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FindLanes.py
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FindLanes.py
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import os
import sys
import cv2
import glob
import pickle
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from moviepy.editor import VideoFileClip
from collections import deque
from itertools import chain
# All the tunable settings in the program
settings = {
'diagnostic': False,
'calibDims': (9, 6),
'persrc': np.array([
[703, 461], # top right
[1000,650], # bottom right
[308, 650], # bottom left
[580, 461] # top left
], dtype=np.float32),
'perdst': np.array([
[1000, 0 ], # top right
[1000, 700], # bottom right
[300 , 700], # bottom left
[300 , 0 ] # top left
], dtype=np.float32),
'minPixelsForFit': 150,
'minStdForFit': 100,
'maskSearchThickness':150,
'lpf': 0.85,
'nFilt': 8,
'yScale': 30/800, # m per pixel
'xScale': 3.7/700, # m per pixel
'dropThresh': 10
}
# Helper functions for plotting and display
def readFolderToStack(path='./test_images/'):
"""
Loads all images in the path into a list. Also stores
the filnames of those images
"""
imgList = []
imgNames = []
for file in os.listdir(path):
imgList.append(mpimg.imread(path + file))
filename = os.path.splitext(file)[0]
imgNames.append(file)
return imgList, imgNames
def displayImagelist(imgList, cmap=None, cols=2):
"""
Display all images in a the list imgList
"""
rows = np.ceil(len(imgList)/cols)
plt.figure()
for i, img in enumerate(imgList):
plt.subplot(rows, cols, i+1)
if len(img.shape) == 2:
cmap = 'gray'
plt.imshow(img, cmap)
plt.xticks([])
plt.yticks([])
plt.tight_layout(pad=0.5, h_pad=0.5, w_pad=0.5)
plt.show()
def compareImageList(imgListLeft, imgListRight, cmap=None):
"""
Here
"""
cols = 2
rows = np.ceil(len(imgListLeft))
plt.figure()
for row in range(len(imgListLeft)):
imgLeft = imgListLeft[row]
imgRight = imgListRight[row]
plt.subplot(rows, cols, 2*row + 1)
plt.imshow(imgLeft, 'gray' if len(imgLeft.shape) == 2 else cmap)
plt.xticks([])
plt.yticks([])
plt.subplot(rows, cols, 2*row + 2)
plt.imshow(imgRight, 'gray' if len(imgRight.shape) == 2 else cmap)
plt.xticks([])
plt.yticks([])
plt.tight_layout(pad=0, h_pad=0, w_pad=0)
plt.show()
def setupAndCalib(calibPath):
# Chessboard settings
nx, ny = settings['calibDims']
imgStack = glob.glob(calibPath)
mtx, dist = cameraCalib(imgStack, nx, ny)
return mtx, dist
def cameraCalib(imgStack, nx, ny):
objpoints = []
imgpoints = []
objp = np.zeros((nx*ny,3), np.float32)
objp[:,:2] = np.mgrid[0:nx, 0:ny].T.reshape(-1,2)
for fname in imgStack:
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
if ret:
objpoints.append(objp)
imgpoints.append(corners)
_, mtx, dist, _, _ = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
return mtx, dist
class Line():
def __init__(self, imgSize):
