如何检测真实图像上的椭圆

Question

我正在尝试识别足球场的中场椭圆。我尝试过在网上找到的不同解决方案，例如霍夫变换和模板匹配，但不幸的是我无法使它们起作用。 图片取自真实的足球比赛，因此有不同的镜头和方向。

目前，我获得了最好的结果，过滤掉音高之外的像素（使用颜色掩模）并使用一些变换，如高斯模糊、Canny，以及边缘轮廓和openCV中的fitEllipse()等技术 .

蒙版后的起始图像：

精明的图像：

这里我执行了边缘轮廓并使用aspect_ratio过滤掉不相关的边缘：

contours, _ = cv2.findContours(canny_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

min_aspect_ratio = 3  # Minimum aspect ratio threshold (adjust as needed)
max_aspect_ratio = 50  # Maximum aspect ratio threshold (adjust as needed)

# Create a copy of the original image to draw bounding rectangles
image_with_rectangles = image.copy()

# Iterate through the contours
for contour in contours:
    # Calculate the aspect ratio of the bounding rectangle
    x, y, w, h = cv2.boundingRect(contour)
    aspect_ratio = float(w) / h

    # Check if the aspect ratio falls within the specified range
    if min_aspect_ratio < aspect_ratio < max_aspect_ratio:
        # Draw a green bounding rectangle around the contour
        cv2.rectangle(image_with_rectangles, (x, y), (x + w, y + h), (255, 255, 0), 2)

# Display the image with the bounding rectangles
print("Image with Bounding Rectangles")
cv2_imshow(image_with_rectangles)

filtered_contours = [
    cnt for cnt in contours
    if min_aspect_ratio < (float(cv2.boundingRect(cnt)[2]) / cv2.boundingRect(cnt)[3]) < max_aspect_ratio
]

result_image = np.zeros_like(original_image)  # Create a blank canvas

cv2.drawContours(result_image, filtered_contours, -1, (0, 255, 0), 2)  # Draw the filtered contours in green

# Display or save the result_image as needed
cv2_imshow(result_image)

带有边界矩形的图像：

具有轮廓的图像：

最后一步包括尝试拟合椭圆，我遇到了很多问题以使 cv2.fitEllipse() 起作用，这是我的解决方案：

gray_image = cv2.cvtColor(result_image, cv2.COLOR_BGR2GRAY)
cv2_imshow(gray_image)

# find the contours
contours,hierarchy = cv2.findContours(gray_image, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
print("\nNumber of contours detected:", len(contours))

ellipse_image = original_image.copy()
ellipses = []
orientations = []

for c in filtered_contours:

  #if it contains at least 5 points (higher the value lower the number of ellipses)
  if len(c) >= 10:
    print(len(c))

    ellipse = cv2.fitEllipse(c)

    # Get the major and minor axes of the ellipse
    major_axis, minor_axis = ellipse[1]
    # Calculate the aspect ratio of the ellipse
    aspect_ratio = major_axis / minor_axis
    #we want a high aspect ratio, meaning that is much wider then taller
    if 0.1 <= aspect_ratio <= 0.2:
      print("\nRatio: ", aspect_ratio)
      print("M and m axes: ", major_axis, minor_axis)

      if major_axis < minor_axis:
        ellipses.append(ellipse)
        orientations.append(int(ellipse[2]))

for e in ellipses:
   cv2.ellipse(ellipse_image, e, (255, 255, 0), 2)


print("\nMiddlefield contour:")
cv2_imshow(ellipse_image)

最终结果：

如何只保留一个椭圆（正确的椭圆）？

Answer 1

我所做的是用形态算子闭合椭圆。并选择最大的椭圆（面积最大）。

• 导入库并读取灰度图像

import cv2
import matplotlib.pyplot as plt
import numpy as np

img = cv.imread('K8hNf.png',0)

• 形态学运算

kernel_square = cv.getStructuringElement(cv.MORPH_ELLIPSE,(50,50))
img = cv.morphologyEx(img,cv.MORPH_CLOSE,kernel_square,iterations=1)

• 找到计数并将图像从灰色更改为 RGB，以便我们可以用绿色绘制椭圆。

contours, _ = cv.findContours(img, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
img = cv.cvtColor(img,cv.COLOR_GRAY2RGB)

• 以下算法找到最大面积椭圆。这很简单。您还可以使用

  cv2.contourArea

  。

largest_ellipse = None
max_area = 0

for contour in contours:
    if len(contour) >= 5:
        ellipse = cv.fitEllipse(contour)
        area = ellipse[1][0] * ellipse[1][1] * np.pi # Calculate the area of the fitted ellipse
        if area > max_area:
            max_area = area
            largest_ellipse = ellipse


if largest_ellipse is not None:
    cv.ellipse(img, largest_ellipse, (0,255,0),2)
else:
    print('No ellipses found')


plt.imshow(img,cmap='gray')

通过删除图像顶部不需要的线条并删除玩家骨骼，您可以改善椭圆的拟合。

由于相机角度失真，贴合可能永远不会完美。