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 # Width of the image (number of columns) width = img.shape # 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 # 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 # Extract the intercept b intercept = parameters # 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,
I get this image after gaussian blur and the canny algorithm,
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”.
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)