CBIR | 🏞 A content-based image retrieval system | Computer Vision library

Β by Β  pochih Python Version: Current License: No License

kandi X-RAY | CBIR Summary

kandi X-RAY | CBIR Summary

CBIR is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. CBIR has no bugs, it has no vulnerabilities and it has low support. However CBIR build file is not available. You can download it from GitHub.

🏞 A content-based image retrieval (CBIR) system
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            kandi-support Support

              CBIR has a low active ecosystem.
              It has 563 star(s) with 200 fork(s). There are 17 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 10 open issues and 4 have been closed. On average issues are closed in 313 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of CBIR is current.

            kandi-Quality Quality

              CBIR has 0 bugs and 39 code smells.

            kandi-Security Security

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

            kandi-License License

              CBIR does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              CBIR releases are not available. You will need to build from source code and install.
              CBIR has no build file. You will be need to create the build yourself to build the component from source.
              CBIR saves you 608 person hours of effort in developing the same functionality from scratch.
              It has 1416 lines of code, 53 functions and 12 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed CBIR and discovered the below as its top functions. This is intended to give you an instant insight into CBIR implemented functionality, and help decide if they suit your requirements.
            • Make samples from the database
            • Make samples from a residual model
            • Generate a histogram of the image
            • Count the number of pixels in the input image
            • Infer distance from a query
            • Compute the distance between two vectors
            • Compute the AP precision
            • Make samples from the cluster
            • Calculate the histogram of the input image
            • Gabor of the image
            • Make histogram for each edge
            • Compute the histogram of the given image
            • Performs the convolution
            • Create samples from a histogram
            • Calculate the histogram of the given image
            • R Compute the distance between two vectors
            • Eval a database
            • Infer distance from query
            • Evaluate features
            • Create samples from a residual model
            • Evaluate a database
            • Concatenate a single feat
            • Performs random projection on the network
            • Convert a feat to a dictionary
            • Evaluate a database
            • Generate a list of kernels
            • Create samples from the database
            • Convert a feat to a dict
            Get all kandi verified functions for this library.

            CBIR Key Features

            No Key Features are available at this moment for CBIR.

            CBIR Examples and Code Snippets

            No Code Snippets are available at this moment for CBIR.

            Community Discussions

            QUESTION

            Encrypting and then decrypting same string in python
            Asked 2020-Feb-19 at 01:50

            Here is the question:

            In cryptography, a simple substitution cypher is a method of encryption in which each letter of a phrase is replaced by a different letter or sequence of letters. The phrase can then be decrypted by performing the inverse of the substitution.

            In this question, we will implement a simple substitution cypher in which a character (letter, number, or special symbol) is substituted by a two-digit number between 00 and 99 (inclusive). For example, the letter β€˜a’ could be encrypted to become the number β€˜06’, or the special symbol β€˜!’ could become the number β€˜57’.

            We will use a dictionary to store the mapping between each character and its corresponding encrypted form. That is, the keys of the dictionary will be the regular characters (letters, numbers and special symbols), and the values will be the encrypted forms of the characters (the two-digit numbers between 00 and 99).

            Your code should implement the following five functions for this question.

            • Name: create cypher dictionary

              • Parameters: No parameters.
              • Return value: A dictionary where each key is a regular character and each value is a random two-digit number between 00 and 99, inclusive.
              • What it should do: At the top of the code provided to you, the string LETTERS is defined. This string contains all the letters, numbers and special symbols that should be keys in the dictionary. You will have to loop over this string, and generate a two-digit random number between 00 and 99 for each character. The two-digit numbers will be the values in the dictionary; each character (key) will have one two-digit number (value).
              • Note that the numbers 0, 1, 2, ..., 9 should not be values; instead, they should be the numbers 00, 01, 02, ..., 09. Further, note that each character should have a unique random number. That is, if the character β€˜a’ maps to the value β€˜57’, no other character should map to the value β€˜57’. (You will need to use some sort of loop to keep generating new random numbers until a unique one is found.)
            • Name: encrypt

              • Parameters: A string s.
              • Return value: The encrypted version of the string s. Each character of the original string s should be replaced by its corresponding two-digit number in the cypher dictionary.
            • Name: decrypt

              • Parameters: A string s. Return value: The decrypted version of the string s. Each two-digit number of the encrypted string s should be replaced by its corresponding character in the cypher dictionary. Note that here, we have the encrypted form (value in the dictionary), and are looking to find the regular character (key 7 in the dictionary). To do this, we will need to use the reverse lookup function as seen in class. This function has been provided to you in the included encryption.py file.

