lane_utils.py

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
# %matplotlib inline

import numpy as np
import cv2
import glob

def find_lane_pixels(binary_warped, img_name):
    """
    Get the X and Y coordinates belonging to the left and
    right lane lines in the binary warped image.
    
    """
    
    
    # Take a histogram of the bottom half of the image
    histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
    # plt.plot(histogram)
    # plt.savefig('media/output_images/hist_{}'.format(img_name), bbox_inches='tight')
    # plt.close()
    # Create an output image to draw on and visualize the result
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))
    # Find the peak of the left and right halves of the histogram
    # These will be the starting point for the left and right lines
    midpoint = np.int(histogram.shape[0]//2)
    leftx_base = np.argmax(histogram[:midpoint])
    rightx_base = np.argmax(histogram[midpoint:]) + midpoint

    # HYPERPARAMETERS
    # Choose the number of sliding windows
    nwindows = 9
    # Set the width of the windows +/- margin
    margin = 100
    # Set minimum number of pixels found to recenter window
    minpix = 50

    # Set height of windows - based on nwindows above and image shape
    window_height = np.int(binary_warped.shape[0]//nwindows)
    # Identify the x and y positions of all nonzero pixels in the image
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    # Current positions to be updated later for each window in nwindows
    leftx_current = leftx_base
    rightx_current = rightx_base

    # Create empty lists to receive left and right lane pixel indices
    left_lane_inds = []
    right_lane_inds = []

    # Step through the windows one by one
    for window in range(nwindows):
        # Identify window boundaries in x and y (and right and left)
        win_y_low = binary_warped.shape[0] - (window+1)*window_height
        win_y_high = binary_warped.shape[0] - window*window_height
        ### TO-DO: Find the four below boundaries of the window ###
        win_xleft_low = leftx_current - margin  # Update this
        win_xleft_high = leftx_current + margin  # Update this
        win_xright_low = rightx_current - margin  # Update this
        win_xright_high = rightx_current + margin  # Update this
        
        # Draw the windows on the visualization image
#         cv2.rectangle(out_img,(win_xleft_low,win_y_low),
#         (win_xleft_high,win_y_high),(0,255,0), 2) 
#         cv2.rectangle(out_img,(win_xright_low,win_y_low),
#         (win_xright_high,win_y_high),(0,255,0), 2) 
        
        ### TO-DO: Identify the nonzero pixels in x and y within the window ###
        good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & 
        (nonzerox >= win_xleft_low) &  (nonzerox < win_xleft_high)).nonzero()[0]
        good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & 
        (nonzerox >= win_xright_low) &  (nonzerox < win_xright_high)).nonzero()[0]
        
        # Append these indices to the lists
        left_lane_inds.append(good_left_inds)
        right_lane_inds.append(good_right_inds)

        # print(good_left_inds.shape)
        ### TO-DO: If you found > minpix pixels, recenter next window ###
        ### (`right` or `leftx_current`) on their mean position ###
        if len(good_left_inds) > minpix:
            leftx_current = np.mean(nonzerox[good_left_inds]).astype(np.int)
        if len(good_right_inds) > minpix:
            rightx_current = np.mean(nonzerox[good_right_inds]).astype(np.int)

    # Concatenate the arrays of indices (previously was a list of lists of pixels)
    try:
        left_lane_inds = np.concatenate(left_lane_inds)
        right_lane_inds = np.concatenate(right_lane_inds)
    except ValueError:
        # Avoids an error if the above is not implemented fully
        pass

    # Extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]

    return leftx, lefty, rightx, righty, out_img


def fit_polynomial(binary_warped, img_name):
    """
    Given the binary warped image, find the pixels belonging
    to both the lane lines and fit a quadratic polynomial to
    those pixels to return two polynomial fits - one for each 
    line.
    
    """
    
    # Find our lane pixels first
    leftx, lefty, rightx, righty, out_img = find_lane_pixels(binary_warped, img_name)

    ### TO-DO: Fit a second order polynomial to each using `np.polyfit` ###
    left_fit = np.polyfit(lefty, leftx, deg=2)
    right_fit = np.polyfit(righty, rightx, deg=2)

    # Generate x and y values for plotting
    ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
    try:
        left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
        right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
    except TypeError:
        # Avoids an error if `left` and `right_fit` are still none or incorrect
        print('The function failed to fit a line!')
        left_fitx = 1*ploty**2 + 1*ploty
        right_fitx = 1*ploty**2 + 1*ploty

    ## Visualization ##
    # Colors in the left and right lane regions
    out_img[lefty, leftx] = [255, 0, 0]
    out_img[righty, rightx] = [0, 0, 255]

    # Plots the left and right polynomials on the lane lines
#     plt.plot(left_fitx, ploty, color='yellow')
#     plt.plot(right_fitx, ploty, color='yellow')

    return out_img, left_fitx, right_fitx, ploty, left_fit, right_fit, leftx, lefty, rightx, righty

def fit_poly(img_shape, leftx, lefty, rightx, righty):
    """
    Given the left and right lane line coordinates,
    fit two polynomials to them.
    
