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lanelines.py
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# Peter Schüllermann
# Udacity Lane Lines Project for the CarND 2017
import cv2
import numpy as np
from calibration import warp_image
def HLS_Gradient(image):
# Convert to HLS color space and separate the S channel
# Note: img is the undistorted image
hls = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)
s_channel = hls[:, :, 2]
# Grayscale image
# NOTE: we already saw that standard grayscaling lost color information for the lane lines
# Explore gradients in other colors spaces / color channels to see what might work better
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Sobel x
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0) # Take the derivative in x
abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
scaled_sobel = np.uint8(255 * abs_sobelx / np.max(abs_sobelx))
# Threshold x gradient
thresh_min = 20
thresh_max = 100
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
# Threshold color channel
s_thresh_min = 170
s_thresh_max = 255
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh_min) & (s_channel <= s_thresh_max)] = 1
# Stack each channel to view their individual contributions in green and blue respectively
# This returns a stack of the two binary images, whose components you can see as different colors
color_binary = np.dstack((np.zeros_like(sxbinary), sxbinary, s_binary))
# Combine the two binary thresholds
combined_binary = np.zeros_like(sxbinary)
combined_binary[(s_binary == 1) | (sxbinary == 1)] = 1
return combined_binary
def detect_lane_lines(image):
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(image[360:, :], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((image, image, image)) # * 255
# 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
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(image.shape[0] / nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = image.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# 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 = image.shape[0] - (window + 1) * window_height
win_y_high = image.shape[0] - window * window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# 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)
# 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)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
return image, left_lane_inds, nonzerox, nonzeroy, out_img, right_lane_inds
def calculate_radius_and_center(image, left_lane_inds, nonzerox, nonzeroy, out_img, right_lane_inds):
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# 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]
ploty = np.linspace(0, image.shape[0] - 1, image.shape[0])
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]
# RADIUS
# calculate curve radius
y_eval = np.max(ploty)
left_fit = np.polyfit(lefty, leftx, 2)
left_fitx = left_fit[0] * ploty ** 2 + left_fit[1] * ploty + left_fit[2]
right_fit = np.polyfit(righty, rightx, 2)
right_fitx = right_fit[0] * ploty ** 2 + right_fit[1] * ploty + right_fit[2]
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30.0 / 720 # meters per pixel in y dimension
xm_per_pix = 3.7 / 700 # meters per pixel in x dimension
# Fit new polynomials to x,y in world space
left_fit_cr = np.polyfit(ploty * ym_per_pix, left_fitx * xm_per_pix, 2)
right_fit_cr = np.polyfit(ploty * ym_per_pix, right_fitx * xm_per_pix, 2)
# Calculate the new radii of curvature
left_curverad = ((1 + (2 * left_fit_cr[0] * y_eval * ym_per_pix + left_fit_cr[1]) ** 2) ** 1.5) / np.absolute(
2 * left_fit_cr[0])
right_curverad = ((1 + (2 * right_fit_cr[0] * y_eval * ym_per_pix + right_fit_cr[1]) ** 2) ** 1.5) / np.absolute(
2 * right_fit_cr[0])
# calculate center displacemet
left_distance = (left_fitx[719] * xm_per_pix)
right_distance = (right_fitx[719] * xm_per_pix)
center = (1280/2)*xm_per_pix
center_distance = center - (left_distance + right_distance)/2
return left_fitx, ploty, right_fitx, left_curverad, right_curverad, center_distance
def draw_lines_to_image(image, left_fitx, original, ploty, right_fitx, warp_matrix_inverse):
warp_zero = np.zeros_like(image).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0, 255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
new_warp = cv2.warpPerspective(color_warp, warp_matrix_inverse, (image.shape[1], image.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(original, 1, new_warp, 0.3, 0)
return result
def write_info_to_image(image, left_curverad, right_curverad, center_distance):
font = cv2.FONT_HERSHEY_SIMPLEX
curve_radius = (left_curverad + right_curverad)/2
# print curve radius
if curve_radius >= 3000.0:
cv2.putText(image, 'straight',
(320, 40), font, 1, (255, 255, 255), 2)
else:
cv2.putText(image, 'curve radius = ' + "{0:.2f}".format(round(curve_radius, 2)) + 'm',
(320, 40), font, 1, (255, 255, 255), 2)
# print center distance
cv2.putText(image, 'center distance = ' + "{0:.2f}".format(round(center_distance, 2)) + 'm',
(320, 100), font, 1, (255, 255, 255), 2)
return image
def detect(image, original, warp_matrix, warp_matrix_inverse):
# Convert to HLS and detect Lane Pixels
image = HLS_Gradient(image)
# Perspective Transform
image = warp_image(image, warp_matrix) * 255
# detect lane lines
image, left_lane_inds, nonzerox, nonzeroy, out_img, right_lane_inds = detect_lane_lines(image)
# calculate curve radius and center displacement
left_fitx, ploty, right_fitx, left_curverad, right_curverad, center_distance = \
calculate_radius_and_center(image, left_lane_inds, nonzerox, nonzeroy, out_img, right_lane_inds)
# draw area to image
image = draw_lines_to_image(image, left_fitx, original, ploty, right_fitx, warp_matrix_inverse)
# write curve radius and center displacement to image
image = write_info_to_image(image, left_curverad, right_curverad, center_distance)
return image