def getLaneCurve(img,display=2):
imgCopy = img.copy()
imgResult = img.copy()
#### STEP 1
imgThres = utlis.thresholding(img)
#### STEP 2
hT, wT, c = img.shape
points = utlis.valTrackbars()
imgWarp = utlis.warpImg(imgThres,points,wT,hT)
imgWarpPoints = utlis.drawPoints(imgCopy,points)
#### STEP 3
middlePoint,imgHist = utlis.getHistogram(imgWarp,display=True,minPer=0.5,region=4)
curveAveragePoint, imgHist = utlis.getHistogram(imgWarp, display=True, minPer=0.9)
curveRaw = curveAveragePoint - middlePoint
#### SETP 4
curveList.append(curveRaw)
if len(curveList)>avgVal:
curveList.pop(0)
curve = int(sum(curveList)/len(curveList))
#### STEP 5
if display != 0:
imgInvWarp = utlis.warpImg(imgWarp, points, wT, hT, inv=True)
imgInvWarp = cv2.cvtColor(imgInvWarp, cv2.COLOR_GRAY2BGR)
imgInvWarp[0:hT // 3, 0:wT] = 0, 0, 0
imgLaneColor = np.zeros_like(img)
imgLaneColor[:] = 0, 255, 0
imgLaneColor = cv2.bitwise_and(imgInvWarp, imgLaneColor)
imgResult = cv2.addWeighted(imgResult, 1, imgLaneColor, 1, 0)
midY = 450
cv2.putText(imgResult, str(curve), (wT // 2 - 80, 85), cv2.FONT_HERSHEY_COMPLEX, 2, (255, 0, 255), 3)
cv2.line(imgResult, (wT // 2, midY), (wT // 2 + (curve * 3), midY), (255, 0, 255), 5)
cv2.line(imgResult, ((wT // 2 + (curve * 3)), midY - 25), (wT // 2 + (curve * 3), midY + 25), (0, 255, 0), 5)
for x in range(-30, 30):
w = wT // 20
cv2.line(imgResult, (w * x + int(curve // 50), midY - 10),
(w * x + int(curve // 50), midY + 10), (0, 0, 255), 2)
#fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer);
#cv2.putText(imgResult, 'FPS ' + str(int(fps)), (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (230, 50, 50), 3);
if display == 2:
imgStacked = utlis.stackImages(0.7, ([img, imgWarpPoints, imgWarp],
[imgHist, imgLaneColor, imgResult]))
cv2.imshow('ImageStack', imgStacked)
cv2.imshow('Warp Points', imgWarpPoints)
elif display == 1:
cv2.imshow('Resutlt', imgResult)
#### NORMALIZATION
curve = curve/100
if curve>1: curve ==1
if curve<-1:curve == -1
return curve
def lane_detect(img):
frameWidth = w
frameHeight = h
intialTracbarVals = (38, 60, 12, 90)
count = 0
noOfArrayValues = 11
global arrayCurve, arrayCounter
arrayCounter = 0
arrayCurve = np.zeros([noOfArrayValues])
myVals = []
#utlis.initializeTrackbars(intialTracbarVals)
# imgWarpPoints = img.copy()
imgFinal = img.copy()
imgCanny = img.copy()
try:
# imgUndis = utlis.undistort(img)
imgThres, imgCanny, imgColor = utlis.thresholding(img)
src = utlis.valTrackbars()
imgWarp = utlis.perspective_warp(imgThres,
dst_size=(frameWidth, frameHeight),
src=src)
# imgWarpPoints = utlis.drawPoints(imgWarpPoints, src)
imgSliding, curves, lanes, ploty = utlis.sliding_window(
imgWarp, draw_windows=False)
curverad = utlis.get_curve(imgFinal, curves[0], curves[1])
lane_curve = np.mean([curverad[0], curverad[1]])
imgFinal = utlis.draw_lanes(img,
curves[0],
curves[1],
frameWidth,
frameHeight,
src=src)
#currentCurve = lane_curve // 50
# if int(np.sum(arrayCurve)) == 0:averageCurve = currentCurve
# else:
# averageCurve = np.sum(arrayCurve) // arrayCurve.shape[0]
# if abs(averageCurve-currentCurve) >200: arrayCurve[arrayCounter] = averageCurve
# else :arrayCurve[arrayCounter] = currentCurve
# arrayCounter +=1
# if arrayCounter >=noOfArrayValues : arrayCounter=0
# cv2.putText(imgFinal, str(int(averageCurve)), (frameWidth//2-70, 70), cv2.FONT_HERSHEY_DUPLEX, 1.75, (0, 0, 255), 2, cv2.LINE_AA)
# imgFinal= utlis.drawLines(imgFinal,lane_curve)
except:
# print("NO Lane Detected")
imgFinal = img
cv2.putText(imgFinal, "No Lane Detected", (10, 20),
cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0, 255), 1, cv2.LINE_AA)
return imgFinal
def getLaneCurve(img):
imgCopy = img.copy()
#Step1
imgThres = utlis.thresholding(img)
#Step2
h,w,c = img.shape
points = utlis.valTrackbars()
imgWarp = utlis.warpImg(img,points,w,h)
imgWarpPoints = utlis.drawPoints(imgCopy,points)
cv2.imshow("Thres", imgThres)
cv2.imshow("Warp", imgWarp)
cv2.imshow("Warp Points", imgWarpPoints)
return None
myVals=[]
utlis.initializeTrackbars(intialTracbarVals)
while True:
success, img = cap.read()
#img = cv2.imread('test3.jpg')
