imagehash | ๐ŸŒ„ Perceptual image hashing for PHP | Hashing library

ย by ย  jenssegers PHP Version: v0.8.0 License: MIT

kandi X-RAY | imagehash Summary

kandi X-RAY | imagehash Summary

imagehash is a PHP library typically used in Security, Hashing applications. imagehash has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

[Donate] Perceptual hashes are a different concept compared to cryptographic hash functions like MD5 and SHA1. With cryptographic hashes, the hash values are random. The data used to generate the hash acts like a random seed, so the same data will generate the same result, but different data will create different results. Comparing two SHA1 hash values really only tells you two things. If the hashes are different, then the data is different. And if the hashes are the same, then the data is likely the same. In contrast, perceptual hashes can be comparedโ€‰โ€”โ€‰giving you a sense of similarity between the two data sets. This code was inspired/based on: - - - - - -
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              imagehash has a medium active ecosystem.
              It has 1855 star(s) with 161 fork(s). There are 67 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 30 open issues and 19 have been closed. On average issues are closed in 85 days. There are 4 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of imagehash is v0.8.0

            kandi-Quality Quality

              imagehash has 0 bugs and 0 code smells.

            kandi-Security Security

              imagehash has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              imagehash code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              imagehash is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              imagehash releases are available to install and integrate.
              Installation instructions, examples and code snippets are available.
              imagehash saves you 163 person hours of effort in developing the same functionality from scratch.
              It has 406 lines of code, 36 functions and 7 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed imagehash and discovered the below as its top functions. This is intended to give you an instant insight into imagehash implemented functionality, and help decide if they suit your requirements.
            • Calculate the uneven blocks .
            • Returns an array with odd values .
            • Convert blocks to bits .
            • Calculate the DCT .
            • Get the default image manager .
            • Get median .
            • Creates a BigInteger from an array of bits .
            • Compares two resources .
            • Calculate the average value in pixels .
            • Hash image .
            Get all kandi verified functions for this library.

            imagehash Key Features

            No Key Features are available at this moment for imagehash.

            imagehash Examples and Code Snippets

            Step 1: Fingerprinting a Dataset
            Pythondot img1Lines of Code : 33dot img1no licencesLicense : No License
            copy iconCopy
            # import the necessary packages
            from PIL import Image
            import imagehash
            import argparse
            import shelve
            import glob
            
            # construct the argument parse and parse the arguments
            ap = argparse.ArgumentParser()
            ap.add_argument("-d", "--dataset", required = True  

            Community Discussions

            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):

            Image 2 (b1.jpg):

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

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

            rs_a1.jpg

            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

            Pair 2

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

            ...

            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:

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

            QUESTION

            detach().cpu() kills kernel
            Asked 2022-Feb-28 at 22:25

            Background
            I am trying to plot an image noise using pytorch, however, when I reach to that point, the kernel dies. I am attempting the same code at Google Colab where I do get results

            Result at Google Colab

            Result at Jupyter

            I do not think that it has something to do with the code itself, but I am posting the function to plot the grid:

            ...

            ANSWER

            Answered 2022-Feb-28 at 22:25

            After a few days I was able to find the solution

            Firstly, my code needed to be fixed to correctly call the params needed with the proper name

            Source https://stackoverflow.com/questions/71225998

            QUESTION

            As a result of the Euclidean distance many images are mistakenly identified as identical
            Asked 2021-Oct-19 at 02:51

            Sorry for my bad english, i have a small database that contains hashes of photos, when I try to find similar photos to the one below:

            for which the following hash was calculated: "0f3f2764ecc482c2" using the method average_hash()

            ...

            ANSWER

            Answered 2021-Oct-19 at 02:51

            You need to convert the hex strings to integers before doing xor operations

            Source https://stackoverflow.com/questions/69424949

            QUESTION

            Multipoint(df['geometry']) key error from dataframe but key exist. KeyError: 13 geopandas
            Asked 2021-Oct-11 at 14:51

            data source: https://catalog.data.gov/dataset/nyc-transit-subway-entrance-and-exit-data

            I tried looking for a similar problem but I can't find an answer and the error does not help much. I'm kinda frustrated at this point. Thanks for the help. I'm calculating the closest distance from a point.

            ...

            ANSWER

            Answered 2021-Oct-11 at 14:21

            geopandas 0.10.1

            • have noted that your data is on kaggle, so start by sourcing it
            • there really is only one issue shapely.geometry.MultiPoint() constructor does not work with a filtered series. Pass it a numpy array instead and it works.
            • full code below, have randomly selected a point to serve as gpdPoint

            Source https://stackoverflow.com/questions/69521034

            QUESTION

            Only change the state for the particular card component
            Asked 2021-Jul-26 at 15:12

            I have a state called "itemQty" where i want it to increase whenever the plus button is pressed and decrease whenever the minus button is pressed. However, now when i pressed the button, the state changes for every card in the page. I only one it to change in the card where the button is. I understand I have to use the key of the mapping. But I am not sure how to. Can someone help me? This is where my both my qty will change to the same. [![enter image description here][1]][1]

            This is my state where the default is set to 1. But when i press one the plus button on the first card, both quantity becomes 2.

            ...

            ANSWER

            Answered 2021-Jul-26 at 15:12

            So there are 2 options to do what you want:

            1. Make every card item a component with its own state, which will include quantity and change it on single item level

            Source https://stackoverflow.com/questions/68527414

            QUESTION

            How to install PyCaret in AWS Glue
            Asked 2021-Jul-08 at 17:01

            How can I properly install PyCaret in AWS Glue?

            Methods I tried:

            I am using Glue Version 2.0. I used --additional-python-modules and set to pycaret as shown in the picture.

            Then I got this error log.

