class FindLanes:
def __init__(self):
self.search_start = -1
self.search_end = -1
self.error_counter = 0
self.smooth_value = 10
self.update_data = 10
self.frame = 0
self.left_curvature = 0
self.right_curvature = 0
self.off_center = 0
self.leftx = deque(maxlen=self.smooth_value)
self.lefty = deque(maxlen=self.smooth_value)
self.rightx = deque(maxlen=self.smooth_value)
self.righty = deque(maxlen=self.smooth_value)
self.ym_per_pix = 30/720 # meters per pixel in y dimension
self.xm_per_pix = 3.7/700 # meters per pixel in x dimension
def compute_calibartion_points(self):
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('./camera_cal/calibration*.jpg')
# Step through the list and search for chessboard corners
for fname in images:
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9,6),None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
self.objpoints = objpoints
self.imgpoints = imgpoints
def undistort_image(self, img):
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(self.objpoints, self.imgpoints,
(img.shape[1], img.shape[0]),None,None)
return cv2.undistort(img, mtx, dist, None, mtx)
def binary_pipeline(self, img, s_thresh=(170, 255), sx_thresh=(20, 100)):
img = np.copy(img)
# Convert to HLS color space
hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HLS).astype(np.float)
l_channel = hsv[:,:,1]
s_channel = hsv[:,:,2]
# Sobel x
sobelx = cv2.Sobel(l_channel, 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
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1
# Threshold color channel
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1
#combine
color_binary = np.zeros_like(scaled_sobel)
color_binary[(sxbinary == 1) | (s_binary ==1)] = 1
return color_binary
def warper(self, img):
img_size = (img.shape[1], img.shape[0])
src = np.float32(
[[(img_size[0] / 2) - 55, img_size[1] / 2 + 100],
[((img_size[0] / 6) - 10), img_size[1]],
[(img_size[0] * 5 / 6) + 60, img_size[1]],
[(img_size[0] / 2 + 55), img_size[1] / 2 + 100]])
dst = np.float32(
[[(img_size[0] / 4), 0],
[(img_size[0] / 4), img_size[1]],
[(img_size[0] * 3 / 4), img_size[1]],
[(img_size[0] * 3 / 4), 0]])
M = cv2.getPerspectiveTransform(src, dst)
Minv = cv2.getPerspectiveTransform(dst, src)
warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_NEAREST) # keep same size as input image
return warped, Minv
def get_histogram_points(self, binary_warped):
if self.search_start == -1:
search_start = 150
else:
search_start = self.search_start
if self.search_end == -1:
search_end = 1150
else:
search_end = min(self.search_end, binary_warped.shape[1])
histogram = np.sum(binary_warped[binary_warped.shape[0]/2:,search_start:search_end], axis=0)
midpoint = np.int(histogram.shape[0]/2)
leftx_base = search_start + np.argmax(histogram[:midpoint])
rightx_base = search_start + np.argmax(histogram[midpoint:]) + midpoint
return midpoint, leftx_base, rightx_base
def polynomial_pipeline(self, binary_warped, Minv, image, on_road, nwindows=9, margin=100, minpix=50):
# histogram of bottom 1/2
midpoint, leftx_base, rightx_base = self.get_histogram_points(binary_warped)
if rightx_base - leftx_base < 700:
self.search_start = -1
self.search_end = -1
midpoint, leftx_base, rightx_base = self.get_histogram_points(binary_warped)
self.search_start = max(leftx_base - margin, 0)
self.search_end = rightx_base + margin
# 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
# 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
# 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. x and y values
self.leftx.append(nonzerox[left_lane_inds])
self.lefty.append(nonzeroy[left_lane_inds])
self.rightx.append(nonzerox[right_lane_inds])
self.righty.append(nonzeroy[right_lane_inds])
leftx = self.get_all_pixels(self.leftx)
lefty = self.get_all_pixels(self.lefty)
rightx = self.get_all_pixels(self.rightx)
righty = self.get_all_pixels(self.righty)
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
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]
if (self.frame % self.update_data == 0):
self.get_curvature(binary_warped, lefty, leftx, righty, rightx)
y_eval = binary_warped.shape[0]
left_bot = left_fit[0]*y_eval**2 + left_fit[1]*y_eval + left_fit[2]
right_bot = right_fit[0]*y_eval**2 + right_fit[1]*y_eval + right_fit[2]
mid_bot = (left_bot + right_bot) / 2
mid_photo = image.shape[1] / 2
#positive is right of center. in meters
self.off_center = (mid_bot - mid_photo) * self.xm_per_pix
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(image,'Left Curvature: ' + str(int(self.left_curvature)),(10,60), font, 2,(255,255,255),2,cv2.LINE_AA)
cv2.putText(image,'Right Curvature: ' + str(int(self.right_curvature)),(10,125), font, 2,(255,255,255),2,cv2.LINE_AA)
cv2.putText(image,'Center offset: ' + str(round(self.off_center,2)),(10,190), font, 2,(255,255,255),2,cv2.LINE_AA)
if on_road:
return self.plot_on_road(binary_warped, image, left_fitx, right_fitx, ploty, Minv)
else:
return left_fitx, right_fitx, ploty
def get_all_pixels(self, q):
all_pixels = []
for v in q:
all_pixels.extend(v)
return np.array(all_pixels)
def plot_on_road(self, binary_warped, image, left_fitx, right_fitx, ploty, Minv):
# Create an image to draw the lines on
warp_zero = np.zeros_like(binary_warped).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)
newwarp = cv2.warpPerspective(color_warp, Minv, (image.shape[1], image.shape[0]))
# Combine the result with the original image
return cv2.addWeighted(image, 1, newwarp, 0.3, 0)
def process_image(self, img):
undistort = self.undistort_image(img)
binary = self.binary_pipeline(undistort)
warped, Minv = self.warper(binary)
on_road = self.polynomial_pipeline(warped, Minv, img, True)
self.frame = self.frame + 1
return on_road
def get_binary_warped(self, img):
undistort = self.undistort_image(img)
binary = self.binary_pipeline(undistort)
return self.warper(binary)
def get_curvature(self, binary_warped, lefty, leftx, righty, rightx):
ym_per_pix = self.ym_per_pix
xm_per_pix = self.xm_per_pix
y_eval = binary_warped.shape[0]/2
# 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
self.left_curvature = left_curverad
self.right_curvature = right_curverad