functions.py

# print("Functions")

import math
import numpy as np
import cv2

from utils import globalvars


def perp(a) :
    b = np.empty_like(a)
    b[0] = -a[1]
    b[1] = a[0]
    return b

# line segment a given by endpoints a1, a2
# line segment b given by endpoints b1, b2
# return 
def seg_intersect(a1,a2, b1,b2):
    da = a2-a1
    db = b2-b1
    dp = a1-b1
    dap = perp(da)
    denom = np.dot( dap, db)
    num = np.dot( dap, dp )
    return (num / denom.astype(float))*db + b1


def movingAverage(avg, new_sample, N=20):
    '''
    Given a series of numbers and a fixed subset size, the first 
    element of the moving average is obtained by taking the average
    of the initial fixed subset of the number series. 
    Then the subset is modified by "shifting forward"; 
    that is, excluding the first number of the series and including the next value in the subset.
    '''
    if (avg == 0):
        return new_sample
    avg -= avg / N;
    avg += new_sample / N;
    return avg;


def grayscale(img):
    """Applies the Grayscale transform
    This will return an image with only one color channel
    but NOTE: to see the returned image as grayscale
    you should call plt.imshow(gray, cmap='gray')"""
    return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)


def bgr_to_hsv(img):
    """Applies HSV transform"""
    return cv2.cvtColor(img, cv2.COLOR_BGR2HSV)


def bgr_to_hls(img):
    """Applies HLS transform"""
    return cv2.cvtColor(img, cv2.COLOR_BGR2HLS)


def bgr_to_lab(img):
    """Applies the CIE-LAB colorspace
    It yields best results for low brightness im images"""
    return cv2.cvtColor(img, cv2.COLOR_BGR2LAB)


def bgr_to_y(img):
    """Applies the YCrCb transform"""
    return cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB)
  

def canny(img, low_threshold, high_threshold):
    """Applies the Canny transform"""
    return cv2.Canny(img, low_threshold, high_threshold)


def gaussian_blur(img, kernel_size):
    """Applies a Gaussian Noise kernel"""
    return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)


def region_of_interest(img, vertices):
    """
    Applies an image mask.
    
    Only keeps the region of the image defined by the polygon
    formed from `vertices`. The rest of the image is set to black.
    """
    #defining a blank mask to start with
    mask = np.zeros_like(img)   
    
    #defining a 3 channel or 1 channel color to fill the mask with depending on the input image
    if len(img.shape) > 2:
        channel_count = img.shape[2]  # i.e. 3 or 4 depending on your image
        ignore_mask_color = (255,) * channel_count
    else:
        ignore_mask_color = 255
        
    #filling pixels inside the polygon defined by "vertices" with the fill color    
    cv2.fillPoly(mask, vertices, ignore_mask_color)
    
    #returning the image only where mask pixels are nonzero
    masked_image = cv2.bitwise_and(img, mask)
    return masked_image


def draw_lines(img, lines, color=[255, 0, 0], thickness=2):
    print(globalvars.avgLeft, globalvars.avgRight)

    """
    NOTE: this is the function you might want to use as a starting point once you want to 
    average/extrapolate the line segments you detect to map out the full
    extent of the lane (going from the result shown in raw-lines-example.mp4
    to that shown in P1_example.mp4).  
    
    Think about things like separating line segments by their 
    slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left
    line vs. the right line.  Then, you can average the position of each of 
    the lines and extrapolate to the top and bottom of the lane.
    
    This function draws `lines` with `color` and `thickness`.    
    Lines are drawn on the image inplace (mutates the image).
    If you want to make the lines semi-transparent, think about combining
    this function with the weighted_img() function below
    """
    # state variables to keep track of most dominant segment
    largestLeftLineSize = 0
    largestRightLineSize = 0
    largestLeftLine = (0,0,0,0)
    largestRightLine = (0,0,0,0)

    if lines is None:
        avgx1, avgy1, avgx2, avgy2 = globalvars.avgLeft
        cv2.line(img, (int(avgx1), int(avgy1)), (int(avgx2), int(avgy2)), [255,255,255], 12) #draw left line
        avgx1, avgy1, avgx2, avgy2 = globalvars.avgRight
        cv2.line(img, (int(avgx1), int(avgy1)), (int(avgx2), int(avgy2)), [255,255,255], 12) #draw right line
        return
    
    '''
    Find largest left and largest right line.
    '''
    for line in lines:
        for x1,y1,x2,y2 in line:
            size = math.hypot(x2 - x1, y2 - y1)
            slope = ((y2-y1)/(x2-x1))
            # Filter slope based on incline and
            # find the most dominent segment based on length
            if (slope > 0.5): #right
                if (size > largestRightLineSize):
                    largestRightLine = (x1, y1, x2, y2)                    
                cv2.line(img, (x1, y1), (x2, y2), color, thickness)
            elif (slope < -0.5): #left
                if (size > largestLeftLineSize):
                    largestLeftLine = (x1, y1, x2, y2)
                cv2.line(img, (x1, y1), (x2, y2), color, thickness)

