Normaly houghtransfer uses mindistance between 2 circle centers but this does not work. included is my code and one of my lab results. I use multiple morphological steps to enhance the result.

My code:

# -*- coding: utf-8 -*-
"""
Created on Mon Mar  2 14:22:03 2020

@author: Fabio Mirisola
"""

    import numpy as np
    import cv2 as cv
    from skimage import morphology

    # Import image
    data = cv.imread("data.tif",cv.IMREAD_GRAYSCALE)

    # Import background
    background = cv.imread("background.tif",cv.IMREAD_GRAYSCALE)

    # Subtracting background from data
    cv_subt = cv.subtract(data,background)

    # Cropping the image
    Xcoord = 350 
    YCoord = 250
    Width = 1330
    Height = 1330
    crop_img = cv_subt[YCoord:YCoord+Height, Xcoord:Xcoord+Width]
    ret,thresh = cv.threshold(crop_img,25,255, cv.THRESH_BINARY)

    # Removing small pixels 
    thresh_bool = thresh.astype('bool')
    cleaned = morphology.remove_small_objects(thresh_bool, min_size=70, connectivity=5)
    im_bin = cleaned.astype('uint8')
    image = (im_bin)*255

    # Inversing the image
    ret,inverse = cv.threshold(image,5,255, cv.THRESH_BINARY_INV)

    # Opening the holes
    kernel = cv.getStructuringElement(shape=cv.MORPH_RECT, ksize=(4,4))
    opening = cv.morphologyEx(inverse, cv.MORPH_OPEN, kernel, iterations = 3)

    closing = cv.morphologyEx(opening, cv.MORPH_CLOSE, kernel, iterations = 1)
    closing_bool = closing.astype('bool')
    cleaned2 = morphology.remove_small_objects(thresh_bool, min_size=300, connectivity=400)
    cleaned2_bin = cleaned2.astype('uint8')
    image2 = (cleaned2_bin)*255


    # Detected circles

    circles = cv.HoughCircles(image2,3,cv.HOUGH_GRADIENT,1,30,param1=140,param2=45,minRadius=10,maxRadius=45)
    circles = np.uint16(np.around(circles))

    """
    Parameters 1 and 2 don't affect accuracy as such, more reliability. 
    Param 1 will set the sensitivity; how strong the edges of the circles need to be. 
    Too high and it won't detect anything, too low and it will find too much clutter. 
    Param 2 will set how many edge points it needs to find to declare that it's found a circle. 
    Again, too high will detect nothing, too low will declare anything to be a circle. 
    The ideal value of param 2 will be related to the circumference of the circles. 
    """

    #Drawing circles

    detected = cv.cvtColor(crop_img,cv.COLOR_GRAY2BGR)

    for i in circles[0,:]:
            #   draw    the outer   circle
            cv.circle(detected,(i[0],i[1]),i[2],(0,255,0),1)
            #   draw    the center  of  the circle
            cv.circle(detected,(i[0],i[1]),2,(0,0,255),3)



    #Show img
    """
    cv.imshow("detected", detected)
    cv.waitKey(0)
    cv.destroyWindow("detected")
    """

    #Save image

    cv.imwrite("detected.jpg", detected)

In the picture you can see that it clearly detects the bubble correctly but then it detects is plenty more time.

Normaly houghtransfer uses mindistance between 2 circle centers but this does not work. included is my code and one of my lab results. I use multiple morphological steps to enhance the result.

My code:

# -*- coding: utf-8 -*-
"""
Created on Mon Mar  2 14:22:03 2020

@author: Fabio Mirisola
"""

    import numpy as np
    import cv2 as cv
    from skimage import morphology

    # Import image
    data = cv.imread("data.tif",cv.IMREAD_GRAYSCALE)

    # Import background
    background = cv.imread("background.tif",cv.IMREAD_GRAYSCALE)

    # Subtracting background from data
    cv_subt = cv.subtract(data,background)

    # Cropping the image
    Xcoord = 350 
    YCoord = 250
    Width = 1330
    Height = 1330
    crop_img = cv_subt[YCoord:YCoord+Height, Xcoord:Xcoord+Width]
    ret,thresh = cv.threshold(crop_img,25,255, cv.THRESH_BINARY)

    # Removing small pixels 
    thresh_bool = thresh.astype('bool')
    cleaned = morphology.remove_small_objects(thresh_bool, min_size=70, connectivity=5)
    im_bin = cleaned.astype('uint8')
    image = (im_bin)*255

