful for managing photos of my whiteboard work. I figured I should share this with you guys, since the HN community seems like the kind of people who'd make use of it.

Visit product View on Hacker NewsApr 2, 2014

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: presentations, using · Missing: mac, agents, macos

47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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