            We will write two further functions to increase the strength of our encryption. One function will repeatedly encrypt the string multiple times. The other function will attempt to decrypt the string, without knowing the number of times it was encrypted (thus defeating the point of the stronger encryption).

            • Name: encrypt multiple times

              • Parameters: A string s and integer value n for the number of times to encrypt the string.
              • Return value: The string s encrypted n times. I.e., the string will be encrypted a first time, with each character turned into its two-digit representation. Then, the encrypted string will be re- encrypted, with each digit turned into its two-digit representation. (The length of the string will thus double after every encryption.) (Hint: You will have to call your encrypt function multiple times.)
            • Name: decrypt multiple times

              • Parameters: A string s.
              • Return value: The decrypted version of the string s. As we do not know how many times to decrypt the string, we will keep calling decrypt on the string until the string contains a common word in the English language. A list of common words, COMMON WORDS, has been provided for you in the encryption.py file. If, after decrypting once, the string contains any word in this list, then you should immediately return the decrypted string. Otherwise, continue calling decrypt on the string until it does contain one of the common words. (We will assume that the fully decrypted string will always contain at least one common word.)

            Finally, at the end of the file encryption.py, you will notice some code already written for you. This code asks the user to input a string, then calls the various functions and prints what they return (to produce the examples seen below). You must alter this code so that if the user enters a string that does not contain any of the words in the COMMON WORDS list, then the program should output ”Invalid input.” and not execute the rest of the code. Otherwise, the program should continue with the rest of the code. (Hint: Think back to Assignment 1 and if/else branches.)

            Here is what I tried as an input:

            ...

            ANSWER

            Answered 2020-Feb-19 at 01:49

            You're converting the ASCII incorrectly by taking only one digit at a time, when you should be taking two digits. Just change the step of the for loop in your decrypt function:

            Source https://stackoverflow.com/questions/60291691

            QUESTION

            AttributeError: 'function' object has no attribute 'predict' while using Alexnet in Keras
            Asked 2020-Jan-06 at 16:33

            I need to use Alexnet model for an image classification task. I took the architecture implementation from this source. I want to apply the model with imagenet weights directly (no finetuning required) and get some predictions for the imageNet dataset. Here is the code:

            ...

            ANSWER

            Answered 2020-Jan-06 at 16:33

            You're not calling alexnet_model.

            Do

            Source https://stackoverflow.com/questions/59615684

            QUESTION

            Displaying images using euclidean distance
            Asked 2020-Jan-06 at 04:42

            I am doing a project using python and opencv(cv2). Here I am calculating the dataset's image's red, green and blue mean separately and also calculating the GLCM( contrast, energy, homogeneity, and correlation) and saving it in different list's. Now I have calculated the euclidean distance between query image with DB images, but I am unable to display the images with least distance. i have done the code partially, and it is like:

            ...

            ANSWER

            Answered 2020-Jan-02 at 18:21

            You can use numpy functions to get the euclidean distance between the DB images and query image in one go with minimum computations.

            Source https://stackoverflow.com/questions/59554712

            QUESTION

            Cognos v11 SDK export to pdf
            Asked 2018-Sep-17 at 16:27

            Does anyone have an example that will generate a report as PDF using the sdk? The SDK.pdf only has html examples. I can't figure it out. I'm using a c# controller to call cognos to generate the report.

            The runOptionStringArray value must be PDF.

            ...

            ANSWER

            Answered 2018-Sep-17 at 16:27

            QUESTION

            Compute Recall and Precision to evaluate CBIR system
            Asked 2018-Apr-27 at 07:57

            I implemented a CBIR with SIFT combined with other feature-based algorithms (with OpenCV and Python3), now I have to evaluate how the combination of them (i.e. SIFT/SURF, ORB/BRISK...) perform.

