kandi background
Explore Kits

OpenBlocks | Random collection of blocks | Hashing library

 by   OpenMods Java Version: Current License: Non-SPDX

 by   OpenMods Java Version: Current License: Non-SPDX

Download this library from

kandi X-RAY | OpenBlocks Summary

OpenBlocks is a Java library typically used in Security, Hashing applications. OpenBlocks has no vulnerabilities, it has build file available and it has low support. However OpenBlocks has 35 bugs and it has a Non-SPDX License. You can download it from GitHub.
An open source random collection of blocks and miscellaneous cool stuff. For stable release downloads, click [here](https://www.openmods.info). For snapshot versions, check the [Jenkins](https://builds.openmods.info). What does it contain?.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • OpenBlocks has a low active ecosystem.
  • It has 285 star(s) with 141 fork(s). There are 54 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 145 open issues and 680 have been closed. On average issues are closed in 33 days. There are 10 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of OpenBlocks is current.
OpenBlocks Support
Best in #Hashing
Average in #Hashing
OpenBlocks Support
Best in #Hashing
Average in #Hashing

quality kandi Quality

  • OpenBlocks has 35 bugs (0 blocker, 1 critical, 28 major, 6 minor) and 963 code smells.
OpenBlocks Quality
Best in #Hashing
Average in #Hashing
OpenBlocks Quality
Best in #Hashing
Average in #Hashing

securitySecurity

  • OpenBlocks has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • OpenBlocks code analysis shows 0 unresolved vulnerabilities.
  • There are 23 security hotspots that need review.
OpenBlocks Security
Best in #Hashing
Average in #Hashing
OpenBlocks Security
Best in #Hashing
Average in #Hashing

license License

  • OpenBlocks has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
OpenBlocks License
Best in #Hashing
Average in #Hashing
OpenBlocks License
Best in #Hashing
Average in #Hashing

buildReuse

  • OpenBlocks releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • OpenBlocks saves you 15786 person hours of effort in developing the same functionality from scratch.
  • It has 31454 lines of code, 2863 functions and 381 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
OpenBlocks Reuse
Best in #Hashing
Average in #Hashing
OpenBlocks Reuse
Best in #Hashing
Average in #Hashing
Top functions reviewed by kandi - BETA

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

  • Registers default icons .
    • Pre - initialization of the Minecraft blocks .
      • Prepare the base quad .
        • Render static part .
          • Select a random tool .
            • Restores the inventory of the player .
              • Render the fluid .
                • Dimise world .
                  • Draws the background layer .
                    • Render a star .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      OpenBlocks Key Features

                      Random collection of blocks

                      default

                      copy iconCopydownload iconDownload
                      **In (mostly) chronological order:**
                      
