Labelme | automatic tagging data , the training data prepare | Data Labeling library

by shuyucool Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | Labelme Summary

Labelme is a Python library typically used in Artificial Intelligence, Data Labeling applications. Labelme has no bugs, it has no vulnerabilities and it has low support. However Labelme build file is not available. You can download it from GitHub.

automatic tagging data, the training data prepare for mask-rcnn

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Quality

Security

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Support

Labelme has a low active ecosystem.

It has 85 star(s) with 41 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 1 have been closed. On average issues are closed in 10 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Labelme is current.

Quality

Labelme has 0 bugs and 0 code smells.

Security

Labelme has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Labelme code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Labelme does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

Labelme releases are not available. You will need to build from source code and install.

Labelme has no build file. You will be need to create the build yourself to build the component from source.

Labelme saves you 133 person hours of effort in developing the same functionality from scratch.

It has 333 lines of code, 16 functions and 6 files.

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

Top functions reviewed by kandi - BETA

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

Return the visualization
Creates a dictionary for JSON output
Convert coordinates x and y to float
Creates a dictionary of shapely shapes
Draw label
Convert label to RGB
Create a colormap
Convert shapes to labels
Converts a list of polygons to an image
Encode and decode

Get all kandi verified functions for this library.

Labelme Key Features

No Key Features are available at this moment for Labelme.

Labelme Examples and Code Snippets

No Code Snippets are available at this moment for Labelme.

Community Discussions

Trending Discussions on Labelme

Inference on image dataset without annotations in detectron2

Json file is save like a folder rather than a file

pip install labelme failing because FreeType version 2.3 or higher is required

pycharm error happening at random time: Process finished with exit code 134 (interrupted by signal 6: SIGABRT)

Unable to Load Images to train model on Custom Datasets

When I run train.py with YOLACT, I get the error KeyError: 0

convert labelImg XML rectangles to labelMe JSON polygons with Image data

Get Array with json without _chartjs: {…}, push: ƒ, pop: ƒ, shift: ƒ, splice: ƒ, …]

To convert a batch Labelme json files to a set of images and labels using anaconda prompt

Torchvision transforms.toPILImage() TypeError: function takes exactly 1 argument (3 given)

QUESTION

Inference on image dataset without annotations in detectron2

Asked 2022-Mar-01 at 03:12

Motivation

I have a detectron2 Mask R-CNN baseline model that is good enough to predict some object boundaries accurately.
I'd like to convert these predicted boundaries to COCO polygons to annotate the next dataset (supervised labeling).
To do this, I need to run inference on an image dataset that does not have annotations.
The detectron2 methods register_coco_instances and load_coco_json require images with annotations to properly label the predicted objects.

Questions

Can I register the test dataset without an annotations file?
If not, what's the easiest way to generate COCO or Labelme JSON files with basic image info without annotations?

Code ...

ANSWER

Answered 2021-Dec-02 at 14:39

Here's a method to generate the image details from a directory of images and write it to an existing COCO JSON file:

Source https://stackoverflow.com/questions/70186513

QUESTION

Json file is save like a folder rather than a file

Asked 2022-Feb-18 at 19:01

I'm using pixellib package to run machine learning, and when I run my program there is a proccess to create coco dataset with name: train.json. But a folder with train.json is created wrongly and inside this folder there is a file dataset.json, this the file that I need. There is something wrong with the proccess, I found the command that made the coco dataset inside pixellib:

...

ANSWER

Answered 2022-Feb-18 at 19:01

The new version didn't work. I used an old version and now is working.

Source https://stackoverflow.com/questions/71175202

QUESTION

pip install labelme failing because FreeType version 2.3 or higher is required

Asked 2021-Dec-09 at 02:54

I can't install labelme because I get the error "#error "FreeType version 2.3 or higher is required."

I did pip install freetype-py==2.3

But I get the error:

...

ANSWER

Answered 2021-Dec-08 at 11:32

You can download labelme directly through the releases page of their github here.

Source https://stackoverflow.com/questions/70274218

QUESTION

pycharm error happening at random time: Process finished with exit code 134 (interrupted by signal 6: SIGABRT)

Asked 2021-Nov-30 at 20:26

I am working with pycharm community on a tensorflow model with keras backend, sometimes this error appears in a random time, specially after I call pyplot and close the window:

...

ANSWER

Answered 2021-Nov-30 at 20:26

Looks like this is part of an actively tracked issue with Tkinter in PyCharm. You can find the reference on the Pycharm's developer's issue tracker. The issue is undiagnosed, but if looking at similar problems with Tk on StackOverflow, one can see that it's an issue with thread safety and/or multithreading.

All I can do is speculate as to the cause, which is not helpful, however, I can at least collate the suggested workarounds:

Switch to a different Matplotlib backend (See docs: What is a backend?). Such as Qt5, but you will need to make sure they are available in your environment.

