def find_lines(frame):
# Resize to 640x480
frame_small = cv.CreateMat(480, 640, cv.CV_8UC3)
cv.Resize(frame, frame_small)
# Threshold by distance: blank out all top pixels
cv.Rectangle(frame_small, (0, 0), (640, 80), (0, 0, 0, 0), cv.CV_FILLED)
frame_size = cv.GetSize(frame_small)
frame_gray = cv.CreateImage(frame_size, cv.IPL_DEPTH_8U, 1)
edges = cv.CreateImage(frame_size, cv.IPL_DEPTH_8U, 1)
cv.CvtColor(frame_small, frame_gray, cv.CV_BGR2GRAY)
cv.Canny(frame_gray, edges, 400, 400)
cv.Dilate(edges, edges,
cv.CreateStructuringElementEx(3, 3, 0, 0, cv.CV_SHAPE_RECT))
cv.Erode(edges, edges,
cv.CreateStructuringElementEx(1, 1, 0, 0, cv.CV_SHAPE_RECT))
line_storage = cv.CreateMemStorage()
lines = cv.HoughLines2(edges, line_storage, cv.CV_HOUGH_PROBABILISTIC, 1,
cv.CV_PI / 180.0, 300, 100, 40)
print len(lines), 'lines found'
for i in range(len(lines)):
line = lines[i]
cv.Line(frame_small, line[0], line[1],
hv2rgb(360.0 * i / len(lines), 1.0), 3, 8)
cv.ShowImage('frame', frame_small)
cv.ShowImage('edges', edges)
def get_hand(img):
global h_lower, h_upper, s_lower, s_upper
storage = cv.CreateMemStorage(0)
contours, hull, max_contours, max_hull = (0, 0, 0, 0)
max_rect = (1, 1, 100, 100)
dst = cv.CreateImage((img.width, img.height), 8, 3)
hsv = cv.CreateImage((img.width, img.height), 8, 3)
frame = cv.CreateImage((img.width, img.height), 8, 1)
con = cv.CreateImage((img.width, img.height), 8, 1)
cv.Zero(con)
cv.Smooth(img, dst, cv.CV_GAUSSIAN, 5, 5)
for i in range(3): cv.Smooth(dst, dst, cv.CV_GAUSSIAN, 5, 5)
cv.CvtColor(dst, hsv, cv.CV_RGB2HSV)
cv.InRangeS(hsv, (h_lower, s_lower, 0), (h_upper, s_upper, 256), frame)
kernel = cv.CreateStructuringElementEx(3, 3, 0, 0, cv.CV_SHAPE_RECT)
#cv.MorphologyEx(frame, frame, None, kernel, cv.CV_MOP_CLOSE , 7)
# cv.MorphologyEx(frame, frame, None, kernel, cv.CV_MOP_OPEN , 3)
#contours = im.find_contours(frame)
#hull = im.find_convex_hull(contours)
print contours
#max_hull_area, max_contour_area = (0, 0)
#print "xxxxxxxx"
#contour = contours.h_next()
#print "........"
#while (contour != 0):
# hull = cv.ConvexHull2(contour, storage, cv.CV_CLOCKWISE, 1);
# maxv = cv.ContourArea(hull)
# contour = contour.h_next()
#cv.DrawContours(con, contours, red, blue, 1, 3, 8)
cv.ShowImage("result", con)
def __init__(self,img):
small_img = cv.CreateImage((cv.Round(img.width / image_scale),cv.Round(img.height / image_scale)), 8, 3)
cv.Resize(img, small_img, cv.CV_INTER_LINEAR)
if H!=0 and S !=0:
getSkinColor(small_img, hasColor)
imgHSV = cv.CreateImage(cv.GetSize(small_img), 8, 3)
cv.CvtColor(small_img, imgHSV, cv.CV_BGR2HSV);
hueImg = cv.CreateMat(small_img.height, small_img.width, cv.CV_8UC1)
satImg = cv.CreateMat(small_img.height, small_img.width, cv.CV_8UC1)
valImg = cv.CreateMat(small_img.height, small_img.width, cv.CV_8UC1)
cv.Split(imgHSV, hueImg, satImg, valImg, None)
cv.ShowImage("hueImg", hueImg)
hueTrshld = cv.CreateMat(hueImg.height, hueImg.width, cv.CV_8UC1)
hueDiff = 30
satDiff = 80
for x in range(0, hueTrshld.height):
for y in range(0, hueTrshld.width):
hueTrshld[x,y] = 0
if hueImg[x,y]>(H-hueDiff) and hueImg[x,y]>(1) and hueImg[x,y]<(H+hueDiff):
if satImg[x,y]>(S-satDiff) and satImg[x,y]<(S+satDiff):
hueTrshld[x,y] = 255
hueTrshldErode = cv.CreateMat(hueImg.height, hueImg.width, cv.CV_8UC1)
hueTrshldDilate = cv.CreateMat(hueImg.height, hueImg.width, cv.CV_8UC1)
kernel10 = cv.CreateStructuringElementEx(10,10,0,0, cv.CV_SHAPE_RECT)
kernel8 = cv.CreateStructuringElementEx(8,8,0,0, cv.CV_SHAPE_RECT)
kernel6 = cv.CreateStructuringElementEx(6,6,0,0, cv.CV_SHAPE_RECT)
kernel4 = cv.CreateStructuringElementEx(4,4,0,0, cv.CV_SHAPE_RECT)
cv.Erode(hueTrshld, hueTrshldErode, kernel6, 1)
cv.Dilate(hueTrshldErode, hueTrshldDilate, kernel10, 1)
cv.ShowImage("hueTrshldOr", hueTrshld) #original
cv.ShowImage("hueTrshldDi", hueTrshldDilate) #dilated
cv.ShowImage("hueTrshldEr", hueTrshldErode) #eroded
def find_blobs(self, frame, debug_image):
'''Find blobs in an image.
Hopefully this gets blobs that correspond with
buoys, but any intelligent checking is done outside of this function.
'''
# Get Channels
hsv = cv.CreateImage(cv.GetSize(frame), 8, 3)
cv.CvtColor(frame, hsv, cv.CV_BGR2HSV)
saturation = libvision.misc.get_channel(hsv, 1)
red = libvision.misc.get_channel(frame, 2)
# Adaptive Threshold
cv.AdaptiveThreshold(
saturation,
saturation,
255,
cv.CV_ADAPTIVE_THRESH_MEAN_C,
cv.CV_THRESH_BINARY_INV,
self.saturation_adaptive_thresh_blocksize -
self.saturation_adaptive_thresh_blocksize % 2 + 1,
self.saturation_adaptive_thresh,
)
cv.AdaptiveThreshold(
red,
red,
255,
cv.CV_ADAPTIVE_THRESH_MEAN_C,
cv.CV_THRESH_BINARY,
self.red_adaptive_thresh_blocksize -
self.red_adaptive_thresh_blocksize % 2 + 1,
-1 * self.red_adaptive_thresh,
)
kernel = cv.CreateStructuringElementEx(9, 9, 4, 4, cv.CV_SHAPE_ELLIPSE)
cv.Erode(saturation, saturation, kernel, 1)
cv.Dilate(saturation, saturation, kernel, 1)
cv.Erode(red, red, kernel, 1)
cv.Dilate(red, red, kernel, 1)
buoys_filter = cv.CreateImage(cv.GetSize(frame), 8, 1)
cv.And(saturation, red, buoys_filter)
if debug_image:
svr.debug("Saturation", saturation)
svr.debug("Red", red)
svr.debug("AdaptiveThreshold", buoys_filter)
# Get blobs
labeled_image = cv.CreateImage(cv.GetSize(buoys_filter), 8, 1)
blobs = libvision.blob.find_blobs(buoys_filter, labeled_image,
MIN_BLOB_SIZE, 10)
return blobs, labeled_image
def detect_ball(self, hsv_img, erd_mat, erd, dil_mat, dil):
size = cv.GetSize(hsv_img)
# colours on pitch2 (note conversion is from BGR2HSV not RGB2HSV!)
trsh_im = self.red_color_.in_range_s(hsv_img)
cv.Dilate(
trsh_im, trsh_im,
cv.CreateStructuringElementEx(dil_mat[0], dil_mat[1], 0, 0, 0),
dil)
cv.Erode(
trsh_im, trsh_im,
cv.CreateStructuringElementEx(erd_mat[0], erd_mat[1], 0, 0, 0),
erd)
tmp_im = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
cv.Copy(trsh_im, tmp_im)
largest = find_largest_contour(
cv.FindContours(tmp_im, cv.CreateMemStorage(0),
cv.CV_RETR_EXTERNAL, cv.CV_CHAIN_APPROX_NONE))
if not largest: return trsh_im, None
return trsh_im, Math.int_vec(get_contour_center(cv.Moments(largest)))
def max_contrast(image):
"""Maximise the contrast of an image using top and bottom hat filters."""
size = cv.GetSize(image)
bh = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
th = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
s1 = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
s2 = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
el = cv.CreateStructuringElementEx(3, 3, 1, 1, cv.CV_SHAPE_ELLIPSE)
cv.MorphologyEx(image, th, None, el, cv.CV_MOP_TOPHAT, 1)
cv.MorphologyEx(image, bh, None, el, cv.CV_MOP_BLACKHAT, 1)
cv.Add(image, th, s1)
cv.Sub(s1, bh, s2)
return s2
def get_angel(src):
pos = 6
element = cv.CreateStructuringElementEx(pos * 2 + 1, pos * 2 + 1, pos, pos,
element_shape)
ni = cv.CreateImage(cv.GetSize(src), src.depth, src.nChannels)
cv.Erode(src, ni, element, 1)
image_gray = cv.CreateImage(cv.GetSize(ni), 8, 1)
cv.CvtColor(ni, image_gray, cv.CV_RGB2GRAY)
pi = Image.fromstring("L", cv.GetSize(ni), image_gray.tostring())
first = 0 if pi.getpixel((0, 0)) < 240 else 255
xstart = xend = ystart = yend = 0
for x in xrange(1, pi.size[0]):
v = 0 if pi.getpixel((x, 0)) < 240 else 255
if first == 0 and v != 0:
xstart = x
xend = pi.size[0]
break
if first == 255 and v != 255:
xstart = 0
xend = x
break
if first == 255:
for y in xrange(pi.size[1]):
v = 0 if pi.getpixel((0, y)) < 240 else 255
if v != 255:
yend = y
break
else:
for y in xrange(pi.size[1]):
v = 0 if pi.getpixel((pi.size[0] - 1, y)) < 240 else 255
if v != 255:
yend = y
break
a = yend - ystart
b = xend - xstart or 1
alpha = atan(a * 1.0 / b) / (PI * 1.0 / 180)
if first == 255:
alpha = -alpha
return (alpha, pi.size[0] * 1.0 / 2, pi.size[1] * 1.0 / 2)
def camera():
print "# Starting initialization..."
#camera capture
#cap = cv.CaptureFromCAM(0)
intrinsics = cv.CreateMat(3, 3, cv.CV_64FC1)
cv.Zero(intrinsics)
#camera data
intrinsics[0, 0] = 1100.850708957251072
intrinsics[1, 1] = 778.955239997982062
intrinsics[2, 2] = 1.0
intrinsics[0, 2] = 348.898495232253822
intrinsics[1, 2] = 320.213734835526282
dist_coeffs = cv.CreateMat(1, 4, cv.CV_64FC1)
cv.Zero(dist_coeffs)
dist_coeffs[0, 0] = -0.326795877008420
dist_coeffs[0, 1] = 0.139445565548056
dist_coeffs[0, 2] = 0.001245710462327
dist_coeffs[0, 3] = -0.001396618726445
#pFrame = cv.QueryFrame(cap)
print "# intrinsics loaded!"
#prepare memory
capture = cv.CaptureFromCAM(0)
src = cv.QueryFrame(capture)
size = GetSize(src)
dst0 = cv.CreateImage(size, src.depth, src.nChannels)
# bg = cv.LoadImage("00000005.jpg")
image_ROI = (0,70,640,340)
size = (640,340)
red = cv.CreateImage(size, 8, 1)
green = cv.CreateImage(size, 8, 1)
blue = cv.CreateImage(size, 8, 1)
hue = cv.CreateImage(size, 8, 1)
sat = cv.CreateImage(size, 8, 1)
val = cv.CreateImage(size, 8, 1)
ball = cv.CreateImage(size, 8, 1)
yellow = cv.CreateImage(size, 8, 1)
ballx = 0
bally = 0
ballmiss = 0
yellowmiss = 0
bluemiss = 0
dst2 = cv.CreateImage(size, 8, 3)
hsv = cv.CreateImage(size,8,3)
print "# base images created..."
#####------------------ajustment data---------------------###############
#shadow
high = 40
low = 300
#threshold
thresBallInit = 116
thresYellowInit = 94
thresBlueInit = 18
ballRangeInit = 8.0
yellowRangeInit = 5.0
blueRangeInit = 8.0
ballRange = ballRangeInit
yellowRange = yellowRangeInit
blueRange = blueRangeInit
ballMinRange = 1.5
yellowMinRange = 2.5
blueMinRange = 8.0
thresBall = thresBallInit
thresYellow = thresYellowInit
thresBlue = thresBlueInit
#dilate
ex = cv.CreateStructuringElementEx(3,3,1,1,cv.CV_SHAPE_RECT)
ex2 = cv.CreateStructuringElementEx(2,2,1,1,cv.CV_SHAPE_RECT)
ex5 = cv.CreateStructuringElementEx(5,5,1,1,cv.CV_SHAPE_RECT)
#ball
ballcount = 15.0
ballAreaInit = 105.0
ballAreaRangeInit = 60.0
ballArea = ballAreaInit
ballAreaRange = ballAreaRangeInit
ballMinAreaRange = 40.0
ballcompact = 3.0
#blue
bluecount = 30.0
blueAreaInit = 300.0
blueAreaRangeInit = 150.0
blueArea = blueAreaInit
blueAreaRange = blueAreaRangeInit
blueMiniAreaRange = 50.0
bluemaxdepth = 8.0
blueminidepth = 2.5
#yellow
yellowcount = 30.0
yellowAreaInit = 450.0
yellowAreaRangeInit = 200.0
yellowArea = yellowAreaInit
yellowAreaRange = yellowAreaRangeInit
yellowMinAreaRange = 50.0
yellowmaxdepth = 10.0
yellowminidepth = 3.2
#####----------------------------------------
#create window
NamedWindow("camera",cv.CV_WINDOW_AUTOSIZE)
#NamedWindow("ball",cv.CV_WINDOW_AUTOSIZE)
#NamedWindow("yellow",cv.CV_WINDOW_AUTOSIZE)
#NamedWindow("blue",cv.CV_WINDOW_AUTOSIZE)
#NamedWindow("hue",cv.CV_WINDOW_AUTOSIZE)
#NamedWindow("sat",cv.CV_WINDOW_AUTOSIZE)
#NamedWindow("val",cv.CV_WINDOW_AUTOSIZE)
timecount = 0
onesec = time.clock()
storage = cv.CreateMemStorage()
print "# starting capture..."