    # Inversing the image
    ret,inverse = cv.threshold(image,5,255, cv.THRESH_BINARY_INV)

    # Opening the holes
    kernel = cv.getStructuringElement(shape=cv.MORPH_RECT, ksize=(4,4))
    opening = cv.morphologyEx(inverse, cv.MORPH_OPEN, kernel, iterations = 3)

    closing = cv.morphologyEx(opening, cv.MORPH_CLOSE, kernel, iterations = 1)
    closing_bool = closing.astype('bool')
    cleaned2 = morphology.remove_small_objects(thresh_bool, min_size=300, connectivity=400)
    cleaned2_bin = cleaned2.astype('uint8')
    image2 = (cleaned2_bin)*255


    # Detected circles

    circles = cv.HoughCircles(image2,3,cv.HOUGH_GRADIENT,1,30,param1=140,param2=45,minRadius=10,maxRadius=45)
    circles = np.uint16(np.around(circles))

    """
    Parameters 1 and 2 don't affect accuracy as such, more reliability. 
    Param 1 will set the sensitivity; how strong the edges of the circles need to be. 
    Too high and it won't detect anything, too low and it will find too much clutter. 
    Param 2 will set how many edge points it needs to find to declare that it's found a circle. 
    Again, too high will detect nothing, too low will declare anything to be a circle. 
    The ideal value of param 2 will be related to the circumference of the circles. 
    """

    #Drawing circles

    detected = cv.cvtColor(crop_img,cv.COLOR_GRAY2BGR)

    for i in circles[0,:]:
            #   draw    the outer   circle
            cv.circle(detected,(i[0],i[1]),i[2],(0,255,0),1)
            #   draw    the center  of  the circle
            cv.circle(detected,(i[0],i[1]),2,(0,0,255),3)



    #Show img
    """
    cv.imshow("detected", detected)
    cv.waitKey(0)
    cv.destroyWindow("detected")
    """

    #Save image

    cv.imwrite("detected.jpg", detected)

In the picture you can see that it clearly detects the bubble correctly but then it detects is plenty more time.

Normaly houghtransfer uses mindistance between 2 circle centers but this does not work. included is my code and one of my lab results. I use multiple morphological steps to enhance the result.

My code:

# -*- coding: utf-8 -*-
"""
Created on Mon Mar  2 14:22:03 2020

@author: Fabio Mirisola
"""

    import numpy as np
    import cv2 as cv
    from skimage import morphology

    # Import image
    data = cv.imread("data.tif",cv.IMREAD_GRAYSCALE)

    # Import background
    background = cv.imread("background.tif",cv.IMREAD_GRAYSCALE)

    # Subtracting background from data
    cv_subt = cv.subtract(data,background)

    # Cropping the image
    Xcoord = 350 
    YCoord = 250
    Width = 1330
    Height = 1330
    crop_img = cv_subt[YCoord:YCoord+Height, Xcoord:Xcoord+Width]
    ret,thresh = cv.threshold(crop_img,25,255, cv.THRESH_BINARY)

    # Removing small pixels 
    thresh_bool = thresh.astype('bool')
    cleaned = morphology.remove_small_objects(thresh_bool, min_size=70, connectivity=5)
    im_bin = cleaned.astype('uint8')
    image = (im_bin)*255

    # Inversing the image
    ret,inverse = cv.threshold(image,5,255, cv.THRESH_BINARY_INV)

    # Opening the holes
    kernel = cv.getStructuringElement(shape=cv.MORPH_RECT, ksize=(4,4))
    opening = cv.morphologyEx(inverse, cv.MORPH_OPEN, kernel, iterations = 3)

    closing = cv.morphologyEx(opening, cv.MORPH_CLOSE, kernel, iterations = 1)
    closing_bool = closing.astype('bool')
    cleaned2 = morphology.remove_small_objects(thresh_bool, min_size=300, connectivity=400)
    cleaned2_bin = cleaned2.astype('uint8')
    image2 = (cleaned2_bin)*255


    # Detected circles

    circles = cv.HoughCircles(image2,3,cv.HOUGH_GRADIENT,1,30,param1=140,param2=45,minRadius=10,maxRadius=45)
    circles = np.uint16(np.around(circles))