This approach is the most promising as other backends are better able to deal with not being the main thread or are explicitly safe for multi-threading. It's also the simplest unless you have a really specific reason for preferring TkAgg.
Do not use the debugger with interactive plots.
Set "Variables Loading Policy" to "Synchronously" in Debugger as suggested by a PyCharm dev in the linked issue.
Enable "Scientific mode" in Pycharm.
More esoteric workarounds such as closing your figures within the code before drawing the second one. Example, after plotting the first figure:

Source https://stackoverflow.com/questions/70063350

QUESTION

Unable to Load Images to train model on Custom Datasets

Asked 2020-Nov-04 at 06:58

I have just stuck with Image Instance Segmentation for a while. I am trying to train the Yolact model for my custom data. Here is some brief information about what I have done so far

I have annotated the image using labelme annotation tool
I have converted annotation file for each (train & validation data) using labelme2coco -> train.json & test.json
I made changes in the cofig.py file as needed and expected by yolact
As I was following this repository I encountered an error Argument 'bb' has incorrect type to which I have solved with the approach stated in this closed issue

After completing the above task I am stuck here with below-stated issue.

...

ANSWER

Answered 2020-Nov-04 at 06:58

You mispelled your folder names :) YolaDataset needs to be renamed to YolactDataset

Source https://stackoverflow.com/questions/64605776

QUESTION

When I run train.py with YOLACT, I get the error KeyError: 0

Asked 2020-Oct-23 at 15:44

I'm new to machine learning and program. Now I'm trying to develop YOLACT AI using my own data. However, when I run train.py, I get the following error and cannot learn. What can I do to overcome this error?`

...

ANSWER

Answered 2020-Oct-19 at 09:47

Your class id in annotations.json should start from 1 not 0. If they are starting from 0, try this in config.py in your "my_custom_dataset" in label map add this

Source https://stackoverflow.com/questions/64420059

QUESTION

convert labelImg XML rectangles to labelMe JSON polygons with Image data

Asked 2020-Aug-19 at 06:33

I have annotated the images in labelImg tool and got the annotation in XML form. I need to convert this in LabelMe JSON format with imageData encoded in it.

Sample Input:

sample XML:

...

ANSWER

Answered 2020-Jul-23 at 19:05

This is how I solved it.

Step 1: XML to CSV format

Source https://stackoverflow.com/questions/63061428

QUESTION

Get Array with json without _chartjs: {…}, push: ƒ, pop: ƒ, shift: ƒ, splice: ƒ, …]

Asked 2020-Jun-19 at 15:04

I have a problem to load chartjs. I need get an array like : ["5000", "14000", "10000"]

but, I get: ["5000", "14000", "10000", _chartjs: {…}, push: ƒ, pop: ƒ, shift: ƒ, splice: ƒ, …]

This is my ajax response :

...

ANSWER

Answered 2020-Jun-19 at 15:04

I only remove the global array and I declare the array into function, problem resolved.

Source https://stackoverflow.com/questions/62374196

QUESTION

To convert a batch Labelme json files to a set of images and labels using anaconda prompt

Asked 2020-May-04 at 01:28

I have a dataset of image masks that were generated by the Labelme tool as JSON files and on the Github tutorial (https://github.com/wkentaro/labelme/tree/master/examples/tutorial) it shows to change the JSON file into an image file we use the following command line code

...

ANSWER

Answered 2020-May-04 at 01:28

Use the command FOR /? to read about substitution of FOR variable references on the last page of the help output. To get just the file basename, %%~na can be used. Run this without ECHO OFF so that you can see each command.

Source https://stackoverflow.com/questions/61537887

QUESTION

Torchvision transforms.toPILImage() TypeError: function takes exactly 1 argument (3 given)

Asked 2020-Apr-01 at 14:47

From Anaconda pillow -> 7.00 torchvision -> 0.5.0

I installed the latest Anaconda for Linux( Anaconda3-2020.02-Linux-x86_64.sh) today and I run the following commands,

...

ANSWER

Answered 2020-Apr-01 at 14:47

The error comes when transform RandomRotation is invoked and a new instance of Image class is created. Particularly, function core.fill() at line 2544 return im._new( core.fill(mode, size, color)) in function new, in file anaconda3/envs/py36/lib/python3.6/site-packages/PIL/Image.py is the source of 'evil'.

core is the alias of file anaconda3/envs/py36/lib/python3.6/site-packages/PIL/_imaging.cpython-36m-x86_64-linux-gnu.so when imported as from . import _imaging as core at line 69 in the same file( Image.py).

Function core.fill(mode, size, color)) depending on the mode has different expectations about color. color is always a 3 - tuple, lines 726 - 727 in file anaconda3/envs/py36/lib/python3.6/site-packages/torchvision/transforms/functional.py, function rotate i.e. RandomRotation transform.

If mode == RGB everything is fine. If mode == L then TypeError: function takes exactly 1 argument (3 given) is raised. If mode == F, TypeError: must be real number, not tuple is raised. I found that given my images under transformation.

For mode in [ 'L', 'F'], parameter color needs to be a single integer. For other modes that might be also true.

Thus, I solved my problem by updating the code as,

Source https://stackoverflow.com/questions/60958127

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

Vulnerabilities

No vulnerabilities reported

Install Labelme

You can download it from GitHub.
You can use Labelme 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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