semi-auto-image-annotation-tool | Semi Automatic Image Annotation Tool | Data Labeling library

by virajmavani Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | semi-auto-image-annotation-tool Summary

semi-auto-image-annotation-tool is a Python library typically used in Artificial Intelligence, Data Labeling, Deep Learning applications. semi-auto-image-annotation-tool has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Anno-Mage: A Semi Automatic Image Annotation Tool which helps you in annotating images by suggesting you annotations for 80 object classes using a pre-trained model

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Quality

Security

License

Reuse

Support

semi-auto-image-annotation-tool has a low active ecosystem.

It has 546 star(s) with 126 fork(s). There are 18 watchers for this library.

It had no major release in the last 6 months.

There are 12 open issues and 9 have been closed. On average issues are closed in 93 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of semi-auto-image-annotation-tool is current.

Quality

semi-auto-image-annotation-tool has 0 bugs and 0 code smells.

Security

semi-auto-image-annotation-tool has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

semi-auto-image-annotation-tool code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

semi-auto-image-annotation-tool 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.

Reuse

semi-auto-image-annotation-tool 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, examples and code snippets are available.

semi-auto-image-annotation-tool saves you 250 person hours of effort in developing the same functionality from scratch.

It has 609 lines of code, 26 functions and 3 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Handle mouse release events
Updates the bounding box
Mouse move handler
Zoom the view
Add the model to the model
List all available models
Open image directory
Load image
Clears all bounding boxes
Open previous image
Automatically process the image
Save the image
Get TensorFlow Session
Open next image
Open an image dialog

Get all kandi verified functions for this library.

semi-auto-image-annotation-tool Key Features

No Key Features are available at this moment for semi-auto-image-annotation-tool.

semi-auto-image-annotation-tool Examples and Code Snippets

No Code Snippets are available at this moment for semi-auto-image-annotation-tool.

Community Discussions

Trending Discussions on Data Labeling

How can I do this split process in Python?

Replacing a character with a space and dividing the string into two words in R

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

QUESTION

How can I do this split process in Python?

Asked 2021-Dec-30 at 14:06

I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.

What I've done so far is make this representation with the same enumerator class.

A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?

...

ANSWER

Answered 2021-Dec-30 at 13:57

Instead of using Enum you can use a dict mapping. You can avoid loops if you flatten your dataframe:

Source https://stackoverflow.com/questions/70532286

QUESTION

Replacing a character with a space and dividing the string into two words in R

Asked 2020-Nov-18 at 07:32

I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.

In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space

i have this:

...

ANSWER

Answered 2020-Nov-16 at 07:24

Try something like:

Source https://stackoverflow.com/questions/64853962

QUESTION

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

Asked 2020-Oct-28 at 20:31

Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.

Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.

Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.

...

ANSWER

Answered 2020-Oct-27 at 22:39

Is the data behind virtual network by any chance?

Source https://stackoverflow.com/questions/64546521

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

Vulnerabilities

No vulnerabilities reported

Install semi-auto-image-annotation-tool

In the repository, execute pip install -r requirements.txt. Note that due to inconsistencies with how tensorflow should be installed, this package does not define a dependency on tensorflow as it will try to install that (which at least on Arch Linux results in an incorrect installation). Please make sure tensorflow is installed as per your systems requirements. Also, make sure Keras 2.1.3 or higher and OpenCV 3.x is installed. a) For Keras model - Download the pretrained weights and save it in /snapshots/keras. b) For tensorflow model get the desired model from here and extract it in /sanpshots/tensorfow. c) You can even save custom pre trained model in the respective directory.
Clone this repository.
In the repository, execute pip install -r requirements.txt. Note that due to inconsistencies with how tensorflow should be installed, this package does not define a dependency on tensorflow as it will try to install that (which at least on Arch Linux results in an incorrect installation). Please make sure tensorflow is installed as per your systems requirements. Also, make sure Keras 2.1.3 or higher and OpenCV 3.x is installed.
a) For Keras model - Download the pretrained weights and save it in /snapshots/keras. b) For tensorflow model get the desired model from here and extract it in /sanpshots/tensorfow c) You can even save custom pre trained model in the respective directory.

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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