test.py

rightSlope, leftSlope, rightIntercept, leftIntercept = [],[],[],[]
def draw_lines(img, lines, thickness=5):
    global rightSlope, leftSlope, rightIntercept, leftIntercept
    rightColor=[0,255,0]    leftColor=[255,0,0]
    
    #this is used to filter out the outlying lines that can affect the average
    #We then use the slope we determined to find the y-intercept of the filtered lines by solving for b in y=mx+b
    for line in lines:
        for x1,y1,x2,y2 in line:
            slope = (y1-y2)/(x1-x2)
            if slope > 0.3:
                if x1 > 500 :
                    yintercept = y2 - (slope*x2)
                    rightSlope.append(slope)
                    rightIntercept.append(yintercept)
                else: None
            elif slope < -0.3:
                if x1 < 600:
                    yintercept = y2 - (slope*x2)
                    leftSlope.append(slope)
                    leftIntercept.append(yintercept)
    #We use slicing operators and np.mean() to find the averages of the 30 previous frames
    #This makes the lines more stable, and less likely to shift rapidly
    leftavgSlope = np.mean(leftSlope[-30:])
    leftavgIntercept = np.mean(leftIntercept[-30:])
    rightavgSlope = np.mean(rightSlope[-30:])
    rightavgIntercept = np.mean(rightIntercept[-30:])
    #Here we plot the lines and the shape of the lane using the average slope and intercepts
    try:
        left_line_x1 = int((0.65*img.shape[0] - leftavgIntercept)/leftavgSlope)
        left_line_x2 = int((img.shape[0] - leftavgIntercept)/leftavgSlope)
        right_line_x1 = int((0.65*img.shape[0] - rightavgIntercept)/rightavgSlope)
        right_line_x2 = int((img.shape[0] - rightavgIntercept)/rightavgSlope)
        pts = np.array([[left_line_x1, int(0.65*img.shape[0])],[left_line_x2, int(img.shape[0])],[right_line_x2, int(img.shape[0])],[right_line_x1, int(0.65*img.shape[0])]], np.int32)
        pts = pts.reshape((-1,1,2))
        cv2.fillPoly(img,[pts],(0,0,255))
        cv2.line(img, (left_line_x1, int(0.65*img.shape[0])), (left_line_x2, int(img.shape[0])), leftColor, 10)
        cv2.line(img, (right_line_x1, int(0.65*img.shape[0])), (right_line_x2, int(img.shape[0])), rightColor, 10)
    except ValueError:
    #I keep getting errors for some reason, so I put this here. Idk if the error still persists.
        pass
def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):
    """
    `img` should be the output of a Canny transform.
    """
    lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)
    line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
    draw_lines(line_img, lines)
    return line_img
def linedetect(img):
    return hough_lines(img, 1, np.pi/180, 10, 20, 100)
hough_img = list(map(linedetect, canny_img))
display_images(hough_img)