Detecting Lines

import cv2
import numpy as np

image = cv2.imread("photo.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 50, 150)

# METHOD 1: Standard Hough Lines
lines = cv2.HoughLines(
    edges,
    rho=1,           # Distance resolution (pixels)
    theta=np.pi/180, # Angle resolution (radians)
    threshold=150    # Minimum votes (higher = fewer, stronger lines)
)

result = image.copy()
if lines is not None:
    for line in lines:
        rho, theta = line[0]
        a, b = np.cos(theta), np.sin(theta)
        x0, y0 = a * rho, b * rho
        x1 = int(x0 + 1000 * (-b))
        y1 = int(y0 + 1000 * (a))
        x2 = int(x0 - 1000 * (-b))
        y2 = int(y0 - 1000 * (a))
        cv2.line(result, (x1, y1), (x2, y2), (0, 255, 0), 1)
    print(f"Found {len(lines)} lines (standard Hough)")

# METHOD 2: Probabilistic Hough Lines (returns line segments, faster)
lines_p = cv2.HoughLinesP(
    edges, rho=1, theta=np.pi/180,
    threshold=100, minLineLength=50, maxLineGap=10
)

result2 = image.copy()
if lines_p is not None:
    for line in lines_p:
        x1, y1, x2, y2 = line[0]
        cv2.line(result2, (x1, y1), (x2, y2), (0, 255, 0), 2)
    print(f"Found {len(lines_p)} line segments (probabilistic)")

cv2.imwrite("lines_hough.jpg", result2)