    # Define an imaginary horizontal line in the center of the screen
    # and at the bottom of the image, to extrapolate determined segment
    '''
    Two horizontal lines - bottom and 1/3rd distance from bottom.
    '''
    imgHeight, imgWidth = (img.shape[0], img.shape[1])
    upLinePoint1 = np.array( [0, int(imgHeight - (imgHeight/3))] )
    upLinePoint2 = np.array( [int(imgWidth), int(imgHeight - (imgHeight/3))] )
    downLinePoint1 = np.array( [0, int(imgHeight)] )
    downLinePoint2 = np.array( [int(imgWidth), int(imgHeight)] )
    
    # Find the intersection of dominant lane with an imaginary horizontal line
    # in the middle of the image and at the bottom of the image.
    '''
    Intersection of largest left line with horizontal lines.
    '''
    p3 = np.array( [largestLeftLine[0], largestLeftLine[1]] )
    p4 = np.array( [largestLeftLine[2], largestLeftLine[3]] )
    upLeftPoint = seg_intersect(upLinePoint1,upLinePoint2, p3,p4)
    downLeftPoint = seg_intersect(downLinePoint1,downLinePoint2, p3,p4)
    
    if (math.isnan(upLeftPoint[0]) or math.isnan(downLeftPoint[0])):
        avgx1, avgy1, avgx2, avgy2 = globalvars.avgLeft
        cv2.line(img, (int(avgx1), int(avgy1)), (int(avgx2), int(avgy2)), [255,255,255], 12) #draw left line
        avgx1, avgy1, avgx2, avgy2 = globalvars.avgRight
        cv2.line(img, (int(avgx1), int(avgy1)), (int(avgx2), int(avgy2)), [255,255,255], 12) #draw right line
        return
    # cv2.line(img, (int(upLeftPoint[0]), int(upLeftPoint[1])), (int(downLeftPoint[0]), int(downLeftPoint[1])), [0, 0, 255], 8) #draw left line

    # Calculate the average position of detected left lane over multiple video frames and draw
    avgx1, avgy1, avgx2, avgy2 = globalvars.avgLeft
    globalvars.avgLeft = (movingAverage(avgx1, upLeftPoint[0]), movingAverage(avgy1, upLeftPoint[1]), movingAverage(avgx2, downLeftPoint[0]), movingAverage(avgy2, downLeftPoint[1]))
    avgx1, avgy1, avgx2, avgy2 = globalvars.avgLeft
    cv2.line(img, (int(avgx1), int(avgy1)), (int(avgx2), int(avgy2)), [255,255,255], 12) #draw left line

    # Find the intersection of dominant lane with an imaginary horizontal line
    # in the middle of the image and at the bottom of the image.
    '''
    Intersection of largest right line with horizontal lines.
    '''
    p5 = np.array( [largestRightLine[0], largestRightLine[1]] )
    p6 = np.array( [largestRightLine[2], largestRightLine[3]] )
    upRightPoint = seg_intersect(upLinePoint1,upLinePoint2, p5,p6)
    downRightPoint = seg_intersect(downLinePoint1,downLinePoint2, p5,p6)
    if (math.isnan(upRightPoint[0]) or math.isnan(downRightPoint[0])):
        avgx1, avgy1, avgx2, avgy2 = globalvars.avgLeft
        cv2.line(img, (int(avgx1), int(avgy1)), (int(avgx2), int(avgy2)), [255,255,255], 12) #draw left line
        avgx1, avgy1, avgx2, avgy2 = globalvars.avgRight
        cv2.line(img, (int(avgx1), int(avgy1)), (int(avgx2), int(avgy2)), [255,255,255], 12) #draw right line
        return
    # cv2.line(img, (int(upRightPoint[0]), int(upRightPoint[1])), (int(downRightPoint[0]), int(downRightPoint[1])), [0, 0, 255], 8) #draw left line

    # Calculate the average position of detected right lane over multiple video frames and draw
    avgx1, avgy1, avgx2, avgy2 = globalvars.avgRight
    globalvars.avgRight = (movingAverage(avgx1, upRightPoint[0]), movingAverage(avgy1, upRightPoint[1]), movingAverage(avgx2, downRightPoint[0]), movingAverage(avgy2, downRightPoint[1]))
    avgx1, avgy1, avgx2, avgy2 = globalvars.avgRight
    cv2.line(img, (int(avgx1), int(avgy1)), (int(avgx2), int(avgy2)), [255,255,255], 12) #draw left line

    
def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):
    """
    `img` should be the output of a Canny transform.
        
    Returns an image with hough lines drawn.
    """
    lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)
    line_img = np.zeros((*img.shape, 3), dtype=np.uint8)
    draw_lines(line_img, lines)
    return line_img


def weighted_img(img, initial_img, α=0.8, β=1., λ=0.):
    """
    `img` is the output of the hough_lines(), An image with lines drawn on it.
    Should be a blank image (all black) with lines drawn on it.
    
    `initial_img` should be the image before any processing.
    
    The result image is computed as follows:
    
    initial_img * α + img * β + λ
    NOTE: initial_img and img must be the same shape!
    """
    return cv2.addWeighted(initial_img, α, img, β, λ)