def detectEdgesInColorPlanes(self, imageToSplit, colorPlane="red"):
morpher = ImageMorpher()
channels = cv2.split(imageToSplit)
colorEdges = []
for i in range(len(channels)):
blurred = cv2.GaussianBlur(channels[i], (7, 7), 3)
colorEdges.append(morpher.dilateWithSquare(cv2.Canny(blurred, 50, 100), 3))
if (colorPlane == "red"):
print 'anding'
result = cv2.bitwise_and(colorEdges[0], colorEdges[1])
result = morpher.closeWithSquare(result, 2)
#self.showImage(result)
result = cv2.bitwise_and(result, cv2.bitwise_not(colorEdges[2]))
elif (colorPlane == "green"):
result = cv2.bitwise_and(colorEdges[0], colorEdges[2])
result = morpher.closeWithSquare(result, 2)
result = cv2.bitwise_and(result, cv2.bitwise_not(colorEdges[1]))
elif (colorPlane == "blue"):
result = cv2.bitwise_and(colorEdges[1], colorEdges[2])
result = morpher.closeWithSquare(result, 2)
result = cv2.bitwise_and(result, cv2.bitwise_not(colorEdges[0]))
#self.showImage(result)
result = morpher.openWithSquare(result, 2)
#self.showImage(result)
return result
def cutOutNonRed(self, imageToCut):
redHighlighted = self.highlightDarkRed(imageToCut, 160, -1, 180, 20, False)
#self.showImage(redHighlighted)
morpher = ImageMorpher()
#self.showImage(redHighlighted)
mask1 = morpher.dilateWithSquare(redHighlighted, 10)
#self.showImage(mask1)
mask1 = morpher.closeWithSquare(mask1, 50)
#self.showImage(mask1)
maskedImg = cv2.bitwise_and(imageToCut, imageToCut, mask=mask1)
#self.showImage(maskedImg)
gray = self.convertColorToGrayscale(imageToCut)
blurred = cv2.GaussianBlur(gray, (5, 5), 3)
edges = cv2.Canny(blurred, 50, 100)
#self.showImage(edges)
mask2 = morpher.dilateWithSquare(edges, 10)
#self.showImage(mask2)
mask2 = morpher.closeWithSquare(mask2, 50)
#self.showImage(mask2)
secondMaskedImg = cv2.bitwise_and(maskedImg, maskedImg, mask=mask2)
#self.showImage(secondMaskedImg)
totalMask = cv2.bitwise_and(mask1, mask2)
#self.showImage(totalMask)
totalMask = morpher.operateWithVerticalLine(totalMask, morpher.DILATION, 15)
totalMask = morpher.operateWithHorizontalLine(totalMask, morpher.DILATION, 15)
#self.showImage(totalMask)
totalMask = morpher.openWithSquare(totalMask, 20)
#self.showImage(totalMask)
totalMask = morpher.closeWithSquare(totalMask, 40)
#self.showImage(totalMask)
result = cv2.bitwise_and(imageToCut, imageToCut, mask=totalMask)
#self.showImage(result)
return result
def combined(inputfile, debug=False):
start_file = inputfile
img = cv2.imread(start_file)
full_img = cv2.imread(start_file)
original_img = deepcopy(img)
fullheight, fullwidth, fullchannels = full_img.shape
print "fullwidth %s" % fullwidth
print "fullheight %s" % fullheight
img, scale = ScaleImage.scale(full_img,1000)
height, width, channels = img.shape
scaled_img = deepcopy(img)
##First blur image in order to reduce noise
blurred_img = deepcopy(img)
print "Blurring image for filtering"
blurred_img = cv2.GaussianBlur(blurred_img,(9,9),0)
blurred_img = cv2.GaussianBlur(blurred_img,(9,9),0)
blurred_img = cv2.GaussianBlur(blurred_img,(9,9),0)
print "Blurring done"
cv2.imwrite("Test_Images/Output_Images/blurred_img.jpg", blurred_img)
##Then find the average background color
print "Calibrating color filtration"
red,green,blue = Calibrate.findRed(blurred_img)
##Filter the image based on that average color
print "Filtering blurred image"
blurred_img = FilterImage.filter(blurred_img,red,green,blue)
if (debug):
showImage.showImage(blurred_img)
##Mask the original image based on the the blurred filter
print "Masking original image based on blurred image"
