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https://github.com/pooya-mohammadi/deep_utils

An open-source toolkit which is full of handy functions, including the most used models and utilities for deep-learning practitioners!
https://github.com/pooya-mohammadi/deep_utils
augmentation coco computer-vision cutmix deep-learning face-detection face-recognition machine-learning modelcheckpoint nlp object-detection python pytorch senet tensorflow utils vggface2 yolov5
Last synced: 3 months ago
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An open-source toolkit which is full of handy functions, including the most used models and utilities for deep-learning practitioners!
Host: GitHub
URL: https://github.com/pooya-mohammadi/deep_utils
Owner: pooya-mohammadi
License: mit
Created: 2021-05-26T08:53:45.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2024-10-29T20:56:55.000Z (3 months ago)
Last Synced: 2024-10-29T22:53:17.489Z (3 months ago)
Topics: augmentation, coco, computer-vision, cutmix, deep-learning, face-detection, face-recognition, machine-learning, modelcheckpoint, nlp, object-detection, python, pytorch, senet, tensorflow, utils, vggface2, yolov5
Language: Python
Homepage: https://github.com/pooya-mohammadi/deep_utils
Size: 2.99 MB
Stars: 109
Watchers: 2
Forks: 15
Open Issues: 7
Metadata Files:
- Readme: Readme.md
- License: LICENSE
Awesome Lists containing this project

awesome-yolo-object-detection - pooya-mohammadi/deep_utils - mohammadi/deep_utils?style=social"/> : A toolkit full of handy functions including most used models and utilities for deep-learning practitioners! (Other Versions of YOLO)
awesome-yolo-object-detection - pooya-mohammadi/deep_utils - mohammadi/deep_utils?style=social"/> : A toolkit full of handy functions including most used models and utilities for deep-learning practitioners! (Other Versions of YOLO)
README

        [![Downloads](https://static.pepy.tech/badge/deep_utils)](https://pepy.tech/project/deep_utils) [![PyPI](https://img.shields.io/pypi/v/deep_utils.svg)](https://pypi.python.org/pypi/deep_utils)








  

    

  

Deep Utils


  


    A toolkit for deep-learning practitioners!





This repository contains the most frequently used deep learning models and functions. **Deep_Utils** is still under

heavy development, so take into consideration that many features may change in the future and make sure to install the

latest version using pypi.

## Table of contents

* [About The Project](#about-the-project)

* [Installation](#installation)

* [Vision](#vision)

    * [Face Detection](#face-detection)

        * [MTCNN](#mtcnn)

    * [Object Detection](#object-detection)

        * [yolov5](#yolov5)

    * [Visual Grounding](#visual-grounding)

        * [Grounding DINO](#grounding-dino)

    * [Image Editing](#image-editing)

        * [GLIDE](#glide)

* [NLP](#NLP)

    * [NER](#NER)

        * [Replacement Augmentation](replacement-augmentation)

        * [Remove Augmentation](remove-augmentation)

* [Augmentation](#augmentation)

    * [CutMix](#cutmix)

* [Utils](#utils)

    * [DictNamedTuple](#dictnametuple)

    * [multi-label-stratify](#multi-label-stratify)

* [Tests](#tests)

* [Contributing](#Contributing)

* [Licence](#Licence)

* [Collaborators](#Collaborators)

* [Contact](#Contact)

* [References](#references)

* [Citation](#citation)

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## About the Project

Many deep learning toolkits are available on GitHub; however, we couldn't find one that would suit our needs.

So, we created this improved one. This toolkit minimizes the deep learning teams' coding efforts 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 functionalities 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

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## Installation:

```bash

    # 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

```

### Installation types:

1. minimal installation:

    1. `pip install deep-utils`

2. minial vision installation

    1. `pip install deep-utils[cv]`

3. tensorflow installation:

    1. `pip install deep-utils[tf]`

4. torch installation:

    1. `pip install deep-utils[torch]`

5. torchvision installation:

    1. `pip install deep-utils[torchvision]`

6. torch_transformers installation:

    1. `pip install deep-utils[torch_transformers]`

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# Vision

We support two subsets of models in Computer Vision.

* Face Detection

* Object Detection

## Face Detection

We have gathered a rich collection of face detection models which are mentioned in the following list. If you notice any

model missing, feel free to open an issue or create a pull request.

