fragmentation-analysis-HED

Fragmentation Analysis is a key check used by mining engineers after blasting to determine the efficacy of blast or blast accuracy. It focuses on checking the average size of rocks/fragments generated after blast, This is a image processing / computer vision approach using Holistically Nested Edge Detection Algorithm (HED)

This is a standard usecase and can be used to find size of unknown objects with clear boundaries in terms of pixels in a stabilised camera with consistent camera position.

Following is a series of snap showing the two phases (Raw | Fragmented Image)

1. Python3.7
2. If Docker Approach is selcted:
   1. Docker
   2. Docker Compose

The program can run in either of the two following ways:

1. Running the python file directly by passing the video url with the python file (Put your images in the images folder to save all output in the same folder):

   pip install -r dockerized_flask_server/web/requirements.txt
   

   python fragmentation_analysis.py --input ./images/Readme/demo_image.jpg
   

2. I've also made it as a complete dockerized server with proper authentication. Following is the way to run the dockerized version:

   cd dockerized_flask_container
   

   sudo docker-compose build
   

   sudo docker-compose up
   

Resource Chart for Rest API:

Resources	URL	Method	Param	Status	Param Body type
Register	/register	post	uname, pass	200 OK, 301 username already exist	JSON
Fragment	/fragment	post	uname, pass, image_file_base64	200 OK, 301, 302 Incorrect id or pass, 303 Out of token, 304 Invalid input	JSON

Database.ini:

I have used a public database but you can replace it with your credentials in the web/req_files/database.ini file