                      * A combination ladder and trapdoor, also known as the **Jaded Ladder**
                      * A creative-only **healer** block that slowly replenishes your health and food when you stand on it.
                      * A **guide block** to assist in various constructions
                      * Colorable **elevator blocks** to quickly travel between floors. Requires XP by default.
                      * **Light boxes** to display your maps on the wall, ceiling or floor
                      * Archery **targets** for a shooting range
                      * ~~**Torch arrows!**~~ *No longer implemented*
                      * **Player graves**, safekeeping for your last death (assuming you’re capable of regeneration)
                      * Colored **flags** for various purposes
                      * Liquid **tanks**-- portable, practical, and pliable any-size liquid containment
                      * Hang **gliders**!
                      * Random **trophies** that may or may not do odd things when right-clicked
                      * **Bear traps**-- like venus fly traps, but snappier
                      * **Luggage**, the traveling sentient chest
                      * **Sprinklers**, to accelerate the growth of crops and other growables // m1.5.2, v1.0.3
                      * **Item cannons** to shoot items around for transport
                      * **Vaccum hoppers**-- like regular hoppers, but not limited to the top and sucks up XP. Compatible with BuildCraft!
                      * **Sonic goggles**-- echolocation for Steves
                      * **Sponges** for getting rid of liquids fast and effectively
                      * The Redstone Configurable Pulse Lightweight Mega Touch Sensor, also known as the **BIG BUTTON**
                      * Blocks drawn by **pencils** and **crayons** that are only seen by the imaginative eye
                      * **Fans**-- the power of an industrial fan in the size of a desk fan
                      * A wearable **crane** for picking up blocks and entities
                      * **XP Bottler**-- should be self-explanatory
                      * **Magnet turtles**-- turtles don't have CRT screens, so we should be fine
                      * **Village highlighter**-- shows the village borders and where their golem guardians spawn
                      * The **Slimalizer**, which detects slime chunks
                      * **Paths**, an alternative to gravel roads // m1.6.x, v1.2.0
                      * The **Block Breaker** and **Block Placer** are back! A perfect replacement for the now-dormant RedPower mod
                      * **XP Drain**-- stand on one attached to a tank, and your XP will be drained and turned into a liquid state
                      * The **Auto Anvil** and **Auto Enchantment Table**, imported fresh from OpenXP. Uses liquid XP to perform their function automatically.
                      * The **Sleeping Bag**. Finally, a way to sleep on the go without resetting your spawn point!
                      * The **Rope Ladder**-- a magic ladder that doesn’t need support to hold itself up and adjusts its own size to match the ground. Single use.
                      * The **Donation Station**-- tells you which mod a block or item comes from, and lets you support the modder that created it
                      * A painting/staining system for blocks, including: // m1.6.x, v.1.2.2
                        * A **Paint Mixer**-- makes paint using dyes and milk. Millions of colors possible.
                        * A paint **brush** to paint white **canvases** and other blocks.
                        * **Stencils** that are made using the **Drawing Table** to paint specific areas of blocks
                        * A **Squeegee** that liquefies paint to clean it off of blocks
                      * ~~Listen to music among other things with the **radio**!~~ *Killed!*
                      * Exciting graphics and much more to come.
                      
                      Compiling
                      --------------
                      We will try keep the building instructions as up-to-date as possible, but things may change without notice.
                      
                      ### Windows
                      You will require [msysgit](http://code.google.com/p/msysgit/downloads/list) or git installed with cygwin. Alternatively I would suggest installing [GitHub for Windows](http://windows.github.com/) and checking out the repo with that.
                      
                      #### Check out the repo
                      Use Github for Windows or use a command prompt/powershell with git binaries in the Environment path.
                      ```git clone https://github.com/OpenMods/OpenBlocks.git```
                      #### Change directory
                      ```cd OpenBlocks```
                      #### Fetch dependencies (OpenModsLib etc.)
                      ```
                      git submodule init
                      git submodule update
                      ```
                      #### Run gradle build
                      OpenBlocks uses Forge Gradle build system, as almost any other mod this days. There are few good tutorials, so we will include just basic command for building:
                      
                      ```
                      gradle.bat build
                      ```
                      
                      The resulting files should be in the `build/lib` folder. File ending with `-deobf` is unobfuscated version for development use.
                      
                      ### Linux or OSX
                      Much the same as Windows, open a terminal window where you wish to checkout the repo and type
                      ```
                      git clone https://github.com/OpenMods/OpenBlocks.git
                      cd OpenBlocks
                      git submodule init
                      git submodule update
                      ```
                      
                      In **OSX**, git is typically supplied. Otherwise it can be installed through the apps thingy that OSX has (Obviously I know very little about OSX but you want the dev tools stuff).
                      
                      **(To install git and other command line tools on OS X, tun `xcode-select --install` in the terminal (/Applications/Utilities/Terminal.app))**
                      
                      The linux git can be fetched from any package manager. If you're on a Debian based machine (This includes **Ubuntu**) you likely have aptitude, so the command would be ```sudo apt-get install git```.
                      
                      If you're running a RedHat based system, such as **CentOS** or **Fedora** (or some other Distro I've never heard of), then you might have the yum package manager ```sudo yum install git-core```. In the case that this does not work through yum, you might not have the packages added to yum to be able to find git. I wont go in to these here, but if you have issues feel free to contact NeverCast in #OpenMods on irc.esper.net.
                      #### Then run gradle
                      
                      OpenBlocks uses Forge Gradle build system, as almost any other mod this days. There are few good tutorials, so we will include just basic command for building:
                      
                      ```
                      ./gradle build
                      ```
                      
                      The resulting files should be in the `build/lib` folder. File ending with `-deobf` is unobfuscated version for development use.
                      