            ...

            ANSWER

            Answered 2021-Jul-08 at 17:01

            I reached out to AWS support. Meghana was in charge of this case.

            Here is the reply:

            Source https://stackoverflow.com/questions/68260888

            QUESTION

            Is there any way by which I can apply imagehash function in online images in Python
            Asked 2021-Jun-24 at 17:13

            I want to get fingerprints for images with the help of the imagehash function in Python but in order to apply

            ...

            ANSWER

            Answered 2021-Jun-24 at 17:05

            QUESTION

            Returning React State Array Mapping Values
            Asked 2021-May-20 at 05:53

            I'm creating a webapp which uses Solidity, Web3 and React. My issue is that I have a function in my smart contract to return two different arrays at the index submitted by a user from a form. Currently I've been able to save the two results from the contract methods call into a state variable which has two arrays.

            State Constructor

            ...

            ANSWER

            Answered 2021-May-20 at 05:53

            From your comment i believe you have an array with 2 more arrays in it and you need to iterate over those to display its value. Currently ur iterating over the outer array only. Try below code if it works:

            Source https://stackoverflow.com/questions/67603569

            QUESTION

            Numpy error trying to use difference hashing with the ImageHash library
            Asked 2021-Apr-27 at 04:42

            I am trying to perform difference hashing with the python ImageHash library and keep getting a numpy error.

            The error:

            File "/Users/testuser/Desktop/test_folder/test_env/lib/python3.8/site-packages/imagehash.py", line 252, in dhash image = image.convert("L").resize((hash_size + 1, hash_size), Image.ANTIALIAS) AttributeError: 'numpy.ndarray' object has no attribute 'convert'

            The code:

            ...

            ANSWER

            Answered 2021-Apr-27 at 04:42

            as it is mentioned in imagehash library's document, @image must be a PIL instance.. so you cant set numpy array as input of the dshash function.if you want do some preprocess with opencv, you should convert it into PIL array before setting it into dhash, like this :

            Source https://stackoverflow.com/questions/67275803

            QUESTION

            Recognizing license plate characters using template characters in Python
            Asked 2021-Mar-01 at 17:03

            For a university project I have to recognize characters from a license plate. I have to do this using python 3. I am not allowed to use OCR functions or use functions that use deep learning or neural networks. I have reached the point where I am able to segment the characters from a license plate and transform them to a uniform format. A few examples of segmented characters are here.

            The format of the segmented characters is very dependent on the input. However, I can easily convert this to uniform dimensions using opencv. Additionally, I have a set of template characters and numbers that I can use to predict what character / number it is.

            I therefore need a metric to express the similarity between the segmented character and the reference image. In this way, I can say that the reference image with the highest similarity score matches the segmented character. I have tried the following ways to compute the similarity.

            For these operations I have made sure that the reference characters and the segmented characters have the same dimensions.

            • A bitwise XOR-operator
            • Inverting the reference characters and comparing them pixel by pixel. If a pixel matches increment the similarity score, if a pixel does not match decrement the similarity score.
            • hash both the segmented character and the reference character using 'imagehash'. Consequently comparing the hashes and see which ones are most similar.

            None of these methods succeed to give me an accurate prediction for all characters. Most characters are usually correctly predicted. However, the program confuses characters like 8-B, D-0, 7-Z, P-R consistently.

            Does anybody have an idea how to predict the segmented characters? I.e. defining a better similarity score.

            Edit: Unfortunately, cv2.matchTemplate and cv2.matchShapes are not allowed for this assignment...

            ...

            ANSWER

            Answered 2021-Mar-01 at 17:03

            The general procedure for comparing two images consists in the extraction of features from the two images and their subsequent comparison. What you are actually doing in the first two methods is considering the value of every pixel as a feature. The similarity measure is therefore a distance-computation on a space of very high dimension. This methods are, however, subject to noise and this requires very big datasets in order not to obtain acceptable results.

            For this reason, usually one attempts to reduce the space dimensionality. I'm not familiar with the third method, but it seems to go in this direction.

            A way to reduce the space dimensionality consists in defining some custom features meaningful for the problem you are facing.

            A possibility for the character classification problem could be to define features that measure the response of the input image on strategic subshapes of the characters (an upper horizontal line, a lower one, a circle in the upper part of the image, a diagonal line, etc.). You could define a minimal set of shapes that, combined together, can generate every character. Then you should retrieve one feature for each shape, by measuring the response (i.e., integrating the signal of the input image inside the shape) of the original image on that particular shape. Finally, you should determine the class which the image belongs to by taking the nearest reference point in this, smaller, space of the features.

            Source https://stackoverflow.com/questions/66413737

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

            Vulnerabilities

            No vulnerabilities reported

            Install imagehash

            You can download it from GitHub.
            PHP requires the Visual C runtime (CRT). The Microsoft Visual C++ Redistributable for Visual Studio 2019 is suitable for all these PHP versions, see visualstudio.microsoft.com. You MUST download the x86 CRT for PHP x86 builds and the x64 CRT for PHP x64 builds. The CRT installer supports the /quiet and /norestart command-line switches, so you can also script it.

            Support

            To report a security vulnerability, follow [these steps](https://tidelift.com/security).
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            CLONE
          • HTTPS

            https://github.com/jenssegers/imagehash.git

          • CLI

            gh repo clone jenssegers/imagehash

          • sshUrl

            git@github.com:jenssegers/imagehash.git

          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link

            Explore Related Topics

            Consider Popular Hashing Libraries

            Try Top Libraries by jenssegers

            laravel-mongodb

            by jenssegersPHP

            agent

            by jenssegersPHP

            date

            by jenssegersPHP

            optimus

            by jenssegersPHP

            php-proxy

            by jenssegersPHP