if cameraFeed== False:img = cv2.resize(img, (frameWidth, frameHeight), None)
imgWarpPoints = img.copy()
imgFinal = img.copy()
imgCanny = img.copy()
imgUndis = utlis.undistort(img)
imgThres,imgCanny,imgColor = utlis.thresholding(imgUndis)
src = utlis.valTrackbars()
imgWarp = utlis.perspective_warp(imgThres, dst_size=(frameWidth, frameHeight), src=src)
imgWarpPoints = utlis.drawPoints(imgWarpPoints, src)
imgSliding, curves, lanes, ploty = utlis.sliding_window(imgWarp, draw_windows=True)
try:
curverad =utlis.get_curve(imgFinal, curves[0], curves[1])
lane_curve = np.mean([curverad[0], curverad[1]])
imgFinal = utlis.draw_lanes(img, curves[0], curves[1],frameWidth,frameHeight,src=src)
# ## Average
currentCurve = lane_curve // 50
if int(np.sum(arrayCurve)) == 0:averageCurve = currentCurve
else:
averageCurve = np.sum(arrayCurve) // arrayCurve.shape[0]
if abs(averageCurve-currentCurve) >200: arrayCurve[arrayCounter] = averageCurve
heightImg = 1600
widthImg = 1200
utlis.initializeTrackbars()
count=0
while True:
img = cv2.imread(pathImage)#READING IMAGE
img = cv2.resize(img, (widthImg, heightImg)) # RESIZE IMAGE
imgBlank = np.zeros((heightImg,widthImg, 3), np.uint8) # CREATE A BLANK IMAGE FOR TESTING DEBUGING IF REQUIRED
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # CONVERT IMAGE TO GRAY SCALE
imgBlur = cv2.GaussianBlur(imgGray, (5, 5), 1) # ADD GAUSSIAN BLUR
thres=utlis.valTrackbars() # GET TRACK BAR VALUES FOR THRESHOLDS
imgThreshold = cv2.Canny(imgBlur,thres[0],thres[1]) # APPLY CANNY BLUR
kernel = np.ones((5, 5))#INITIALISING KERNEL
imgDial = cv2.dilate(imgThreshold, kernel, iterations=2) # APPLY DILATION
imgThreshold = cv2.erode(imgDial, kernel, iterations=1) # APPLY EROSION
## FIND ALL COUNTOURS
imgContours = img.copy() # COPY IMAGE FOR DISPLAY PURPOSES
imgBigContour = img.copy() # COPY IMAGE FOR DISPLAY PURPOSES
contours, hierarchy = cv2.findContours(imgThreshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # FIND ALL CONTOURS
cv2.drawContours(imgContours, contours, -1, (0, 255, 0), 10) # DRAW ALL DETECTED CONTOURS
# FIND THE BIGGEST COUNTOUR
biggest, maxArea = utlis.biggestContour(contours) # FIND THE BIGGEST CONTOUR
if biggest.size != 0:
count = 0
while True:
# if webCamFeed:success,img = cap.read()
# else:
# img = cv2.imread('03_oswal.png')
img = cv2.imread('03_oswal.png')
img = cv2.resize(img, (widthImg, heightImg)) # RESIZE IMAGE
imgBlank = np.zeros((heightImg, widthImg, 3), np.uint8)
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgBlur = cv2.GaussianBlur(imgGray, (5, 5), 1)
thres = utlis.valTrackbars()
imgThreshold = cv2.Canny(imgBlur, thres[0], thres[1])
kernel = np.ones((5, 5))
imgDial = cv2.dilate(imgThreshold, kernel, iterations=2) #elimina blancos
imgThreshold = cv2.erode(imgDial, kernel,
iterations=1) # engruesa la imagen
imgContours = img.copy()
imgBigContour = img.copy()
contours, hierarchy = cv2.findContours(imgThreshold, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(imgContours, contours, -1, (0, 255, 0), 10)
biggest, maxArea = utlis.biggestContour(contours)
utlis.initializeTrackbars()
count = 0
while True:
imgBlank = np.zeros((heightImg, widthImg, 3), np.uint8)
if webCam: success, img = cap.read()
else: img = cv2.imread(imageLocation)
img = cv2.imread(imageLocation)
img = cv2.resize(img, (widthImg, heightImg))
imgGray = cv2.cvtColor(img,
cv2.COLOR_BGR2GRAY) #convets the image to grayscale
imgBlur = cv2.GaussianBlur(
imgGray, (5, 5), 1) #converts the grayscale image to blured image
thres = utlis.valTrackbars(
) #using this function we get the trackbars which help us adjust the threshold
imgThreshold = cv2.Canny(imgBlur, thres[0], thres[1])
kernel = np.ones((5, 5))
imgDial = cv2.dilate(imgThreshold, kernel, iterations=2) # APPLY DILATION
imgThreshold = cv2.erode(imgDial, kernel, iterations=1) # APPLY EROSION
## FIND ALL COUNTOURS
imgConts = img.copy()
imgBigConts = img.copy()
contours, hierarchy = cv2.findContours(
imgThreshold, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE) # FIND ALL CONTOURS
cv2.drawContours(imgConts, contours, -1, (0, 255, 0),
10) # DRAW ALL DETECTED CONTOURS
# FIND THE LARGEST COUNTOUR IN THE FRAME