print ''
capture = cv.CaptureFromCAM(0)
aa = time.clock()
while(True):
timecount = timecount + 1
src = cv.QueryFrame(capture)
#barrel undistortion
cv.Undistort2(src, dst0, intrinsics, dist_coeffs)
#ROI = Region of Interests, crop the image
cv.SetImageROI(dst0,image_ROI)
dst = GetImage(dst0)
CvtColor(dst,hsv,CV_RGB2HSV)
cv.Split(hsv,hue,sat,val,None)
# ShowImage("hue",hue)
# ShowImage("val",val)
# ShowImage("sat",sat)
# BALL
cv.Threshold(hue,ball,thresBallInit+ballRange, 255,cv.CV_THRESH_TOZERO_INV)
cv.Threshold(hue,ball,thresBallInit-ballRange, 255,cv.CV_THRESH_BINARY)
cv.Erode(ball,ball,ex2,1)
cv.Dilate(ball,ball,ex2,1)
ShowImage("ball",ball)
# YELLOW
cv.Threshold(hue,yellow,thresYellowInit+yellowRange,255,cv.CV_THRESH_TOZERO_INV)
cv.Threshold(yellow,yellow,thresYellowInit-yellowRange,255,cv.CV_THRESH_BINARY)
cv.Erode(yellow,yellow,ex2,1)
cv.Dilate(yellow,yellow,ex2,1)
ShowImage("yellow",yellow)
# BLUE
# CvtColor(dst,hsv,CV_BGR2HSV)
# cv.Split(hsv,hue,sat,val,None)
cv.Threshold(hue,blue,thresBlue+blueRange,255,cv.CV_THRESH_BINARY_INV)
# cv.Threshold(blue,blue,4,255,cv.CV_THRESH_BINARY)
cv.Erode(blue,blue,ex2,1)
cv.Dilate(blue,blue,ex2,1)
ShowImage("blue",blue)
cv.Threshold(val,val,80,255,cv.CV_THRESH_BINARY_INV)
cv.Threshold(sat,sat,80,255,cv.CV_THRESH_BINARY_INV)
ShowImage("sat2",sat)
ShowImage("val2",val)
# Removes the walls
Sub(blue,val,blue)
Sub(blue,sat,blue)
Sub(yellow,val,yellow)
Sub(yellow,sat,yellow)
Sub(ball,val,ball)
Sub(ball,sat,ball)
Set2D(ball,4,4,255)
Set2D(blue,4,4,255)
Set2D(yellow,4,4,255)
ShowImage("yellow3",yellow)
ShowImage("ball3",ball)
ShowImage("blue3",blue)
#find ball
seq = cv.FindContours(ball,storage,cv.CV_RETR_LIST, cv.CV_LINK_RUNS)
if seq != None:
count = 0
while (seq != None and count <= ballcount):
count =count + 1
area = cv.ContourArea(seq)+0.01
compact = ArcLength(seq)*ArcLength(seq)/(4*area*math.pi)
if (area < 4 or area > (ballArea+ballAreaRange) or area < (ballArea-ballAreaRange)): #or compact >= ballcompact ):
seq = seq.h_next()
continue
else:
ballx = 0
bally = 0
for p in seq:
ballx = ballx + p[0]
bally = bally + p[1]
ballx = int(float(ballx)/len(seq))
bally = int(float(bally)/len(seq))
###############--------------Auto ajustment
print "ball area %f" %area
print "ball hue: %f" %hue[bally,ballx]
# thresBall = 0.2*hue[bally,ballx]+0.2*thresBall + 0.5*thresBallInit
# ballArea = area
# if(ballRange > ballMinRange):
# ballRange = ballRange -0.1
# if(ballAreaRange > ballMinAreaRange):
# ballAreaRange = ballAreaRange -1.0
cv.Circle(dst,(ballx,bally),4,cv.CV_RGB(255,255,255),2,8,0)
cv.Circle(dst,(ballx,bally),10,cv.CV_RGB(255,0,0),9,8,0)
break
if(count > ballcount or seq == None):
# thresBall = thresBallInit
# ballRange = 0.5*ballRange + 0.5*ballRangeInit
# ballArea = ballArea + 10.0
# ballAreaRange = ballAreaRange + 0.1
print ballAreaRange
ballx = 0
bally = 0
ballmiss = ballmiss + 1
print "\r# error: ball not found "
#find blue
seq = cv.FindContours(blue,storage,cv.CV_RETR_LIST, cv.CV_LINK_RUNS)
if seq != None:
count = 0
while (seq != None and count <= bluecount):
count =count + 1
area = cv.ContourArea(seq)
if(area < blueArea-blueAreaRange or area > blueArea+blueAreaRange):
seq = seq.h_next()
continue
else:
hull = None
convex = None
hull =cv.ConvexHull2(seq,storage)
convex = cv.ConvexityDefects(seq,hull,storage)
if (len(convex) > 1):
convex = sorted(convex , key = lambda(k1,k2,k3,k4):k4)#sort by depth of the convex defect
if (convex[len(convex)-1][3] < blueminidepth or convex[len(convex)-2][3] < blueminidepth or convex[len(convex)-1][3] > bluemaxdepth or convex[len(convex)-2][3] > bluemaxdepth ):
seq = seq.h_next()
continue
else:
#find the T
blue_start1 = convex[len(convex)-1][0]
blue_end1 = convex[len(convex)-1][1]
blue_depth1 = convex[len(convex)-1][2]
#draw the side line of T
blue_start2 = convex[len(convex)-2][0]
blue_end2 = convex[len(convex)-2][1]
blue_depth2 = convex[len(convex)-2][2]
blue_from = ((blue_depth1[0]+blue_depth2[0])/2,(blue_depth1[1]+blue_depth2[1])/2)#calculate the center of robot
#calculate the end of direction vector, the two end point of the smaller distans
if math.hypot(blue_start1[0]-blue_end2[0],blue_start1[1]-blue_end2[1])>math.hypot(blue_end1[0]-blue_start2[0],blue_end1[1]-blue_start2[1]):
blue_to = ((blue_end1[0]+blue_start2[0])/2,(blue_end1[1]+blue_start2[1])/2)
else:
blue_to = ((blue_start1[0]+blue_end2[0])/2,(blue_start1[1]+blue_end2[1])/2)
cv.Line(dst,blue_from,blue_to,cv.CV_RGB(255,0,255),2,8,0)
cv.Circle(dst,blue_from,1,cv.CV_RGB(255,0,0),2,8,0)
cv.Circle(dst,blue_to,1,cv.CV_RGB(0,0,0),2,8,0)
cv.Circle(dst,blue_from,10,cv.CV_RGB(255,0,0),9,8,0)
#######---------------------------Auto Ajusting
print "blue area %f" %area
# print "blue hue: %f" %hue[blue_from[1],blue_from[0]]
# thresBlue = hue[blue_from[1],blue_from[0]] #+ 0.4*thresBlue + 0.5*thresBlueInit
# blueArea = area
# if(blueAreaRange > blueMiniAreaRange):
# blueAreaRange = blueAreaRange -1.0
# if(blueRange > blueMinRange):
# blueRange = blueRange - 0.1
break
else:
seq = seq.h_next()
continue
if(count > bluecount or seq == None):
# thresBlue = thresBlueInit
# blueAreaRange = blueAreaRange + 10.0
# blueArea = blueArea + 10.0
# blueRange = 0.5*blueRange + 0.5*blueRangeInit
bluemiss = bluemiss + 1
blue_from = (0,0);
blue_to = (0,0);
print "\r# error: blue not found "
#find yellow
seq = cv.FindContours(yellow,storage,cv.CV_RETR_LIST, cv.CV_LINK_RUNS)
if seq != None:
count = 0
while (seq != None and count <= yellowcount):
count =count + 1
area = cv.ContourArea(seq)
if(area < yellowArea-yellowAreaRange or area > yellowArea + yellowAreaRange):
seq = seq.h_next()
continue
else:
hull = None
convex = None
hull =cv.ConvexHull2(seq,storage)
convex = cv.ConvexityDefects(seq,hull,storage)
if (len(convex) > 1):
convex = sorted(convex , key = lambda(k1,k2,k3,k4):k4)#sort by depth of the convex defect
if (convex[len(convex)-1][3] < yellowminidepth or convex[len(convex)-2][3] < yellowminidepth or convex[len(convex)-1][3] > yellowmaxdepth or convex[len(convex)-2][3] > yellowmaxdepth ):
seq = seq.h_next()
continue
else:
#find the T
yellow_start1 = convex[len(convex)-1][0]
yellow_end1 = convex[len(convex)-1][1]
yellow_depth1 = convex[len(convex)-1][2]
#draw the side line of T
yellow_start2 = convex[len(convex)-2][0]
yellow_end2 = convex[len(convex)-2][1]
yellow_depth2 = convex[len(convex)-2][2]
yellow_from = ((yellow_depth1[0]+yellow_depth2[0])/2,(yellow_depth1[1]+yellow_depth2[1])/2)#calculate the center of robot
#calculate the end of direction vector, the two end point of the smaller distans
if math.hypot(yellow_start1[0]-yellow_end2[0],yellow_start1[1]-yellow_end2[1])>math.hypot(yellow_end1[0]-yellow_start2[0],yellow_end1[1]-yellow_start2[1]):
yellow_to = ((yellow_end1[0]+yellow_start2[0])/2,(yellow_end1[1]+yellow_start2[1])/2)
else:
yellow_to = ((yellow_start1[0]+yellow_end2[0])/2,(yellow_start1[1]+yellow_end2[1])/2)
###########------------------------------Auto Ajusting
# print cv.ContourArea(seq)
print "yellow area %f" %area
print "yellow hue: %f" %hue[yellow_from[1],yellow_from[0]]
# thresYellow = hue[yellow_from[1],yellow_from[0]] #+ 0.4*thresYellow + 0.5*thresYellowInit
# yellowArea = area
# if(yellowRange > yellowMinRange):
# yellowRange = yellowRange -0.1
# if(yellowAreaRange > yellowMinAreaRange):
# yellowAreaRange = yellowAreaRange - 1.0
# yellow_miss = ((yellow_from[0]+yellow_to[0])/2,(yellow_from[1]+yellow_to[1])/2)
cv.Line(dst,yellow_from,yellow_to,cv.CV_RGB(255,0,255),2,8,0)
cv.Circle(dst,yellow_from,1,cv.CV_RGB(255,0,0),2,8,0)
cv.Circle(dst,yellow_to,1,cv.CV_RGB(0,0,0),2,8,0)
cv.Circle(dst,yellow_from,10,cv.CV_RGB(255,0,0),9,8,0)
break
else:
seq = seq.h_next()
continue
if(count > yellowcount or seq == None):
#thresYellow = thresYellowInit
# yellowRange = 0.5*yellowRange + 0.5*yellowRangeInit
#yellowArea = yellowArea
# yellowAreaRange = yellowAreaRange + 10.0
print "area:%d" %yellowArea
yellowmiss = yellowmiss + 1
yellow_from = (0,0);
yellow_to = (0,0);
print "\r# error: yellow not found"
ballpos = (ballx,bally)
#output(ballpos,blue_from,blue_to,yellow_from,yellow_to)
ShowImage("camera",dst)
cv.SetImageROI(dst0,(0,0,640,480))
# ShowImage("camera",dst0)
if( cv.WaitKey(2) >= 0 ):
bb = time.clock()
print "frame rate: %f" %(timecount/(bb-aa))
print "ball miss rate: %f" %(ballmiss)
print "blue miss rate: %f" %(bluemiss)
print "yellow miss rate: %f" %(yellowmiss)
break;
self.aktif = True
def getDimensions(self):
return (self.x, self.y, self.width, self.height)
def centerOrigin(self):
return (self.x - self.width / 2, self.y - self.height / 2)
def update(self):
self.x += self.hiz[0]
self.y += self.hiz[1]
python_opencv_modulu.NamedWindow("Python - Bahçeşehir University Game",
python_opencv_modulu.CV_WINDOW_AUTOSIZE)
elips_sekil_kalibi = python_opencv_modulu.CreateStructuringElementEx(
9, 9, 4, 4, python_opencv_modulu.CV_SHAPE_ELLIPSE)
kamera = python_opencv_modulu.CaptureFromCAM(-1)
cerceve_boyutu = (int(
python_opencv_modulu.GetCaptureProperty(
kamera, python_opencv_modulu.CV_CAP_PROP_FRAME_WIDTH)),
int(
python_opencv_modulu.GetCaptureProperty(
kamera,
python_opencv_modulu.CV_CAP_PROP_FRAME_HEIGHT)))
yazdir_goruntuyu = False
fourcc_degeri = python_opencv_modulu.FOURCC('M', 'J', 'P', 'G')
fps_degeri = 30
if yazdir_goruntuyu:
video_yazdir = python_opencv_modulu.CreateVideoWriter(
"film.avi", fourcc_degeri, fps_degeri, cerceve_boyutu)
def find_loop(input_data, IterationClosing=6):
"""
This function detect support (or loop) and return the coordinates if there is a detection,
and -1 if not.
in : filename : string image Filename / Format accepted :
in : IterationClosing : int : Number of iteration for closing contour procedure
Out : tupple of coordiante : (string, coordinate X, coordinate Y) where string take value
'Coord' or 'No loop detected depending if loop was detected or not. If no loop was
detected coordinate X and coordinate y take the value -1.
