如何使用OpenCV检测足球场中的线条

Question

我使用Canny来检测边缘，然后Hough Lines转换以获得线条，我想我找不到适合我使用的正确参数。

imgsrc = cv2.imread("image.png")

# Resize to improve detection accuracy
t = int(img.shape[1] * 1.6)
img = imutils.resize(imgsrc, width=t)

# Apply gaussian blur
kernel_size = 3
img = cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)

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

# Initialize morph-kernel, apply CLOSE before Canny to improve edges detection
kernel0 = np.ones((9,27), np.uint8)
img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel0)

# Detect edges
low_threshold = 5
high_threshold = 50
edges = cv2.Canny(img, low_threshold, high_threshold)

# Initialize morph-kerne, apply CLOSE after Canny to merge edges
kernel2 = np.ones((8,24), np.uint8)
edges = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel2)

# Hough Lines params
rho = 1  # distance resolution in pixels of the Hough grid
theta = np.pi / 180  # angular resolution in radians of the Hough grid
# minimum number of votes (intersections in Hough grid cell)
threshold = 30
min_line_length = 50  # minimum number of pixels making up a line
max_line_gap = 40  # maximum gap in pixels between connectable line segments

# Run Hough on edge detected image
lines = cv2.HoughLinesP(edges, rho, theta, threshold, np.array([]), min_line_length, max_line_gap)

output = np.copy(imgsrc) * 0  # creating a blank to draw lines on
for line in lines:
    for x1, y1, x2, y2 in line:
        cv2.line(output, (int(x1), int(y1)), (int(x2), int(y2)), (255, 255, 255), thickness)

你知道我该如何改进线路检测，最终只得到几条线路吗？

Answer 1

以下是Python中的解决方案代码：

import cv2
from matplotlib import pyplot as plt
import numpy

im = cv2.imread("foot.png")
B = im[:,:,2]
Y = 255-B

thresh = cv2.adaptiveThreshold(Y,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
            cv2.THRESH_BINARY_INV,35,5)

contours, hierarchy = cv2.findContours(thresh,  
    cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) 

x=[]
for i in range(0, len(contours)):
    if cv2.contourArea(contours[i]) > 100:
        x.append(contours[i])
cv2.drawContours(im, x, -1, (255,0,0), 2) 

plt.imshow(im)

玩参数得到想要的结果。