# was the line detected in the last iteration?
self.detected = False
#polynomial coefficients for the most recent fit
self.currFit = [np.array([False])]
#radius of curvature of the line in some units
self.radius = None
self.vehPos = None
#distance in meters of vehicle center from the line
#self.line_base_pos = None
#difference in fit coefficients between last and new fits
#self.diffs = np.array([0,0,0], dtype='float')
# x values for detected line pixels in current frame
self.pixx = None
# y values for detected line pixels in current frame
self.pixy = None
self.pastPixx = deque(maxlen=settings['nFilt'])
self.pastPixy = deque(maxlen=settings['nFilt'])
self.yLine = np.arange(imgSize[0])
self.xLine = None
#self.xFilt = None
self.mask = np.ones(imgSize, dtype=np.uint8)*255
self.mode = 0
self.dropCount = 0
def fitLine(self, x, y):
return np.polyfit(y, x, 2)
def curvature(self, x, y, yEval):
yEval = yEval*settings['yScale']
## Method 1: Fit again
#x = np.float64(x)*settings['xScale']
#y = np.float64(y)*settings['yScale']
#fitCoeffs = self.fitLine(x,y)
#return ((1 + (2*fitCoeffs[0]*yEval + fitCoeffs[1])**2)**1.5) / np.absolute(2*fitCoeffs[0])
## Method 2: Derive analytical expression
fit = self.currFit
sx, sy = settings['xScale'], settings['yScale']
A = fit[0]*sx/sy/sy
B = fit[1]*sx/sy
return ((1 + (2*A*yEval + B)**2)**1.5) / np.absolute(2*A)
def eval(self, y):
return np.polyval(self.currFit, y)
def updateMask(self):
self.mask.fill(0)
def update(self, x, y, mode):
self.mode = mode
if len(y) > settings['minPixelsForFit'] and np.std(y) > settings['minStdForFit']:
self.pixx, self.pixy = x, y
self.pastPixx.append(x)
self.pastPixy.append(y)
# Find the best fit line
fit = self.fitLine(list(chain.from_iterable(self.pastPixx)),
list(chain.from_iterable(self.pastPixy)))
self.currFit = fit
# Remember the current line
self.xLine = self.eval(self.yLine)
self.radius = self.curvature(self.xLine, self.yLine, np.max(self.yLine))
self.vehPos = self.xLine[-1]
# Compute a search mask for the next frame
self.mask.fill(0)
linePts = np.transpose(np.vstack([self.xLine, self.yLine])).reshape((-1,1,2)).astype(np.int32)
cv2.drawContours(self.mask, linePts, -1, (255,255,255), thickness=settings['maskSearchThickness'])
self.detected = True
self.dropCount = 0
else:
self.currFit = [np.array([False])]
self.detected = False
self.dropCount += 1
def searchInMask(self, img):
img = img.astype(np.uint8)
masked = cv2.bitwise_and(img, self.mask)
pts = cv2.findNonZero(masked)
if pts is not None:
pts = pts.reshape((-1,2))
self.update(pts[:,0], pts[:,1], 0)
else:
self.detected = False
def thresholdFrame(img):
# Gray thresholds
grayThres = (20, 255)
gray = 0.5*img[:,:,0] + 0.4*img[:,:,1] + 0.1*img[:,:,2]
grayBin = (gray > grayThres[0]) & (gray <= grayThres[1])
# RGB thresholds
# HSV thresholds
hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
hsvLowYellow = np.array([ 0, 100, 100])
hsvHighYellow = np.array([ 50, 255, 255])
hsvLowWhite = np.array([0, 0, 200])
hsvHighWhite = np.array([15, 20,255])
cBin = cv2.inRange(hsv, hsvLowYellow, hsvHighYellow) | cv2.inRange(hsv, hsvLowWhite, hsvHighWhite)
# HLS thresholds
sThres = (170, 255)
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s = hls[:,:,2]
sBin = (s > sThres[0]) & (s <= sThres[1])
# Gradient thresholds
sobelSize = 15
gradxThresh = (0.05, 0.75)
sobelin = s # choose the input image for gradient calculation
sobelx = np.absolute(cv2.Sobel(sobelin, cv2.CV_64F, 1, 0, ksize=sobelSize))
sobelx = sobelx/np.max(sobelx)