### MTCNN

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pooya-mohammadi/Face/blob/master/_02_mtcnn_tf1/deep_utils_mtcnn.ipynb)

1. After Installing the library, import deep_utils and instantiate the model:

```python

from deep_utils import face_detector_loader, list_face_detection_models

# This line will print all the available models

print(list_face_detection_models())

# Create a face detection model using MTCNN-Torch

face_detector = face_detector_loader('MTCNNTorchFaceDetector')

```

2. The model is instantiated, Now let's Detect an image:

```python

import cv2

from deep_utils import show_destroy_cv2, Box, download_file, Point

# 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-stars.jpg")

# show the image. Press a button to proceed

show_destroy_cv2(img)

# Detect the faces

result = face_detector.detect_faces(img, is_rgb=False)

# Draw detected boxes on the image.

img = Box.put_box(img, result.boxes)

# Draw the landmarks

for landmarks in result.landmarks:

    Point.put_point(img, list(landmarks.logs()), radius=3)

# show the results

show_destroy_cv2(img)

```

The result:



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## Object Detection

### YoloV5

YoloV5 by far is one of the top-5 most used object detection models. The training process is straight forward and the

results

are spectacular. However, using a trained model can be very challenging because of several files that yolov5's model

needs in production.

To tackle this issue we have wrapped yolov5's models in a simple module whose usage will be illustrated in the following

section.


[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pooya-mohammadi/deep-utils-notebooks/blob/main/vision/object-detection/yolov5/deep-utils-yolov5.ipynb)

1. After Installing the library, import deep_utils and instantiate the model:

```python

# import the model

from deep_utils import YOLOV5TorchObjectDetector

# instantiate with the default parameters

yolov5 = YOLOV5TorchObjectDetector()

# print the parameters

print(yolov5)

```

2. Download and visualize the test image

```python

import cv2

from deep_utils import Box, download_file, Point, show_destroy_cv2

from PIL import Image

# Download an image

download_file("https://raw.githubusercontent.com/pooya-mohammadi/deep-utils-notebooks/main/vision/images/dog.jpg")

# Load an image

base_image = cv2.imread("dog.jpg")

# pil.Image is used for visualization

Image.fromarray(base_image[..., ::-1])  # convert to rgb

# visualize using oepncv

# show_destroy_cv2(base_image)

```

The result:



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3. Detect and visualize Objects

```python

# Detect the objects

# the image is opened by cv2 which results to a BGR image. Therefore the `is_rgb` is set to `False`

result = yolov5.detect_objects(base_image, is_rgb=False, confidence=0.5)

# Draw detected boxes on the image.

img = Box.put_box_text(base_image,

                       box=result.boxes,

                       label=[f"{c_n} {c}" for c_n, c in zip(result.class_names, result.confidences)])

# pil.Image is used for visualization

Image.fromarray(img[..., ::-1])  # convert to rgb

# visualize using oepncv

# show_destroy_cv2(img)

```



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## Visual Grounding

### Grounding DINO

DINO is a self-supervised learning method for visual grounding. It is a simple and efficient method that can be used for

visual grounding. Let's see how we can use it in `deep_utils`:

#### Download a sample image

```commandline

wget -q https://github.com/pooya-mohammadi/deep_utils/releases/download/1.0.2/golsa_in_garden.jpg

```

```python

from PIL import Image

from deep_utils import Text2BoxVisualGroundingDino

import numpy as np

import matplotlib.pyplot as plt

model = Text2BoxVisualGroundingDino()

img_path = "golsa_in_garden.jpg"

img = np.asarray(Image.open(img_path))

output = model.text_to_box(text="Hen", img=img)

print(output.boxes, output.scores, output.labels)

annotated_img = model.annotate(img, output)

plt.axis("off")

plt.imshow(annotated_img)

```

Output Image:




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## Image Editing

### GLIDE

GLIDE is a simple and efficient method for image editing/inpainting. It is developed by OpenAI.