"""
#Definition variable Global
global AIRE_MIN_REL
global AIRE_MIN
global NORM_IMG
global NiteClosing
global pointRef
#Chargement image
try:
if type(input_data) == str:
#Image filename is passed
img_ipl = cv.LoadImageM(input_data)
elif type(input_data) == np.ndarray:
img_ipl = cv.fromarray(input_data)
else:
print "ERROR : Input image could not be opened, check format or path"
return (
"ERROR : Input image could not be opened, check format or path",
-10, -10)
except:
print "ERROR : Input image could not be opened, check format or path"
return (
"ERROR : Input image could not be opened, check format or path",
-10, -10)
img_cont = img_ipl # img used for
NORM_IMG = img_ipl.width * img_ipl.height
AIRE_MIN = NORM_IMG * AIRE_MIN_REL
#traitement
#Converting input image in Grey scale image
img_gray_ini = cv.CreateImage((img_ipl.width, img_ipl.height), 8, 1)
cv.CvtColor(img_ipl, img_gray_ini, cv.CV_BGR2GRAY)
#Removing Offset from image
img_gray_resize = cv.CreateImage(
(img_ipl.width - 2 * Offset[0], img_ipl.height - 2 * Offset[1]), 8, 1)
cv.SetImageROI(img_gray_ini,
(Offset[0], Offset[1], img_ipl.width - 2 * Offset[0],
img_ipl.height - 2 * Offset[1]))
cv.Copy(img_gray_ini, img_gray_resize)
# #creat image used for treatment
img_gray = cv.CreateImage((img_gray_resize.width, img_gray_resize.height),
8, 1)
img_trait = cv.CreateImage((img_gray.width, img_gray.height), 8, 1)
# image used for treatment is the same than img_gray_resize
cv.Copy(img_gray_resize, img_gray)
#Img is smooth with asymetric kernel
cv.Smooth(img_gray, img_gray, param1=11, param2=9)
cv.Canny(img_gray, img_trait, 40, 60)
# Laplacian treatment
# Creating buffer image
img_lap_ini = cv.CreateImage((img_gray.width, img_gray.height), 32, 1)
img_lap = cv.CreateImage((img_lap_ini.width - 2 * Offset[0],
img_lap_ini.height - 2 * Offset[1]), 32, 1)
# Creating buffer img
img_lap_tmp = cv.CreateImage((img_lap.width, img_lap.height), 32, 1)
#Computing laplacian
cv.Laplace(img_gray, img_lap_ini, 5)
#Applying Offset to avoid border effect
cv.SetImageROI(img_lap_ini,
(Offset[0], Offset[1], img_lap_ini.width - 2 * Offset[0],
img_lap_ini.height - 2 * Offset[1]))
#Copying laplacian treated image to final laplacian image
cv.Copy(img_lap_ini, img_lap)
#Apply an asymetrique smoothing
cv.Smooth(img_lap, img_lap, param1=21, param2=11)
#Define the Kernel for closing algorythme
MKernel = cv.CreateStructuringElementEx(7, 3, 3, 1, cv.CV_SHAPE_RECT)
# Closing contour procedure
cv.MorphologyEx(img_lap, img_lap, img_lap_tmp, MKernel, cv.CV_MOP_CLOSE,
NiteClosing)
# Conveting img in 8bit image
img_lap8_ini = cv.CreateImage((img_lap.width, img_lap.height), 8, 1)
cv.Convert(img_lap, img_lap8_ini)
# Add white border to image
mat_bord = WhiteBorder(np.asarray(img_lap8_ini[:]), XSize, YSize)
img_lap8 = cv.CreateImage(
(img_lap.width + 2 * XSize, img_lap.height + 2 * YSize), 8, 1)
img_lap8 = cv.fromarray(mat_bord)
#Compute threshold
seuil_tmp = Seuil_var(img_lap8)
#If Seuil_tmp is not null
if seuil_tmp != 0:
seuil = seuil_tmp
#Else seuil is fixed to 20, which prevent from wrong positiv detection
else:
seuil = 20
#Compute thresholded image
img_lap_bi = cv.CreateImage((img_lap8.width, img_lap8.height), 8, 1)
img_lap_color = cv.CreateImage((img_lap8.width, img_lap8.height), 8, 3)
img_trait_lap = cv.CreateImage((img_lap8.width, img_lap8.height), 8, 1)
#Compute thresholded image
cv.Threshold(img_lap8, img_lap_bi, seuil, 255, cv.CV_THRESH_BINARY)
#Gaussian smoothing on laplacian
cv.Smooth(img_lap_bi, img_lap_bi, param1=11, param2=11)
#Convert grayscale laplacian image to binarie image using "seuil" as threshold value
cv.Threshold(img_lap_bi, img_lap_bi, 1, 255, cv.CV_THRESH_BINARY_INV)
cv.CvtColor(img_lap_bi, img_lap_color, cv.CV_GRAY2BGR)
#Compute edge in laplacian image
cv.Canny(img_lap_bi, img_trait_lap, 0, 2)
#Find contour
seqlapbi = cv.FindContours(img_trait_lap, cv.CreateMemStorage(),
cv.CV_RETR_TREE, cv.CV_CHAIN_APPROX_SIMPLE)
#contour is filtered
try:
contour_list = parcourt_contour(seqlapbi, img_lap_color)
except:
# If error is traped then there is no loop detected
return (0, 0, ("No loop detected", -1, -1))
# If there contours's list is not empty
NCont = len(contour_list)
if (NCont > 0):
# The CvSeq is inversed : X(i) became i(X)
indice = MapCont(contour_list[0], img_lap_color.width,
img_lap_color.height)
# The coordinate of target is computed in the traited image
point_shift = integreCont(indice, contour_list[0])
# The coordinate in original image are computed taken into account Offset and white bordure
point = (point_shift[0], point_shift[1] + 2 * Offset[0] - XSize,
point_shift[2] + 2 * Offset[1] - YSize)
else:
#Else no loop is detected
point = ("No loop detected", -1, -1)
Aire_Max = 0
return point
def test_morph(image, do_show=False):
element = cv.CreateStructuringElementEx(30, 30, 15, 15, cv.CV_SHAPE_RECT)
temp = cv.CreateImage(cv.GetSize(image), image.depth, image.channels)
morphed = morph(image, temp, element, cv.CV_MOP_OPEN)
if do_show: show(morphed=morphed)
def test_erode(image, do_show=False):
element = cv.CreateStructuringElementEx(3, 3, 2, 2, cv.CV_SHAPE_RECT)
eroded = erode(image, element)
if do_show: show(eroded=eroded)
image = loadGreyscale()
threshold = 100
color = 255
cv.Threshold(image, image, threshold, color, cv.CV_THRESH_BINARY)
showWindow("binary threshold")
# Otsu threshold
image = loadGreyscale()
cv.Threshold(image, image, threshold, color, cv.CV_THRESH_OTSU)
showWindow("Otsu threshold")
# Dilation
image = loadGreyscale()
element_shape = cv.CV_SHAPE_RECT
pos = 1
element = cv.CreateStructuringElementEx(pos * 2 + 1, pos * 2 + 1, pos, pos,
element_shape)
cv.Dilate(image, image, element, 2)
showWindow("Dilate")
# Erosion
image = loadGreyscale()
cv.Erode(image, image, element, 2)
showWindow("Erode")
# Morphology
image = loadGreyscale()
cv.MorphologyEx(image, image, image, element, cv.CV_MOP_CLOSE, 2)
showWindow("Morphology")
# Laplace
image = loadGreyscale()
def process_frame(self, frame):
debug_frame = cv.CreateImage(cv.GetSize(frame), 8, 3)
cv.Copy(frame, debug_frame)
cv.Smooth(frame, frame, cv.CV_MEDIAN, 7, 7)
# Set binary image to have saturation channel
hsv = cv.CreateImage(cv.GetSize(frame), 8, 3)
binary = cv.CreateImage(cv.GetSize(frame), 8, 1)
cv.CvtColor(frame, hsv, cv.CV_BGR2HSV)
cv.SetImageCOI(hsv, 2)
cv.Copy(hsv, binary)
cv.SetImageCOI(hsv, 0)
cv.AdaptiveThreshold(
binary,
binary,
255,
cv.CV_ADAPTIVE_THRESH_MEAN_C,
cv.CV_THRESH_BINARY_INV,
self.adaptive_thresh_blocksize,
self.adaptive_thresh,
)
# Morphology
kernel = cv.CreateStructuringElementEx(5, 5, 3, 3, cv.CV_SHAPE_ELLIPSE)
cv.Erode(binary, binary, kernel, 1)
cv.Dilate(binary, binary, kernel, 1)
# Get Edges
#cv.Canny(binary, binary, 30, 40)
cv.CvtColor(binary, debug_frame, cv.CV_GRAY2RGB)
# Hough Transform
line_storage = cv.CreateMemStorage()
raw_lines = cv.HoughLines2(binary,
line_storage,
cv.CV_HOUGH_STANDARD,
rho=1,
theta=math.pi / 180,
threshold=self.hough_threshold,
param1=0,
param2=0)
line_groups = [] # A list of line groups which are each a line list
for line in raw_lines:
group_found = False
for line_group in line_groups:
if line_group_accept_test(line_group, line, self.max_range):
line_group.append(line)
group_found = True
if not group_found:
line_groups.append([line])
# Average line groups into lines
lines = []
for line_group in line_groups:
rhos = map(lambda line: line[0], line_group)
angles = map(lambda line: line[1], line_group)
line = (sum(rhos) / len(rhos),
circular_average(angles, math.pi))
lines.append(line)
libvision.misc.draw_lines(debug_frame, raw_lines)
# cv.CvtColor(color_filtered,debug_frame, cv.CV_GRAY2RGB)
svr.debug("Bins", debug_frame)
class Preprocessor:
#cropRect = (0, 80, 640, 400) # Primary pitch
cropRect = (0, 45, 640, 400) # Alt. pitch
bgLearnRate = 0 #.15
bgsub_kernel = \
cv.CreateStructuringElementEx(5,5, #size
2,2, #X,Y offsets
cv.CV_SHAPE_RECT)
def __init__(self, rawSize, threshold, simulator=None, bg=None):
self.rawSize = rawSize
self.cropSize = self.cropRect[2:]
if rawSize[0] < 640:
self.cropSize = rawSize
logging.info("Captured image size: %s", dim2string(self.rawSize))
logging.info("Cropped image size: %s", dim2string(self.cropSize))
self.initMatrices()
self.Idistort = cv.CreateImage(self.rawSize, cv.IPL_DEPTH_8U, 3)
self.Icrop = cv.CreateImage(self.cropSize, cv.IPL_DEPTH_8U, 3)
self.Igray = cv.CreateImage(self.cropSize, cv.IPL_DEPTH_8U, 1)
self.Imask = cv.CreateImage(self.cropSize, cv.IPL_DEPTH_8U, 3)
self.Iobjects = cv.CreateImage(self.cropSize, cv.IPL_DEPTH_8U, 3)
self.bg = cv.CreateImage(self.cropSize, cv.IPL_DEPTH_8U, 3)
self.Ieq = cv.CreateImage(self.cropSize, cv.IPL_DEPTH_8U, 3)
self.R = cv.CreateImage(self.cropSize, cv.IPL_DEPTH_8U, 1)
self.G = cv.CreateImage(self.cropSize, cv.IPL_DEPTH_8U, 1)
self.B = cv.CreateImage(self.cropSize, cv.IPL_DEPTH_8U, 1)
if bg:
logging.debug("Loading dummy background image")
self.bg = cv.CreateImage(self.rawSize, cv.IPL_DEPTH_8U, 3)
cv.Zero(self.bg)
else:
logging.debug("Loading the background image")
#self.bg = cv.LoadImage('alt-pitch-bg.png')
self.bg = cv.LoadImage('alt-pitch-bg.jpg')
logging.debug("Processing the background image:")
self.bg = cv.CloneImage(self.crop(self.undistort(self.bg)))
# cv.SaveImage("calibrated-background.png", self.bg)
self.standardised = simulator is not None
self.threshold = threshold
def get_standard_form(self, frame):
"""Undistort an image, i.e. convert to standard format
Returns an internal buffer.
"""
if self.standardised:
return frame
else:
return self.crop(self.undistort(frame))
def bgsub(self, frame):
"""Preprocess a frame
:: CvMat -> (CvMat, CvMat, CvMat)
This method preprocesses a frame by undistorting it using
prior camera calibration data and then removes the background
using an image of the background.
"""
# if not self.standardised:
# frame = self.get_standard_form(frame)
self.continuousLearnBackground(frame)
bgsub, mask = self.remove_background_values(frame)
return bgsub, mask
def crop(self, frame):
logging.debug("Cropping a frame")
sub_region = cv.GetSubRect(frame, self.cropRect)
cv.Copy(sub_region, self.Icrop)
return self.Icrop
def preprocessBG(self, frame):
ballmask = self.threshold.ball(frame)
def continuousLearnBackground(self, frame):
if self.bgLearnRate == 0: return
cv.AddWeighted(frame, self.bgLearnRate, self.bg,
1.0 - self.bgLearnRate, 0, self.bg)
def remove_background(self, frame):
"""Remove background, leaving robots and some noise.
It is not safe to modify the returned image, as it will be
re-initialised each time preprocess is run.
"""
logging.debug("Performing background subtraction")
#cv.CvtColor(frame, self.Igray, cv.CV_BGR2GRAY)
cv.Sub(frame, self.bg, self.Imask)
return self.Imask
def remove_background_values(self, frame):
self.Imask = self.remove_background(frame)
logging.debug(
"Using thresholded background subtracted image as a mask")
#cv.ShowImage("ASD", self.Imask)
self.Igray = self.threshold.foreground(self.Imask)
cv.CvtColor(self.Imask, self.Igray, cv.CV_BGR2GRAY)
cv.Threshold(self.Igray, self.Igray, 200, 255, cv.CV_THRESH_OTSU)
#cv.EqualizeHist(self.Igray, self.Igray)
cv.CvtColor(self.Igray, self.Imask, cv.CV_GRAY2BGR)
#Finally, return the salient bits of the original frame
cv.And(self.Imask, frame, self.Iobjects)
return self.Iobjects, self.Igray
def background_mask(self, frame):
bgsub_eq = cv.CreateImage(self.pre.cropSize, cv.IPL_DEPTH_8U, 1)
cv.CvtColor(bgsub, bgsub_eq, cv.CV_BGR2GRAY)
cv.EqualizeHist(bgsub_eq, bgsub_eq)
def hist_eq(self, frame):
cv.Split(frame, self.B, self.G, self.R, None)
cv.EqualizeHist(self.R, self.R)
cv.EqualizeHist(self.R, self.B)
cv.EqualizeHist(self.G, self.G)
cv.Merge(self.B, self.G, self.R, None, self.Ieq)
return self.Ieq
def hsv_normalise(self, frame):
"""Should normalise scene lighting
Works by setting the HSV Value component to a constant.
However, turns out that this does not adequately remove shadows.
Maybe parameter tweaking the Value constant might do something? TODO
"""
tmp = cv.CreateImage(cv.GetSize(frame), 8, 3)
cv.CvtColor(frame, tmp, cv.CV_BGR2HSV)
H, S, V = [cv.CreateImage(cv.GetSize(frame), 8, 1) for _ in range(3)]
cv.Split(tmp, H, S, V, None)
cv.Set(V, 140)
cv.Merge(H, S, V, None, tmp)
cv.CvtColor(tmp, tmp, cv.CV_HSV2BGR),
out = tmp
return out
def undistort(self, frame):
logging.debug("Undistorting a frame")
assert frame.width == self.Idistort.width
assert frame.height == self.Idistort.height
cv.Undistort2(frame, self.Idistort, self.Intrinsic, self.Distortion)
return self.Idistort
def initMatrices(self):
"Initialise matrices for camera distortion correction."
logging.debug("Initialising camera matrices")
dmatL = [
-3.1740235091903346e-01, -8.6157434640872499e-02,
9.2026812110876845e-03, 4.4950266773574115e-03
]
imatL = [
8.6980146658682384e+02, 0., 3.7426130495414304e+02, 0.,
8.7340754327613899e+02, 2.8428760615670581e+02, 0., 0., 1.
]
imat = cv.CreateMat(3, 3, cv.CV_32FC1)
dmat = cv.CreateMat(1, 4, cv.CV_32FC1)
for i in range(3):
for j in range(3):
imat[i, j] = imatL[3 * i + j]
for i in range(4):
dmat[0, i] = dmatL[i]
self.Distortion = dmat
self.Intrinsic = imat
def processBag( self, bag ):
FLIP_IMAGE = bool( self.options.frameFlip == "True" )
USING_OPTICAL_FLOW_FOR_MOTION = False
print "frameFlip = ", FLIP_IMAGE
bagFrameIdx = 0
frameIdx = 0
impactFrameIdx = None
# Setup filters
opticalFlowFilter = OpticalFlowFilter(
self.OPTICAL_FLOW_BLOCK_WIDTH, self.OPTICAL_FLOW_BLOCK_HEIGHT,
self.OPTICAL_FLOW_RANGE_WIDTH, self.OPTICAL_FLOW_RANGE_HEIGHT )
motionDetectionFilter = MotionDetectionFilter()
imageFlowFilter = ImageFlowFilter()
residualSaliencyFilter = ResidualSaliencyFilter()
# Process bag file
for topic, msg, t in bag.read_messages():
if self.workCancelled:
# We've been given the signal to quit
break
if msg._type == "sensor_msgs/Image":
bagFrameIdx += 1
if (bagFrameIdx-1)%self.PROCESSED_FRAME_DIFF != 0:
continue
print "Processing image", frameIdx
# Get input image
image = cv.CreateMatHeader( msg.height, msg.width, cv.CV_8UC3 )
cv.SetData( image, msg.data, msg.step )
if FLIP_IMAGE:
cv.Flip( image, None, 1 )
# Convert to grayscale
grayImage = cv.CreateMat( msg.height, msg.width, cv.CV_8UC1 )
cv.CvtColor( image, grayImage, cv.CV_BGR2GRAY )
grayImageNumpPy = np.array( grayImage )
# Calculate optical flow
opticalFlowArrayX, opticalFlowArrayY = \
opticalFlowFilter.calcOpticalFlow( grayImage )
# Detect motion
if USING_OPTICAL_FLOW_FOR_MOTION:
if frameIdx == 0:
motionImage = PyVarFlowLib.createMotionMask(
grayImageNumpPy, grayImageNumpPy )
else:
motionImage = PyVarFlowLib.createMotionMask(
np.array( self.grayScaleImageList[ frameIdx - 1 ] ),
grayImageNumpPy )
else:
motionImage = motionDetectionFilter.calcMotion( grayImage )
# Work out the left most point in the image where motion appears
motionTest = np.copy( motionImage )
cv.Erode( motionTest, motionTest )
if frameIdx == 0:
leftMostMotion = motionImage.shape[ 1 ]
else:
leftMostMotion = self.leftMostMotionList[ frameIdx - 1 ]
leftMostMotionDiff = 0
for i in range( leftMostMotion ):
if motionTest[ :, i ].max() > 0:
leftMostMotionDiff = abs( leftMostMotion - i )
leftMostMotion = i
break
segmentationMask = np.zeros( ( msg.height, msg.width ), dtype=np.uint8 )
FRAMES_BACK = 3
if impactFrameIdx == None:
if leftMostMotionDiff > 18 and leftMostMotion < 0.75*msg.width:
# Found impact frame
impactFrameIdx = frameIdx
else:
PROCESS_IMPACT = False
if PROCESS_IMPACT and frameIdx - impactFrameIdx == FRAMES_BACK:
# Should now have enough info to segment object
impactMotionImage = self.motionImageList[ impactFrameIdx ]
print "Aligning"
postImpactRealFarFlow = imageFlowFilter.calcImageFlow( impactMotionImage, motionImage )
print "Aligning"
postImpactFarFlow = imageFlowFilter.calcImageFlow( impactMotionImage, self.motionImageList[ impactFrameIdx + 2 ] )
print "Aligning"
postImpactNearFlow = imageFlowFilter.calcImageFlow( impactMotionImage, self.motionImageList[ impactFrameIdx + 1 ] )
segmentationMask = np.maximum( np.maximum( np.maximum(
impactMotionImage, postImpactNearFlow[ 3 ] ), postImpactFarFlow[ 3 ] ), postImpactRealFarFlow[ 3 ] )
cv.Dilate( segmentationMask, segmentationMask )
print "Aligning"
preImpactRealFarFlow = imageFlowFilter.calcImageFlow( impactMotionImage, self.motionImageList[ impactFrameIdx - 8 ] )
print "Aligning"
preImpactFarFlow = imageFlowFilter.calcImageFlow( impactMotionImage, self.motionImageList[ impactFrameIdx - 6 ] )
print "Aligning"
preImpactNearFlow = imageFlowFilter.calcImageFlow( impactMotionImage, self.motionImageList[ impactFrameIdx - 4 ] )
subMask = np.maximum( np.maximum(
preImpactRealFarFlow[ 3 ], preImpactFarFlow[ 3 ] ), preImpactNearFlow[ 3 ] )
cv.Erode( subMask, subMask )
cv.Dilate( subMask, subMask )
cv.Dilate( subMask, subMask )
cv.Dilate( subMask, subMask )
subMask[ subMask > 0 ] = 255
diffImage = segmentationMask.astype( np.int32 ) - subMask.astype( np.int32 )
diffImage[ diffImage < 0 ] = 0
diffImage = diffImage.astype( np.uint8 )
cv.Erode( diffImage, diffImage )
#diffImage[ diffImage > 0 ] = 255
#segmentationMask = subMask
segmentationMask = diffImage
#segmentationMask = np.where( diffImage > 128, 255, 0 ).astype( np.uint8 )
# Calculate image flow
#imageFlow = imageFlowFilter.calcImageFlow( motionImage )
## Calculate saliency map
#saliencyMap, largeSaliencyMap = residualSaliencyFilter.calcSaliencyMap( grayImageNumpPy )
#blobMap = np.where( largeSaliencyMap > 128, 255, 0 ).astype( np.uint8 )
#blobMap, numBlobs = PyBlobLib.labelBlobs( blobMap )
#print "found", numBlobs, "blobs"
#largeSaliencyMap = np.where( largeSaliencyMap > 128, 255, 0 ).astype( np.uint8 )
# Threshold the saliency map
#largeSaliencyMap = (largeSaliencyMap > 128).astype(np.uint8) * 255
#cv.AdaptiveThreshold( largeSaliencyMap, largeSaliencyMap, 255 )
# Detect clusters within the saliency map
#NUM_CLUSTERS = 5
#numSamples = np.sum( saliencyMap )
#sampleList = np.ndarray( ( numSamples, 2 ), dtype=np.float32 )
#sampleListIdx = 0
#for y in range( saliencyMap.shape[ 0 ] ):
#for x in range( saliencyMap.shape[ 1 ] ):
#numNewSamples = saliencyMap[ y, x ]
#if numNewSamples > 0:
#sampleList[ sampleListIdx:sampleListIdx+numNewSamples, 0 ] = x
#sampleList[ sampleListIdx:sampleListIdx+numNewSamples, 1 ] = y
#sampleListIdx += numNewSamples
#sampleList[ 0:numSamples/2 ] = ( 20, 20 )
#sampleList[ numSamples/2: ] = ( 200, 200 )
#labelList = np.ndarray( ( numSamples, 1 ), dtype=np.int32 )
#cv.KMeans2( sampleList, NUM_CLUSTERS, labelList,
#(cv.CV_TERMCRIT_ITER | cv.CV_TERMCRIT_EPS, 10, 0.01) )
#saliencyScaleX = float( largeSaliencyMap.shape[ 1 ] ) / saliencyMap.shape[ 1 ]
#saliencyScaleY = float( largeSaliencyMap.shape[ 0 ] ) / saliencyMap.shape[ 0 ]
clusterList = []
#for clusterIdx in range( NUM_CLUSTERS ):
#clusterSamples = sampleList[
#np.where( labelList == clusterIdx )[ 0 ], : ]
#if clusterSamples.size <= 0:
#mean = ( 0.0, 0.0 )
#stdDev = 0.0
#else:
#mean = clusterSamples.mean( axis=0 )
#mean = ( mean[ 0 ]*saliencyScaleX, mean[ 1 ]*saliencyScaleY )
#stdDev = clusterSamples.std()*saliencyScaleX
#clusterList.append( ( mean, stdDev ) )
# Work out the maximum amount of motion we've seen in a single frame so far
#motionCount = motionImage[ motionImage > 0 ].size
#if frameIdx == 0:
#lastMotionCount = 0
#else:
#lastMotionCount = self.maxMotionCounts[ frameIdx - 1 ]
#if motionCount < lastMotionCount:
#motionCount = lastMotionCount
## Work out diffImage
#diffImage = np.array( motionImage, dtype=np.int32 ) \
#- np.array( imageFlow[ 3 ], dtype=np.int32 )
#diffImage = np.array( np.maximum( diffImage, 0 ), dtype=np.uint8 )
# Segment the image
#workingMask = np.copy( motionImage )
#workingMask = np.copy( diffImage )
workingMask = np.copy( segmentationMask )
kernel = cv.CreateStructuringElementEx(
cols=3, rows=3,
anchorX=1, anchorY=1, shape=cv.CV_SHAPE_CROSS )
cv.Erode( workingMask, workingMask, kernel )
cv.Dilate( workingMask, workingMask )
extraExtraMask = np.copy( workingMask )
cv.Dilate( extraExtraMask, extraExtraMask )
cv.Dilate( extraExtraMask, extraExtraMask )
cv.Dilate( extraExtraMask, extraExtraMask )
cv.Dilate( extraExtraMask, extraExtraMask )
cv.Dilate( extraExtraMask, extraExtraMask )
cv.Dilate( extraExtraMask, extraExtraMask )
allMask = np.copy( extraExtraMask )
cv.Dilate( allMask, allMask )
cv.Dilate( allMask, allMask )
cv.Dilate( allMask, allMask )
cv.Dilate( allMask, allMask )
cv.Dilate( allMask, allMask )
cv.Dilate( allMask, allMask )
possibleForeground = workingMask > 0
if workingMask[ possibleForeground ].size >= 100 \
and frameIdx >= 16:
print "Msk size", workingMask[ possibleForeground ].size
print workingMask[ 0, 0:10 ]
fgModel = cv.CreateMat( 1, 5*13, cv.CV_64FC1 )
bgModel = cv.CreateMat( 1, 5*13, cv.CV_64FC1 )
#workingMask[ possibleForeground ] = self.GC_FGD
#workingMask[ possibleForeground == False ] = self.GC_PR_BGD
#workingMask[ : ] = self.GC_PR_BGD
#workingMask[ possibleForeground ] = self.GC_FGD
workingMask[ : ] = self.GC_BGD
workingMask[ allMask > 0 ] = self.GC_PR_BGD
workingMask[ extraExtraMask > 0 ] = self.GC_PR_FGD
workingMask[ possibleForeground ] = self.GC_FGD
if frameIdx == 16:
# Save mask
maskCopy = np.copy( workingMask )
maskCopy[ maskCopy == self.GC_BGD ] = 0
maskCopy[ maskCopy == self.GC_PR_BGD ] = 64
maskCopy[ maskCopy == self.GC_PR_FGD ] = 128
maskCopy[ maskCopy == self.GC_FGD ] = 255
print "Unused pixels", \
maskCopy[ (maskCopy != 255) & (maskCopy != 0) ].size
outputImage = cv.CreateMat( msg.height, msg.width, cv.CV_8UC3 )
cv.CvtColor( maskCopy, outputImage, cv.CV_GRAY2BGR )
cv.SaveImage( "output.png", image );
cv.SaveImage( "outputMask.png", outputImage );
print "Saved images"
#return
#print "Set Msk size", workingMask[ workingMask == self.GC_PR_FGD ].size
imageToSegment = image #self.inputImageList[ frameIdx - FRAMES_BACK ]
imageCopy = np.copy( imageToSegment )
cv.CvtColor( imageCopy, imageCopy, cv.CV_BGR2RGB )
print "Start seg"
cv.GrabCut( imageCopy, workingMask,
(0,0,0,0), fgModel, bgModel, 12, self.GC_INIT_WITH_MASK )
print "Finish seg"
segmentation = np.copy( imageToSegment )
segmentation[ (workingMask != self.GC_PR_FGD) & (workingMask != self.GC_FGD) ] = 0
black = (workingMask != self.GC_PR_FGD) & (workingMask != self.GC_FGD)
#motionImage = np.where( black, 0, 255 ).astype( np.uint8 )
# Refine the segmentation
REFINE_SEG = False
if REFINE_SEG:
motionImageCopy = np.copy( motionImage )
cv.Erode( motionImageCopy, motionImageCopy )
#cv.Erode( motionImageCopy, motionImageCopy )
#cv.Erode( motionImageCopy, motionImageCopy )
workingMask[ motionImageCopy > 0 ] = self.GC_PR_FGD
workingMask[ motionImageCopy == 0 ] = self.GC_PR_BGD
cv.Dilate( motionImageCopy, motionImageCopy )
cv.Dilate( motionImageCopy, motionImageCopy )
cv.Dilate( motionImageCopy, motionImageCopy )
cv.Dilate( motionImageCopy, motionImageCopy )
workingMask[ motionImageCopy == 0 ] = self.GC_BGD
print "Other seg"
cv.GrabCut( imageCopy, workingMask,
(0,0,0,0), fgModel, bgModel, 12, self.GC_INIT_WITH_MASK )
print "Other seg done"
segmentation = np.copy( imageToSegment )
segmentation[ (workingMask != self.GC_PR_FGD) & (workingMask != self.GC_FGD) ] = 0
black = (workingMask != self.GC_PR_FGD) & (workingMask != self.GC_FGD)
motionImage = np.where( black, 0, 255 ).astype( np.uint8 )
else:
segmentation = np.zeros( ( image.height, image.width ), dtype=np.uint8 )
# Save output data
self.inputImageList[ frameIdx ] = image
self.grayScaleImageList[ frameIdx ] = grayImage
self.opticalFlowListX[ frameIdx ] = opticalFlowArrayX
self.opticalFlowListY[ frameIdx ] = opticalFlowArrayY
self.motionImageList[ frameIdx ] = motionImage
self.segmentationList[ frameIdx ] = segmentation
self.segmentationMaskList[ frameIdx ] = segmentationMask
#self.maxMotionCounts[ frameIdx ] = motionCount
#self.imageFlowList[ frameIdx ] = imageFlow
#self.saliencyMapList[ frameIdx ] = largeSaliencyMap
#self.saliencyClusterList[ frameIdx ] = clusterList
self.leftMostMotionList[ frameIdx ] = leftMostMotion
frameIdx += 1
self.numFramesProcessed += 1
if not self.workCancelled:
SAVE_MOTION_IMAGES = True
BASE_MOTION_IMAGE_NAME = self.scriptPath + "/../../test_data/motion_images/motion_{0:03}.png"
if SAVE_MOTION_IMAGES and len( self.motionImageList ) > 0:
width = self.motionImageList[ 0 ].shape[ 1 ]
height = self.motionImageList[ 0 ].shape[ 0 ]
colourImage = np.zeros( ( height, width, 3 ), dtype=np.uint8 )
for frameIdx, motionImage in enumerate( self.motionImageList ):
colourImage[ :, :, 0 ] = motionImage
colourImage[ :, :, 1 ] = motionImage
colourImage[ :, :, 2 ] = motionImage
outputName = BASE_MOTION_IMAGE_NAME.format( frameIdx + 1 )
cv.SaveImage( outputName, colourImage )
# Recalculate impactFrameIdx
width = self.motionImageList[ 0 ].shape[ 1 ]
totalMotionDiff = 0
maxMotionDiff = 0
impactFrameIdx = None
for motionIdx in range( 1, len( self.leftMostMotionList ) ):
motionDiff = abs( self.leftMostMotionList[ motionIdx ] \
- self.leftMostMotionList[ motionIdx - 1 ] )
totalMotionDiff += motionDiff
if motionDiff > maxMotionDiff and totalMotionDiff > 0.5*width:
maxMotionDiff = motionDiff
impactFrameIdx = motionIdx
if maxMotionDiff <= 18:
impactFrameIdx = None
if impactFrameIdx != None:
preMotionImages = []
postMotionImages = []
impactMotionImage = None
NUM_FRAMES_BEFORE = 3
prefix = self.options.outputPrefix
if prefix != "":
prefix += "_"
BASE_MOTION_IMAGE_NAME = self.scriptPath + "/../../test_data/impact_images/" + prefix + "motion_{0:03}.png"
START_MOTION_IMAGE_NAME = self.scriptPath + "/../../test_data/impact_images/" + prefix + "start_motion.png"
START_IMAGE_NAME = self.scriptPath + "/../../test_data/impact_images/" + prefix + "start.png"
IMPACT_IMAGE_NAME = self.scriptPath + "/../../test_data/impact_images/" + prefix + "impact.png"
SEGMENTATION_IMAGE_NAME = self.scriptPath + "/../../test_data/impact_images/" + prefix + "segmentation.png"
NUM_FRAMES_AFTER = 3
width = self.motionImageList[ 0 ].shape[ 1 ]
height = self.motionImageList[ 0 ].shape[ 0 ]
colourImage = np.zeros( ( height, width, 3 ), dtype=np.uint8 )
for frameIdx in range( impactFrameIdx - NUM_FRAMES_BEFORE,
impactFrameIdx + NUM_FRAMES_AFTER + 1 ):
motionImage = self.motionImageList[ frameIdx ]
if frameIdx < impactFrameIdx:
preMotionImages.append( motionImage )
elif frameIdx == impactFrameIdx:
impactMotionImage = motionImage
else: # frameIdx > impactFrameIdx
postMotionImages.append( motionImage )
colourImage[ :, :, 0 ] = motionImage
colourImage[ :, :, 1 ] = motionImage
colourImage[ :, :, 2 ] = motionImage
outputName = BASE_MOTION_IMAGE_NAME.format( frameIdx - impactFrameIdx )
cv.SaveImage( outputName, colourImage )
motionDetectionFilter.calcMotion( self.grayScaleImageList[ 0 ] )
startMotionImage = motionDetectionFilter.calcMotion(
self.grayScaleImageList[ impactFrameIdx ] )
colourImage[ :, :, 0 ] = startMotionImage
colourImage[ :, :, 1 ] = startMotionImage
colourImage[ :, :, 2 ] = startMotionImage
cv.SaveImage( START_MOTION_IMAGE_NAME, colourImage )
cv.CvtColor( self.inputImageList[ 0 ], colourImage, cv.CV_RGB2BGR )
cv.SaveImage( START_IMAGE_NAME, colourImage )
cv.CvtColor( self.inputImageList[ impactFrameIdx ], colourImage, cv.CV_RGB2BGR )
cv.SaveImage( IMPACT_IMAGE_NAME, colourImage )
print "Segmenting..."
