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lane_detection.py
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# lane detection pipeline video
import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle
# convert float image to uint8 image
from skimage import img_as_ubyte
from pp_transform import corners_unwarp
def evalPoly(fit_param, Y):
"""
Evaluate X, based on Y of the polynomial
"""
return fit_param[0]*Y**2 + fit_param[1]*Y + fit_param[2]
def thresholdIMG(img, sx_thresh=(40, 255), l_thresh = (220, 255), b_thresh = (155,255)):
"""
Thresholding original image with 3 different criteria
"""
# Convert to HLS color space and use the L channel
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS).astype(np.float)
l_channel = hls[:,:,1]
l_binary = np.zeros_like(l_channel)
l_binary[(l_channel>=l_thresh[0])&(l_channel<=l_thresh[1])] = 1
# calculate gradient in x direction
sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0)
abs_sobelx = np.absolute(sobelx)
scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1
# Convert to LAB color space, and use the B channel
lab = cv2.cvtColor(img, cv2.COLOR_RGB2LAB).astype(np.float)
b_channel = lab[:,:,2]
b_binary = np.zeros_like(b_channel)
b_binary[(b_channel>=b_thresh[0])&(b_channel<=b_thresh[1])] = 1
# combine detection binaries
img_out = np.dstack(( sxbinary, l_binary, b_binary))
return img_out
def findLanes(top_down):
"""
extract lanes from top_down view of the road
"""
binary_warped = np.zeros((top_down.shape[0], top_down.shape[1]))
binary_warped[(top_down[:,:,0]>0) | (top_down[:,:,1]>0) | (top_down[:,:,2]>0)] = 1
# 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
out_img = np.uint8(out_img)
# 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(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]
pts_raw = [leftx, lefty, rightx, righty]
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]
# 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, top_down.shape[0]-1, top_down.shape[0] )
left_fitx = evalPoly(left_fit, ploty)
right_fitx = evalPoly(right_fit, ploty)
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))], dtype=np.int32)
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))], dtype=np.int32)
pts = np.hstack((pts_left, pts_right))
return pts, pts_raw, out_img
def visualLane(image, pts, pts_raw, perspective_M):
"""
Visualize the detected lane, radius, and car center shift
"""
# plot on original image
# Create an image to draw the lines on
warp_zero = np.zeros_like(image).astype(np.uint8)
# Draw the lane onto the warped blank image
cv2.fillPoly(warp_zero, pts, (0,255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(warp_zero, np.linalg.inv(perspective_M), (image.shape[1], image.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(image, 1, newwarp, 0.3, 0)
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30./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
ymax = float(image.shape[0])
y_eval = ymax
leftx = pts_raw[0]
lefty = pts_raw[1]
rightx = pts_raw[2]
righty = pts_raw[3]
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
# print(left_curverad, 'm', right_curverad, 'm')
# print distance from center and radius on the image
lane_center = (evalPoly(left_fit_cr, ymax*ym_per_pix) + evalPoly(right_fit_cr, ymax*ym_per_pix))/2.0
car_center = image.shape[1]*xm_per_pix/2.0
str1 = "Distance from center: {:2.2f} m".format(car_center-lane_center)
str2 = "Radius of Curvature: {:2.2f} km".format((left_curverad+right_curverad)/2000.)
cv2.putText(result,str1,(430,630), cv2.FONT_HERSHEY_DUPLEX, 1,(0,0,255))
cv2.putText(result,str2,(430,660), cv2.FONT_HERSHEY_DUPLEX, 1,(0,0,255))
return result
if __name__ == "__main__":
# load camera calibration data
dist_pickle = pickle.load( open( "camera_cal/calibration_undistort.p", "rb" ) )
mtx = dist_pickle["mtx"]
dist = dist_pickle["dist"]
# load image
#image = mpimg.imread('test_images/p3.png')
image = mpimg.imread('test_images/test6.jpg')
image = img_as_ubyte(image)
# threshold image
img_thresh = thresholdIMG(image)
# unwarp image
top_down, perspective_M = corners_unwarp(img_thresh, mtx, dist)
# Plot the result
f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=20)
ax2.imshow(img_thresh)
#plt.save_figure(result, "result.jpg")
ax2.set_title('Threshold Result', fontsize=20)
ax3.imshow(top_down)
#plt.save_figure(result, "result.jpg")
ax3.set_title('Pipeline Result', fontsize=20)
#plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
fig = plt.figure(2)
# find lane line pixels
pts, pts_raw, out_img = findLanes(top_down)
plt.imshow(np.uint8(out_img))
N = pts.shape[1]
plt.plot(pts[0, 0:N/2, 0], pts[0, 0:N/2, 1], color='yellow')
plt.plot(pts[0, N/2:, 0], pts[0, N/2:, 1], color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
plt.figure(3)
result = visualLane(image, pts, pts_raw, perspective_M)
plt.imshow(result)
plt.show()
print("finish plotting")