OpenCV 中的霍夫线问题，如何将大量霍夫线变为每条红色胶带一条线，而不是像这样

Question

我现在有了这段代码，它非常适合检测视频中的红线，问题是我目前一条红线大约有 40 行（见图），我想将其减少到仅1 这样我就可以轻松确定红色方块的交点以及随后的角点，有人有想法吗？:

代码：

import cv2
import numpy as np


def detect_grid(frame):
   
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

 
    lower_red = np.array([0, 100, 100])
    upper_red = np.array([10, 255, 255])

    mask1 = cv2.inRange(hsv, lower_red, upper_red)


    lower_red = np.array([140, 20, 20])
    upper_red = np.array([180, 255, 255])

  
    mask2 = cv2.inRange(hsv, lower_red, upper_red)

  
    red_mask = mask1 + mask2

 
    kernel = np.ones((5, 5), np.uint8)
    red_mask = cv2.morphologyEx(red_mask, cv2.MORPH_OPEN, kernel)
    red_mask = cv2.morphologyEx(red_mask, cv2.MORPH_CLOSE, kernel)

    lines = cv2.HoughLinesP(red_mask, 1, np.pi / 180, threshold=50, minLineLength=50, maxLineGap=5)

    if lines is not None:
        for line in lines:
            x1, y1, x2, y2 = line[0]
            cv2.line(frame, (x1, y1), (x2, y2), (0, 255, 0), 5)


cap = cv2.VideoCapture('redline_rectified.mp4')

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

  
    detect_grid(frame)

  
    cv2.imshow('Grid Detection', frame)

   
    if cv2.waitKey(1) & 0xFF == ord(' '):
        break

cap.release()
cv2.destroyAllWindows()

我现在得到的：

Answer 1

通常，我基本上通过轮廓检测和分析轮廓来手动解决线检测问题。由于您的输入很清楚，我继续修改您的代码。

以下是我针对您的情况应用的步骤：如果两条线彼此靠近，则忽略其中一条。我在最后得到了这个输出，其中有清晰的线条。

这是代码。开头部分相同，只是修改了函数的结尾：

import cv2
import numpy as np


def detect_grid(frame):
   
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    lower_red = np.array([0, 100, 100])
    upper_red = np.array([10, 255, 255])

    mask1 = cv2.inRange(hsv, lower_red, upper_red)

    lower_red = np.array([140, 20, 20])
    upper_red = np.array([180, 255, 255])

    mask2 = cv2.inRange(hsv, lower_red, upper_red)

    red_mask = mask1 + mask2

    kernel = np.ones((5, 5), np.uint8)
    red_mask = cv2.morphologyEx(red_mask, cv2.MORPH_OPEN, kernel)
    red_mask = cv2.morphologyEx(red_mask, cv2.MORPH_CLOSE, kernel)

    lines = cv2.HoughLinesP(red_mask, 1, np.pi / 180, threshold=350, minLineLength=50, maxLineGap=50)

    if lines is not None:
        # Filter out lines that are too close
        filtered_lines = []
        for line in lines:
            x1, y1, x2, y2 = line[0]
            p1 = np.array([x1, y1])
            p2 = np.array([x2, y2])
            too_close = False

            for other_line in filtered_lines:
                x3, y3, x4, y4 = other_line
                p3 = np.array([x3, y3])
                p4 = np.array([x4, y4])

                # Calculate Euclidean distance
                distance = np.linalg.norm(np.cross(p2 - p1, p1 - p3)) / np.linalg.norm(p2 - p1)

                if distance < 10:
                    too_close = True
                    break

            if not too_close:
                filtered_lines.append(line[0])

        for line in filtered_lines:
            x1, y1, x2, y2 = line
            cv2.line(frame, (x1, y1), (x2, y2), (0, 255, 255), 3)

    cv2.imwrite("/home/ubuntu/a.jpg", frame)

img = cv2.imread("/home/ubuntu/lines.png")
detect_grid(img)