segmentation = self.produceSegmentation( self.inputImageList[ 0 ],
impactMotionImage, preMotionImages, postMotionImages )
cv.CvtColor( segmentation, colourImage, cv.CV_RGB2BGR )
cv.SaveImage( SEGMENTATION_IMAGE_NAME, colourImage )
self.refreshGraphDisplay()
print "Finished processing bag file"
if bool( self.options.quitAfterFirstSegmentation == "True" ):
print "Trying to quit"
self.onWinMainDestroy( None )
else:
print "Not trying to quit so neeah"
def find_lines(frame):
# Resize to 640x480
frame_size = cv.GetSize(frame)
if frame_size[0] != 640:
frame_small = cv.CreateMat(480, 640, cv.CV_8UC3)
cv.Resize(frame, frame_small)
else:
frame_small = frame
# Threshold by distance: blank out all top pixels
cv.Rectangle(frame_small, (0, 0), (640, 80), (0, 0, 0, 0), cv.CV_FILLED)
# Convert to grayscale
frame_size = cv.GetSize(frame_small)
frame_gray = cv.CreateImage(frame_size, cv.IPL_DEPTH_8U, 1)
cv.CvtColor(frame_small, frame_gray, cv.CV_BGR2GRAY)
# Use color thresholding to get white lines
threshold = cv.CreateImage(frame_size, cv.IPL_DEPTH_8U, 1)
cv.Threshold(frame_gray, threshold, 190, 255, cv.CV_THRESH_BINARY)
# Morphological ops to reduce noise
# TODO try to reduce sizes to increase detection of faraway lines
openElement = cv.CreateStructuringElementEx(7, 7, 3, 3, cv.CV_SHAPE_RECT)
closeElement = cv.CreateStructuringElementEx(11, 11, 5, 5,
cv.CV_SHAPE_RECT)
cvOpen(threshold, threshold, openElement)
cvClose(threshold, threshold, closeElement)
# Use Canny edge detection to find edges
edges = cv.CreateImage(frame_size, cv.IPL_DEPTH_8U, 1)
cv.Canny(threshold, edges, 100, 200)
# Use Hough transform to find equations for lines
line_storage = cv.CreateMemStorage()
lines = cv.HoughLines2(edges, line_storage, cv.CV_HOUGH_STANDARD, 1,
cv.CV_PI / 180.0, 120)
lines = list(lines)
# Remove spurious line from the black rectangle up top
for line in lines[:]:
if (abs(180 - line[0]) < 10
and abs(util.normalizeRadians(cv.CV_PI / 2 - line[1])) < 0.01):
lines.remove(line)
# Group lines that are within r +/-12 and theta +/- 5 degrees
grouped_lines = []
r_threshold = 12 # in px
theta_threshold = cv.CV_PI * 5 / 180 # in radians
while len(lines) > 0:
line1 = normalizeLine(lines.pop())
avg_line = line1
matched_lines = [line1]
for j in range(len(lines) - 1, -1, -1):
line2 = normalizeLine(lines[j])
if verbose:
# Print which criteria were matched
if (abs(avg_line[0] - line2[0]) < r_threshold):
print 1,
if (abs(util.normalizeRadians(avg_line[1] - line2[1])) <
theta_threshold):
print 2,
print avg_line, line2
if (abs(avg_line[0] - line2[0]) < r_threshold and
abs(util.normalizeRadians(avg_line[1] - line2[1])) < \
theta_threshold):
matched_lines.append(line2)
avg_line = avgLines(matched_lines)
lines.pop(j)
if verbose: print matched_lines
grouped_lines.append(avg_line)
lines = grouped_lines
# Group possible pairs of lines by smallest angle difference
grouped_lines = []
while len(lines) > 0:
line1 = normalizeLine(lines.pop())
closest = None
for j in range(len(lines) - 1, -1, -1):
line2 = normalizeLine(lines[j])
# Find the closest match
if ((closest is None
or abs(util.normalizeRadians(line1[1] - line2[1])) < \
abs(util.normalizeRadians(line1[1] - closest[1])))
# Make sure difference < pi/4 to reduce errors
and abs(util.normalizeRadians(line1[1] - line2[1])) < \
cv.CV_PI / 4):
closest = line2
if closest is not None:
lines.remove(closest)
# Sort list by line[0] (radius)
if line1[0] > closest[0]:
line1, closest = closest, line1
# Make a tuple (line1, line2) or (line,) if no match found
grouped_lines.append((line1, closest))
else:
grouped_lines.append((line1, ))
# Print lines
if len(grouped_lines) > 0:
print 'Groups of lines:', grouped_lines
i = 0
for group in grouped_lines:
for j in range(len(group)):
rho, theta = group[j]
a, b = np.cos(theta), np.sin(theta)
x0, y0 = a * rho, b * rho
pt1 = (cv.Round(x0 + 1000 * (-b)), cv.Round(y0 + 1000 * (a)))
pt2 = (cv.Round(x0 - 1000 * (-b)), cv.Round(y0 - 1000 * (a)))
#cv.Line(frame_small, pt1, pt2,
# hv2rgb(360.0*i/len(grouped_lines), 1.0), 1, 8)
i += 1
# If 2+ groups of lines, find corners (intersection point of lines)
intersection_pts = []
if len(grouped_lines) > 1:
for i in range(len(grouped_lines)):
pair1 = grouped_lines[i]
for j in range(i + 1, len(grouped_lines)):
pair2 = grouped_lines[j]
# Make sure their angles differ by more than 10 deg to
# reduce errors
if (abs(util.normalizeRadians(pair1[0][1] - pair2[0][1])) <
cv.CV_PI * 10 / 180):
break
# Enumerate intersection points
pts = []
for line1 in pair1:
for line2 in pair2:
pts.append(lineIntersection(line1, line2))
# Find average of intersection points
x = sum(pt[0] for pt in pts) / len(pts)
y = sum(pt[1] for pt in pts) / len(pts)
pt = (x, y)
print 'Intersection:', pt,
intersection_pts.append(pt)
pt = (cv.Round(x), cv.Round(y))
cv.Circle(frame_small, pt, 4, (0, 255, 0, 0))
# Find direction of intersection by following each line
# (direction is defined as the point of the T)
angles = []
for pair in grouped_lines:
angles.append(pair[0][1] + cv.CV_PI / 2)
angles.append(pair[0][1] - cv.CV_PI / 2)
for angle in angles:
# Look 50px down the line for white pixels
# TODO look a variable amount
x1 = x + 50 * np.cos(angle)
y1 = y + 50 * np.sin(angle)
# Enforce limits
# TODO handle when intersection is off the bounds of the image
# Currently orientation of the intersection is not being used
# by the particle filter
x1 = min(max(0, x1), frame_size[0] - 1)
y1 = min(max(0, y1), frame_size[1] - 1)
srchPt = cv.Round(x1), cv.Round(y1)
if threshold[srchPt[1], srchPt[0]] == 0:
x1 = x + 50 * np.cos(angle + cv.CV_PI)
y1 = y + 50 * np.sin(angle + cv.CV_PI)
invSrchPt = cv.Round(x1), cv.Round(y1)
cv.Line(frame_small, pt, invSrchPt, (0, 255, 0, 0), 1,
8)
print 'Angle:', angle + cv.CV_PI
break
# Convert line equations into line segments
line_segments = []
for group in grouped_lines:
if len(group) == 2:
# Get the average of the lines in a pair
line1, line2 = group
line = ((line1[0] + line2[0]) / 2.0, (line1[1] + line2[1]) / 2.0)
else:
line = group[0]
# Look down the line for the endpoints of the white region
line_segment = lineToVisibleSegment(line, threshold)
if line_segment != None:
line_segments.append(line_segment)
print 'Line segments:', line_segments
i = 0
for pt1, pt2 in line_segments:
pt1 = cv.Round(pt1[0]), cv.Round(pt1[1])
pt2 = cv.Round(pt2[0]), cv.Round(pt2[1])
cv.Line(frame_small, pt1, pt2,
hv2rgb(360.0 * i / len(line_segments), 1.0), 2, 8)
i += 1
cv.ShowImage('frame', frame_small)
cv.ShowImage('edges', threshold)
return line_segments, intersection_pts
cv.Resize(image2, small, cv.CV_INTER_LINEAR)
size = cv.GetSize(small)
x = size[0]
y = size[1]
temp = cv.CreateImage(size, 8, 3)
sat = cv.CreateImage(size, 8, 1)
notsat = cv.CreateImage(size, 8, 1)
new = cv.CreateImage(size, 8, 1)
cv.Split(small, sat, None, None, None)
cv.Threshold(sat, notsat, 100, 255, cv.CV_THRESH_BINARY)
cv.Dilate(notsat, notsat,
cv.CreateStructuringElementEx(3, 3, 1, 1, cv.CV_SHAPE_ELLIPSE), 2)
storage = cv.CreateMemStorage(0)
contour = cv.FindContours(notsat, storage, cv.CV_RETR_EXTERNAL,
cv.CV_CHAIN_APPROX_NONE)
while contour:
if cv.ContourArea(contour) < 10:
contour2 = contour
cv.DrawContours(new, contour2, cv.RGB(255, 255, 255),
cv.RGB(255, 255, 255), -1, cv.CV_FILLED, cv.CV_AA)
contour = contour.h_next()
cv.SaveImage("stars.png", new)
cv.SaveImage("small.png", small)
# The magic number M determines how deep the polar transformation goes.
M = 69
#This is the main loop
while True:
cam = cv.QueryFrame(capture)
cv.LogPolar(cam, polar, (centerX, centerY), M+1, cv.CV_INTER_NN) #possible speedup - get subrect src
#unwrapped = cv.GetSubRect(polar,(280,0,40,360))
#cv.Transpose(unwrapped, unwrapped)
cv.Transpose(cv.GetSubRect(polar,(280,0,40,360)), unwrapped)
cv.Flip(unwrapped) #just for viewing (possible speedup)
cv.InRangeS(unwrapped, lower, upper, cones)
cv.Erode(cones, cones) # just once might be too much, but unavoidable
k = cv.CreateStructuringElementEx(3, 43, 1, 1, cv.CV_SHAPE_RECT) # create a 3x43 rectangular dilation element k
cv.Dilate(cones, cones, k)
#Display (should probably be disabled with a usage flag)
cv.ShowImage('cam', cam)
cv.ShowImage('unwrapped', unwrapped)
cv.ShowImage('cones', cones)
#cv.ShowImage('polar', polar)
#cv.ShowImage('hsvcopy', hsvcopy)
#scan top row of thresholded, eroded, dilated image, find the number of contiguous segments and their location
s = 0 # size of contiguous segment
ss = 0 #number of contiguous segments
bearingToLandmarks = []
for i in xrange(360-2):
c = cones[0, i] #current
def Closing(pos):
element = cv.CreateStructuringElementEx(pos*2+1, pos*2+1, pos, pos, element_shape)
cv.Dilate(src, image, element, 1)
cv.Erode(image, dest, element, 1)
cv.ShowImage("Opening & Closing", dest)
def test_dilate(image, do_show=False):
element = cv.CreateStructuringElementEx(3, 3, 2, 2, cv.CV_SHAPE_RECT)
dilated = dilate(image, element)
if do_show: show(dilated=dilated)
def Dilation(pos):
element = cv.CreateStructuringElementEx(pos*2+1, pos*2+1, pos, pos, element_shape)
cv.Dilate(src, dest, element, 1)
cv.ShowImage("Erosion & Dilation", dest)
files = os.listdir('data/examples')
counter = 0
for f in files:
image = cv.LoadImage('data/examples/' + f)
for plate in anpr.detect_plates(image):
zzz = cv.CreateImage(cv.GetSize(plate), cv.IPL_DEPTH_8U, 3)
cv.Smooth(plate, zzz)
#
cv.PyrMeanShiftFiltering(plate, zzz, 40, 15)
foo = anpr.greyscale(plate)
segmented = cv.CreateImage(cv.GetSize(plate), cv.IPL_DEPTH_8U, 1)
bar = cv.CreateImage(cv.GetSize(plate), cv.IPL_DEPTH_8U, 1)
cv.EqualizeHist(foo, segmented)
cv.AdaptiveThreshold(
segmented, bar, 255, cv.CV_ADAPTIVE_THRESH_GAUSSIAN_C,
cv.CV_THRESH_BINARY_INV,
plate.height % 2 == 0 and (plate.height + 1) or plate.height,
plate.height / 2)
baz = cv.CreateImage(cv.GetSize(plate), cv.IPL_DEPTH_8U, 1)
el = cv.CreateStructuringElementEx(1, 2, 0, 0, cv.CV_SHAPE_RECT)
cv.Erode(bar, baz, el)
#quick_show(plate)
#quick_show(segmented)
#quick_show(bar)
quick_show(baz)
for char in anpr.find_characters(foo, baz):
cv.Rectangle(plate, (int(char.x1), int(char.y1)),
(int(char.x2), int(char.y2)), (255, 0, 0))
quick_show(plate)
# some constants
OPENNI_INITIALIZATION_FILE = "../config/BasicColorAndDepth.xml"
KEY_ESC = 27
hist_height = 64
# initialization stuff
print 'loading ..'
save_count = 0
objects = []
stats = []
current_key = -1
timing = {'t_draw': 0, 't_histo': 0, 't_track': 0, 't_pre': 0}
loop_c = 0
font = cv.InitFont(cv.CV_FONT_HERSHEY_PLAIN, 1, 1, 0, 1, 8) # opencv font
elem = cv.CreateStructuringElementEx(8, 8, 4, 4, cv.CV_SHAPE_RECT) # used by the morphological operator (erode)
storage = cv.CreateMemStorage(0) # needed by find contours
writer = None
# compute a timestamp for output file
outpath += datetime.datetime.now().strftime('%d%m%y_%H%M%S')
os.mkdir(outpath)
# create a list of random colors
color_tab = [random_color() for i in range(n_colors)]
help = """
press 'v' to start or stop video recording
press 's' to save the current images
press 'c' to draw the contours
press 'b' to draw the boxes
press 'k' to draow the keypoints
def process_frame(self, frame):
################
#setup CV ######
################
print "processing frame"
(w, h) = cv.GetSize(frame)
#generate hue selection frames
ones = np.ones((h, w, 1), dtype='uint8')
a = ones * (180 - self.target_hue)
b = ones * (180 - self.target_hue + 20)
a_array = cv.fromarray(a)
b_array = cv.fromarray(b)
#create locations for the test frame and binary frame
frametest = cv.CreateImage(cv.GetSize(frame), 8, 3)
binarytest = cv.CreateImage(cv.GetSize(frame), 8, 1)
#use the red channel for the binary frame (just for debugging purposes)
cv.Copy(frame, frametest)
cv.SetImageCOI(frametest, 3)
cv.Copy(frametest, binarytest)
#reset the COI for test frame to RGB.
cv.SetImageCOI(frametest, 0)
# Resize image to 320x240
#copy = cv.CreateImage(cv.GetSize(frame), 8, 3)
#cv.Copy(frame, copy)
#cv.SetImageROI(frame, (0, 0, 320, 240))
#cv.Resize(copy, frame, cv.CV_INTER_NN)
found_gate = False
#create a new frame for comparison purposes
unchanged_frame = cv.CreateImage(cv.GetSize(frame), 8, 3)
cv.Copy(frame, unchanged_frame)
#apply noise filter #1
cv.Smooth(frame, frame, cv.CV_MEDIAN, 7, 7)
# Set binary image to have saturation channel
hsv = cv.CreateImage(cv.GetSize(frame), 8, 3)
binary = cv.CreateImage(cv.GetSize(frame), 8, 1)
cv.CvtColor(frame, hsv, cv.CV_BGR2HSV)
cv.SetImageCOI(hsv, 1)
cv.Copy(hsv, binary)
#spin the color wheel (psuedo-code for later if necessary)
# truncate spectrum marked as end
# shift all values up based on truncating value (mask out 0 regions)
# take truncated bits, and flip them (180->0, 179->1...)
# dnow that truncated bits are flipped, add them back in to final image
#Reset hsv COI
cv.SetImageCOI(hsv, 0)
#correct for wraparound on red spectrum
cv.InRange(binary, a_array, b_array, binarytest) #generate mask
cv.Add(binary, cv.fromarray(ones * 180), binary,
mask=binarytest) #use mask to selectively add values
#run adaptive threshold for edge detection
cv.AdaptiveThreshold(
binary,
binary,
255,
cv.CV_ADAPTIVE_THRESH_MEAN_C,
cv.CV_THRESH_BINARY_INV,
self.adaptive_thresh_blocksize,
self.adaptive_thresh,
)
# Morphology
kernel = cv.CreateStructuringElementEx(5, 5, 3, 3, cv.CV_SHAPE_ELLIPSE)
cv.Erode(binary, binary, kernel, 1)
cv.Dilate(binary, binary, kernel, 1)
if self.debug:
color_filtered = cv.CloneImage(binary)
# Get Edges
cv.Canny(binary, binary, 30, 40)
# Hough Transform
line_storage = cv.CreateMemStorage()
raw_lines = cv.HoughLines2(binary,
line_storage,
cv.CV_HOUGH_STANDARD,
rho=1,
theta=math.pi / 180,
threshold=self.hough_threshold,
param1=0,
param2=0)
# Get vertical lines
vertical_lines = []
i = 0
for line in raw_lines:
if line[1] < self.vertical_threshold or \
line[1] > (math.pi-self.vertical_threshold):
#absolute value does better grouping currently
vertical_lines.append((abs(line[0]), line[1]))
i += 1
# print message to user for performance purposes
logging.debug("{} possibilities reduced to {} lines".format(
i, len(vertical_lines)))
# Group vertical lines
vertical_line_groups = [
] #A list of line groups which are each a line list
i = 0
for line in vertical_lines:
group_found = False
for line_group in vertical_line_groups:
i += 1
if line_group_accept_test(line_group, line, self.max_range):
line_group.append(line)
group_found = True
if not group_found:
vertical_line_groups.append([line])
#quick debugging statement
logging.debug("{} internal iterations for {} groups".format(
i, len(vertical_line_groups)))
# Average line groups into lines
vertical_lines = []
for line_group in vertical_line_groups:
rhos = map(lambda line: line[0], line_group) #get rho of each line
angles = map(lambda line: line[1], line_group)
line = (sum(rhos) / len(rhos), circular_average(angles, math.pi))
vertical_lines.append(line)
self.left_pole = None
self.right_pole = None
self.returning = 0
self.found = False
if len(vertical_lines) is 2:
roi = cv.GetImageROI(frame)
width = roi[2]
height = roi[3]
self.left_pole = round(
min(vertical_lines[0][0], vertical_lines[1][0]), 2) - width / 2
self.right_pole = round(
max(vertical_lines[0][0], vertical_lines[1][0]), 2) - width / 2
self.returning = (self.left_pole + self.right_pole) / 2
logging.info("Returning {}".format(self.returning))
#If this is first iteration, count this as seeing the gate
if self.last_seen < 0:
self.last_center = None
self.last_seen = 0
#increment a counter if result is good.
if self.last_center is None:
self.last_center = self.returning
self.seen_count = 1
elif math.fabs(self.last_center -
self.returning) < self.center_trans_thresh:
self.seen_count += 1
self.last_seen += 2
else:
self.last_seen -= 1
#if not convinced, forget left/right pole. Else, proclaim success.
if self.seen_count < self.seen_count_thresh:
self.left_pole = None
self.right_pole = None
else:
print "FOUND CENTER AND RETURNED IT"
self.found = True
else:
self.returning = 0
if self.last_seen < 0:
self.last_center = None
self.last_seen = 0
self.last_seen -= 1
self.left_pole = None
self.right_POLE = None
#extra debugging stuff
if self.debug:
cv.CvtColor(color_filtered, frame, cv.CV_GRAY2RGB)
libvision.misc.draw_lines(frame, vertical_lines)
if self.found:
cv.Circle(frame, (int(frame.width / 2 + self.returning),
int(frame.height / 2)), 15, (0, 255, 0), 2,
8, 0)
font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 1, 1, 1, 3)
cv.PutText(frame, "Gate Sent to Mission Control", (100, 400),
font, (255, 255, 0))
#print frame.width
#cv.ShowImage("Gate", cv.CloneImage(frame))
svr.debug("Gate", cv.CloneImage(frame))
svr.debug("Unchanged", cv.CloneImage(unchanged_frame))
self.return_output()
def camera():
found_goals = False
print "# Starting initialization..."
intrinsics = cv.CreateMat(3, 3, cv.CV_64FC1)
cv.Zero(intrinsics)
#camera data
intrinsics[0, 0] = 850.850708957251072
intrinsics[1, 1] = 778.955239997982062
intrinsics[2, 2] = 1
intrinsics[0, 2] = 320.898495232253822
intrinsics[1, 2] = 380.213734835526282
dist_coeffs = cv.CreateMat(1, 4, cv.CV_64FC1)
cv.Zero(dist_coeffs)
dist_coeffs[0, 0] = -0.226795877008420
dist_coeffs[0, 1] = 0.139445565548056
dist_coeffs[0, 2] = 0.001245710462327
dist_coeffs[0, 3] = -0.001396618726445
print "# intrinsics loaded!"
