initial commit

.gitignore

@@ -0,0 +1,134 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+# vsc env 
+.vscode
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# doc
+*.pdf

README.MD

@@ -0,0 +1,96 @@
+# Computer Vision Hello World!
+
+## Overview
+
+Computer Vision Hello World is a collection of computer vision projects tailored for aspiring Computer Vision Engineers embarking on their journey in the field.
+
+The project has 3 parts:
+
+- Part 1: Getting Started with Basics 
+- Part 2: Hands-on Computer Vision with Deep Learning
+- Part 3: Vision LLMs: GANs, Image generations, AI Art & Image Captioning
+
+## Requirements
+
+```sh
+pip install -r requirements.txt
+```
+
+## Part 1: Getting Started with Basics 
+
+||Project|Description|
+|--|--|--|
+|[1](#)| Image Loading and Display|	Load an image, display it, and apply basic transformations.|
+|[2](#)| Image Filtering|	Enhance or manipulate image features using filtering techniques.|
+|[3](#)| Color Space Conversion|	Convert images between different color spaces like RGB, HSV, and YUV.|
+|[4](#)| Image Thresholding|	Segment an image into two or more regions based on pixel intensity values.|
+|[5](#)| Blob Detection|	Identify and extract connected regions in an image.|
+|[6](#)| Contour Tracking|	Follow the movement of objects in a video sequence.|
+|[7](#)| Template Matching|	Find instances of a template image within a larger image.|
+|||
+
+## Part 1.2: Getting Started with Basics 2 
+
+||Project|Description|
+|--|--|--|
+|[1](#)|Face Detection|Detect and localize human faces in images and videos with high accuracy.|
+|[2](#)|Face-Detection-using-deep-learning |Identify and localize human faces in images and videos with high accuracy using deep convolutional neural networks.|
+|[3](#)|Feature Extraction|Extract relevant features from raw data to improve machine learning algorithms.|
+|[4](#)|Face Boundary Detection|Detect, localize, and segment the face boundaries and facial features in an image.|
+|[5](#)|Rectangle Drawing|Visualize 2D rectangles with precise dimensions and orientations using computer vision techniques.|
+|[6](#)|2D-Object-Detection-using-Deep-Learning |Identify and locate objects in images using deep neural networks for computer vision tasks.|
+|[7](#)|2D-Object-Tracking |Identify and follow objects in videos, maintaining their positions and movements over time|
+|[8](#)|3D-object-tracking |Monitor and localize objects in 3D space, establishing unique IDs across frames.|
+|[9](#)|Human-Pose-Estimation-using-Deep-Learning |Track and analyze the positions of human joints in images and videos using deep neural networks to identify body postures and gestures.|
+|[10](#)|Image to Text|output a text from a given image|
+|||
+
+## Part 2: Hands-on Computer Vision with Deep Learning
+
+||Project|Description|
+|--|--|--|
+|[1](#)|MNIST Handwritten Digit Recognition	|Train a simple neural network to classify handwritten digits from the MNIST dataset.|
+|[2](#)|CIFAR-10 Image Classification	|Utilize convolutional neural networks (CNNs) to classify images of different types of objects from the CIFAR-10 dataset.|
+|[3](#)|Image Captioning	|Explore image captioning using CNNs to generate natural language descriptions of images.|
+|[4](#)|Object Detection with YOLOv5	|Implement YOLOv5, a real-time object detection algorithm, to detect objects in images and videos.|
+|[5](#)|Semantic Segmentation with DeepLabv3+	|Utilize DeepLabv3+, a semantic segmentation model, to segment images into different semantic categories.|
+|[6](#)|Facial Recognition with OpenFace	|Explore facial recognition using OpenFace, a facial recognition library, to identify individuals in images.|
+|[7](#)|Style Transfer with Neural Style |Transfer	Introduce neural style transfer to transfer the style of one artwork to another image.|
+|||
+
+
+## Part 3: Vision LLMs: GANs, Image generations, AI Art & Image Captioning 
+
+||Project|Description|
+|--|--|--|
+|[1](#)|Creative Image Generation with GANs	|Generate novel images of different styles using GANs.|
+|[2](#)|Text-to-Image Synthesis with LLMs and Diffusion Models	|Create realistic and creative images from text descriptions using LLMs and diffusion models.|
+|[3](#)|AI-Powered Image Restoration and Enhancement	|Restore and enhance images using AI methods.|
+|[4](#)|Style Transfer with GANs and Image Processing	|Transfer the artistic style of one image to another.|
+|[5](#)|AI-Driven Image Captioning and Storytelling	|Generate comprehensive and creative captions and stories from images using LLMs.|
+|[6](#)|AI-Assisted Image Editing and Manipulation	|Automate image editing and manipulation tasks using AI.|
+|[7](#)|AI-Powered Image Analysis and Classification	|Analyze and classify images using AI models|
+|||
+
+## Usage
+
+Some projects are implemented in Jupyter notebooks, while others have a `main.py` file.
+
+For projects implemented in Jupyter notebooks, run the notebook using:
+
+- jupyter notebook/lab
+- Google Colab
+
+For projects with a main.py file, run the following command:
+
+```
+python main.py
+```
+
+## Contributing
+
+Feel free to open a pull request or create an issue if you encounter any problem running one these projects.
+
+## LICENSE
+
+- MIT

requirements.txt

@@ -0,0 +1,4 @@
+numpy
+pandas
+cv2
+matplotlib