                      Contact
                      -
                      Feel free to chat with us in #OpenMods on *irc.esper.net*
                      
                      License
                      -
                      
                      OpenBlocks is open source, please check the Licence.txt for more information and the licences of individual components of this mod.

                      Community Discussions

                      Trending Discussions on Hashing
                      • Find near duplicate and faked images
                      • Is there a need for transitivity in Python __eq__?
                      • Unhashing a hashed (MD5) email address
                      • Channel hangs, probably not closing at the right place
                      • How can I join two lists in less than O(N*M)?
                      • How reproducible / deterministic is Parquet format?
                      • Angular 12 app still being cached with output-hashing=all
                      • Where to store access token and how to keep track of user (using JWT token in Http only cookie)
                      • Flutter Web Page Routing Issue
                      • Ionic + Fastlane | Android "error: package android.support.v4.content does not exist"
                      Trending Discussions on Hashing

                      QUESTION

                      Find near duplicate and faked images

                      Asked 2022-Mar-24 at 01:32

                      I am using Perceptual hashing technique to find near-duplicate and exact-duplicate images. The code is working perfectly for finding exact-duplicate images. However, finding near-duplicate and slightly modified images seems to be difficult. As the difference score between their hashing is generally similar to the hashing difference of completely different random images.

                      To tackle this, I tried to reduce the pixelation of the near-duplicate images to 50x50 pixel and make them black/white, but I still don't have what I need (small difference score).

                      This is a sample of a near duplicate image pair:

                      Image 1 (a1.jpg):

                      enter image description here

                      Image 2 (b1.jpg):

                      enter image description here

                      The difference between the hashing score of these images is : 24

                      When pixeld (50x50 pixels), they look like this:

                      enter image description here

                      rs_a1.jpg

                      enter image description here

                      rs_b1.jpg

                      The hashing difference score of the pixeled images is even bigger! : 26

                      Below two more examples of near duplicate image pairs as requested by @ann zen:

                      Pair 1

                      enter image description here

                      Pair 2

                      enter image description here

                      The code I use to reduce the image size is this :

                      from PIL import Image    
                      with Image.open(image_path) as image:
                                  reduced_image = image.resize((50, 50)).convert('RGB').convert("1")
                      

                      And the code for comparing two image hashing:

                      from PIL import Image
                      import imagehash        
                      with Image.open(image1_path) as img1:
                                  hashing1 =  imagehash.phash(img1)
                      with Image.open(image2_path) as img2:
                                  hashing2 =  imagehash.phash(img2)           
                      print('difference :  ', hashing1-hashing2)
                      

                      ANSWER

                      Answered 2022-Mar-22 at 12:48

                      Rather than using pixelisation to process the images before finding the difference/similarity between them, simply give them some blur using the cv2.GaussianBlur() method, and then use the cv2.matchTemplate() method to find the similarity between them:

                      import cv2
                      import numpy as np
                      
                      def process(img):
                          img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
                          return cv2.GaussianBlur(img_gray, (43, 43), 21)
                      
                      def confidence(img1, img2):
                          res = cv2.matchTemplate(process(img1), process(img2), cv2.TM_CCOEFF_NORMED)
                          return res.max()
                      
                      img1s = list(map(cv2.imread, ["img1_1.jpg", "img1_2.jpg", "img1_3.jpg"]))
                      img2s = list(map(cv2.imread, ["img2_1.jpg", "img2_2.jpg", "img2_3.jpg"]))
                      
                      for img1, img2 in zip(img1s, img2s):
                          conf = confidence(img1, img2)
                          print(f"Confidence: {round(conf * 100, 2)}%")
                      

                      Output:

                      Confidence: 83.6%
                      Confidence: 84.62%
                      Confidence: 87.24%
                      

