erinlin / junkyard Goto Github PK

1. General object classifier
2. OpenCV Filtering GUI
3. Python notes and links to interesting places
4. Manual image annotation with polygons
5. Manual image annotation with rectangles
6. Image viewer
7. Advanced zoom
8. C extension for Python
9. OpenCV + Tkinter snapshot GUI
10. OpenCV features
11. Multilanguage for Python
12. Dynamic menu
13. Rolling window for 2D array
14. SIFT object tracking
15. Tkinter progressbar

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:

1. model training script - Jupyter (Colab) script to train a classification model.
2. web app - starter project to deploy a trained classification model to the web.
3. 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.

Bookmarks to remember and re-visit.

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.

Example of the rolling window for 2D array. It has zero Python cycles inside, so the speed is the same as in C programming language.

Example of the objects tracking using SIFT algorithm. SIFT algorithm is free of charge for non-commercial projects.

1. Open GUI: python sift_tracking.py.
2. Place object in front of webcamera, so it take all visible space.
3. Press "Snapshot!" button. Application will make snapshort of the object to track.
4. After taking snapshort there will be blue rectangle around tracking object and green lines connecting specian points of the image.

Rectangular objects, like books, are tracked better than faces.

Example of the Tkinter progressbar GUI.