EagleView

Project: Binary Mask Creation

Overview: This project processes a set of .jpg, .jpeg and .png images by creating a binary mask for each image and stores them. The binary mask identifies pixels where all three channels (RGB) have values above 200 (on an 8-bit scale).

• For each mask:
  • Pixels meeting the condition (i.e., all channels are above 200) are marked as white (255).
  • Pixels not meeting the condition are marked as black (0).
  • Along with mask creation, the program stores the number of white pixel values in the current image.
  • It sums up the count across all images and, at the end, displays the total count of white pixels.

Features:

• Parallel processing: Used to improve performance when handling multiple images.
• Handles a variety of images: Supports variety image formats .jpg, .jpeg and .png formats.

Requirements:

• Python 3.12.2
• OpenCV library 4.5.5.64
• numpy 1.26.4

Building the Project:

1. Run activate.cmd

   • This will activate the virtual environment and install the required dependencies.
   • Once the environment is activated, place the required .jpg/.png images in the input_data folder.

2. Run the program

   python parallel_processing.py
   

Example: In the input_data folder already 3 input images are there. once the script is run it will create the masked images and stores them in the output_data folder with corresponding image names suffixed with _mask and prints the output: Total number of max pixels in all images: 23650