#prepare memory
capture = cv.CaptureFromCAM(0)
src = cv.QueryFrame(capture)
size = GetSize(src)
dst0 = cv.CreateImage(size, src.depth, src.nChannels)
image_ROI = (0, 60, 640, 340)
size = (640, 340)
hue = cv.CreateImage(size, 8, 1)
sat = cv.CreateImage(size, 8, 1)
val = cv.CreateImage(size, 8, 1)
ball = cv.CreateImage(size, 8, 1)
yellow = cv.CreateImage(size, 8, 1)
blue = cv.CreateImage(size, 8, 1)
Set2D(hue, 4, 4, 255)
Set2D(sat, 4, 4, 255)
Set2D(val, 4, 4, 255)
Set2D(ball, 4, 4, 255)
Set2D(yellow, 4, 4, 255)
Set2D(blue, 4, 4, 255)
ballx = 0
bally = 0
print "# base images created..."
#####------------------adjustment data---------------------###############
#shadow
high = 40
low = 300
#threshold
thresred = 160
thresgreen = 220
thresblue = 254
#dilate
ex = cv.CreateStructuringElementEx(3, 3, 1, 1, cv.CV_SHAPE_RECT)
ex2 = cv.CreateStructuringElementEx(2, 2, 1, 1, cv.CV_SHAPE_RECT)
ex5 = cv.CreateStructuringElementEx(5, 5, 1, 1, cv.CV_SHAPE_RECT)
tHack = cv.CreateStructuringElementEx(3, 3, 1, 1, cv.CV_SHAPE_CROSS)
#ball
ballcount = 15
ballmaxarea = 200
ballminiarea = 45
ballcompact = 1.3
#blue
bluecount = 30
bluemaxarea = 1500
blueminiarea = 50
bluemaxdepth = 10
blueminidepth = 2
#yellow
yellowcount = 30
yellowmaxarea = 1000
yellowminiarea = 50
yellowmaxdepth = 10
yellowminidepth = 3.2
#####----------------------------------------
aa = time.time()
storage = cv.CreateMemStorage()
first = True
pitch = 0 # 0 for main pitch, 1 for alt pitch
countf = 0
print "# starting capture..."
print ''
capture = cv.CaptureFromCAM(0)
while (True):
src = cv.QueryFrame(capture)
#ShowImage('src',src)
cv.SetImageROI(dst0, (0, 0, 640, 480))
average = cv.CreateImage(size, 8, 3)
#barrel undistortion
cv.Undistort2(src, dst0, intrinsics, dist_coeffs)
#ROI = Region of Interests, crop the image
cv.SetImageROI(dst0, image_ROI)
dst = GetImage(dst0)
dst2 = cv.CreateImage(size, 8, 3)
Set2D(dst2, 4, 4, 255)
hsv = cv.CreateImage(size, 8, 3)
CvtColor(dst, hsv, CV_RGB2HSV)
cv.Split(hsv, hue, sat, val, None)
if (first):
#hist = cv.CreateHist([32,64], CV_HIST_ARRAY, [[0,180], [0,256]], 1)
#cv.CalcHist([hue, sat], hist, 0, None)
#values = cv.GetMinMaxHistValue(hist)
#print values
#tweak = values[3][0]
#if tweak >= 12:
# pitch = 1
#print ">>> tweak=",tweak,"pitch selected =",pitch
pitch = pitchSet
if pitch == 1:
base = cv.LoadImage("base.jpg", cv.CV_LOAD_IMAGE_UNCHANGED)
baseInv = cv.CreateImage(size, 8, 1)
cv.Not(base, baseInv)
#huecorr = cv.LoadImage("huecorr.jpg",cv.CV_LOAD_IMAGE_UNCHANGED)
#cv.Smooth(huecorr,huecorr)
#ShowImage("base",base)
#base = cv.CreateImage(size,8,1)
#base = GetImage(val)
#cv.Threshold(hue,hue,75,255,cv.CV_THRESH_BINARY_INV)
#cv.SaveImage("huecorr.jpg", hue)
#cv.Threshold(base,base,110,255,cv.CV_THRESH_BINARY)
#cv.SaveImage("base.jpg", base)
#cv.WaitKey(-1)
first = False
if (debug):
ShowImage("hue", hue)
ShowImage("sat", sat)
ShowImage("val", val)
if pitch == 1:
walls = cv.CreateImage(size, 8, 1)
cv.Threshold(val, walls, 50, 255, cv.CV_THRESH_BINARY_INV)
Set2D(walls, 4, 4, 255)
# BALL
# fixed this cause with another robot it was finding the ball on it. seems to work
Add(sat, hue, ball)
Sub(ball, walls, ball)
cv.SubS(ball, 60, ball, baseInv)
cv.Threshold(ball, ball, 170, 255, cv.CV_THRESH_BINARY)
cv.Erode(ball, ball, ex, 1)
cv.Dilate(ball, ball, ex2, 1)
Set2D(ball, 4, 4, 255)
# YELLOW
# cv.Threshold(hue,yellow,80,255,cv.CV_THRESH_BINARY)
cv.Threshold(val, yellow, 250, 255, cv.CV_THRESH_BINARY)
Sub(yellow, walls, yellow)
cv.Erode(yellow, yellow, ex, 1)
Set2D(yellow, 4, 4, 255)
# blue
cv.Add(walls, hue, blue)
cv.Threshold(blue, blue, 40, 255, cv.CV_THRESH_BINARY_INV)
cv.Erode(blue, blue, ex2, 2)
Set2D(blue, 4, 4, 255)
cv.Dilate(blue, blue, tHack, 2)
if pitch == 0:
ballcompact = 2.0
walls = cv.CreateImage(size, 8, 1)
cv.Threshold(val, walls, 50, 255, cv.CV_THRESH_BINARY_INV)
Set2D(walls, 4, 4, 255)
# BALL
#cv.Add(sat,val,ball)
#ShowImage("rawB",ball)
cv.Threshold(hue, ball, 110, 255, cv.CV_THRESH_BINARY)
cv.Erode(ball, ball, ex2, 1)
cv.Dilate(ball, ball, ex, 1)
# YELLOW
cv.Threshold(val, yellow, 240, 255, cv.CV_THRESH_BINARY)
# cv.Threshold(hue,yellow,80,255,cv.CV_THRESH_TOZERO)
# cv.Threshold(yellow,yellow,105,255,cv.CV_THRESH_TOZERO_INV)
# cv.Threshold(yellow,yellow,50,255,cv.CV_THRESH_BINARY)
cv.Erode(yellow, yellow, ex, 1)
cv.Dilate(yellow, yellow, tHack, 1)
# BLUE
CvtColor(dst, hsv, CV_BGR2HSV)
cv.Split(hsv, hue, sat, val, None)
cv.Threshold(hue, blue, 80, 255, cv.CV_THRESH_BINARY)
cv.Threshold(val, val, 80, 255, cv.CV_THRESH_BINARY_INV)
# Removes the walls
Sub(blue, val, blue)
Sub(yellow, val, yellow)
Sub(ball, val, ball)
cv.Erode(blue, blue, ex, 1)
Set2D(ball, 4, 4, 255)
Set2D(yellow, 4, 4, 255)
Set2D(blue, 4, 4, 255)
if (debug):
ShowImage("blue", blue)
ShowImage("yellow", yellow)
ShowImage("ball", ball)
#find ball
#seq = None
seq = cv.FindContours(ball, storage, cv.CV_RETR_LIST, cv.CV_LINK_RUNS)
if seq != None:
count = 0
#print seq
while seq != None:
compact = 0
count = count + 1
if (count > ballcount):
break
#removed and pitch==0 no idea why it was there
if (cv.ContourArea(seq) != 0):
compact = ArcLength(seq) * ArcLength(seq) / (
4 * cv.ContourArea(seq) * math.pi)
if compact >= ballcompact:
print ">> compact: ", compact, ballcompact
seq = seq.h_next()
continue
area = cv.ContourArea(seq)
if (area == 0 or area > ballmaxarea
or area < ballminiarea): # or compact > ballcompact):
print ">> area: ", area, ballmaxarea, ballminiarea
seq = seq.h_next()
continue
else:
ballx = 0
bally = 0
for p in seq:
ballx = ballx + p[0]
bally = bally + p[1]
ballx = int(float(ballx) / len(seq))
bally = int(float(bally) / len(seq))
# print "compact=%f,area=%f" %(compact,area)
cv.Circle(dst, (ballx, bally), 4, cv.CV_RGB(255, 255, 255),
2, 8, 0)
cv.Circle(dst2, (ballx, bally), 4,
cv.CV_RGB(255, 255, 255), 2, 8, 0)
break
if (count > 15 or seq == None):
ballx = -1
bally = -1
print "# error: ball not found "
#find blue
seq = None
seq = cv.FindContours(blue, storage, cv.CV_RETR_LIST, cv.CV_LINK_RUNS)
if seq != None:
count = 0
while seq != None:
count = count + 1
if (count > bluecount):
break
if (cv.ContourArea(seq) < blueminiarea
or cv.ContourArea(seq) > bluemaxarea):
seq = seq.h_next()
continue
else:
hull = None
convex = None
#
hull = cv.ConvexHull2(seq, storage)
convex = cv.ConvexityDefects(seq, hull, storage)
if (len(convex) > 1):
convex = sorted(convex,
key=lambda (k1, k2, k3, k4): k4
) #sort by depth of the convex defect
if (convex[len(convex) - 1][3] < blueminidepth
or convex[len(convex) - 2][3] < blueminidepth
or convex[len(convex) - 1][3] > bluemaxdepth
or convex[len(convex) - 2][3] > bluemaxdepth):
cv.Line(dst, convex[len(convex) - 1][0],
convex[len(convex) - 1][2],
cv.CV_RGB(0, 0, 255), 2, 8, 0)
cv.Line(dst, convex[len(convex) - 1][2],
convex[len(convex) - 1][1],
cv.CV_RGB(0, 255, 255), 2, 8, 0)
cv.Line(dst, convex[len(convex) - 2][0],
convex[len(convex) - 2][2],
cv.CV_RGB(0, 0, 255), 2, 8, 0)
cv.Line(dst, convex[len(convex) - 2][2],
convex[len(convex) - 2][1],
cv.CV_RGB(0, 255, 255), 2, 8, 0)
seq = seq.h_next()
continue
else:
#find the T
blue_start1 = convex[len(convex) - 1][0]
blue_end1 = convex[len(convex) - 1][1]
blue_depth1 = convex[len(convex) - 1][2]
#draw the side line of T
cv.Line(dst, blue_start1, blue_depth1,
cv.CV_RGB(0, 0, 255), 2, 8, 0)
cv.Line(dst, blue_depth1, blue_end1,
cv.CV_RGB(0, 255, 255), 2, 8, 0)
cv.Line(dst2, blue_start1, blue_depth1,
cv.CV_RGB(0, 0, 255), 2, 8, 0)
cv.Line(dst2, blue_depth1, blue_end1,
cv.CV_RGB(0, 255, 255), 2, 8, 0)
blue_start2 = convex[len(convex) - 2][0]
blue_end2 = convex[len(convex) - 2][1]
blue_depth2 = convex[len(convex) - 2][2]
cv.Line(dst, blue_start2, blue_depth2,
cv.CV_RGB(0, 0, 255), 2, 8, 0)
cv.Line(dst, blue_depth2, blue_end2,
cv.CV_RGB(0, 255, 255), 2, 8, 0)
cv.Line(dst2, blue_start2, blue_depth2,
cv.CV_RGB(0, 0, 255), 2, 8, 0)
cv.Line(dst2, blue_depth2, blue_end2,
cv.CV_RGB(0, 255, 255), 2, 8, 0)
blue_from = ((blue_depth1[0] + blue_depth2[0]) / 2,
(blue_depth1[1] + blue_depth2[1]) / 2
) #calculate the center of robot
#calculate the end of direction vector, the two end point of the smaller distans
if math.hypot(blue_start1[0] - blue_end2[0],
blue_start1[1] -
blue_end2[1]) > math.hypot(
blue_end1[0] - blue_start2[0],
blue_end1[1] - blue_start2[1]):
blue_to = ((blue_end1[0] + blue_start2[0]) / 2,
(blue_end1[1] + blue_start2[1]) / 2)
else:
blue_to = ((blue_start1[0] + blue_end2[0]) / 2,
(blue_start1[1] + blue_end2[1]) / 2)
cv.Line(dst, blue_from, blue_to,
cv.CV_RGB(255, 0, 255), 2, 8, 0)
cv.Circle(dst, blue_from, 1, cv.CV_RGB(255, 0, 0),
2, 8, 0)
cv.Circle(dst, blue_to, 1, cv.CV_RGB(0, 0, 0), 2,
8, 0)
cv.Line(dst2, blue_from, blue_to,
cv.CV_RGB(255, 0, 255), 2, 8, 0)
cv.Circle(dst2, blue_from, 1, cv.CV_RGB(255, 0, 0),
2, 8, 0)
cv.Circle(dst2, blue_to, 1,
cv.CV_RGB(255, 255, 255), 2, 8, 0)
break
else:
seq = seq.h_next()
continue
if (count > bluecount or seq == None):
blue_from = (0, 0)
blue_to = (0, 0)
print "# error: blue not found "
#find yellow
seq = None
seq = cv.FindContours(yellow, storage, cv.CV_RETR_LIST,
cv.CV_LINK_RUNS)
if seq != None:
count = 0
while seq != None:
count = count + 1
if (count > yellowcount):
break
area = cv.ContourArea(seq)
if (area < yellowminiarea or area > yellowmaxarea):
seq = seq.h_next()
continue
else:
hull = None
convex = None
#
hull = cv.ConvexHull2(seq, storage)
convex = cv.ConvexityDefects(seq, hull, storage)
if (len(convex) > 1):
convex = sorted(convex,
key=lambda (k1, k2, k3, k4): k4
) #sort by depth of the convex defect
if (convex[len(convex) - 1][3] < yellowminidepth
or convex[len(convex) - 2][3] < yellowminidepth
or convex[len(convex) - 1][3] > yellowmaxdepth
or
convex[len(convex) - 2][3] > yellowmaxdepth):
seq = seq.h_next()
continue
else:
#find the T
yellow_start1 = convex[len(convex) - 1][0]
yellow_end1 = convex[len(convex) - 1][1]
yellow_depth1 = convex[len(convex) - 1][2]
#draw the side line of T
cv.Line(dst, yellow_start1, yellow_depth1,
cv.CV_RGB(0, 0, 255), 2, 8, 0)
cv.Line(dst, yellow_depth1, yellow_end1,
cv.CV_RGB(0, 255, 255), 2, 8, 0)
cv.Line(dst2, yellow_start1, yellow_depth1,
cv.CV_RGB(0, 0, 255), 2, 8, 0)
cv.Line(dst2, yellow_depth1, yellow_end1,
cv.CV_RGB(0, 255, 255), 2, 8, 0)
yellow_start2 = convex[len(convex) - 2][0]
yellow_end2 = convex[len(convex) - 2][1]
yellow_depth2 = convex[len(convex) - 2][2]
cv.Line(dst, yellow_start2, yellow_depth2,
cv.CV_RGB(0, 0, 255), 2, 8, 0)
cv.Line(dst, yellow_depth2, yellow_end2,
cv.CV_RGB(0, 255, 255), 2, 8, 0)
cv.Line(dst2, yellow_start2, yellow_depth2,
cv.CV_RGB(0, 0, 255), 2, 8, 0)
cv.Line(dst2, yellow_depth2, yellow_end2,
cv.CV_RGB(0, 255, 255), 2, 8, 0)
