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line_fit.py
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line_fit.py
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import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle
from combined_thresh import combined_thresh
from perspective_transform import perspective_transform
def line_fit(binary_warped):
"""
Find and fit lane lines
"""
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = (np.dstack((binary_warped, binary_warped, binary_warped))*255).astype('uint8')
# 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[100:midpoint]) + 100
rightx_base = np.argmax(histogram[midpoint:-100]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
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 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 = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.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]))
# 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]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Return a dict of relevant variables
ret = {}
ret['left_fit'] = left_fit
ret['right_fit'] = right_fit
ret['nonzerox'] = nonzerox
ret['nonzeroy'] = nonzeroy
ret['out_img'] = out_img
ret['left_lane_inds'] = left_lane_inds
ret['right_lane_inds'] = right_lane_inds
return ret
def tune_fit(binary_warped, left_fit, right_fit):
"""
Given a previously fit line, quickly try to find the line based on previous lines
"""
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped")
# It's now much easier to find line pixels!
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
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]
# If we don't find enough relevant points, return all None (this means error)
min_inds = 10
if lefty.shape[0] < min_inds or righty.shape[0] < min_inds:
return None
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
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 a dict of relevant variables
ret = {}
ret['left_fit'] = left_fit
ret['right_fit'] = right_fit
ret['nonzerox'] = nonzerox
ret['nonzeroy'] = nonzeroy
ret['left_lane_inds'] = left_lane_inds
ret['right_lane_inds'] = right_lane_inds
return ret
def viz1(binary_warped, ret, save_file=None):
"""
Visualize each sliding window location and predicted lane lines, on binary warped image
save_file is a string representing where to save the image (if None, then just display)
"""
# Grab variables from ret dictionary
left_fit = ret['left_fit']
right_fit = ret['right_fit']
nonzerox = ret['nonzerox']
nonzeroy = ret['nonzeroy']
out_img = ret['out_img']
left_lane_inds = ret['left_lane_inds']
right_lane_inds = ret['right_lane_inds']
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
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]
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]
plt.imshow(out_img)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 640)
plt.ylim(360, 0)
if save_file is None:
plt.show()
else:
plt.savefig(save_file)
plt.gcf().clear()
def viz2(binary_warped, ret, save_file=None):
"""
Visualize the predicted lane lines with margin, on binary warped image
save_file is a string representing where to save the image (if None, then just display)
"""
# Grab variables from ret dictionary
left_fit = ret['left_fit']
right_fit = ret['right_fit']
nonzerox = ret['nonzerox']
nonzeroy = ret['nonzeroy']
left_lane_inds = ret['left_lane_inds']
right_lane_inds = ret['right_lane_inds']
# 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).astype('uint8')
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 x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0])
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]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
margin = 100 # NOTE: Keep this in sync with *_fit()
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, 0.3, 0)
plt.imshow(result)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 640)
plt.ylim(360, 0)
if save_file is None:
plt.show()
else:
plt.savefig(save_file)
plt.gcf().clear()
def calc_curve(left_lane_inds, right_lane_inds, nonzerox, nonzeroy):
"""
Calculate radius of curvature in meters
"""
#y_eval = 719 # 360p video/image, so last (lowest on screen) y index is 719
y_eval = 359
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/360*2 # meters per pixel in y dimension
xm_per_pix = 3.7/700*2 # meters per pixel in x dimension
# 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 to x,y in world space
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*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])
# Now our radius of curvature is in meters
return left_curverad, right_curverad
def calc_vehicle_offset(undist, left_fit, right_fit):
"""
Calculate vehicle offset from lane center, in meters
"""
# Calculate vehicle center offset in pixels
bottom_y = undist.shape[0] - 1
bottom_x_left = left_fit[0]*(bottom_y**2) + left_fit[1]*bottom_y + left_fit[2]
bottom_x_right = right_fit[0]*(bottom_y**2) + right_fit[1]*bottom_y + right_fit[2]
vehicle_offset = undist.shape[1]/2 - (bottom_x_left + bottom_x_right)/2
# Convert pixel offset to meters
xm_per_pix = 3.7/700*2 # meters per pixel in x dimension
vehicle_offset *= xm_per_pix
return vehicle_offset
def final_viz(undist, left_fit, right_fit, m_inv, left_curve, right_curve, vehicle_offset):
"""
Final lane line prediction visualized and overlayed on top of original image
"""
# Generate x and y values for plotting
ploty = np.linspace(0, undist.shape[0]-1, undist.shape[0])
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]
# Create an image to draw the lines on
#warp_zero = np.zeros_like(warped).astype(np.uint8)
#color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
color_warp = np.zeros((360, 640, 3), dtype='uint8') # NOTE: Hard-coded image dimensions
# 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)
newwarp = cv2.warpPerspective(color_warp, m_inv, (undist.shape[1], undist.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)
# Annotate lane curvature values and vehicle offset from center
avg_curve = (left_curve + right_curve)/2
label_str = 'Radius of curvature: %.1f m' % avg_curve
result = cv2.putText(result, label_str, (30,40), 0, 1, (0,0,0), 2, cv2.LINE_AA)
label_str = 'Vehicle offset from lane center: %.1f m' % vehicle_offset
result = cv2.putText(result, label_str, (30,70), 0, 1, (0,0,0), 2, cv2.LINE_AA)
return result