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9-Patch-Resizer | resizer tool to automaticaly resize png files | Image Editing library

 by   redwarp Java Version: 1.4.2 License: Apache-2.0

 by   redwarp Java Version: 1.4.2 License: Apache-2.0

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kandi X-RAY | 9-Patch-Resizer Summary

9-Patch-Resizer is a Java library typically used in Media, Image Editing, Nodejs applications. 9-Patch-Resizer has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.
Let's face it : juggling with densities for Android is a bit of a pain, especially when dealing with 9 patch png. And then comes this tool, that takes a xhdpi PNG file, or 9.png file, and generates ldpi, mdpi and hdpi png files automatically. As simple as drag and drop can get. And here is the changelog. Current version : 1.4.2.
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Quality
Quality
Security
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kandi-support Support

  • 9-Patch-Resizer has a medium active ecosystem.
  • It has 984 star(s) with 166 fork(s). There are 50 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 5 open issues and 29 have been closed. On average issues are closed in 205 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of 9-Patch-Resizer is 1.4.2
9-Patch-Resizer Support
Best in #Image Editing
Average in #Image Editing
9-Patch-Resizer Support
Best in #Image Editing
Average in #Image Editing

quality kandi Quality

  • 9-Patch-Resizer has 0 bugs and 0 code smells.
9-Patch-Resizer Quality
Best in #Image Editing
Average in #Image Editing
9-Patch-Resizer Quality
Best in #Image Editing
Average in #Image Editing

securitySecurity

  • 9-Patch-Resizer has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • 9-Patch-Resizer code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
9-Patch-Resizer Security
Best in #Image Editing
Average in #Image Editing
9-Patch-Resizer Security
Best in #Image Editing
Average in #Image Editing

license License

  • 9-Patch-Resizer is licensed under the Apache-2.0 License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
9-Patch-Resizer License
Best in #Image Editing
Average in #Image Editing
9-Patch-Resizer License
Best in #Image Editing
Average in #Image Editing

buildReuse

  • 9-Patch-Resizer releases are available to install and integrate.
  • Build file is available. You can build the component from source.
  • Installation instructions are available. Examples and code snippets are not available.
  • 9-Patch-Resizer saves you 821 person hours of effort in developing the same functionality from scratch.
  • It has 1884 lines of code, 104 functions and 23 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
9-Patch-Resizer Reuse
Best in #Image Editing
Average in #Image Editing
9-Patch-Resizer Reuse
Best in #Image Editing
Average in #Image Editing
Top functions reviewed by kandi - BETA

kandi has reviewed 9-Patch-Resizer and discovered the below as its top functions. This is intended to give you an instant insight into 9-Patch-Resizer implemented functionality, and help decide if they suit your requirements.

  • This method executes the image .
    • Creates the input panel .
      • Create the drop target .
        • Loads the density dictionary from a JSON file .
          • Main entry point .
            • Overrides the default implementation to set the label and icon .
              • Writes an image to a file .
                • Copies the given file to the given output directory .
                  • Removes all operations from the table .
                    • Post the process .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      9-Patch-Resizer Key Features

                      A resizer tool to automaticaly resize png files and 9 patches in several densities

                      Community Discussions

                      Trending Discussions on Image Editing
                      • Why did my new python edited photo lose brightness?
                      • Python tkinter, resizing transparent image on canvas
                      • Application/script for merging images, applying color, black & white filter in batch?
                      • Changing <QBitmap> / <QImage>'s contrast
                      • Trying to make 3 x 3 montage of 9 images using ImageMagick but getting error for command line command
                      • How can I write a cv2 / Pillow-modified image out to disk?
                      • How to process each page of a multi-page PDF in place with imagemagick?
                      • Crop the image from encoded data on submission - ( File Pond - FilePondPluginFileEncode - imageEditEditor- Doka - Crop - PHP )
                      Trending Discussions on Image Editing

                      QUESTION

                      Why did my new python edited photo lose brightness?

                      Asked 2021-Jun-07 at 13:18

                      I am working on a photo editing project and I am curious about why did my new photo lose it's brightness. The program shoud get 2 photos out of the original one. One of them shoud contain only RED value and the other shoud contain BLUE and GREEN values. But when I put them back together the brightness is not the same as in original picture.

                      Here is my code:

                      import io, re, requests
                      from PIL import  Image, ImageOps, ImageEnhance, ImageChops
                      import cv2
                      import numpy as np
                      
                      imgpth ='image.jpg'
                      
                      
                      #red image
                      img2 =  Image.open(imgpth).convert('RGB')
                      source = img2.split()
                      R, G, B = 0, 1, 2
                      out = source[G].point(lambda i: i * 0)
                      source[G].paste(out, None, None)
                      out = source[B].point(lambda i: i * 0)
                      source[B].paste(out, None, None)
                      img2 = Image.merge(img2.mode, source)
                      
                      
                      #green and blue image
                      img =  Image.open(imgpth).convert('RGB')
                      source = img.split()
                      R, G, B = 0, 1, 2
                      out = source[R].point(lambda i: i * 0)
                      source[R].paste(out, None, None)
                      img = Image.merge(img.mode, source)
                      
                      blend2 = Image.blend(img, img2, 0.5)
                      blend2.show()
                      

                      ANSWER

                      Answered 2021-Jun-07 at 13:18

                      blend2 = Image.blend(img, img2, 0.5)

                      The third argument, 0.5, is the alpha level of each layer. Essentially, you are setting each layer to be 50% transparent. This effectively reduces the brightness. Instead, you should read in img1 and img2 and then set the red layer of the second to the red layer of the first.

                      img2[R] = img1[R]
                      

                      Source https://stackoverflow.com/questions/67872281

                      Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                      Vulnerabilities

                      No vulnerabilities reported

                      Install 9-Patch-Resizer

                      To get the latest build (.jar or .exe file), check the release page on the github project: https://github.com/redwarp/9-Patch-Resizer/releases. The .exe file is just a wrapper around the executable .jar file, use it if you don't feel comfortable with a java archive ^_^.

                      Support

                      Images and stuff found on http://www.clker.com/ (The online royalty free public domain clip art)Images are downsized using an optimized incremental scaling algorithm proposed by Chris Campbell (whoever that is) - http://today.java.net/pub/a/today/2007/04/03/perils-of-image-getscaledinstance.html

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