gmxBin = (sobelx > gradxThresh[0]) & (sobelx <= gradxThresh[1])
#sobely = np.absolute(cv2.Sobel(sobelin, cv2.CV_64F, 0, 1, ksize=sobelSize))
#sobely = sobely/np.max(sobely)
##gmyBin
## Calculate the gradient magnitude
#gradMagThres = (0.25, 1)
#gradmag = np.sqrt(sobelx**2 + sobely**2)
#gradmag = gradmag/np.max(gradmag)
#gmBin = (gradmag > gradMagThres[0]) & (gradmag <= gradMagThres[1])
## Calculate the gradient direction
#gradDirThres = (0.8, 1.2)
#graddir = np.arctan2(sobely, sobelx)
#gdBin = (graddir > gradDirThres[0]) & (graddir <= gradDirThres[1])
## Calculate the laplacian of gaussian
#gaussianKernelShape = (5,5)
#laplacianKernelSize = 21
#laplacianThresh = 0.1
#logImg = cv2.GaussianBlur(s, gaussianKernelShape, 0)
#logImg = cv2.Laplacian(logImg, cv2.CV_64F, ksize=laplacianKernelSize)
#logBin = (logImg < laplacianThresh*np.min(logImg))
#return ((grayBin & (gmxBin | sBin))*255).astype(np.uint8)
return ((cBin==255) | (grayBin & gmxBin))*255
def computePerpectiveTransforms():
M = cv2.getPerspectiveTransform(settings['persrc'], settings['perdst'])
Minv = cv2.getPerspectiveTransform(settings['perdst'], settings['persrc'])
return M, Minv
def changePerpective(img, M, visualize=False):
changed = cv2.warpPerspective(img, M, (img.shape[1], img.shape[0]), flags=cv2.INTER_LINEAR)
if(visualize):
fig = plt.figure()
plt.subplot(1,2,1)
plt.imshow(img)
colors = ['ro', 'go', 'bo', 'wo']
pts = settings['persrc']
for i in range(4):
plt.plot(pts[i,0], pts[i,1], colors[i])
plt.subplot(1,2,2)
plt.imshow(changed)
pts = state['perdst']
for i in range(4):
plt.plot(pts[i,0], pts[i,1], colors[i])
plt.show()
return changed
def rejectOutliers(x, y, m = 2.):
# Since lines are mostly verticals, we reject outliers mostly in the xdirection
x, y = np.array(x), np.array(y)
d = np.abs(x - np.median(x))
mdev = np.median(d)
s = d/(mdev if mdev else 1.)
select = s<m
return list(x[select]), list(y[select])
def calcBaseIdx(signal, threshold=10):
if np.any(signal):
idxMax = np.argmax(signal)
if signal[idxMax] >= threshold:
return idxMax
return signal.shape[0]//2
def slidingLanePixelSearch(img, nWin=10, margin=200, pixThres = 50, visualize=False):
height, width = img.shape[0], img.shape[1]
hist = np.sum(img[:height//2, :], axis=0)
idxMid = hist.shape[0]//2
## Starting indices
idxStartLeft = calcBaseIdx(hist[:idxMid])
idxStartRight = calcBaseIdx(hist[idxMid:]) + idxMid
#plt.plot(hist)
#plt.plot(idxStartLeft, hist[idxStartLeft], 'ro')
#plt.plot(idxStartRight, hist[idxStartRight], 'bo')
#plt.show()
winHeight = np.int(img.shape[0]/nWin)
pltImg = np.zeros(img.shape, dtype=np.uint8) if visualize else None
yLeft, xLeft, yRight, xRight = [], [], [], []
for i in range(nWin)[::-1]:
winWdith = 2*margin if i==nWin-1 else margin
# Read out pixels in the windows
lWin = img[i*winHeight:(i+1)*winHeight, idxStartLeft-winWdith//2:idxStartLeft+winWdith//2]
rWin = img[i*winHeight:(i+1)*winHeight, idxStartRight-winWdith//2:idxStartRight+winWdith//2]
if visualize:
cv2.rectangle(pltImg, (idxStartLeft-winWdith//2, i*winHeight),
(idxStartLeft+winWdith//2, (i+1)*winHeight),
65, 2)
cv2.rectangle(pltImg, (idxStartRight-winWdith//2, i*winHeight),
(idxStartRight+winWdith//2, (i+1)*winHeight),
65, 2)
# Get the non-zero pixels in the windows
yLeftCur , xLeftCur = lWin.nonzero()
yRightCur, xRightCur = rWin.nonzero()
# Append to lane pixels
yLeft.extend( yLeftCur + winHeight*i)
xLeft.extend( xLeftCur + idxStartLeft - winWdith//2)
yRight.extend(yRightCur + winHeight*i)
