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laneDetection.py
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laneDetection.py
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import cv2
import numpy as np
import serial
import os
from matplotlib import pyplot as plt, cm, colors
ser = serial.Serial('COM6', 9600)
ym_per_pix = 30 / 720
xm_per_pix = 3.7 / 720
CWD_PATH = os.getcwd()
def readVideo():
inpImage = cv2.VideoCapture(os.path.join(CWD_PATH, 'drive.mp4'))
return inpImage
def processImage(inpImage):
hls = cv2.cvtColor(inpImage, cv2.COLOR_BGR2HLS)
lower_white = np.array([0, 160, 10])
upper_white = np.array([255, 255, 255])
mask = cv2.inRange(inpImage, lower_white, upper_white)
hls_result = cv2.bitwise_and(inpImage, inpImage, mask = mask)
gray = cv2.cvtColor(hls_result, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 160, 255, cv2.THRESH_BINARY)
blur = cv2.GaussianBlur(thresh,(3, 3), 0)
canny = cv2.Canny(blur, 40, 60)
return image, hls_result, gray, thresh, blur, canny
def perspectiveWarp(inpImage):
img_size = (inpImage.shape[1], inpImage.shape[0])
src = np.float32([[590, 440],
[690, 440],
[200, 640],
[1000, 640]])
dst = np.float32([[200, 0],
[1200, 0],
[200, 710],
[1200, 710]])
matrix = cv2.getPerspectiveTransform(src, dst)
minv = cv2.getPerspectiveTransform(dst, src)
birdseye = cv2.warpPerspective(inpImage, matrix, img_size)
height, width = birdseye.shape[:2]
birdseyeLeft = birdseye[0:height, 0:width // 2]
birdseyeRight = birdseye[0:height, width // 2:width]
return birdseye, birdseyeLeft, birdseyeRight, minv
def plotHistogram(inpImage):
histogram = np.sum(inpImage[inpImage.shape[0] // 2:, :], axis = 0)
midpoint = int(histogram.shape[0] / 2)
leftxBase = np.argmax(histogram[:midpoint])
rightxBase = np.argmax(histogram[midpoint:]) + midpoint
plt.xlabel("Image X Coordinates")
plt.ylabel("Number of White Pixels")
return histogram, leftxBase, rightxBase
def slide_window_search(binary_warped, histogram):
out_img = np.dstack((binary_warped, binary_warped, binary_warped)) * 255
midpoint = int(histogram.shape[0] / 2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
nwindows = 9
window_height = int(binary_warped.shape[0] / nwindows)
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
leftx_current = leftx_base
rightx_current = rightx_base
margin = 100
minpix = 50
left_lane_inds = []
right_lane_inds = []
for window in range(nwindows):
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
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)
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]
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
if len(good_left_inds) > minpix:
leftx_current = int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = int(np.mean(nonzerox[good_right_inds]))
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
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]
ltx = np.trunc(left_fitx)
rtx = np.trunc(right_fitx)
plt.plot(right_fitx)
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.plot(left_fitx, ploty, color = 'yellow')
plt.plot(right_fitx, ploty, color = 'yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
return ploty, left_fit, right_fit, ltx, rtx
def general_search(binary_warped, left_fit, right_fit):
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)))
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
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]
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
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]
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))
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.plot(left_fitx, ploty, color = 'yellow')
plt.plot(right_fitx, ploty, color = 'yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
ret = {}
ret['leftx'] = leftx
ret['rightx'] = rightx
ret['left_fitx'] = left_fitx
ret['right_fitx'] = right_fitx
ret['ploty'] = ploty
return ret
def measure_lane_curvature(ploty, leftx, rightx):
leftx = leftx[::-1]
rightx = rightx[::-1]
y_eval = np.max(ploty)
left_fit_cr = np.polyfit(ploty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(ploty*ym_per_pix, rightx*xm_per_pix, 2)
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])
if leftx[0] - leftx[-1] > 60:
curve_direction = 'Left Curve'
elif leftx[-1] - leftx[0] > 60:
curve_direction = 'Right Curve'
else:
curve_direction = 'Straight'
return (left_curverad + right_curverad) / 2.0, curve_direction
def draw_lane_lines(original_image, warped_image, Minv, draw_info):
leftx = draw_info['leftx']
rightx = draw_info['rightx']
left_fitx = draw_info['left_fitx']
right_fitx = draw_info['right_fitx']
ploty = draw_info['ploty']
warp_zero = np.zeros_like(warped_image).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
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))
mean_x = np.mean((left_fitx, right_fitx), axis=0)
pts_mean = np.array([np.flipud(np.transpose(np.vstack([mean_x, ploty])))])
cv2.fillPoly(color_warp, np.int_([pts]), (0, 255, 0))
cv2.fillPoly(color_warp, np.int_([pts_mean]), (0, 255, 255))
newwarp = cv2.warpPerspective(color_warp, Minv, (original_image.shape[1], original_image.shape[0]))
result = cv2.addWeighted(original_image, 1, newwarp, 0.3, 0)
return pts_mean, result
def offCenter(meanPts, inpFrame):
mpts = meanPts[-1][-1][-2].astype(int)
pixelDeviation = inpFrame.shape[1] / 2 - abs(mpts)
deviation = pixelDeviation * xm_per_pix
direction = "left" if deviation < 0 else "right"
return deviation, direction
def addText(img, radius, direction, deviation, devDirection):
font = cv2.FONT_HERSHEY_TRIPLEX
if (direction != 'Straight'):
text = 'Radius of Curvature: ' + '{:04.0f}'.format(radius) + 'm'
text1 = 'Curve Direction: ' + (direction)
else:
text = 'Radius of Curvature: ' + 'N/A'
text1 = 'Curve Direction: ' + (direction)
cv2.putText(img, text , (50,100), font, 0.8, (0,100, 200), 2, cv2.LINE_AA)
cv2.putText(img, text1, (50,150), font, 0.8, (0,100, 200), 2, cv2.LINE_AA)
deviation_text = 'Off Center: ' + str(round(abs(deviation), 3)) + 'm' + ' to the ' + devDirection
cv2.putText(img, deviation_text, (50, 200), cv2.FONT_HERSHEY_TRIPLEX, 0.8, (0,100, 200), 2, cv2.LINE_AA)
return img
image = readVideo()
while True:
_, frame = image.read()
birdView, birdViewL, birdViewR, minverse = perspectiveWarp(frame)
img, hls, grayscale, thresh, blur, canny = processImage(birdView)
imgL, hlsL, grayscaleL, threshL, blurL, cannyL = processImage(birdViewL)
imgR, hlsR, grayscaleR, threshR, blurR, cannyR = processImage(birdViewR)
hist, leftBase, rightBase = plotHistogram(thresh)
plt.plot(hist)
ploty, left_fit, right_fit, left_fitx, right_fitx = slide_window_search(thresh, hist)
plt.plot(left_fit)
draw_info = general_search(thresh, left_fit, right_fit)
curveRad, curveDir = measure_lane_curvature(ploty, left_fitx, right_fitx)
meanPts, result = draw_lane_lines(frame, thresh, minverse, draw_info)
deviation, directionDev = offCenter(meanPts, frame)
ser.write(str(deviation).encode())
finalImg = addText(result, curveRad, curveDir, deviation, directionDev)
cv2.imshow("Final", finalImg)
if cv2.waitKey(1) == 13:
image.release()
cv2.destroyAllWindows()