lane detection example

straight-lines.py

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
import math


def region_of_interest(img, vertices):
    mask = np.zeros_like(img)
    match_mask_color = 255 # <-- This line altered for grayscale.
    
    cv2.fillPoly(mask, vertices, match_mask_color)
    masked_image = cv2.bitwise_and(img, mask)
    return masked_image

def draw_lines(img, lines, color=[255, 0, 0], thickness=3):
  # If there are no lines to draw, exit.
  if lines is None:
      return
  # Make a copy of the original image.
  img = np.copy(img)
  # Create a blank image that matches the original in size.
  line_img = np.zeros(
      (
          img.shape[0],
          img.shape[1],
          3
      ),
      dtype=np.uint8,
  )
  # Loop over all lines and draw them on the blank image.
  for line in lines:
      for x1, y1, x2, y2 in line:
          cv2.line(line_img, (x1, y1), (x2, y2), color, thickness)
  # Merge the image with the lines onto the original.
  img = cv2.addWeighted(img, 0.8, line_img, 1.0, 0.0)
  # Return the modified image.
  return img


image = mpimg.imread('test.jpg')
height = image.shape[0]
width = image.shape[1]
region_of_interest_vertices = [
    (0, height),
    (width / 2, height / 2),
    (width, height),
]

plt.figure()
plt.imshow(image)
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
cannyed_image = cv2.Canny(gray_image, 100, 200)
# Moved the cropping operation to the end of the pipeline.
cropped_image = region_of_interest(
    cannyed_image,
    np.array(
        [region_of_interest_vertices],
        np.int32
    ),
)
lines = cv2.HoughLinesP(
    cropped_image,
    rho=6,
    theta=np.pi / 60,
    threshold=160,
    lines=np.array([]),
    minLineLength=40,
    maxLineGap=25
)
line_image = draw_lines(image, lines) # <---- Add this call.
plt.figure()
plt.imshow(line_image)
plt.show()