使用 OpenCV 检测图像中的粗黑线

Question

我有下面的乐高板图像，上面有一些积木

现在我尝试用 OpenCV 检测粗黑线（连接白色方块）。我已经用 HoughLinesP 进行了很多实验，之前将图像转换为灰色或黑白，应用了模糊，...Nonthing 产生了可用的结果。

# Read image
img = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

# Resize Image
img =  cv2.resize(img, (0,0), fx=0.25, fy=0.25) 

# Initialize output
out = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)

# Median blurring to get rid of the noise; invert image
img = cv2.medianBlur(img, 5)

# Adaptive Treshold
bw = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
            cv2.THRESH_BINARY,15,8)

# HoughLinesP
linesP = cv2.HoughLinesP(bw, 500, np.pi / 180, 50, None, 50, 10)

# Draw Lines
if linesP is not None:
    for i in range(0, len(linesP)):
        l = linesP[i][0]
        cv2.line(out, (l[0], l[1]), (l[2], l[3]), (0,0,255), 3, cv2.LINE_AA)

自适应阈值可以让您很好地看到边缘，但使用 HoughLinesP，您​​无法从中获得任何可用的东西

我究竟做错了什么？

---

感谢@fmw42 和@jeru-luke 为这个问题提供了很好的解决方案！我喜欢隔离/遮盖绿板，所以我将两者结合起来：

import cv2
import numpy as np
 
img = cv2.imread("image.jpg")

scale_percent = 50 # percent of original size
width = int(img.shape[1] * scale_percent / 100)
height = int(img.shape[0] * scale_percent / 100)
dim = (width, height)
  
# resize image
img = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
a_component = lab[:,:,1]

# binary threshold the a-channel
th = cv2.threshold(a_component,127,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)[1]

# numpy black
black = np.zeros((img.shape[0],img.shape[1]),np.uint8)

# function to obtain the largest contour in given image after filling it
def get_region(image):
    contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    c = max(contours, key = cv2.contourArea)
    mask = cv2.drawContours(black,[c],0,255, -1)
    return mask

mask = get_region(th)

# turning the region outside the green block white
green_block = cv2.bitwise_and(img, img, mask = mask)
green_block[black==0]=(255,255,255)

# median blur
median = cv2.medianBlur(green_block, 5)

# threshold on black
lower = (0,0,0)
upper = (15,15,15)
thresh = cv2.inRange(median, lower, upper)

# apply morphology open and close
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
morph = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (29,29))
morph = cv2.morphologyEx(morph, cv2.MORPH_CLOSE, kernel)

# filter contours on area
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
result = green_block.copy()
for c in contours:
    area = cv2.contourArea(c)
    if area > 1000:
            cv2.drawContours(result, [c], -1, (0, 0, 255), 2)
  

# view result
cv2.imshow("result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()

Answer 1

这是在 Python/OpenCV 中执行此操作的一种方法。

• 阅读图像
• 应用中值模糊
• 使用 cv2.inRange() 的黑色阈值
• 应用形态学来清理它
• 获取轮廓并过滤区域
• 在输入上绘制轮廓
• 保存结果

输入：

import cv2
import numpy as np

# read image
img = cv2.imread('black_lines.jpg')

# median blur
median = cv2.medianBlur(img, 5)

# threshold on black
lower = (0,0,0)
upper = (15,15,15)
thresh = cv2.inRange(median, lower, upper)

# apply morphology open and close
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
morph = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (29,29))
morph = cv2.morphologyEx(morph, cv2.MORPH_CLOSE, kernel)

# filter contours on area
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
result = img.copy()
for c in contours:
    area = cv2.contourArea(c)
    if area > 1000:
            cv2.drawContours(result, [c], -1, (0, 0, 255), 2)
  
# save result
cv2.imwrite("black_lines_threshold.jpg", thresh)
cv2.imwrite("black_lines_morphology.jpg", morph)
cv2.imwrite("black_lines_result.jpg", result)

# view result
cv2.imshow("threshold", thresh)
cv2.imshow("morphology", morph)
cv2.imshow("result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()

阈值图像：

形态图像：

结果：

Answer 2

在这里，我提出了一种使用颜色的重复分割方法。这个答案基于LAB 色彩空间的使用

1. 隔离绿色乐高积木

img = cv2.imread(image_path)
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
a_component = lab[:,:,1]

# binary threshold the a-channel
th = cv2.threshold(a_component,127,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)[1]

th

# function to obtain the largest contour in given image after filling it
def get_region(image):
    contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    c = max(contours, key = cv2.contourArea)
    black = np.zeros((image.shape[0], image.shape[1]), np.uint8)
    mask = cv2.drawContours(black,[c],0,255, -1)
    return mask

mask = get_region(th)

mask

# turning the region outside the green block white
green_block = cv2.bitwise_and(img, img, mask = mask)
green_block[black==0]=(255,255,255)

green_block

2. 道路分段

• 为了获得道路的大致区域，我减去了mask和th。

cv2.subtract()执行算术减法，其中cv2将处理负值。

road = cv2.subtract(mask,th)
# `road` contains some unwanted spots/contours which are removed using the function "get_region"
only_road = get_region(road)

only_road

仅用原始图像掩盖路段给出

road_colored = cv2.bitwise_and(img, img, mask = only_road)
road_colored[only_road==0]=(255,255,255)

road_colored

从上图中仅存在黑色区域（道路），这很容易分割：

# converting to grayscale and applying threshold
th2 = cv2.threshold(road_colored[:,:,1],127,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)[1]

# using portion of the code from fmw42's answer, to get contours above certain area
contours = cv2.findContours(th2, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
result = img.copy()
for c in contours:
    area = cv2.contourArea(c)
    if area > 1000:
        cv2.drawContours(result, [c], -1, (0, 0, 255), 4)

result

注意： 要清理最终结果，您可以th2在绘制轮廓之前应用形态学操作。