measure distance between two elements using opencv

Question

I have video taken from car. My program is measuring distance between front wheel and white line on road. This script is running well for left side video and right side video.

But Some times it measures the wrong distance between the front wheel and white line for the right side.

thresh = 150
distance_of_wood_plank = 80
pixel_of_wood_plank = 150
origin_width = 0
origin_height = 0
wheel_x = 0; wheel_y = 0 #xpoint and ypoint of wheel

df = pandas.DataFrame(columns=["Frame_No", "Distance", "TimeStrap"])
cap = cv2.VideoCapture(args.video)
frame_count = 0;
while(cap.isOpened()): #Reading input video by VideoCapture of Opencv
    try:
        frame_count += 1
        ret, source = cap.read() # get frame from video
        origin_height, origin_width, channels = source.shape

        timestamps = [cap.get(cv2.CAP_PROP_POS_MSEC)]
        milisecond = int(timestamps[0]) / 1000
        current_time = str(datetime.timedelta(seconds = milisecond))
        cv2.waitKey(1)
        grayImage = cv2.cvtColor(source, cv2.COLOR_RGB2GRAY) # get gray image
        crop_y = int(origin_height / 3 * 2) - 30
        crop_img = grayImage[crop_y:crop_y + 100, 0:0 + origin_width] # get interest area
        blur_image = cv2.blur(crop_img,(3,3))
        ret, th_wheel = cv2.threshold(blur_image, 10, 255, cv2.THRESH_BINARY) #get only wheel
        ret, th_line = cv2.threshold(blur_image, 150, 255, cv2.THRESH_BINARY) #get only white line
        contours, hierarchy = cv2.findContours(th_wheel, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[-2:]
        # get xpoint and ypoint of wheel
        for cnt in contours:
            x, y, w, h = cv2.boundingRect(cnt)
            if (x < origin_width/ 4):
                continue
            elif (w < 10):
                continue
            elif (w > 80):
                continue
            elif (x > origin_width / 4 * 3):
                continue
            wheel_x = int(x)
            wheel_y = int(y + h / 2 - 8)
        pixel_count = 0 # count of pixel between wheel and white line
        # get distance between wheel and white line
        if (wheel_x > origin_width/2):
            wheel_x -= 7
            for i in range(wheel_x, 0, -1):
                pixel_count += 1
                suit_point = th_line[wheel_y,i]
                if (suit_point == 255):
                    break
                if (i == 1):
                    pixel_count = 0
            pixel_count -= 4
            cv2.line(source, (wheel_x - pixel_count, wheel_y + crop_y), (wheel_x, wheel_y + crop_y), (255, 0, 0), 2)
        else :
            wheel_x += 7
            for i in range(wheel_x , origin_width):
                pixel_count += 1
                suit_point = th_line[wheel_y,i]
                if (suit_point == 255):
                    break
                if (i == origin_width - 1):
                    pixel_count = 0
            pixel_count += 4
            cv2.line(source, (wheel_x, wheel_y + crop_y), (wheel_x + pixel_count, wheel_y + crop_y), (255, 0, 0), 2)
        distance_Cm = int(pixel_count * 80 / pixel_of_wood_plank)
        str_distance = ""
        if distance_Cm > 10:
            str_distance = str(distance_Cm) + "Cm"
        else:
            str_distance = "No white line"

        cv2.putText(source, str_distance, (50, 250), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)

        df = df.append({'Frame_No': frame_count,'Distance': str_distance ,'TimeStrap': current_time}, ignore_index = True)

        df.to_csv("result.csv")
        cv2.imshow("Distance_window", source)
        cv2.waitKey(1)
    except:
        pass

Here is the link for the video - https://drive.google.com/file/d/1IjJ-FA2LTGv8Cz-ReL7fFI7HPTiEhyxF/view?usp=sharing

Answer 1

You are actually doing a really good job at measuring the distance between the tire and the white line. You need to consider is how much noise you have in your samples. Unless you stop the truck, get out, and measure the distance from the tire to the line with a tape, you are never really going to know how far it is. You also need to consider that (unless you wreck the truck) the distance from the tire to the white line won't vary by more than a few pixels between each frame.

The best solution would be a Kalman filter, but that is pretty complex. I used a more simple solution. To find the line position, I averaged the last four values to reduce noise.

import numpy as np, cv2

thresh = 150
distance_of_wood_plank = 80
pixel_of_wood_plank = 150
origin_width = 0
origin_height = 0
wheel_x = 0; wheel_y = 0 #xpoint and ypoint of wheel

cap = cv2.VideoCapture('/home/stephen/Desktop/20180301 1100 VW Right.mp4')
frame_count = 0;
vid_writer = cv2.VideoWriter('/home/stephen/Desktop/writer.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (480,360))

positions = []

import math
def distance(a,b): return math.sqrt((a[0]-b[0])**2 + (a[1]-b[1])**2)

while(cap.isOpened()): #Reading input video by VideoCapture of Opencv
    frame_count += 1
    ret, source = cap.read() # get frame from video
    origin_height, origin_width, channels = source.shape
    grayImage = cv2.cvtColor(source, cv2.COLOR_RGB2GRAY) # get gray image
    crop_y = int(origin_height / 3 * 2) - 30
    crop_img = grayImage[crop_y:crop_y + 100, 0:0 + origin_width] # get interest area
    blur_image = cv2.blur(crop_img,(3,3))
    ret, th_wheel = cv2.threshold(blur_image, 10, 255, cv2.THRESH_BINARY) #get only wheel
    ret, th_line = cv2.threshold(blur_image, 150, 255, cv2.THRESH_BINARY) #get only white line
    contours, hierarchy = cv2.findContours(th_wheel, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[-2:]
    # get xpoint and ypoint of wheel
    for cnt in contours:
        x, y, w, h = cv2.boundingRect(cnt)
        if (x < origin_width/ 4):
            continue
        elif (w < 10):
            continue
        elif (w > 80):
            continue
        elif (x > origin_width / 4 * 3):
            continue
        wheel_x = int(x)
        wheel_y = int(y + h / 2 - 8)
    pixel_count = 0 # count of pixel between wheel and white line
    # get distance between wheel and white line
    if (wheel_x > origin_width/2):
        wheel_x -= 7
        for i in range(wheel_x, 0, -1):
            pixel_count += 1
            suit_point = th_line[wheel_y,i]
            if (suit_point == 255):
                break
            if (i == 1):
                pixel_count = 0
        pixel_count -= 4
    else :
        wheel_x += 7
        for i in range(wheel_x , origin_width):
            pixel_count += 1
            suit_point = th_line[wheel_y,i]
            if (suit_point == 255):
                break
            if (i == origin_width - 1):
                pixel_count = 0
        pixel_count += 4
        a,b = (wheel_x - pixel_count, wheel_y + crop_y), (wheel_x, wheel_y + crop_y)
        if distance(a,b)>10: positions.append((wheel_x + pixel_count, wheel_y + crop_y))

    if len(positions)>10:
        radius = 2
        for position in positions[-10:]:
            radius += 2
            center = tuple(np.array(position, int))
            color = 255,255,0
            cv2.circle(source, center, radius, color, -1)
        x,y = zip(*positions[-4:])
        xa, ya = np.average(x), np.average(y)
        center = int(xa), int(ya)
        cv2.circle(source, center, 20, (0,0,255), 10)

    cv2.imshow("Distance_window", source)
    vid_writer.write(cv2.resize(source, (480,360)))
    k = cv2.waitKey(1)
    if k == 27: break

cv2.destroyAllWindows()