xRight.extend(xRightCur + idxStartRight - winWdith//2)
#xLeft, yLeft = rejectOutliers(xLeft, yLeft)
#xRight, yRight = rejectOutliers(xRight, yRight)
if len(xLeftCur) > pixThres:
idxStartLeft = np.int(np.median(xLeftCur)) + idxStartLeft - winWdith//2
if len(xRightCur) > pixThres:
idxStartRight = np.int(np.median(xRightCur)) + idxStartRight - winWdith//2
return yLeft, xLeft, yRight, xRight, pltImg
def processFrame(img):
rightLine, leftLine = state['rightLine'], state['leftLine']
if rightLine is None:
rightLine = Line((*img.shape[:2],))
if leftLine is None:
leftLine = Line((*img.shape[:2],))
undistImg = cv2.undistort(img, state['mtx'], state['dist'], None, state['mtx'])
perspImg = changePerpective(undistImg, state['per_m'])
threshImg = thresholdFrame(perspImg).astype(np.uint8)
if (leftLine.xLine is None) or (rightLine.xLine is None) or \
(leftLine.dropCount > settings['dropThresh']) or (rightLine.dropCount > settings['dropThresh']):
yLeft, xLeft, yRight, xRight, winOver = slidingLanePixelSearch(threshImg, visualize=True)
leftLine.update(xLeft, yLeft, 0)
rightLine.update(xRight, yRight, 0)
else:
leftLine.searchInMask(threshImg)
rightLine.searchInMask(threshImg)
winOver = rightLine.mask | leftLine.mask
detected = leftLine.detected and rightLine.detected
## Create image for draw output on
diagImg = np.zeros((*threshImg.shape[:2], 3), dtype=np.uint8)
#if detected:
yLine = leftLine.yLine
xLineLeft = leftLine.xLine #np.polyval(leftLineCoeffs, yLine)
xLineRight = rightLine.xLine #np.polyval(rightLineCoeffs, yLine)
markImg = np.zeros((*threshImg.shape, 3), dtype=np.uint8)
# Recast the x and y points into usable format for cv2.fillPoly()
ptsLeft = np.array([np.transpose(np.vstack([xLineLeft, yLine]))])
ptsRight = np.array([np.flipud(np.transpose(np.vstack([xLineRight, yLine])))])
pts = np.hstack((ptsLeft, ptsRight))
# Draw the lane onto the warped blank image
cv2.fillPoly(markImg, np.int_([pts]), (0,255,0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
markImg = cv2.warpPerspective(markImg, state['per_minv'], (img.shape[1], img.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(undistImg, 1, markImg, 0.3, 0)
# Add text annotations
error = (img.shape[1] - (rightLine.vehPos + leftLine.vehPos))*settings['xScale']/2
cv2.putText(result,
'Mean radius of curvature: %d m'%( np.int(np.mean([leftLine.radius, rightLine.radius]))), (50,50),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)
cv2.putText(result,
'Cross-track error: %.3f m'%(error), (50,100),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)
## Create a lane detection diagnostic image
diagImg[leftLine.pixy, leftLine.pixx] = [255,0,0]
diagImg[rightLine.pixy, rightLine.pixx]=[0,0,255]
# Change the annotation based on mode
diagImg[:,:,1] = winOver
cv2.polylines(diagImg, np.int32([ptsLeft]), isClosed=False, color=[255, 255, 0], thickness=8)
cv2.polylines(diagImg, np.int32([ptsRight]), isClosed=False, color=[255, 255, 0], thickness=8)
if settings['diagnostic']:
imgTopLeft = undistImg
imgTopRight = np.dstack((threshImg,threshImg,threshImg))
imgBotLeft = diagImg
imgBotRight = result
result = np.vstack((
np.hstack( (imgTopLeft, imgTopRight) ),
np.hstack( (imgBotLeft, imgBotRight) )
)).astype(np.uint8)
state['rightLine'], state['leftLine'] = rightLine, leftLine
return result
if __name__=='__main__':
inFile = sys.argv[1]
outFile = sys.argv[2]
calDataFile = 'calibData.p'
calImageFiles = 'camera_cal/calibration*.jpg'
if os.path.isfile(calDataFile):
print('Loading camera calibration ...')