            I found that I can use Precision |TP| / (|TP| + |FP|) and Recall |TP| / (|TP| + |FN|). I know that the TP is the correct positive, that FN is the relevant documents that are not returned and that the FP is the documents that are returned but are not relevant

            I calculate my matches with BF and I presume that:

            • matches=bf.knnMatch(descriptor1, descriptor2, k=2) are my TP+FP
            • the matches finded with ration test are my TP

            How can I calculate my FN? Such as the matches that are relevant but not returned?

            Note that I'm just formulating a hypothesis, so please correct me if I'm wrong.

            I would like to have some help on the concrete implementation, such as where are these data in a concrete case of images matching.

            In alternative can you please suggest me how to evaluate a CBIR system based on feature detection and description?

            ...

            ANSWER

            Answered 2018-Apr-27 at 07:57

            I finally found an answer to my question, maybe it can help someone!

            There is a difference between PRECISION and RECALL calculate in INFORMATION RETRIEVAL CONTEXT and in CLASSIFICATION CONTEXT.

            For information retrieval:

            Source https://stackoverflow.com/questions/49511941

            QUESTION

            Bag of Visual Words (obtained from features) for CBIR. Steps?
            Asked 2018-Feb-02 at 14:48

            I'm very confused about the steps to follow to use BOVW for CBIR. I found a lot of literature about classification, machine learning and SVM but it is not quite what I'm looking for.
            My problem is related to searching image similarity in a database with an image query.

            My steps until now:

            1. extract features (example: ORB, BRISK, SIFT...).
            2. store all images' features to disk.
            3. read features and calculate K-means in order to obtain centroids (my vocabulary, right?)

            And now I'm stuck. I found many different ways to proceed.

            This is my hypothesis:

            1. for each k-means compute nearest neighbour (FLANN?)
            2. Build histogram with set of nearest neighbour

            Do I have to extract a dictionary also for every single image and then indexing the images?
            Why is vector quantization (step 4. and 5.) necessary?

            Can you suggest me a possible way to proceed, or any article, tutorial on the topic?

            NOTE: For the implementation of BOVW I cannot use OpenCV because it does not work with binary descriptors so I need to try with sklearn library.

            ...

            ANSWER

            Answered 2018-Feb-02 at 14:48

            Ok, this is pretty much what I was looking for:

            https://stackoverflow.com/a/8549874/8894489

            Hope that can be helpful for someone.

            Source https://stackoverflow.com/questions/48566520

            QUESTION

            Image Edge Detection: Comparing extracted edge data of two images for CBIR
            Asked 2017-Jul-05 at 12:23

            I am fairly new in image processing. For making a Content Based Image Retrieval(CBIR) system i have to match image feature information of the query image with that of the images in the image database to find images from the database that are same or similar to the query image. I have selected Sobel Edge Detection as feature for now.

            I can extract edge information from a subject image in the form of an edge-image by Sobel edge detection algorithm. The result is a black picture with white pixels representing edges of the original image. (These descriptions might seem very basic and unnecessary but I want to clarify exactly what amount of data I have in hand)

            I have to compare this edge information of two images to find out how similar/dissimilar they are. Actually I need to compare the query image with all the images of the database in this manner to find similar images and how similar they are to the query image. I need a numeric measurement to tell the distance between two images after comparison (like manhattan distance/chi square distance etc).

            So, after extracting the edge detection by applying the Sobel Operator, how should I 'compare' two edge images? Should I make a histogram from the edge image and calculate difference between the two histograms? Or should follow some other method?

            I need suggestion. Every paper I find online describes the same thing again and again, what is edge detection and how to do it. I can't find any useful exact suggestion on what I should do after I detect the edges to use in a CBIR system. And also, any software/language specific answer is not going to be useful for me. I need an algorithm and I will implement it myself.

            ...

            ANSWER

            Answered 2017-Jul-05 at 12:23

            On your images first apply contorlet transform and extract the mean and variance values which becomes the edge features of your image then on these edge features you apply any similarity check test, best one is the Euclidean distance metric.

            Source https://stackoverflow.com/questions/41931795

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

            Vulnerabilities

            No vulnerabilities reported

            Install CBIR

            You can download it from GitHub.
            You can use CBIR like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            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 .
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