def getDirection(img, color):
width = img.width
height = img.height
if color == 'blue':
filtered = blueOnly(img)
elif color == 'red':
filtered = redOnly(img)
elif color == 'green':
filtered = greenOnly(img)
db.showImg(filtered)
sumLeft = np.sum(filtered.regionSelect(0, 0, width/2, height).getGrayNumpy())
sumRight = np.sum(filtered.regionSelect(width/2, 0, width, height).getGrayNumpy())
sumMid = np.sum(filtered.regionSelect(width/5*2, 0, width/5*3, height).getGrayNumpy())
sumBoth = sumLeft + sumRight
x = width*height
#print("sumleft: " + `sumLeft` + ". sumMid: " + `sumMid` + ". sumRight: " + `sumRight` + ". sumBoth: " + `sumBoth`)
if sumBoth > 40*x or sumMid > 1*x:
return 'forward: ' + `sumBoth/x`
elif sumLeft > 1*x:
return 'left: ' + `sumLeft/x`
elif sumRight > 1*x:
return 'right: ' + `sumRight/x`
else:
return 'nothing'
def getDirection(img, color):
#applies 2 different blurs to get rid of noise and "normalizes" the colors a bit
img = img.copy()
blurred = img.gaussianBlur((5, 5), 3, 3)
blurred = blurred.medianFilter(11)
width = img.width
height = img.height
if color == 'blue':
filtered = blueOnly(blurred)
elif color == 'red':
filtered = redOnly(blurred)
elif color == 'green':
filtered = greenOnly(blurred)
db.showImg(filtered)
#finds all the blobs and check whether they are balloon-y by checking the hu-moments with known (cached) blobs
balloons = filterBalloons(filtered)
if balloons:
#[b.drawOutline(layer=filtered.dl(), color=SimpleCV.Color.CRIMSON, width=3) for b in balloons]
filtered = blobsToImage(balloons)
else:
return "idle"
#sum of balloon-pixels in each section
sumLeft = np.sum(filtered.regionSelect(0, 0, width/2, height).getGrayNumpy())
sumRight = np.sum(filtered.regionSelect(width/2, 0, width, height).getGrayNumpy())
sumMid = np.sum(filtered.regionSelect(width/5*2, 0, width/5*3, height).getGrayNumpy())
sumBoth = sumLeft + sumRight
#the x factor is to make the sums independent of resolution
x = width*height
#the image is split up in 3 sections, each having a threshold sum
#"forward" is a bit of an exception, since a balloon right in front of us can "fill" multiple sections
if sumBoth > 40*x or sumMid > 0.7*x:
ret = "forward"#: " + `sumBoth/x`
elif sumLeft > 0.7*x:
ret = "left"#: " + `sumLeft/x`
elif sumRight > 0.7*x:
ret = "right"#: " + `sumRight/x`
else:
ret = "idle"
db.showImg(filtered)
return ret
def findColorOrder(self, img):
if COMPENSATEYELLOW == True:
t1 = 100; t2 = 130; t3 = 35
t4 = 100; t5 = 40; t6 = 100
t7 = 5; t8 = 5; t9 = 180
t4 = db.t(0)
t5 = db.t(1)
t6 = db.t(2)
else:
t1 = 100; t2 = 130; t3 = 35
t4 = 30; t5 = 30; t6 = 60
t7 = 150; t8 = 5; t9 = 200
#basic blurs to remove noise and even out the colors
img = img.copy()
median = img.gaussianBlur(3, 3)
median = median.medianFilter(7)
#RED = (213, 78, 29) if COMPENSATEYELLOW else (132, 168, 44)
GREEN = (213, 78, 29) if COMPENSATEYELLOW else (63, 57, 89)
#BLUE = (213, 78, 29) if COMPENSATEYELLOW else (132, 168, 44)
#apply (hueDistance . treshold . invert), multiplies by values which somehow improve detection..
#todo adaptive thresholding (binarize). perhaps to create a mask for more precise thresholding later on?
red = (median.hueDistance(SimpleCV.Color.RED, minsaturation=t1, minvalue=t2) *2).threshold(t3).invert()
green = (median.hueDistance(SimpleCV.Color.GREEN, minsaturation=t4, minvalue=t5)*1).threshold(t6).invert()
blue = (median.hueDistance(SimpleCV.Color.BLUE, minsaturation=t7, minvalue=t8) ).threshold(t9).invert()
#blue = blue.invert() if COMPENSATEYELLOW == False else blue
#(erode . dilate) to remove "messy" blobs and make the card-blob more square-like
redBlobs = red.erode(10).dilate(15).findBlobs(minsize=1000)
greenBlobs = green.erode(10).dilate(10).findBlobs(minsize=1000)
blueBlobs = blue.erode(10).dilate(10).findBlobs(minsize=1000)
median.addDrawingLayer(red.dl())
if redBlobs and greenBlobs and blueBlobs:
#filter all blobs with a square-y ratio
#s1 = float(db.t(0))/100
#s2 = float(db.t(1))/100
s1 = 1.50
s2 = 0.20
redsquares = (redBlobs).filter([b.isSquare(s1, s2) for b in redBlobs])
greensquares = (greenBlobs).filter([b.isSquare(s1, s2) for b in greenBlobs])
bluesquares = (blueBlobs).filter([b.isSquare(s1, s2) for b in blueBlobs])
if redsquares and greensquares and bluesquares:
#draw the blobs to our initial blurred image, for debug and spider-o-vision!
redsquares[-1].draw(layer=median.dl())
greensquares[-1].draw(layer=median.dl())
bluesquares[-1].draw(layer=median.dl())
greensquares[-1].drawOutline()
db.showImg(median)
#find the 3 blobs, order by height, extract the ordered color strings
colors = [('red', redsquares), ('green', greensquares), ('blue', bluesquares)]
ordersTuple = sorted(colors, key=lambda color: (color[1][-1].y))
orders = [x[0] for x in ordersTuple]
return orders
#display failing blob-image here.
db.showImg(red)
db.showImg(green)
db.showImg(blue)
#didn't find all 3 RGB squares
return None
