Python script to generate IOS icons

Hacker News

Starting with IOS development, I was looking for a way to generate icons in various sizes as required by apple. I couldn't find anything quickly, so I hacked together a Python script. Here it is: [code] from PIL import Image import glob, os sizes = [57, 114, 72, 144, 512, 1024] for infile in glob.glob("*.jpg"): print("File: " + infile) if "_" in infile: continue file, ext = os.path.splitext(infile) for size in sizes: print("=> New size: %dx%d"%(size, size)) im = Image.open(infile) im.thumbnail((size, size), Image.ANTIALIAS) im.save(file + "_" + str(size) + ".png", "PNG") [/code]

Visit product View on Hacker NewsFeb 14, 2013

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2points

Did not reach leaderboard

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

57%57% 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.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: apple, new, code · Missing: mac, agents, macos

40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

22%22% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

20%20% 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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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