    """
    ### Fit a second order polynomial to each with np.polyfit() ###
    left_fit = np.polyfit(lefty, leftx, deg=2)
    right_fit = np.polyfit(righty, rightx, deg=2)
    # Generate x and y values for plotting
    ploty = np.linspace(0, img_shape[0]-1, img_shape[0])
    ### Calc both polynomials using ploty, left_fit and right_fit ###
    left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
    right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
    
    return left_fit, right_fit, left_fitx, right_fitx, ploty

def search_around_poly(binary_warped, left_fit, right_fit):
    """
    In case of an already calculated fit, search for pixels within
    a margin to identify the pixels belonging to the lane lines, 
    instead of using a sliding window based approach again.
    
    """
    
    # HYPERPARAMETER
    # Choose the width of the margin around the previous polynomial to search
    # The quiz grader expects 100 here, but feel free to tune on your own!
    margin = 100

    # Grab activated pixels
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    
    ### Set the area of search based on activated x-values ###
    ### within the +/- margin of our polynomial function ###
    left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + 
                    left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) + 
                    left_fit[1]*nonzeroy + left_fit[2] + margin)))
    right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + 
                    right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) + 
                    right_fit[1]*nonzeroy + right_fit[2] + margin)))
    
    # Again, extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]

    # Fit new polynomials
    left_fit, right_fit, left_fitx, right_fitx, ploty = fit_poly(binary_warped.shape, leftx, lefty, rightx, righty)
    
    ## Visualization ##
    # Create an image to draw on and an image to show the selection window
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
    window_img = np.zeros_like(out_img)
    # Color in left and right line pixels
    out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
    out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]

    # Generate a polygon to illustrate the search window area
    # And recast the x and y points into usable format for cv2.fillPoly()
    left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
    left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, 
                              ploty])))])
    left_line_pts = np.hstack((left_line_window1, left_line_window2))
    right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
    right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, 
                              ploty])))])
    right_line_pts = np.hstack((right_line_window1, right_line_window2))

    # Draw the lane onto the warped blank image
    cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
    cv2.fillPoly(window_img, np.int_([right_line_pts]), (0, 255, 0))
    result = cv2.addWeighted(out_img, 1, window_img, 0.5, 0)
    
    # Plot the polynomial lines onto the image
#     plt.plot(left_fitx, ploty, color='yellow')
#     plt.plot(right_fitx, ploty, color='yellow')
    ## End visualization steps ##
    
    return result, left_fitx, right_fitx, ploty, left_fit, right_fit, leftx, lefty, rightx, righty


def get_scaled_fits(leftx, lefty, rightx, righty, ym_per_pix, xm_per_pix):
    """
    Return the left and polynomial fits taking real-world scale into account
    
    """
    left_fit = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
    right_fit = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
    return left_fit, right_fit


def measure_curvature_pixels(im, ploty, left_fit, right_fit, xm_per_pix, ym_per_pix):
    '''
    Calculates the curvature of polynomial functions in pixels.
    '''
    # Define y-value where we want radius of curvature
    # We'll choose the maximum y-value, corresponding to the bottom of the image
    
    y_eval = np.max(ploty)
#     print(ym_per_pix)
#     print(left_fit)
#     print(y_eval)
    # Calculation of R_curve (radius of curvature)
    left_curverad = ((1 + (2*left_fit[0]*y_eval*ym_per_pix + left_fit[1])**2)**1.5) / np.absolute(2*left_fit[0])
    right_curverad = ((1 + (2*right_fit[0]*y_eval*ym_per_pix + right_fit[1])**2)**1.5) / np.absolute(2*right_fit[0])
    
    y = im.shape[0]-1
    bottom_left = left_fit[0]*y**2 + left_fit[1]*y + left_fit[2]
    bottom_right = right_fit[0]*y**2 + right_fit[1]*y + right_fit[2]
    center = (bottom_left + bottom_right) / 2
    diff = center - im.shape[1]//2
    left_diff = im.shape[1]//2 - bottom_left
    right_diff = bottom_right - im.shape[1]//2 
    diff_real = diff * xm_per_pix
    ldiff_real = left_diff * xm_per_pix
    rdiff_real = right_diff * xm_per_pix
    
    return left_curverad, right_curverad, np.round(diff_real, 2),  np.round(ldiff_real, 2),  np.round(rdiff_real, 2)