应用圆形霍夫变换后从一张图像中提取圆圈

Question

I'm trying to extract the detected circles in one image using the circular hough transform. My idea is get every circle or separate each one to then get his color histogram features and after send this features to one classifier as SVM, ANN, KNN etc.. This is my input image:

I'm getting the circles of this way:

import numpy as np
import cv2
import matplotlib.pyplot as plt
cv2.__version__
#read image
file = "lemon.png"
image = cv2.imread(file)
#BGR to RGB
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
#convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

circles = cv2.HoughCircles(gray,
                           cv2.HOUGH_GRADIENT,
                           15, 
                           41,
                           param1=31,
                           param2=31,
                           minRadius=0,
                           maxRadius=33)

circles = np.uint16(np.around(circles))
for i in circles[0,:]:
    # draw the outer circle
    cv2.circle(image,(i[0],i[1]),i[2],(0,255,0),2)
    # draw the center of the circle
    cv2.circle(image,(i[0],i[1]),2,(0,0,255),3)


print("Number of circles: "+ str(len(circles[0,:])))


plt.imshow(image, cmap='gray', vmin=0, vmax=255)
plt.show()

Output:

The next step is try to extract those circles but I don't have idea how to do it.

Well guys I would like to see your suggestions, any I idea I will apreciate it. Thanks so much.

Answer 1

You can create a binary mask for every circle you detect. Use this mask to extract only the ROIs from the input image. Additionally, you can crop these ROIs and store them in a list to pass them to your classifier.

Here's the code:

import numpy as np
import cv2

# image path
path = "C://opencvImages//"
file = path + "LLfN7.png"

image = cv2.imread(file)

# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

circles = cv2.HoughCircles(gray,
                           cv2.HOUGH_GRADIENT,
                           15,
                           41,
                           param1=31,
                           param2=31,
                           minRadius=0,
                           maxRadius=33)

# Here are your circles:
circles = np.uint16(np.around(circles))

# Get input size:
dimensions = image.shape

# height, width
height = image.shape[0]
width = image.shape[1]

# Prepare a list to store each ROI:
lemonROIs = []

The idea is that you process one circle at a step. Get the current circle, create a mask, mask the original input, crop the ROI and store it inside the list:

for i in circles[0, :]:

    # Prepare a black canvas:
    canvas = np.zeros((height, width))

    # Draw the outer circle:
    color = (255, 255, 255)
    thickness = -1
    centerX = i[0]
    centerY = i[1]
    radius = i[2]
    cv2.circle(canvas, (centerX, centerY), radius, color, thickness)

    # Create a copy of the input and mask input:
    imageCopy = image.copy()
    imageCopy[canvas == 0] = (0, 0, 0)

    # Crop the roi:
    x = centerX - radius
    y = centerY - radius
    h = 2 * radius
    w = 2 * radius

    croppedImg = imageCopy[y:y + h, x:x + w]

    # Store the ROI:
    lemonROIs.append(croppedImg) 

For each circle you get a cropped ROI:

You can pass that info to your classifier.