ImageProcessing

Defect (Blobs) detection using Python in 3D Printed artifacts This code performs defect detection using blob detection in a collection of positive and negative images, It calculates the accuracy of defect detection based on the number of images where defects were correctly detected.

Requirements

• Python 3.x
• OpenCV (cv2) library

Installation

1. Clone the repository or download the code files.
2. Install the required dependencies using pip:

Usage

1. Prepare your positive and negative images:

• Create two separate directories for positive and negative images.
• Place your positive images in one directory and negative images in the other.

2. Update the code:

• Open the code file in a text editor.
• Update the positive_directory and negative_directory variables with the paths to your positive and negative image directories.

3. Run the code:

• Execute the code in a Python environment.
• The code will resize the images, perform defect detection using blob detection, and display the images with detected blobs.
• The accuracy of defect detection will be calculated and displayed.

Customization

You can customize the blob detection parameters by modifying the params object in the detect_defects function. Adjust the threshold values, area constraints, circularity, convexity, and inertia parameters based on your specific requirements.

Notes

• The code assumes that the images are in grayscale format. If your images are in color, you may need to adjust the code accordingly.
• The accuracy calculation is based on the number of images where defects were detected. It does not take into account the type or severity of the defects.