A Python Library for Computer-Vision related Tasks
Project description
Xtreme-Vision
Go to PyPI page
> Here
This is the Official Repository of Xtreme-Vision. Xtreme-Vision is a High Level Python Library which is built with simplicity in mind for Computer Vision Tasks, such as Object-Detection, Human-Pose-Estimation, Segmentation Tasks, it provides the support of a list of state-of-the-art algorithms, You can Start Detecting with Pretrained Weights as well as You can train the Models On Custom Dataset and with Xtreme-Vision you have the Power to detect/segment only the Objects of your interest
Currently, It Provides the Solution for the following Tasks:
- Object-Detection
- Pose-Estimation
- Segmentation
For Detection with pre-trained models it provides:
- RetinaNet
- CenterNet
- YOLOv4
- TinyYOLOv4
- Mask-RCNN
- DeepLabv3+
For Custom Training It Provides:
- YOLOv4
- TinyYOLOv4
- RetinaNet with (resnet50, resnet101, resnet152)
In Future it will provide solution for a wide variety of Computer-Vision Tasks such as Object-Detection, Pose-Estimation, Object Segmentation, Image-Prediction, Auto-Encoders and GANs and it will support More State-Of-the-Art Algorithms.
If You Like this Project Please do support it by donating here
Dependencies:
- tensorflow >= 2.3.0
- keras
- opencv-python
- numpy
- pillow
- matplotlib
- pandas
- scikit-learn
- scikit-image
- imgaug
- labelme2coco
- progressbar2
- scipy
- h5py
Get Started:
!pip install xtreme-vision
For More Tutorials of Xtreme-Vision, Click
Here
YOLOv4
Example
Image Object Detection
Using YOLOv4
from xtreme_vision.Detection import Object_Detection
model = Object_Detection()
model.Use_YOLOv4()
model.Detect_From_Image(input_path='kite.jpg',
output_path='./output.jpg')
from PIL import Image
Image.open('output.jpg')
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for xtreme_vision-1.4-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | bd561ca869d472907be3b84e01dd890142eb75fd06ae4e6cac66a9e596d1e31e |
|
MD5 | ec1e81faeb235746819126f949530071 |
|
BLAKE2b-256 | 2c82ee9ea4f741a17944027f0f054ff77d9c2aa0e14bc3d2a8b228b249a32144 |