yellow_from = (
(yellow_depth1[0] + yellow_depth2[0]) / 2,
(yellow_depth1[1] + yellow_depth2[1]) / 2
) #calculate the center of robot
#calculate the end of direction vector, the two end point of the smaller distans
if math.hypot(
yellow_start1[0] - yellow_end2[0],
yellow_start1[1] -
yellow_end2[1]) > math.hypot(
yellow_end1[0] - yellow_start2[0],
yellow_end1[1] - yellow_start2[1]):
yellow_to = (
(yellow_end1[0] + yellow_start2[0]) / 2,
(yellow_end1[1] + yellow_start2[1]) / 2)
else:
yellow_to = (
(yellow_start1[0] + yellow_end2[0]) / 2,
(yellow_start1[1] + yellow_end2[1]) / 2)
# print cv.ContourArea(seq)
cv.Line(dst, yellow_from, yellow_to,
cv.CV_RGB(255, 0, 255), 2, 8, 0)
cv.Circle(dst, yellow_from, 1,
cv.CV_RGB(255, 0, 0), 2, 8, 0)
cv.Circle(dst, yellow_to, 1, cv.CV_RGB(0, 0, 0), 2,
8, 0)
cv.Line(dst2, yellow_from, yellow_to,
cv.CV_RGB(255, 0, 255), 2, 8, 0)
cv.Circle(dst2, yellow_from, 1,
cv.CV_RGB(255, 0, 0), 2, 8, 0)
cv.Circle(dst2, yellow_to, 1,
cv.CV_RGB(255, 255, 255), 2, 8, 0)
break
else:
seq = seq.h_next()
continue
if (count > yellowcount or seq == None):
yellow_from = (0, 0)
yellow_to = (0, 0)
print "# error: yellow not found"
ballpos = (ballx, bally)
ShowImage("camera", dst)
if (found_goals == False):
if (us == "yellow"):
goals = find_goals(size, yellow_from)
stewies_goal = goals[0]
loiss_goal = goals[1]
found_goals = True
elif (us == "blue"):
goals = find_goals(size, blue_from)
stewies_goal = goals[0]
loiss_goal = goals[1]
found_goals = True
#if (ballx >= 0):
output(ballpos, blue_from, blue_to, yellow_from, yellow_to,
stewies_goal, loiss_goal)
time_passed = time.time() - aa
countf += 1
if (time_passed >= 1):
print "frame per second: " + str(countf),
countf = 0
aa = time.time()
cv.WaitKey(2)
maxProbInt = probIntensity
cv.ShowImage("skinProb",
skinProbImg) #Original skin probability image
#threshold
cv.InRangeS(skinProbImg, 100, 255, skinProbImg)
cv.ShowImage(
"skinProbThresholded1", skinProbImg
) #Original skin probability image after thresholding
#smooth
cv.Smooth(skinProbImg, skinProbImg, cv.CV_BLUR_NO_SCALE)
cv.ShowImage(
"skinProbSmoothed", skinProbImg
) #Original skin probability image after thresholding
#erode
kernelEr = cv.CreateStructuringElementEx(
4, 4, 0, 0, cv.CV_SHAPE_RECT)
cv.Erode(skinProbImg, skinProbImg, kernelEr, 1)
cv.ShowImage(
"skinProbEroded", skinProbImg
) #Original skin probability image after thresholding
#dilate
kernelDi = cv.CreateStructuringElementEx(
14, 14, 0, 0, cv.CV_SHAPE_ELLIPSE)
cv.Dilate(skinProbImg, skinProbImg, kernelDi, 1)
cv.ShowImage(
"skinProbDilated", skinProbImg
) #Original skin probability image after thresholding
#smooth
cv.Smooth(skinProbImg, skinProbImg, cv.CV_BLUR_NO_SCALE)
cv.ShowImage("skinProbSmoothed", skinProbImg) #Original image
def process_frame(self, frame):
self.debug_frame = cv.CreateImage(cv.GetSize(frame), 8, 3)
og_frame = cv.CreateImage(cv.GetSize(frame), 8, 3)
cv.Copy(frame, self.debug_frame)
cv.Copy(self.debug_frame, og_frame)
cv.Smooth(frame, frame, cv.CV_MEDIAN, 7, 7)
# Set binary image to have saturation channel
hsv = cv.CreateImage(cv.GetSize(frame), 8, 3)
binary = cv.CreateImage(cv.GetSize(frame), 8, 1)
cv.CvtColor(frame, hsv, cv.CV_BGR2HSV)
cv.SetImageCOI(hsv, 1) #3 before competition #2 at competition
cv.Copy(hsv, binary)
cv.SetImageCOI(hsv, 0)
cv.AdaptiveThreshold(binary, binary,
255,
cv.CV_ADAPTIVE_THRESH_MEAN_C,
cv.CV_THRESH_BINARY_INV,
self.adaptive_thresh_blocksize,
self.adaptive_thresh,
)
# Morphology
kernel = cv.CreateStructuringElementEx(5, 5, 3, 3, cv.CV_SHAPE_ELLIPSE)
cv.Erode(binary, binary, kernel, 1)
cv.Dilate(binary, binary, kernel, 1)
# Get Edges
#cv.Canny(binary, binary, 30, 40)
cv.CvtColor(binary, self.debug_frame, cv.CV_GRAY2RGB)
# Hough Transform
line_storage = cv.CreateMemStorage()
raw_lines = cv.HoughLines2(binary, line_storage, cv.CV_HOUGH_PROBABILISTIC,
rho=1,
theta=math.pi / 180,
threshold=self.hough_threshold,
param1=self.min_length,
param2=self.max_gap
)
lines = []
for line in raw_lines:
lines.append(line)
#Grouping lines depending on endpoint similarities
for line1 in lines[:]:
for line2 in lines[:]:
if line1 in lines and line2 in lines and line1 != line2:
if math.fabs(line1[0][0] - line2[0][0]) < self.max_corner_range and \
math.fabs(line1[0][1] - line2[0][1]) < self.max_corner_range and \
math.fabs(line1[1][0] - line2[1][0]) < self.max_corner_range and \
math.fabs(line1[1][1] - line2[1][1]) < self.max_corner_range:
if line_distance(line1[0], line1[1]) > line_distance(line2[0], line2[1]):
lines.remove(line2)
else:
lines.remove(line1)
elif math.fabs(line1[0][0] - line2[1][0]) < self.max_corner_range and \
math.fabs(line1[0][1] - line2[1][1]) < self.max_corner_range and \
math.fabs(line1[1][0] - line2[0][0]) < self.max_corner_range and \
math.fabs(line1[1][1] - line2[0][1]) < self.max_corner_range:
if line_distance(line1[0], line1[1]) > line_distance(line2[0], line2[1]):
lines.remove(line2)
else:
lines.remove(line1)
self.hough_corners = []
for line in lines:
self.hough_corners.append(line[0])
self.hough_corners.append(line[1])
for corner1 in self.hough_corners[:]:
for corner2 in self.hough_corners[:]:
if corner1 is not corner2 and corner1 in self.hough_corners and corner2 in self.hough_corners:
if math.fabs(corner1[0] - corner2[0]) < self.max_corner_range4 and \
math.fabs(corner1[1] - corner2[1]) < self.max_corner_range4:
corner1 = [(corner1[0] + corner2[0]) / 2, (corner1[1] + corner2[1]) / 2]
self.hough_corners.remove(corner2)
for line1 in lines:
#cv.Line(self.debug_frame,line1[0],line1[1], (0,0,255), 10, cv.CV_AA, 0)
for line2 in lines:
if line1 is not line2:
self.find_corners(line1, line2)
for corner1 in self.corners:
for corner2 in self.corners:
if math.fabs(corner1[1][0] - corner2[1][0]) < self.max_corner_range2 and \
math.fabs(corner1[1][1] - corner2[1][1]) < self.max_corner_range2 and \
math.fabs(corner1[2][0] - corner2[2][0]) < self.max_corner_range2 and \
math.fabs(corner1[2][1] - corner2[2][1]) < self.max_corner_range2 and \
math.fabs(corner1[0][0] - corner2[0][0]) > self.max_corner_range2 and \
math.fabs(corner1[0][1] - corner2[0][1]) > self.max_corner_range2:
pt1 = (int(corner1[0][0]), int(corner1[0][1]))
pt4 = (int(corner2[0][0]), int(corner2[0][1]))
pt3 = (int(corner1[1][0]), int(corner1[1][1]))
pt2 = (int(corner1[2][0]), int(corner1[2][1]))
#line_color = (0,255,0)s
#cv.Line(self.debug_frame,pt1,pt2, line_color, 10, cv.CV_AA, 0)
#cv.Line(self.debug_frame,pt1,pt3, line_color, 10, cv.CV_AA, 0)
#cv.Line(self.debug_frame,pt4,pt2, line_color, 10, cv.CV_AA, 0)
#cv.Line(self.debug_frame,pt4,pt3, line_color, 10, cv.CV_AA, 0)
new_bin = Bin(pt1, pt2, pt3, pt4)
new_bin.id = self.bin_id
self.bin_id += 1
if math.fabs(line_distance(pt1, pt2) - line_distance(pt3, pt4)) < self.parallel_sides_length_thresh and \
math.fabs(line_distance(pt1, pt3) - line_distance(pt2, pt4)) < self.parallel_sides_length_thresh:
self.Bins.append(new_bin)
print "new_bin"
elif (math.fabs(corner1[1][0] - corner2[2][0]) < self.max_corner_range2 and
math.fabs(corner1[1][1] - corner2[2][1]) < self.max_corner_range2 and
math.fabs(corner1[2][0] - corner2[1][0]) < self.max_corner_range2 and
math.fabs(corner1[2][1] - corner2[1][1]) < self.max_corner_range2 and
math.fabs(corner1[0][0] - corner2[0][0]) > self.max_corner_range2 and
math.fabs(corner1[0][1] - corner2[0][1]) > self.max_corner_range2):
continue
self.corners = []
self.final_corners = self.sort_corners() #Results are not used. Experimental corners which have been seen twice, should be only the corners we want, but there were problems
self.sort_bins()
self.update_bins()
self.group_bins()
self.draw_bins()
for corner in self.hough_corners:
line_color = [255, 0, 0]
cv.Circle(self.debug_frame, corner, 15, (255, 0, 0), 2, 8, 0)
for line in lines:
line_color = [255, 0, 0]
cv.Line(self.debug_frame, line[0], line[1], line_color, 5, cv.CV_AA, 0)
#cv.Circle(self.debug_frame, line[0], 15, (255,0,0), 2,8,0)
#cv.Circle(self.debug_frame, line[1], 15, (255,0,0), 2,8,0)
#Output bins
self.output.bins = self.Bins
anglesum = 0
for bins in self.output.bins:
bins.theta = (bins.center[0] - frame.width / 2) * 37 / (frame.width / 2)
bins.phi = -1 * (bins.center[1] - frame.height / 2) * 36 / (frame.height / 2)
anglesum += bins.angle
# bins.orientation = bins.angle
if len(self.output.bins) > 0:
self.output.orientation = anglesum / len(self.output.bins)
else:
self.output.orientation = None
self.return_output()
svr.debug("Bins", self.debug_frame)
svr.debug("Original", og_frame)
#BEGIN SHAPE PROCESSING
#constants
img_width = 128
img_height = 256
number_x = 23
number_y = 111
number_w = 82
number_h = 90
bin_thresh_blocksize = 11
bin_thresh = 1.9
red_significance_threshold = 0.4
#load templates - run once, accessible to number processor
number_templates = [
(10, cv.LoadImage("number_templates/10.png")),
(16, cv.LoadImage("number_templates/16.png")),
(37, cv.LoadImage("number_templates/37.png")),
(98, cv.LoadImage("number_templates/98.png")),
]
#Begin Bin Contents Processing
for bin in self.Bins:
#Take the bin's corners, and get an image containing an img_width x img_height rectangle of it
transf = cv.CreateMat(3, 3, cv.CV_32FC1)
cv.GetPerspectiveTransform(
[bin.corner1, bin.corner2, bin.corner3, bin.corner4],
[(0, 0), (0, img_height), (img_width, 0), (img_width, img_height)],
transf
)
bin_image = cv.CreateImage([img_width, img_height], 8, 3)
cv.WarpPerspective(frame, bin_image, transf)
#AdaptaveThreshold to get black and white image highlighting the number (still works better than my yellow-vs-red threshold attempt
hsv = cv.CreateImage(cv.GetSize(bin_image), 8, 3)
bin_thresh_image = cv.CreateImage(cv.GetSize(bin_image), 8, 1)
cv.CvtColor(bin_image, hsv, cv.CV_BGR2HSV)
cv.SetImageCOI(hsv, 3)
cv.Copy(hsv, bin_thresh_image)
cv.SetImageCOI(hsv, 0)
cv.AdaptiveThreshold(bin_thresh_image, bin_thresh_image,
255,
cv.CV_ADAPTIVE_THRESH_MEAN_C,
cv.CV_THRESH_BINARY_INV,
bin_thresh_blocksize,
bin_thresh,
)
kernel = cv.CreateStructuringElementEx(5, 5, 3, 3, cv.CV_SHAPE_ELLIPSE)
cv.Erode(bin_thresh_image, bin_thresh_image, kernel, 1)
cv.Dilate(bin_thresh_image, bin_thresh_image, kernel, 1)
#Here, we loop through all four different templates, and figure out which one we think is most likely.
#The comparison function counts corresponding pixels that are non-zero in each image, and then corresponding pixels that are different in each image. The ratio of diff_count/both_count is our "unconfidence" ratio. The lower it is, the more confident we are.
#There are two nearly identical pieces of code within this loop. One checks the bin right-side-up, and the other one checks it flipped 180.
