Minos_CodeExample01.py

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import os
import cvb.minos
import cvb.foundation
 
if __name__ == '__main__':
 
    # Load the image
    image = cvb.Image.load(os.path.join(cvb.install_path(), "Tutorial/Minos/Images/Clara/Clara1.bmp"))
 
    # Load the classifier
    classifier = cvb.minos.Classifier(os.path.join(cvb.install_path(), "Tutorial/Minos/Images/Clara/Clara.clf"))
 
    # define the search area: complete image
    w = image.width
    h = image.height
    search_aoi = cvb.Area2D(cvb.Point2D(0.0, 0.0),      # top left
                            cvb.Point2D(w-1, 0.0),      # top right
                            cvb.Point2D(0.0, h-1))      # bottom left
 
    # Perform pattern recognition
    search_result = classifier.search(image.planes[0], cvb.minos.SearchMode.FindFirst, search_aoi, density=1000)
    x = search_result.position.x
    y = search_result.position.y
    q = search_result.quality
    print(x, y, q)     # x=94.0, y=135.0, q=26.0
 
    # Refine the result to subpixel accuracy
    search_aoi = cvb.Area2D(cvb.Point2D(x-10, y-10),      # top left
                            cvb.Point2D(x+10, y-10),      # top right
                            cvb.Point2D(x-10, y+10))      # bottom left
    search_result = classifier.search(image.planes[0], cvb.minos.SearchMode.FindBestSubPixel, search_aoi, density=1000)
    x = search_result.position.x
    y = search_result.position.y
    q = search_result.quality
    print(x, y, q)     # x=93.999, y=137.001, q=1.0
 
    # Let's add some noise to the image
    image = cvb.foundation.add_uniform_noise(image, -150, 150, seed=42)
    search_result = classifier.search(image.planes[0], cvb.minos.SearchMode.FindBestSubPixel, search_aoi, density=1000)
    x = search_result.position.x
    y = search_result.position.y
    q = search_result.quality
    print(x, y, q)     # x=94.964, y=137.121, q=0.802