Line detection issue - OpenCV in Python

Question

I have written the following script with which I aim to detect lines in Gazebo (a simulation environment):

#!/usr/bin/env python
# rospy for the subscriber
import rospy
# ROS Image message
from sensor_msgs.msg import Image
# ROS Image message -> OpenCV2 image converter
from cv_bridge import CvBridge, CvBridgeError
# OpenCV2 for saving an image
import cv2
import matplotlib.pyplot as plt
import numpy as np

def gradient(img):
    # grayscale the image
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # gaussian blur of image with a 5x5 kernel
    gauss = cv2.GaussianBlur(gray,(5,5),0)
    # Return the canny of the image
    return cv2.Canny(gauss,20,30)

def region_of_interest(img):
    # Height of image (number of rows)
    height = img.shape[0]
    # Width of the image (number of columns)
    width = img.shape[1]
    # Create an array of polygons to use for the masking of the canny image
    polygons = np.array([
    [(200,height), (200,500), (600,500), (600,height)]
    ])
    # Create the mask image's background (black color)
    mask_bg = np.zeros_like(img)
    # Create the mask image (image with black background an white region of interest)
    mask = cv2.fillPoly(mask_bg, polygons, 255)
    # Isolate the area of interest using the bitwise operator of the mask and canny image
    masked_image = cv2.bitwise_and(img,cv2.fillPoly(mask_bg, polygons, 255))
    # Return the updated image
    return masked_image

def make_coordinates(img, line_parameters):
    # Extract the average slope and intercept of the line
    slope, intercept = line_parameters
    # Coordinate y(1) of the calculated line
    y1 = img.shape[0]
    # Coordinate y(2) of the calculated line
    y2 = int(y1*0.5)
    # Coordinate x(1) of the calculated line
    x1 = int((y1-intercept)/slope)
    # Coordinate x(2) of the calculated line
    x2 = int((y2-intercept)/slope)
    # Return the coordinates of the average line
    return np.array([x1,y1,x2,y2])

def average_slope_intercep(img,lines):
    # Create an empty list containing the coordinates of the detected line
    line_fit = []
    # Loop through all the detected lines
    for line in lines:
        # Store the coordinates of the detected lines into an 1D array of 4 elements
        x1,y1,x2,y2 = line.reshape(4)
        # Create a line y = mx+b based on the coordinates
        parameters = np.polyfit((x1,x2),(y1,y2),1)
        # Extract the slope m
        slope = parameters[0]
        # Extract the intercept b
        intercept = parameters[1]
        # Add elements on the list
        line_fit.append((slope,intercept))
        # Check slope of line
        # if slope < 0:
        #     continue
        # else:
        #     continue
    # Calculate the average of the line fit parameters list
    line_fit_average = np.average(line_fit,axis=0)
    # Extract the coordinates of the calculated line
    main_line = make_coordinates(img,line_fit_average)

    return np.array([main_line])


def display_lines(img,lines):
    # Create a mask image that will have the drawn lines
    line_image = np.zeros_like(img)
    # If no lines were detected
    if lines is not None:
        # Loop through all the lines
        for line in lines:
            # Store the coordinates of the first and last point of the lines into 1D arrays
            x1, y1, x2, y2 = line.reshape(4)
            # Draw the lines on the image with blue color and thicknes of 10
            cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),10)
    # Return the mask image with the drawn lines
    return line_image

def image_callback(msg):
    # print("Received an image!")
    # Instantiate CvBridge
    bridge = CvBridge()
    try:
        # Convert your ROS Image message to OpenCV2
        frame = bridge.imgmsg_to_cv2(msg, "bgr8")
    except CvBridgeError, e:
        print(e)
    else:
        # Copy of the original frame
        frame_copy = np.copy(frame)
        # Canny of image
        canny_frame = gradient(frame_copy)
        # Apply mask in region of interest
        cropped_image = region_of_interest(canny_frame)
        # Apply Hough Transform on the region of interest
        lines = cv2.HoughLinesP(cropped_image,1,np.pi/180,30,np.array([]),minLineLength=10,maxLineGap=2)
        # Calculate the average slope of the detected lines
        averaged_lines = average_slope_intercep(frame_copy,lines)
        # Create a mask image with the drawn lines
        line_image = display_lines(frame_copy,averaged_lines)
        # Plot lines on the camera feed frame
        combo_image = cv2.addWeighted(frame_copy,0.8,line_image,1,1)
        #Show manipulated image feed
        cv2.imshow("Result feed", frame_copy)
        # plt.imshow(canny_frame)
        cv2.waitKey(1)
        # plt.show()

def main():
    rospy.init_node('image_listener')
    # Define your image topic
    image_topic = "rover/camera1/image_raw"
    # Set up your subscriber and define its callback
    rospy.Subscriber(image_topic, Image, image_callback)
    # Spin until ctrl + c
    rospy.spin()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()

The code is integrated in ROS, so please focus your attention at the image_callback function. My issue is that the line that I want to detect is quite noisy and I cannot figure out how to detect it correctly.

To be more specific, from the following frame,

Original Frame

I get this image after gaussian blur and the canny algorithm,

Canny Frame

How could I filter the "noise" I see in the canny frame? I played a lot with the canny and gausian blur parameters but all that I have achieved is removing gradients instead of actually making it less "noisy".

Answer 1

This method might help you to remove noise from the frame.

import cv2
import numpy as np
from skimage.morphology import skeletonize


def get_skeleton_iamge(threshold_image):
    skeleton = skeletonize(threshold_image / 255)
    skeleton = skeleton.astype(np.uint8)
    skeleton *= 255
    return skeleton


image = cv2.imread("road.png", 0)
image = cv2.resize(image, (300, 300))

bilateral = cv2.bilateralFilter(image, 15, 100, 100)
cv2.imshow("bilateral_image", bilateral)

canny_image = cv2.Canny(bilateral, 20, 30)
cv2.imshow("canny_image", canny_image)

kernel = np.ones((10, 10))
dilate_image = cv2.dilate(canny_image, kernel, iterations=1)
erode_image = cv2.erode(dilate_image, kernel, iterations=1)
cv2.imshow("erode_image", erode_image)

skeleton_iamge = get_skeleton_iamge(erode_image)
cv2.imshow("skeleton_iamge", skeleton_iamge)

cv2.waitKey(0)