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Safety-Helmet-Wearing-Dataset | Safety helmet wearing detect dataset, with pretrained model | Computer Vision library

 by   njvisionpower Python Version: Current License: MIT

 by   njvisionpower Python Version: Current License: MIT

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kandi X-RAY | Safety-Helmet-Wearing-Dataset Summary

Safety-Helmet-Wearing-Dataset is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch, Tensorflow, Transformer applications. Safety-Helmet-Wearing-Dataset has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However Safety-Helmet-Wearing-Dataset build file is not available. You can download it from GitHub.
SHWD provide the dataset used for both safety helmet wearing and human head detection. It includes 7581 images with 9044 human safety helmet wearing objects(positive) and 111514 normal head objects(not wearing or negative). The positive objects got from goolge or baidu, and we manually labeld with LabelImg. Some of negative objects got from SCUT-HEAD. We fixed some bugs for original SCUT-HEAD and make the data can be directly loaded as normal Pascal VOC format. Also we provide some pretrained models with MXNet GluonCV.
Support
Support
Quality
Quality
Security
Security
License
License
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Reuse

kandi-support Support

  • Safety-Helmet-Wearing-Dataset has a medium active ecosystem.
  • It has 870 star(s) with 314 fork(s). There are 29 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 37 open issues and 41 have been closed. On average issues are closed in 8 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of Safety-Helmet-Wearing-Dataset is current.
This Library - Support
Best in #Computer Vision
Average in #Computer Vision
This Library - Support
Best in #Computer Vision
Average in #Computer Vision

quality kandi Quality

  • Safety-Helmet-Wearing-Dataset has 0 bugs and 0 code smells.
This Library - Quality
Best in #Computer Vision
Average in #Computer Vision
This Library - Quality
Best in #Computer Vision
Average in #Computer Vision

securitySecurity

  • Safety-Helmet-Wearing-Dataset has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • Safety-Helmet-Wearing-Dataset code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
This Library - Security
Best in #Computer Vision
Average in #Computer Vision
This Library - Security
Best in #Computer Vision
Average in #Computer Vision

license License

  • Safety-Helmet-Wearing-Dataset is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
This Library - License
Best in #Computer Vision
Average in #Computer Vision
This Library - License
Best in #Computer Vision
Average in #Computer Vision

buildReuse

  • Safety-Helmet-Wearing-Dataset releases are not available. You will need to build from source code and install.
  • Safety-Helmet-Wearing-Dataset has no build file. You will be need to create the build yourself to build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • It has 365 lines of code, 8 functions and 3 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
This Library - Reuse
Best in #Computer Vision
Average in #Computer Vision
This Library - Reuse
Best in #Computer Vision
Average in #Computer Vision
Top functions reviewed by kandi - BETA

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

  • Train model .
  • Parse the command line arguments .
  • Validate the prediction .
  • get data loader
  • Create and return a dataset .
  • Saves the params to the best map .
  • Initialize a VOCLike object .

Safety-Helmet-Wearing-Dataset Key Features

Safety helmet wearing detect dataset, with pretrained model

How to use dataset

copy iconCopydownload iconDownload
---VOC2028    
    ---Annotations    
    ---ImageSets    
    ---JPEGImages   

Test with pretrained models

copy iconCopydownload iconDownload
Run "python test_yolo.py" with default settings, or change options:  
--network: darknet/mobile1.0/mobile0.25 network, default darknet53;  
--threshold: confidence that filter object;  
--gpu: use gpu or cpu, default gpu;  
--short: short side input size for original image.

How to train

copy iconCopydownload iconDownload
train_dataset = VOCLike(root='D:\VOCdevkit', splits=[(2028, 'trainval')])
val_dataset = VOCLike(root='D:\VOCdevkit', splits=[(2028, 'test')])

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QUESTION

Image similarity in swift

Asked 2022-Mar-25 at 11:42

The swift vision similarity feature is able to assign a number to the variance between 2 images. Where 0 variance between the images, means the images are the same. As the number increases this that there is more and more variance between the images.

What I am trying to do is turn this into a percentage of similarity. So one image is for example 80% similar to the other image. Any ideas how I could arrange the logic to accomplish this:

import UIKit
import Vision
func featureprintObservationForImage(atURL url: URL) -> VNFeaturePrintObservation? {
let requestHandler = VNImageRequestHandler(url: url, options: [:])
let request = VNGenerateImageFeaturePrintRequest()
do {
  try requestHandler.perform([request])
  return request.results?.first as? VNFeaturePrintObservation
} catch {
  print("Vision error: \(error)")
  return nil
}
  }
 let apple1 = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"apple1", withExtension: "jpg")!)
let apple2 = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"apple2", withExtension: "jpg")!)
let pear = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"pear", withExtension: "jpg")!)
var distance = Float(0)
try apple1!.computeDistance(&distance, to: apple2!)
var distance2 = Float(0)
try apple1!.computeDistance(&distance2, to: pear!)

ANSWER

Answered 2022-Mar-25 at 10:26

It depends on how you want to scale it. If you just want the percentage you could just use Float.greatestFiniteMagnitude as the maximum value.

1-(distance/Float.greatestFiniteMagnitude)*100

A better solution would probably be to set a lower ceiling and everything above that ceiling would just be 0% similarity.

1-(min(distance, 10)/10)*100

Here the artificial ceiling would be 10, but it can be any arbitrary number.

Source https://stackoverflow.com/questions/71615277

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

Vulnerabilities

No vulnerabilities reported

Install Safety-Helmet-Wearing-Dataset

You can download it from GitHub.
You can use Safety-Helmet-Wearing-Dataset 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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