Ecosyste.ms: Repos
An open API service providing repository metadata for many open source software ecosystems.
GitHub topics: image-labelling-tool
xtreme1-io/xtreme1
Xtreme1 is an all-in-one data labeling and annotation platform for multimodal data training and supports 3D LiDAR point cloud, image, and LLM.
Language: TypeScript - Size: 49.8 MB - Last synced: 5 days ago - Pushed: 5 days ago - Stars: 743 - Forks: 117
selbs/speedy_qc
Python GUI to view and label DICOM images
Language: Python - Size: 21.7 MB - Last synced: 16 days ago - Pushed: 16 days ago - Stars: 0 - Forks: 0
ESA-PhiLab/iris
Semi-automatic tool for manual segmentation of multi-spectral and geo-spatial imagery.
Language: JavaScript - Size: 30.3 MB - Last synced: 11 days ago - Pushed: 6 months ago - Stars: 120 - Forks: 35
cvat-ai/cvat
Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.
Language: TypeScript - Size: 246 MB - Last synced: 27 days ago - Pushed: 27 days ago - Stars: 11,417 - Forks: 2,833
mpl-extensions/mpl-image-labeller
Matplotlib Image labeller for classifying images
Language: Python - Size: 2.77 MB - Last synced: 24 days ago - Pushed: about 2 months ago - Stars: 9 - Forks: 1
HumanSignal/label-studio
Label Studio is a multi-type data labeling and annotation tool with standardized output format
Language: JavaScript - Size: 1.86 GB - Last synced: about 1 month ago - Pushed: about 1 month ago - Stars: 16,367 - Forks: 2,018
robertarvind/Interactive-Semi-Automatic-Image-2D-Bounding-Box-Annotation-Tool-using-Multi-Template_Matching
Interactive Semi Automatic Image 2D Bounding Box Annotation and Labelling Tool using Multi Template Matching An Interactive Semi Automatic Image 2D Bounding Box Annotation/Labelling Tool to aid the Annotater/User to rapidly create 2D Bounding Box Single Object Detection masks for large number of training images in a semi automatic manner in order to train an object detection deep neural network such as Mask R-CNN or U-Net. As the Annotater/User starts annotating/labelling by drawing a bounding box for a few number of images in the selected folder then the algorithm suggests bounding box predictions for the rest of the yet to be annotated/labelled images in the folder. If the predictions are right then the user/annotater can simply press the keyboard key 'y' which indicates that the detected bounding box is correct. If the prediction is wrong then the user/annotater can manually draw a rectangular 2D bounding box over the correct ROI (Region of interest) in the image and then press the key 'y' to proceed further to the rest of the images in the folder. If the user/annotater made a mistake while drawing the 2D bounding box, then he/she can press the key 'n' in order to remove the incorrectly marked 2D bounding box and he/she can repeat the process for the same image until he/she draws the correct 2D bounding box and then after drawing the correct 2D bounding box, the user/annotater may press the key 'y' to continue to the rest of the images. The 2D bounding box prediction over the whole image data set improves as the user/annotater annotates/labels more number of images by drawing 2D bounding boxes. This tool allows the user/annotater to not only interactively and rapidly annotate large number of images but also to validate the predictions at the same time interactively. This tool helps the user/annotater to save a lot of time when annotating/labelling and validating the predictions for a large number of training images in a folder. Instructions to use:- 1. If the training images are in JPEG or any other format, then convert them to PNG format using some other tool or program before using these images for annotation. 2. All the training images must contain the object of interest which is to be annotated. 3. Currently the application only supports 2D bounding box annotation for single object detection per image, but in the future semantic segmentation based annotation features will be added which will allow precise boundary segmentation masks of an object in an image. 4. If some or all of the training images have varying dimensions(shapes/resolutions), then resize them to the same dimensions using this tool by providing the height and width to which all the training images need to be resized to. The height and width are inputed separately in two different dialog boxes which pop up once the program is executed. If the training images need not be resized then press the cancel button in the dialog boxes requesting the height and width. 5. Select the folder containing the training images by navigating to the folder containing the training images through a dialog box which pops up after the program is executed. If the images need to be resized then two dialog boxes pop up. The first dialog box is to navigate to the destination folder containing the unresized raw training images and after resizing another dialog box pops up to navigate to the folder containing the saved resized training images named as "resized_data". If the images need not be resized then only one dialog box pops up so that the user can navigate to the raw training images folder directly. 6. The images in the folder pop up one by one. After drawing the correct 2D bounding box over the ROI (region of Interest), press the 'y' key. Except the first image, the rest of the images will have a 2D bounding box drawn over them. If the predicted box is accurate, then continue by pressing the 'y' key. If the prediction is incorrect, then draw the accurate bounding box and press the 'y' key. If any mistake occured while drawing the 2D box, then reset the image by removing the incorrect drawing by pressing the 'n' key and then draw the correct box and press the 'y' key. 7. The output images are stored in four different folders in the same directory containing the training images folder. among the four folders, one contains the cropped templates of the bounding boxes, black and white mask images, training images and the images with 2D box detection markings.
Language: Python - Size: 7.36 MB - Last synced: 7 months ago - Pushed: over 3 years ago - Stars: 17 - Forks: 5
selbs/speedy_iqa
Python GUI for labelling images against a reference for IQA tool development
Language: Python - Size: 7.7 MB - Last synced: 2 months ago - Pushed: 2 months ago - Stars: 0 - Forks: 0
SirLYC/ImageLabelView
A view for data-labeling(a tool for machine learning).
Language: Kotlin - Size: 16.3 MB - Last synced: about 1 year ago - Pushed: over 3 years ago - Stars: 25 - Forks: 7
masum035/Dataset-preparation-for-YOLO
With this repository, image annotation can be performed for already labaled image on open image dataset
Language: Python - Size: 1.03 MB - Last synced: about 1 month ago - Pushed: almost 2 years ago - Stars: 0 - Forks: 0
Labelix/Labelix
Labelix, a image labeling tool which aims to keep usability and modularity in mind.
Language: TypeScript - Size: 35 MB - Last synced: about 1 year ago - Pushed: over 1 year ago - Stars: 2 - Forks: 1
Cynwell/Image-Labeling-Helper
Makes image or CAPTCHA labeling work faster.
Language: Python - Size: 12.7 KB - Last synced: about 1 year ago - Pushed: almost 4 years ago - Stars: 4 - Forks: 0
ktrk115/paircomp Fork of pallets/flask
The simple app of pairwise comparison for image data.
Language: Python - Size: 8.61 MB - Last synced: about 1 year ago - Pushed: almost 4 years ago - Stars: 0 - Forks: 1
Luca96/imgann
Image Annotator Tool (WIP)
Language: Python - Size: 9.38 MB - Last synced: about 1 year ago - Pushed: over 5 years ago - Stars: 1 - Forks: 1
