Question

我正在尝试检测这张图片中的某些圆圈：

这是我成功的最好结果：

您可以看到有4个圆圈检测到我没有检测到，有1个圆圈错过了。

这是我使用的代码：

def draw_circles(img, circles):
    cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
    for i in circles[0,:]:
    # draw the outer circle
        cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
        # draw the center of the circle
        cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
        cv2.putText(cimg,str(i[0])+str(',')+str(i[1]), (i[0],i[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.4, 255)
    return cimg

def detect_circles(image_path):
    gray = cv2.imread(image_path, 0)
    gray_blur = cv2.medianBlur(gray, 13)  # Remove noise before laplacian
    gray_lap = cv2.Laplacian(gray_blur, cv2.CV_8UC1, ksize=5)
    dilate_lap = cv2.dilate(gray_lap, (3, 3))  # Fill in gaps from blurring. This helps to detect circles with broken edges.
    # Furture remove noise introduced by laplacian. This removes false pos in space between the two groups of circles.
    lap_blur = cv2.bilateralFilter(dilate_lap, 5, 9, 9)
    # Fix the resolution to 16. This helps it find more circles. Also, set distance between circles to 55 by measuring dist in image.
    # Minimum radius and max radius are also set by examining the image.
    circles = cv2.HoughCircles(lap_blur, cv2.HOUGH_GRADIENT, 16, 80, param2=450, minRadius=20, maxRadius=40)
    cimg = draw_circles(gray, circles)
    print("{} circles detected.".format(circles[0].shape[0]))
    # There are some false positives left in the regions containing the numbers.
    # They can be filtered out based on their y-coordinates if your images are aligned to a canonical axis.
    # I'll leave that to you.
    return cimg

plt.imshow(detect_circles('test.jpeg'))

我尝试过使用最小和最大半径参数，但是没有真正的成功，任何建议都会有所帮助。

Answer 1

我不明白为什么您需要所有这些预处理。您的图片中有完美的圆，您知道它们的确切半径...

您要做的所有预处理就是将良好的输入转换为不良的输入。您删除了已有的漂亮圆圈轮廓，然后尝试将图像中值至死后从剩下的内容中重新创建它们：）

def detect_circles(image_path):
    gray = cv2.imread(image_path, 0)
    circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 2, 60, param2=100, minRadius=20, maxRadius=40)
    cimg = draw_circles(gray, circles)
    print("{} circles detected.".format(circles[0].shape[0]))
    return cimg

我认为您应该真正研究那些算法的工作方式。您的代码看起来像是盲目地应用了在网上找到的内容。

Answer 2

也许此解决方案会有所帮助。

def draw_circles(img, circles):
    cimg = img.copy()
    for i in circles[0,:]:
    # draw the outer circle
        cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
        # draw the center of the circle
        cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
        cv2.putText(cimg,str(i[0])+str(',')+str(i[1]), (i[0],i[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.4, 255)
    return cimg

def detect_circles(image_path):
    # open color image
    image = cv2.imread(image_path)
    image_blur = cv2.medianBlur(image, 3)
    # find edges
    edges = cv2.Canny(image_blur, 5, 50)
    # let's clean the neighboring pixels
    edges = cv2.medianBlur(edges, 3)

    circles = cv2.HoughCircles(edges, cv2.HOUGH_GRADIENT, 2, 80, param1=100, param2=45, minRadius=20, maxRadius=40)
    cimg = draw_circles(image, circles)
    print("{} circles detected.".format(circles[0].shape[0]))
    return cimg

The result of the code above

如何改善圆圈检测

2 个答案: