Transform contour line as straight line

Question

I have this image: I extracted lines using

    contours, hierarchy = cv2.findContours(image, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)

I have 4 contours then. My goal is to convert this lines as infinite straight lines (so I can detect overlap points of those lines then. Is this somehow possible? I tried hough lines, but it gives unpredictable solutions, in some cases lines doesnt have enough length, or there is much more lines than just one.

Answer 1

You can use fitLine to fit a straight line to a contour.

void cv::fitLine (
        InputArray      points,
        OutputArray     line,
        int     distType,
        double      param,
        double      reps,
        double      aeps 
    ) 

For 2D lines, it'll output the line as (vx, vy, x0, y0):

where (vx, vy) is a normalized vector collinear to the line and (x0, y0) is a point on the line

Answer 2

Maybe a bit late but you can use:

0. Prepare data from your image (not contours ):

import cv2
import numpy as np

# Load image
image = cv2.imread('test2.png')
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
edged = cv2.Canny(gray, 30, 100)

1. A clustering method to group your contours like KMeans :

from sklearn.cluster import KMeans

# Get the list of points
points = np.argwhere(edged)

# Create 4 clusters of points
kmeans = KMeans(n_clusters=4).fit(points)

2. Now, find the slope m and the intercept b coefficients from y = mx + b with LinearRegression (or np.polyfit ):

from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

# Find coefficients
coeffs = []
for i in range(4):
    idx = np.where(i == kmeans.labels_)[0]
    x = points[idx, 0].reshape(-1, 1)
    y = points[idx, 1]

    reg = LinearRegression().fit(x, y)
    m, b = reg.coef_[0], reg.intercept_  
    coeffs.append((m, b))

    plt.scatter(x, y, s=0.1)
    plt.axline(xy1=(0, b), slope=m, color='k', zorder=-1, lw=0.5)

plt.xlim(0, image.shape[0])
plt.ylim(0, image.shape[1])
plt.show()

Output:

>>> coeffs
[(101.53675590518964, -6345.8426544453905),
 (-68.00736350967681, 62626.8080715293),
 (-0.00030668339485539364, 318.2125762056594),
 (0.001297826071265324, 1622.8759316729752)]

Now you can find the intersection between equations.