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Kaki Detection Script

This script processes an image to detect and estimate the number of kakis (persimmons) by identifying the common orange areas using a Gaussian fit method with least squares estimation.

Overview

The script performs the following steps:

  • Reads an image of kakis.
  • Converts the image to the HSV color space.
  • Applies a median blur to reduce noise.
  • Masks the image to isolate the orange color of the kakis.
  • Dilates the mask to ensure kakis are properly highlighted.
  • Extracts the kakis from the original image using the mask.
  • Finds contours of the masked kakis.
  • Estimates the number of kakis based on the most common area obtained from a Gaussian fit of the area histogram.
  • Draws contours around the detected kakis and saves the output images.

Requirements

To run this script, you'll need Python installed on your system along with the following libraries:

  • numpy
  • opencv-python
  • scipy

You can install these libraries using pip with the following command:

pip install numpy opencv-python scipy

Usage

To use the script, follow these steps:

  1. Place your image of kakis in the ./img/ directory and name it kaki.JPG.
  2. Run the script with the command python kaki_detection.py.
  3. The script will output two images:
    • masked_kaki.jpg: Shows the kakis with the mask applied.
    • contoured_kaki.jpg: Displays the original kakis with contours drawn around them.
  4. The estimated count of kakis will be printed to the console.

Files

  • kaki_detection.py: The main script file.
  • ./img/kaki.JPG: Input image file (you need to provide this).
  • ./img/masked_kaki.jpg: Output image with kakis masked.
  • ./img/contoured_kaki.jpg: Output image with contours drawn around kakis.

This document was generated by an AI model from OpenAI.