- Study topics and tasks for students
- Fine-tuning and feature extraction
- Examples of data visualization
- Coral TPU
- PyTorch Tutorial
- Object tracking
- Python notes and links to interesting places
- Image Processing with Python
- General object classifier
- OpenCV Filtering GUI
- Sending emails with Python
- Manual image annotation with polygons
- Manual image annotation with rectangles
- Image viewer
- Advanced zoom
- C extension for Python
- OpenCV + Tkinter snapshot GUI
- OpenCV features
- Multilanguage for Python
- Dynamic menu
- Rolling window on NumPy arrays
- Tkinter progressbar
Exercises for students and programmers with independent work on assignment.
Image-to-text training pipeline. Feature extractors, i.e., dots in the feature space, for ViT, ResNet50 and VAE models.
Examples of data visualization using matplotlib library.
Python scripts for Google Coral USB Accelerator.
PyTorch tutorial with Aladdin Persson
in Google CoLab.
In this folder are Jupyter *.ipynb copies.
Here is the online CoLab
replica of this
original course.
Object tracking using OpenCV feature detectors (detectors) and descriptor extractors (descriptors) algorithms with GUI for fun, tests and education.
Previous simple script is here SIFT object tracking. SIFT algorithm became free since March 2020.
Bookmarks to remember and re-visit.
My replica of this original course: Image Processing with Python.
Classifies 3 types of bears: bronw, black and teddy bear.
Object classifier is based on:
- Python and fast.ai for model training through deep learning;
- Render cloud provider to deploy your code in web.
- Flutter mobile development framework with a single code base for Android and iOS applications;
- Firebase for Google Analytics.
And consists of 3 components:
- model training script - Jupyter (Colab) script to train a classification model.
- web app - starter project to deploy a trained classification model to the web.
- mobile app - mobile application which connect your web app with mobile phone (tested for Android).
OpenCV Filtering GUI is a set of various realtime filters to process images from the webcam. This GUI is based on the previous OpenCV features demo with enhanced Tkinter controls for user-friendly OpenCV real-time filters demonstration.
Sending emails with Python.
Manual image annotation opens image where user can select polygon areas around the objects of interest. After selecting region of interest user presses menu button and program cuts rectangular images from selected polygons with a scanning window.
Manual image annotation creates rectangular images with selected areas of interest (ROI). User opens image and selects rectangular areas of interest. After selecting rectangles and pressing menu button program cuts rectangle images from the bigger image.
Image viewer shows image and prints coordinates of the rectangular area in the console.
Advanced zoom for images of various formats and sizes from small to huge up to several GB.
C language extension for Python language by example of co-occurrence matrix calculation.
Take shapshot using webcamera, OpenCV and Tkinter. Example is well documented and has many comments inside.
Demo of various OpenCV features. Application is tested for Windows OS and requires webcam. There is a newer version with GUI.
How-to implement multilanguage for Python.
Example of the dynamic menu for Tkinter GUI.
General examples for 1D, 2D, 3D and MD rolling window arrays in the on-line CoLab notebook.
It has zero Python cycles inside, so the speed is the same as in C programming language.
My previous examples of the rolling window for 2D array are here and here.
Example of the Tkinter progressbar GUI.