last_thought_number = -1
last_unconfidence_ratio = number_w * number_h + 2
for i in range(0, len(number_templates)):
both_count = 0
diff_count = 0
this_number_image = number_templates[i][1]
for y in range(0, number_h):
for x in range(0, number_w):
if (bin_thresh_image[y + number_y, x + number_x] != 0) and (this_number_image[y, x][0] != 0):
both_count += 1
elif (bin_thresh_image[y + number_y, x + number_x] != 0) or (this_number_image[y, x][0] != 0):
diff_count += 1
if both_count == 0:
unconfidence_ratio = number_w * number_h + 1 # max unconfidence
else:
unconfidence_ratio = 1.0 * diff_count / both_count
if unconfidence_ratio < last_unconfidence_ratio:
last_thought_number = number_templates[i][0]
last_unconfidence_ratio = unconfidence_ratio
both_count = 0
diff_count = 0
for y in range(0, number_h):
for x in range(0, number_w):
if (bin_thresh_image[img_height - number_y - 1 - y, img_width - number_x - 1 - x] != 0) and (
this_number_image[y, x][0] != 0):
both_count += 1
elif (bin_thresh_image[img_height - number_y - 1 - y, img_width - number_x - 1 - x] != 0) or (
this_number_image[y, x][0] != 0):
diff_count += 1
if both_count == 0:
unconfidence_ratio = number_w * number_h + 1 # max unconfidence
else:
unconfidence_ratio = 1.0 * diff_count / both_count
if unconfidence_ratio < last_unconfidence_ratio:
last_thought_number = number_templates[i][0]
last_unconfidence_ratio = unconfidence_ratio
print str(last_thought_number) + " | " + str(last_unconfidence_ratio)
try: #check if it's defined
bin.number_unconfidence_ratio
except:
bin.number_unconfidence_ratio = last_unconfidence_ratio
bin.number = last_thought_number
print "Set Speed Limit Number"
else:
if last_unconfidence_ratio < bin.number_unconfidence_ratio:
bin.number_unconfidence_ratio = last_unconfidence_ratio
if bin.number == last_thought_number:
print "More Confident on Same Number: Updated"
else:
print "More Confident on Different Number: Updated"
bin.icon = last_thought_number
def process_frame(self, frame):
self.debug_frame = cv.CreateImage(cv.GetSize(frame), 8, 3)
self.test_frame = cv.CreateImage(cv.GetSize(frame), 8, 3)
cv.Copy(frame, self.debug_frame)
cv.Copy(frame, self.test_frame)
cv.Smooth(frame, frame, cv.CV_MEDIAN, 7, 7)
# Set binary image to have saturation channel
hsv = cv.CreateImage(cv.GetSize(frame), 8, 3)
binary = cv.CreateImage(cv.GetSize(frame), 8, 1)
cv.CvtColor(frame, hsv, cv.CV_BGR2HSV)
cv.SetImageCOI(hsv, 1)
cv.Copy(hsv, binary)
cv.SetImageCOI(hsv, 0)
# Adaptive Threshold
cv.AdaptiveThreshold(binary, binary,
255,
cv.CV_ADAPTIVE_THRESH_MEAN_C,
cv.CV_THRESH_BINARY_INV,
self.adaptive_thresh_blocksize,
self.adaptive_thresh,
)
# Morphology
kernel = cv.CreateStructuringElementEx(5, 5, 3, 3, cv.CV_SHAPE_ELLIPSE)
cv.Erode(binary, binary, kernel, 1)
cv.Dilate(binary, binary, kernel, 1)
cv.CvtColor(binary, self.debug_frame, cv.CV_GRAY2RGB)
# Find Corners
temp1 = cv.CreateImage(cv.GetSize(frame), 8, 1)
temp2 = cv.CreateImage(cv.GetSize(frame), 8, 1)
self.corners = cv.GoodFeaturesToTrack(binary, temp1, temp2, self.max_corners, self.quality_level, self.min_distance, None, self.good_features_blocksize, 0, 0.4)
# Display Corners
for corner in self.corners:
corner_color = (0, 0, 255)
text_color = (0, 255, 0)
font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, .6, .6, 0, 1, 1)
cv.Circle(self.debug_frame, (int(corner[0]), int(corner[1])), 15, corner_color, 2, 8, 0)
# Find Candidates
for confirmed in self.confirmed:
confirmed.corner1_repl_check = 0
confirmed.corner2_repl_check = 0
confirmed.corner3_repl_check = 0
confirmed.corner4_repl_check = 0
for corner in self.corners:
if math.fabs(confirmed.corner1[0] - corner[0]) < self.MaxCornerTrans and \
math.fabs(confirmed.corner1[1] - corner[1]) < self.MaxCornerTrans:
confirmed.corner1_repl_check = 1
confirmed.corner1_repl = corner
elif math.fabs(confirmed.corner2[0] - corner[0]) < self.MaxCornerTrans and \
math.fabs(confirmed.corner2[1] - corner[1]) < self.MaxCornerTrans:
confirmed.corner2_repl_check = 1
confirmed.corner2_repl = corner
elif math.fabs(confirmed.corner3[0] - corner[0]) < self.MaxCornerTrans and \
math.fabs(confirmed.corner3[1] - corner[1]) < self.MaxCornerTrans:
confirmed.corner3_repl_check = 1
confirmed.corner3_repl = corner
elif math.fabs(confirmed.corner4[0] - corner[0]) < self.MaxCornerTrans and \
math.fabs(confirmed.corner4[1] - corner[1]) < self.MaxCornerTrans:
confirmed.corner4_repl_check = 1
confirmed.corner4_repl = corner
if confirmed.corner4_repl_check == 1 and confirmed.corner3_repl_check == 1 and confirmed.corner2_repl_check == 1 and confirmed.corner1_repl_check == 1:
confirmed.corner1 = confirmed.corner1_repl
confirmed.corner2 = confirmed.corner2_repl
confirmed.corner3 = confirmed.corner3_repl
confirmed.corner4 = confirmed.corner4_repl
confirmed.midx = rect_midpointx(confirmed.corner1, confirmed.corner2, confirmed.corner3, confirmed.corner4)
confirmed.midy = rect_midpointy(confirmed.corner1, confirmed.corner2, confirmed.corner3, confirmed.corner4)
if confirmed.last_seen < self.last_seen_max:
confirmed.last_seen += 5
for corner1 in self.corners:
for corner2 in self.corners:
for corner3 in self.corners:
for corner4 in self.corners:
# Checks that corners are not the same and are in the proper orientation
if corner4[0] != corner3[0] and corner4[0] != corner2[0] and corner4[0] != corner1[0] and \
corner3[0] != corner2[0] and corner3[0] != corner1[0] and corner2[0] != corner1[0] and \
corner4[1] != corner3[1] and corner4[1] != corner2[1] and corner4[1] != corner1[1] and \
corner3[1] != corner2[1] and corner3[1] != corner1[1] and corner2[1] != corner1[1] and \
corner2[0] >= corner3[0] and corner1[1] >= corner4[1] and corner2[0] >= corner1[0]:
# Checks that the side ratios are correct
if math.fabs(line_distance(corner1, corner3) - line_distance(corner2, corner4)) < self.size_threshold and \
math.fabs(line_distance(corner1, corner2) - line_distance(corner3, corner4)) < self.size_threshold and \
math.fabs(line_distance(corner1, corner3) / line_distance(corner1, corner2)) < self.ratio_threshold or \
math.fabs(line_distance(corner1, corner2) / line_distance(corner1, corner3)) < self.ratio_threshold:
# Checks that angles are roughly 90 degrees
angle_cnr_2 = math.fabs(angle_between_lines(line_slope(corner1, corner2), line_slope(corner2, corner4)))
if self.angle_min < angle_cnr_2 < self.angle_max:
angle_cnr_3 = math.fabs(angle_between_lines(line_slope(corner1, corner3), line_slope(corner3, corner4)))
if self.angle_min2 < angle_cnr_3 < self.angle_max2:
new_bin = Bin(corner1, corner2, corner3, corner4)
self.match_bins(new_bin)
self.sort_bins()
'''
#START SHAPE PROCESSING
#TODO load these ONCE somewhere
samples = np.loadtxt('generalsamples.data',np.float32)
responses = np.loadtxt('generalresponses.data',np.float32)
responses = responses.reshape((responses.size,1))
model = cv2.KNearest()
model.train(samples,responses)
for bin in self.confirmed:
try:
bin.speedlimit
except:
continue
transf = cv.CreateMat(3, 3, cv.CV_32FC1)
corner_orders = [
[bin.corner1, bin.corner2, bin.corner3, bin.corner4], #0 degrees
[bin.corner4, bin.corner3, bin.corner2, bin.corner1], #180 degrees
[bin.corner2, bin.corner4, bin.corner1, bin.corner3], #90 degrees
[bin.corner3, bin.corner1, bin.corner4, bin.corner2], #270 degrees
[bin.corner3, bin.corner4, bin.corner1, bin.corner2], #0 degrees and flipped X
[bin.corner2, bin.corner1, bin.corner4, bin.corner3], #180 degrees and flipped X
[bin.corner1, bin.corner3, bin.corner2, bin.corner4], #90 degrees and flipped X
[bin.corner4, bin.corner2, bin.corner3, bin.corner1]] #270 degrees andf flipped X
for i in range(0, 8):
cv.GetPerspectiveTransform(
corner_orders[i],
[(0, 0), (0, 256), (128, 0), (128, 256)],
transf
)
shape = cv.CreateImage([128, 256], 8, 3)
cv.WarpPerspective(frame, shape, transf)
shape_thresh = np.zeros((256-104,128,1), np.uint8)
j = 104
while j<256:
i = 0
while i<128:
pixel = cv.Get2D(shape, j, i)
if int(pixel[2]) > (int(pixel[1]) + int(pixel[0])) * 0.7:
shape_thresh[j-104,i] = 255
else:
shape_thresh[j-104,i] = 0
i = i+1
j = j+1
cv2.imshow("Bin " + str(i), shape_thresh)
contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_NONE)
for cnt in contours:
if cv2.contourArea(cnt)>50:
[x,y,w,h] = cv2.boundingRect(cnt)
if h>54 and w>36:
roi = thresh[y:y+h,x:x+w]
roismall = cv2.resize(roi,(10,10))
roismall = roismall.reshape((1,100))
roismall = np.float32(roismall)
retval, results, neigh_resp, dists = model.find_nearest(roismall, k = 1)
digit_tuples.append( (x, int((results[0][0]))) )
if len(digit_tuples) == 2:
digit_tuples_sorted = sorted(digit_tuples, key=lambda digit_tuple: digit_tuple[0])
speedlimit = 0
for i in range(0, len(digit_tuples_sorted)):
speedlimit = speedlimit * 10 + digit_tuples_sorted[i][1]
bin.speedlimit = speedlimit
print "Found speed limit: " + str(speedlimit)
break
else:
print "Unable to determine speed limit"
#... TODO more
#END SHAPE PROCESSING
'''
svr.debug("Bins", self.debug_frame)
svr.debug("Bins2", self.test_frame)
# Output bins
self.output.bins = self.confirmed
anglesum = 0
for bins in self.output.bins:
bins.theta = (bins.midx - frame.width / 2) * 37 / (frame.width / 2)
bins.phi = -1 * (bins.midy - frame.height / 2) * 36 / (frame.height / 2)
bins.shape = bins.object
anglesum += bins.angle
# bins.orientation = bins.angle
if len(self.output.bins) > 0:
self.output.orientation = anglesum / len(self.output.bins)
else:
self.output.orientation = None
self.return_output()
def process_frame(self, frame):
(w, h) = cv.GetSize(frame)
#generate hue selection frames
#create locations for the a pair of test frames
frametest = cv.CreateImage(cv.GetSize(frame), 8, 3)
binarytest = cv.CreateImage(cv.GetSize(frame), 8, 1)
#use the red channel for the binary frame (just for debugging purposes)
cv.Copy(frame, frametest)
cv.SetImageCOI(frametest, 3)
cv.Copy(frametest, binarytest)
cv.SetImageCOI(frametest, 0) #reset COI
#svr.debug("R?",binarytest)
# Resize image to 320x240
#copy = cv.CreateImage(cv.GetSize(frame), 8, 3)
#cv.Copy(frame, copy)
#cv.SetImageROI(frame, (0, 0, 320, 240))
#cv.Resize(copy, frame, cv.CV_INTER_NN)
found_gate = False
#create a new frame just for comparison purposes
unchanged_frame = cv.CreateImage(cv.GetSize(frame), 8, 3)
cv.Copy(frame, unchanged_frame)
#apply a course noise filter
cv.Smooth(frame, frame, cv.CV_MEDIAN, 7, 7)
# Set binary image to have saturation channel
hsv = cv.CreateImage(cv.GetSize(frame), 8, 3)
binary = cv.CreateImage(cv.GetSize(frame), 8, 1)
cv.CvtColor(frame, hsv, cv.CV_BGR2HSV)
cv.SetImageCOI(hsv, 1)
cv.Copy(hsv, binary)
cv.SetImageCOI(hsv, 0) #reset COI
#shift hue of image such that orange->red are at top of spectrum
'''
binary = libvision.misc.cv_to_cv2(binary)
binary = libvision.misc.shift_hueCV2(binary, self.target_shift)
binary = libvision.misc.cv2_to_cv(binary)
'''
#correct for wraparound on red spectrum
#cv.InRange(binary,a_array,b_array,binarytest) #generate mask
#cv.Add(binary,cv.fromarray(ones*180),binary,mask=binarytest) #use mask to selectively add values
svr.debug("R2?", binary)
svr.debug("R2?", binary)
#run adaptive threshold for edge detection and more noise filtering
cv.AdaptiveThreshold(
binary,
binary,
255,
cv.CV_ADAPTIVE_THRESH_MEAN_C,
cv.CV_THRESH_BINARY_INV,
self.adaptive_thresh_blocksize,
self.adaptive_thresh,
)
# Morphology
kernel = cv.CreateStructuringElementEx(5, 5, 3, 3, cv.CV_SHAPE_ELLIPSE)
cv.Erode(binary, binary, kernel, 1)
cv.Dilate(binary, binary, kernel, 1)
if self.debug:
color_filtered = cv.CloneImage(binary)
# Get Edges
cv.Canny(binary, binary, 30, 40)
# Hough Transform
line_storage = cv.CreateMemStorage()
raw_lines = cv.HoughLines2(binary,
line_storage,
cv.CV_HOUGH_STANDARD,
rho=1,
theta=math.pi / 180,
threshold=self.hough_threshold,
param1=0,
param2=0)
# Get vertical lines
vertical_lines = []
for line in raw_lines:
if line[1] < self.vertical_threshold or \
line[1] > math.pi-self.vertical_threshold:
#absolute value does better grouping currently
vertical_lines.append((abs(line[0]), line[1]))
#print message to user for performance purposes
logging.debug("{} possibilities reduced to {} lines".format(
len(raw_lines), len(vertical_lines)))
# Group vertical lines
vertical_line_groups = [
] # A list of line groups which are each a line list
i = 0
for line in vertical_lines:
group_found = False
for line_group in vertical_line_groups:
i += 1
if line_group_accept_test(line_group, line, self.max_range):
line_group.append(line)
group_found = True
if not group_found:
vertical_line_groups.append([line])
#quick debugging statement
logging.debug("{} internal iterations for {} groups".format(
i, len(vertical_line_groups)))
# Average line groups into lines
vertical_lines = []
for line_group in vertical_line_groups:
rhos = map(lambda line: line[0], line_group)
angles = map(lambda line: line[1], line_group)
line = (sum(rhos) / len(rhos), circular_average(angles, math.pi))
vertical_lines.append(line)
####################################################
#vvvv Horizontal line code isn't used for anything
# Get horizontal lines
horizontal_lines = []
for line in raw_lines:
dist_from_horizontal = (math.pi / 2 + line[1]) % math.pi
if dist_from_horizontal < self.horizontal_threshold or \
dist_from_horizontal > math.pi-self.horizontal_threshold:
horizontal_lines.append((abs(line[0]), line[1]))
# Group horizontal lines
horizontal_line_groups = [
] # A list of line groups which are each a line list
for line in horizontal_lines:
group_found = False
for line_group in horizontal_line_groups:
if line_group_accept_test(line_group, line, self.max_range):
line_group.append(line)
group_found = True
if not group_found:
horizontal_line_groups.append([line])
if len(horizontal_line_groups) is 1:
self.seen_crossbar = True
if self.debug:
rhos = map(lambda line: line[0], horizontal_line_groups[0])
angles = map(lambda line: line[1], horizontal_line_groups[0])
line = (sum(rhos) / len(rhos),
circular_average(angles, math.pi))
horizontal_lines = [line]
else:
self.seen_crossbar = False
horizontal_lines = []
#^^^ Horizontal line code isn't used for anything
###################################################
self.left_pole = None
self.right_pole = None
#print vertical_lines
self.returning = 0
self.found = False
if len(vertical_lines) is 2:
roi = cv.GetImageROI(frame)
width = roi[2]
height = roi[3]
self.left_pole = round(
min(vertical_lines[0][0], vertical_lines[1][0]), 2) - width / 2
self.right_pole = round(
max(vertical_lines[0][0], vertical_lines[1][0]), 2) - width / 2
self.returning = (self.left_pole + self.right_pole) / 2
logging.info("Returning {} as gate center delta.".format(
self.returning))
#initialize first iteration with 2 known poles
if self.last_seen < 0:
self.last_center = None
self.last_seen = 0
#increment a counter if result is good.
if self.last_center is None:
self.last_center = self.returning
self.seen_count = 1
elif math.fabs(self.last_center -
self.returning) < self.center_trans_thresh:
self.seen_count += 1
self.last_seen += 2
else:
self.last_seen -= 1
#if not conviced, forget left/right pole. Else proclaim success.
if self.seen_count < self.seen_count_thresh:
self.left_pole = None
self.right_pole = None
else:
print "FOUND CENTER AND RETURNED IT"
self.found = True
else:
self.returning = 0
if self.last_seen < 0:
self.last_center = None
self.last_seen = 0
self.last_seen -= 1
self.left_pole = None
self.right_pole = None
#TODO: If one pole is seen, is it left or right pole?
if self.debug:
cv.CvtColor(color_filtered, frame, cv.CV_GRAY2RGB)
libvision.misc.draw_lines(frame, vertical_lines)
libvision.misc.draw_lines(frame, horizontal_lines)
if self.found:
cv.Circle(frame, (int(frame.width / 2 + self.returning),
int(frame.height / 2)), 15, (0, 255, 0), 2,
8, 0)
font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 1, 1, 1, 3)
cv.PutText(frame, "Gate Sent to Mission Control", (100, 400),
font, (255, 255, 0))
#print frame.width
#cv.ShowImage("Gate", cv.CloneImage(frame))
svr.debug("Gate", cv.CloneImage(frame))
svr.debug("Unchanged", cv.CloneImage(unchanged_frame))
#populate self.output with infos
self.output.seen_crossbar = self.seen_crossbar
self.output.left_pole = self.left_pole
self.output.right_pole = self.right_pole
self.return_output()
