Deep Utils
Project description
This repository contains the most frequently used deep learning models and functions. Deep_Utils is still under heavy development, so please remember that many things may change. Please install the latest version using pypi.
Table of contents
About the Project
Many deep learning toolkits are available on GitHub; however, we didn't find one that suited our needs. So, we created this enhanced one. This toolkit minimizes the deep learning teams' coding effort to utilize the functionalities of famous deep learning models such as MTCNN in face detection, yolov5 in object detection, and many other repositories and models in various fields. In addition, it provides functions for preprocessing, monitoring, and manipulating datasets that can come in handy in any programming project.
What we have done so far:
- The outputs of all the models are standard numpy
- Single predict and batch predict of all models are ready
- handy functions and tools are tested and ready to use
Installation:
# pip: recommended
pip install -U deep_utils
# repository
pip install git+https://github.com/pooya-mohammadi/deep_utils.git
# clone the repo
git clone https://github.com/pooya-mohammadi/deep_utils.git deep_utils
pip install -U deep_utils
Face Detection
We have gathered a rich collection of face detection models that are as follows. If you need a model that we don't have, please feel free to open an issue or create a pull request.
MTCNN-Torch
- After Installing the library, import deep_utils and instantiate models:
from deep_utils import face_detector_loader, list_face_detection_models
# This line will print all the available models
list_face_detection_models()
# Create a face detection model using MTCNN-Torch
face_detector = face_detector_loader('MTCNNTorchFaceDetector')
- The model is instantiated, Now let's Detect an image:
import cv2
from deep_utils import show_destroy_cv2, Box, download_file
# Download an image
download_file("https://raw.githubusercontent.com/pooya-mohammadi/deep_utils/master/examples/vision/data/movie-stars.jpg")
# Load an image
img = cv2.imread("movie-starts.jpg")
# show the image. Press a button to proceed
show_destroy_cv2(img)
# Detect the faces
boxes, confidences = face_detector.detect_faces(img)
# Draw detected boxes on the image.
img = Box.put_box(img, boxes)
# show the results
show_destroy_cv2(img)
The result:
Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this toolkit enhanced, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature
) - Commit your Changes (
git commit -m 'Add some AmazingFeature'
) - Push to the Branch (
git push origin feature/AmazingFeature
) - Open a Pull Request
License
Distributed under the MIT License. See LICENSE
for more information.
🤝 Collaborators
Pooya Mohammadi Kazaj |
Vargha Khallokhi |
Dorna Sabet |
Alireza Kazemipour |
Contact
Pooya Mohammadi:
- LinkedIn www.linkedin.com/in/pooya-mohammadi
- Email: pooyamohammadikazaj@gmail.com
Project Link: https://github.com/pooya-mohammadi/deep_utils
References
- Tim Esler's facenet-pytorch repo: https://github.com/timesler/facenet-pytorch
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