                      Here are the images used for the program above:

                      img1_1.jpg & img2_1.jpg:

                      enter image description here enter image description here

                      img1_2.jpg & img2_2.jpg:

                      enter image description here enter image description here

                      img1_3.jpg & img2_3.jpg:

                      enter image description here enter image description here

                      To prove that the blur doesn't produce really off false-positives, I ran this program:

                      import cv2
                      import numpy as np
                      
                      def process(img):
                          h, w, _ = img.shape
                          img = cv2.resize(img, (350, h * w // 350))
                          img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
                          return cv2.GaussianBlur(img_gray, (43, 43), 21)
                      
                      def confidence(img1, img2):
                          res = cv2.matchTemplate(process(img1), process(img2), cv2.TM_CCOEFF_NORMED)
                          return res.max()
                      
                      img1s = list(map(cv2.imread, ["img1_1.jpg", "img1_2.jpg", "img1_3.jpg"]))
                      img2s = list(map(cv2.imread, ["img2_1.jpg", "img2_2.jpg", "img2_3.jpg"]))
                      
                      for i, img1 in enumerate(img1s, 1):
                          for j, img2 in enumerate(img2s, 1):
                              conf = confidence(img1, img2)
                              print(f"img1_{i} img2_{j} Confidence: {round(conf * 100, 2)}%")
                      

                      Output:

                      img1_1 img2_1 Confidence: 84.2% # Corresponding images
                      img1_1 img2_2 Confidence: -10.86%
                      img1_1 img2_3 Confidence: 16.11%
                      img1_2 img2_1 Confidence: -2.5%
                      img1_2 img2_2 Confidence: 84.61% # Corresponding images
                      img1_2 img2_3 Confidence: 43.91%
                      img1_3 img2_1 Confidence: 14.49%
                      img1_3 img2_2 Confidence: 59.15%
                      img1_3 img2_3 Confidence: 87.25% # Corresponding images
                      

                      Notice how only when matching the images with their corresponding images does the program output high confidence levels (84+%).

                      For comparison, here are the results without blurring the images:

                      import cv2
                      import numpy as np
                      
                      def process(img):
                          h, w, _ = img.shape
                          img = cv2.resize(img, (350, h * w // 350))
                          return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
                      
                      def confidence(img1, img2):
                          res = cv2.matchTemplate(process(img1), process(img2), cv2.TM_CCOEFF_NORMED)
                          return res.max()
                      
                      img1s = list(map(cv2.imread, ["img1_1.jpg", "img1_2.jpg", "img1_3.jpg"]))
                      img2s = list(map(cv2.imread, ["img2_1.jpg", "img2_2.jpg", "img2_3.jpg"]))
                      
                      for i, img1 in enumerate(img1s, 1):
                          for j, img2 in enumerate(img2s, 1):
                              conf = confidence(img1, img2)
                              print(f"img1_{i} img2_{j} Confidence: {round(conf * 100, 2)}%")
                      

                      Output:

                      img1_1 img2_1 Confidence: 66.73%
                      img1_1 img2_2 Confidence: -6.97%
                      img1_1 img2_3 Confidence: 11.01%
                      img1_2 img2_1 Confidence: 0.31%
                      img1_2 img2_2 Confidence: 65.33%
                      img1_2 img2_3 Confidence: 31.8%
                      img1_3 img2_1 Confidence: 9.57%
                      img1_3 img2_2 Confidence: 39.74%
                      img1_3 img2_3 Confidence: 61.16%
                      

                      Source https://stackoverflow.com/questions/71514124

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

                      Vulnerabilities

                      No vulnerabilities reported

                      Install OpenBlocks

                      You can download it from GitHub.
                      You can use OpenBlocks like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the OpenBlocks component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

                      Support

                      For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

                      DOWNLOAD this Library from

                      Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
                      over 430 million Knowledge Items
                      Find more libraries
                      Reuse Solution Kits and Libraries Curated by Popular Use Cases
                      Explore Kits

                      Save this library and start creating your kit

                      Explore Related Topics

                      Share this Page

                      share link
                      Consider Popular Hashing Libraries
                      Try Top Libraries by OpenMods
                      Compare Hashing Libraries with Highest Support
                      Compare Hashing Libraries with Highest Quality
                      Compare Hashing Libraries with Highest Security
                      Compare Hashing Libraries with Permissive License
                      Compare Hashing Libraries with Highest Reuse
                      Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
                      over 430 million Knowledge Items
                      Find more libraries
                      Reuse Solution Kits and Libraries Curated by Popular Use Cases
                      Explore Kits

                      Save this library and start creating your kit

                      • © 2022 Open Weaver Inc.