for y in range(0,height):
for x in range(0,width):
pxR = blurred_img[y,x,2]
pxB = blurred_img[y,x,1]
pxG = blurred_img[y,x,0]
if ( (pxR == 0) and (pxG == 0) and (pxB == 0) ):
img[y,x] = 0
cv2.imwrite("Test_Images/Output_Images/justFiltered.jpg", img)
if (debug):
showImage.showImage(img, "Just filtered")
morpher = ImageMorpher()
openimg = deepcopy(img)
## Use morphology to get rid of erratic blobs and specs
print "Doing morphology to fix blobbies"
openimg = morpher.openWithSquare(openimg,7)
openimg = morpher.closeWithSquare(openimg,7)
openimg = cv2.cvtColor(openimg,cv2.COLOR_BGR2GRAY)
for x in range(0,height):
for y in range(0,width):
px = openimg[x,y]
if ( (px == 0) ):
openimg[x,y] = 0
else:
openimg[x,y] = 255
##Get contours for remaining blobs
print "Contouring blobbies"
contoured_img, contours, hierarchy = cv2.findContours(openimg,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
##Get rid of the tiny remaining specs
print "Removing small blobs"
for i in range(0, len(contours)):
cnt = contours[i]
if (cv2.contourArea(cnt) < 2000):
cv2.drawContours(contoured_img,cnt,-1,0,thickness=cv2.FILLED)
##Create a new mask to remove contours
print "Masking image based on removed contours"
for x in range(0,height):
for y in range(0,width):
px = contoured_img[x,y]
if ( (px == 0) ):
img[x,y] = [0,0,0]
else:
continue
if (debug):
showImage.showImage(img, "Filtered and corrected")
color_filtered_img = deepcopy(img)
cv2.imwrite("Test_Images/Output_Images/1_Template_Color_Filtered.jpg",img)
temp = deepcopy(color_filtered_img)
##Watershed works best with blurred image
print "Blurring image for watershed algorithm"
img = cv2.GaussianBlur(img,(9,9),0)
img = cv2.GaussianBlur(img,(9,9),0)
#####
#Watershed segmentation
b,g,r = cv2.split(img)
rgb_img = cv2.merge([r,g,b])
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
# noise removal
kernel = np.ones((2,2),np.uint8)
closing = cv2.morphologyEx(thresh,cv2.MORPH_CLOSE,kernel, iterations = 2)
# sure background area
sure_bg = cv2.dilate(closing,kernel,iterations=1)
# Finding sure foreground area
dist_transform = cv2.distanceTransform(sure_bg,cv2.DIST_L2,3)
# Threshold
ret, sure_fg = cv2.threshold(dist_transform,0.1*dist_transform.max(),255,0)
# Finding unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg,sure_fg)
# Marker labelling
ret, markers = cv2.connectedComponents(sure_fg)
# Add one to all labels so that sure background is not 0, but 1
markers = markers+1
# Now, mark the region of unknown with zero
markers[unknown==255] = 0
markers = cv2.watershed(img,markers)
img[markers == -1] = [255,0,0]
cv2.imwrite('Test_Images/Output_Images/2_Template_Watershedded.jpg',img)
if (debug):
showImage.showImage(img,"Template_Watershedded")
water_img = deepcopy(img)
new_img = img
for x in range(1,height-1):
for y in range(1,width-1):
if markers[x,y] == -1:
new_img[x,y] = [255,255,255]
else:
new_img[x,y] = [0,0,0]
cv2.imwrite('Test_Images/Output_Images/3_Template_Just_Watershed_Edges.jpg',new_img)
#####
#Hough Circles
print "Doing Hough cirlces on watershedded edges"
scimg = cv2.imread('Test_Images/Output_Images/3_Template_Just_Watershed_Edges.jpg',0)
#scimg = cv2.medianBlur(scimg,5)
sccimg = cv2.cvtColor(scimg,cv2.COLOR_GRAY2BGR)
circles = cv2.HoughCircles(scimg,cv2.HOUGH_GRADIENT,1,100,
param1=50,param2=20,minRadius=30,maxRadius=100)
##circles = cv2.HoughCircles(scimg,cv2.HOUGH_GRADIENT,1,50,
## param1=50,param2=15,minRadius=15,maxRadius=50)
counter = 0
radsum = 0
try:
print circles.shape
except Exception, e:
print e