loaded = pickle.load(open(calDataFile, 'rb'))
mtx, dist = loaded['mtx'], loaded['dist']
else:
print('Computing camera calibration ...')
mtx, dist = setupAndCalib(calImageFiles)
toPickle = {}
toPickle['mtx'] = mtx
toPickle['dist'] = dist
pickle.dump(toPickle, open(calDataFile, 'wb'))
state = {'mtx': mtx,
'dist': dist,
'rightLine': None,
'leftLine': None
}
state['per_m'], state['per_minv'] = computePerpectiveTransforms()
#videoIn = VideoFileClip(inFile).subclip(20, 26)
#videoIn = VideoFileClip(inFile).subclip(37, 42)
videoIn = VideoFileClip(inFile)
videoOut = videoIn.fl_image(processFrame)
videoOut.write_videofile(outFile, audio=False)
## Test thresholding
#inFile = 'test_images\straight_lines1.jpg'
#inImg = mpimg.imread(inFile)
#outImg = processFrame(inImg)
#f, (ax1, ax2) = plt.subplots(1, 2)
#ax1.set_xticks([])
#ax1.set_yticks([])
#ax2.set_xticks([])
#ax2.set_yticks([])
#ax1.imshow(inImg)
#ax1.set_title('Input colour')
#ax2.imshow(outImg, cmap='gray')
#ax2.set_title('Thresholded binary')
#plt.savefig('output_images/threshresult.png', bbox_inches='tight')
#plt.show()
## Show perspective correction
#inFile = 'test_images\straight_lines1.jpg'
#inImg = mpimg.imread(inFile)
#outImg = processFrame(inImg)
#f, (ax1, ax2) = plt.subplots(1, 2)
#ax1.set_xticks([])
#ax1.set_yticks([])
#ax2.set_xticks([])
#ax2.set_yticks([])
#ax1.imshow(inImg)
#ax1.set_title('Camera')
#x, y = np.hsplit(settings['persrc'], 2)
#ax1.plot(x, y, 'r', linewidth=3)
#ax2.imshow(outImg)
#ax2.set_title('Corrected')
#x, y = np.hsplit(settings['perdst'], 2)
#ax2.plot(x, y, 'r', linewidth=3)
#plt.savefig('output_images/perspresult.png', bbox_inches='tight')
#plt.show()
## Test camera calibration
#inFile = 'camera_cal\calibration1.jpg'
#inImg = mpimg.imread(inFile)
#outImg = cv2.undistort(inImg, state['mtx'], state['dist'], None, state['mtx'])
#f, (ax1, ax2) = plt.subplots(1, 2)
#ax1.set_xticks([])
#ax1.set_yticks([])
#ax2.set_xticks([])
#ax2.set_yticks([])
#ax1.imshow(inImg)
#ax1.set_title('Uncalibrated')
#ax2.imshow(outImg)
#ax2.set_title('Calibrated')
#plt.savefig('output_images/calibresult.png', bbox_inches='tight')
#plt.show()
#imgStack, _ = readFolderToStack()
#outStack = [processFrame(img) for img in imgStack]
##compareImageList(imgStack, outStack)
#displayImagelist(outStack)
#inFile = 'test_images/test1.jpg'
#inImg = mpimg.imread(inFile)
#outImg = processFrame(inImg)
#fig = plt.figure()
#plt.subplot(1, 2, 1)
#plt.imshow(inImg)
##plt.subplot(1, 2, 2)
##plt.imshow(outImg, cmap='gray' )
#plt.show()