#### Download a sample image

```commandline

wget -q https://github.com/pooya-mohammadi/deep_utils/releases/download/1.0.2/golsa_in_garden.jpg

```

**Input Image**:




```python

import matplotlib.pyplot as plt

from deep_utils import ImageEditingGLIDE

from PIL import Image

pil_img = Image.open("golsa_in_garden.jpg")

# position of the editing box. Here the hen in the image. The  

box = [340.6672668457031, 403.7683410644531, 372.0812072753906, 439.3288879394531]

glide_model = ImageEditingGLIDE()

text = "dead leaves"

edited_image = glide_model.edit_box(pil_img, text=text, box=box)

plt.imshow(edited_image)

```

**Output Image**: The `hen` is removed and replaced with `dead leaves` as the background




**Note:** The best way to get the box is to use the `Text2BoxVisualGroundingDino` model. See the example in the previous

section. Or check the following full sample [easy_image_editing](https://github.com/pooya-mohammadi/easy_image_editing)

## NLP

In this section, models and utilities for nlp projects are provided

### NER

Name Entity Recognition

#### multi-label-stratify

## Augmentation

### CutMix

   

CutMix is one of the best augmentation methods that's proven to be very effective in different vision-based project.

Therefore, CutMix is now

available on `deep_utils` to be used both for segmentation and classification tasks.Let some examples:

#### Segmentation

```python

import cv2

import numpy as np

from deep_utils import CutMixTF, group_show, repeat_dimension

# creating random images, the code for this section can be found in the colab notebook

image_a = np.zeros((300, 300, 3), np.uint8) * 255

mask_a = np.zeros_like(image_a)

pt1 = (150, 100)

pt2 = (100, 200)

pt3 = (200, 200)

triangle_cnt = np.array([pt1, pt2, pt3])

image_a = cv2.drawContours(image_a, [triangle_cnt], 0, (0, 255, 0), -1)

mask_a = cv2.drawContours(mask_a, [triangle_cnt], 0, (255, 255, 255), -1)

image_b = np.zeros((300, 300, 3), np.uint8) * 255

mask_b = np.zeros_like(image_b)

pt1 = (150, 150)

image_b = cv2.circle(image_b, pt1, 50, (0, 255, 0), -1)

mask_b = cv2.circle(mask_b, pt1, 50, (255, 255, 255), -1)

# CutMix for two individual images:

cutmix_img, cutmix_mask = CutMixTF.seg_cutmix(image_a, mask_a[..., 0], image_b, mask_b[..., 0], beta=1)

```

The input and output are as follows:

**Input:**




**Output:**



As it illustrated in the above image a section of the triangle and the circle are combined together. By

changing `seg_cutmix` to `seg_cutmix_batch` one can use CutMix augmentation for batch of images.

```python

cutmix_img, cutmix_mask = CutMixTF.seg_cutmix_batch(a_images=batch_img, a_masks=batch_mask[..., 0], beta=1)

```

**Input:**




**Output:**



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## Utils

In this section, various utility functions are provided.

### DictNamedTuple

In this custom data type, we have added the methods of the Dict type to the NamedTuple type. You have access to .get(),

.values(), .items() alongside all of the functionalities of a NamedTuple. Also, all the outputs of our models are

DictNamedTuple, and you can modify and manipulate them easily. Let's see how to use it:

```

from deep_utils import dictnamedtuple

# create a new object

dict_object = dictnamedtuple(typename='letters', field_names=['firstname', 'lastname'])

# pass the values

instance_dict = dict_object(firstname='pooya', lastname='mohammadi')

# get items and ...

print("items: ", instance_dict.items())

print("keys: ", instance_dict.keys())

print("values: ", instance_dict.values())

print("firstname: ", instance_dict.firstname)

print("firstname: ", instance_dict['firstname'])

print("lastname: ", instance_dict.lastname)

print("lastname: ", instance_dict['lastname'])

```

```

# results

items:  [('firstname', 'pooya'), ('lastname', 'mohammadi')]

keys:  ['firstname', 'lastname']

values:  ['pooya', 'mohammadi']

firstname:  pooya

firstname:  pooya

lastname:  mohammadi

lastname:  mohammadi

```

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### Multi-Label-Stratify

While splitting a dataset for NER or Object detection tasks, you might have noticed that there is no way to split the

dataset using

stratify functionality of `train_test_split` of the `scikit-learn` library because not only does each sample in these

two tasks may

have

more than one tag/object, but also each tag/object of each class may appear more than once. For example, an image/sample

may

contain two dogs and three cats, which means the label/y of that sample would be like [2, 3] in which the index zero

corresponds

to the dog class, and the index one corresponds to the cat class.