    """
    Parameters 1 and 2 don't affect accuracy as such, more reliability. 
    Param 1 will set the sensitivity; how strong the edges of the circles need to be. 
    Too high and it won't detect anything, too low and it will find too much clutter. 
    Param 2 will set how many edge points it needs to find to declare that it's found a circle. 
    Again, too high will detect nothing, too low will declare anything to be a circle. 
    The ideal value of param 2 will be related to the circumference of the circles. 
    """

    #Drawing circles

    detected = cv.cvtColor(crop_img,cv.COLOR_GRAY2BGR)

    for i in circles[0,:]:
            #   draw    the outer   circle
            cv.circle(detected,(i[0],i[1]),i[2],(0,255,0),1)
            #   draw    the center  of  the circle
            cv.circle(detected,(i[0],i[1]),2,(0,0,255),3)



    #Show img
    """
    cv.imshow("detected", detected)
    cv.waitKey(0)
    cv.destroyWindow("detected")
    """

    #Save image

    cv.imwrite("detected.jpg", detected)

In the picture you can see that it clearly detects the bubble correctly but then it detects is plenty more time.

Normaly houghtransfer uses mindistance between 2 circle centers but this does not work. included is my code and one of my lab results. I use multiple morphological steps to enhance the result.

My code:

# -*- coding: utf-8 -*-
"""
Created on Mon Mar  2 14:22:03 2020

@author: Fabio Mirisola
"""

    import numpy as np
    import cv2 as cv
    from skimage import morphology

    # Import image
    data = cv.imread("data.tif",cv.IMREAD_GRAYSCALE)

    # Import background
    background = cv.imread("background.tif",cv.IMREAD_GRAYSCALE)

    # Subtracting background from data
    cv_subt = cv.subtract(data,background)

    # Cropping the image
    Xcoord = 350 
    YCoord = 250
    Width = 1330
    Height = 1330
    crop_img = cv_subt[YCoord:YCoord+Height, Xcoord:Xcoord+Width]
    ret,thresh = cv.threshold(crop_img,25,255, cv.THRESH_BINARY)

    # Removing small pixels 
    thresh_bool = thresh.astype('bool')
    cleaned = morphology.remove_small_objects(thresh_bool, min_size=70, connectivity=5)
    im_bin = cleaned.astype('uint8')
    image = (im_bin)*255

    # Inversing the image
    ret,inverse = cv.threshold(image,5,255, cv.THRESH_BINARY_INV)

    # Opening the holes
    kernel = cv.getStructuringElement(shape=cv.MORPH_RECT, ksize=(4,4))
    opening = cv.morphologyEx(inverse, cv.MORPH_OPEN, kernel, iterations = 3)

    closing = cv.morphologyEx(opening, cv.MORPH_CLOSE, kernel, iterations = 1)
    closing_bool = closing.astype('bool')
    cleaned2 = morphology.remove_small_objects(thresh_bool, min_size=300, connectivity=400)
    cleaned2_bin = cleaned2.astype('uint8')
    image2 = (cleaned2_bin)*255


    # Detected circles

    circles = cv.HoughCircles(image2,3,cv.HOUGH_GRADIENT,1,30,param1=140,param2=45,minRadius=10,maxRadius=45)
    circles = np.uint16(np.around(circles))

    """
    Parameters 1 and 2 don't affect accuracy as such, more reliability. 
    Param 1 will set the sensitivity; how strong the edges of the circles need to be. 
    Too high and it won't detect anything, too low and it will find too much clutter. 
    Param 2 will set how many edge points it needs to find to declare that it's found a circle. 
    Again, too high will detect nothing, too low will declare anything to be a circle. 
    The ideal value of param 2 will be related to the circumference of the circles. 
    """

    #Drawing circles

    detected = cv.cvtColor(crop_img,cv.COLOR_GRAY2BGR)

    for i in circles[0,:]:
            #   draw    the outer   circle
            cv.circle(detected,(i[0],i[1]),i[2],(0,255,0),1)
            #   draw    the center  of  the circle
            cv.circle(detected,(i[0],i[1]),2,(0,0,255),3)



    #Show img
    """
    cv.imshow("detected", detected)
    cv.waitKey(0)
    cv.destroyWindow("detected")
    """

    #Save image

    cv.imwrite("detected.jpg", detected)

In the picture you can see that it clearly detects the bubble correctly but then it detects is plenty more time.