To split these types of datasets, the following function is

developed in the `deep_utils` library which is very easy to use. To use this function, two arrays are needed. The first

is an array

or list containing the input samples. The type of these samples could be anything; they could be a list of sentences, a

list of

paths to input images, or even structured data like the one in the following example. The other array, however, must be

a 2D ndarray whose first dimension is equal to the number of samples, and the second dimension is equal to the number

of the classes. Likewise, each index is correspondent to a class, and each element of this array shows the number of

each sample in a specific class. For example, the element in index `[0, 0]` of the following array

`[[1, 0], [3, 3]]`, which is equal to 1, shows that the sample 0 contains 1 item of the first class or the class that

corresponds to index zero. Now, let's see an example:

```commandline

>>> from deep_utils import stratify_train_test_split_multi_label

>>> x = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [1, 2], [3, 4], [1, 2], [3, 4]])

>>> y = np.array([[0, 0], [0, 0], [0, 1], [0, 1], [1, 1], [1, 1], [1, 0], [1, 0]])

>>> x_train, x_test, y_train, y_test = stratify_train_test_split_multi_label(x, y, test_size=0.5, closest_ratio=False)

>>> x_train

    array([[1, 2],

           [3, 4],

           [1, 2],

           [3, 4]])

>>> x_test

array([[1, 2],

       [3, 4],

       [1, 2],

       [3, 4]])

>>> y_train

array([[0, 1],

       [0, 1],

       [1, 0],

       [1, 0]])

>>> y_test

array([[1, 1],

       [1, 1],

       [0, 0],

       [0, 0]])

>>> print("class ratio:", tuple(y_test.sum(0) / y.sum(0)))

class ratio: (0.5, 0.5)

>>> print("sample ratio:", y_test.shape[0] / y.shape[0])

sample ratio: 0.5

```

As clearly shown in the results, both the sample and the class ratios are preserved. In some datasets,

it's impossible to get the exact expected ratio, so the function will split the input dataset in a way that it would

be the closest ratio to the expected one. Link to code:

https://github.com/pooya-mohammadi/deep_utils/blob/master/deep_utils/utils/multi_label_utils/stratify/stratify_train_test_split.py

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## Tests

Tests are done for python 3.8 and 3.9. Deep-Utils will probably run without any errors on lower versions as well.

**Note**: Model tests are done on CPU devices provided by GitHub Actions. GPU based models are tested manually by the

authors.

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## 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 ⭐️! Thanks again!

1. Fork the Project

2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)

3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)

4. Push to the Branch (`git push origin feature/AmazingFeature`)

5. Open a Pull Request

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## 🌟 Spread the word!

If you want to say thank you and/or support active development of the repo:

- Add a GitHub Star to the project!

- Join our discord servers [Deep Utils](https://discord.gg/pWe3yChw) .

- Follow my profile [pooya-mohammadi](https://github.com/pooya-mohammadi)

Thanks so much for your interest in growing the reach of the repo!

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## ⚠️ License

Distributed under the MIT License. See `LICENSE` for more information.

The LICENSE of each model is located inside its corresponding directory.

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## 🤝 Collaborators

  

    

      

        


        _{Pooya Mohammadi Kazaj}

      

    

    

      

        


        _{Vargha Khallokhi}

      

    

    

      

        


        _{Zahra Zamanshoar}

      

    

    

      

        


        _{Dorna Sabet}

      

    

    

      

        


        _{Menua Bedrosian}

      

    

    

      

        


        _{Alireza Kazemipour}

      

    

  

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## Contact

Pooya Mohammadi:

* LinkedIn [www.linkedin.com/in/pooya-mohammadi](www.linkedin.com/in/pooya-mohammadi)

* Email: [[email protected]]([email protected])

Project's Link: [https://github.com/pooya-mohammadi/deep_utils](https://github.com/your_username/repo_name)

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## References

1. Tim Esler's facenet-pytorch

   repo: [https://github.com/timesler/facenet-pytorch](https://github.com/timesler/facenet-pytorch)

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## Citation

Please cite deep-utils if it helps your research. You can use the following BibTeX entry:

```

@misc{deep_utils,

	title = {deep_utils},

	author = {Mohammadi Kazaj, Pooya},

	howpublished = {\url{github.com/pooya-mohammadi/deep_utils}},

	year = {2021}

}

```

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