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
class LabelRecognizer:
# constants for letters
B_FROM_LABEL = 1
R_FROM_LABEL = 2
A_FROM_LABEL = 3
C_FROM_LABEL = 4
H_FROM_LABEL = 5
S_FROM_LABEL = 6
whRatioCutoff = 6
perimAreaCutoff = 3
correlationCutoff = 1
CONTOUR_MATCHING = 1
# may add other letter matching methods later
def __init__(self):
self.basicFunctions = BasicFunctions()
self.morpher = ImageMorpher()
def findLetterFromLabel(self, searchImage, letterEnum, matchMethod=CONTOUR_MATCHING):
self.searchImage = searchImage
print letterEnum
self.initializeLetterTemplate(letterEnum)
self.getContours(searchImage)
return self.findMatchingContours()
def initializeLetterTemplate(self, letterEnum):
if letterEnum == LabelRecognizer.B_FROM_LABEL:
fileName = "letterTemplates/Bcrop.JPG"
elif letterEnum == LabelRecognizer.R_FROM_LABEL:
# retake Rcrop (wrinkle in wrapper)
fileName = "letterTemplates/Rcrop.JPG"
elif letterEnum == LabelRecognizer.A_FROM_LABEL:
fileName = "letterTemplates/Acrop.JPG"
elif letterEnum == LabelRecognizer.C_FROM_LABEL:
fileName = "letterTemplates/Ccrop.JPG"
elif letterEnum == LabelRecognizer.H_FROM_LABEL:
fileName = "letterTemplates/Hcrop.JPG"
elif letterEnum == LabelRecognizer.S_FROM_LABEL:
fileName = "letterTemplates/Scrop.JPG"
img = self.basicFunctions.readColorImageFromFile(fileName)
gray = self.basicFunctions.convertColorToGrayscale(img)
if letterEnum == LabelRecognizer.R_FROM_LABEL:
gray = cv2.equalizeHist(gray)
blurred = cv2.GaussianBlur(gray, (101, 101), 3)
else:
blurred = cv2.GaussianBlur(gray, (7, 7), 3)
edges = cv2.Canny(blurred, 50, 100)
edges = self.morpher.dilateWithSquare(edges, 2)
edges = self.morpher.closeWithSquare(edges, 3)
self.basicFunctions.showImage(edges)
contours = self.basicFunctions.findContours(edges)
drawnOn = self.basicFunctions.drawContours(img, contours[1])
drawOn = self.basicFunctions.drawContours(img, contours[1], contourIdx=(len(contours[1]) - 1))
self.basicFunctions.showImage(drawOn)
print type(contours[1])
if letterEnum == LabelRecognizer.R_FROM_LABEL:
self.letterContour = contours[1][len(contours[1]) - 2]
else:
self.letterContour = contours[1][len(contours[1]) - 1]
self.basicFunctions.showImage(self.basicFunctions.drawContours(img, [self.letterContour]))
self.letterWHRatio = calcBoundingRectRatio(self.letterContour)
self.letterPerimAreaRatio = calculatePerimAreaRatio(self.letterContour)
def findMatchingContours(self):
matchingContours = []
for i in range(len(self.imgContours)):
result = cv2.matchShapes(self.letterContour, self.imgContours[i], 2, 0)
if result <= self.correlationCutoff:
if abs(self.letterPerimAreaRatio - calculatePerimAreaRatio(self.imgContours[i]) < self.perimAreaCutoff):
if abs(self.letterWHRatio - calcBoundingRectRatio(self.imgContours[i]) < self.whRatioCutoff):
matchingContours.append(self.imgContours[i])
print len(matchingContours)
return tuple(matchingContours)
def getContours(self, searchImage):
# search image assumed to be color image
"""
gray2 = self.basicFunctions.convertColorToGrayscale(searchImage)
blurred = cv2.GaussianBlur(gray2, (5, 5), 3)
edges2 = cv2.Canny(blurred, 50, 100)
edges = self.morpher.dilateWithSquare(edges2, 4)
edges = self.morpher.closeWithSquare(edges2, 5)
self.basicFunctions.showImage(edges2)
"""
gray = self.basicFunctions.convertColorToGrayscale(searchImage)
blurred = cv2.GaussianBlur(gray, (5, 5), 3)
edges = cv2.Canny(blurred, 50, 100)
edges = self.morpher.dilateWithSquare(edges, 2)
edges = self.morpher.closeWithSquare(edges, 3)
contours = self.basicFunctions.findContours(edges)
self.imgContours = contours[1]
self.basicFunctions.showImage(self.basicFunctions.drawContours(searchImage, contours[1]))
print len(self.imgContours)
def __init__(self):
self.basicFunctions = BasicFunctions()
self.morpher = ImageMorpher()
def filter2( image, debug ):
#####
#Filter out image
wrapperBool = False
basicFunctions = BasicFunctions()
img = image
original = deepcopy(image)
maskLogo = deepcopy(img)
maskWrapper = deepcopy(img)
height, width, channels = img.shape
counter = 0
pixcounter = 0
##########
#LOGO MASK
for x in range(0,height):
for y in range(0,width):
pxR = img[x,y,2]
pxG = img[x,y,1]
pxB = img[x,y,0]
if ~((pxR == 0) and (pxB == 0) and (pxG == 0)):
pixcounter = pixcounter + 1
if (isPurple(pxR,pxG,pxB)):
counter = counter + 1
if ( (isPink(pxR,pxG,pxB)) | (isPurple(pxR,pxG,pxB)) ):
maskLogo[x,y] = [255,255,255]
else:
maskLogo[x,y] = [0,0,0]
##Get the logo score from the amount of purple in the image
logoperc = float(counter)/(pixcounter)
print "Logo Percentage = %s" % logoperc
if (logoperc >= 0.01) and (logoperc <= 0.50):
wrapperBool = True
morpher = ImageMorpher()
maskLogo = morpher.closeWithSquare(maskLogo,1)
maskLogo = morpher.dilateWithSquare(maskLogo,1)
for x in range(0,height):
for y in range(0,width):
pxR = img[x,y,2]
pxG = img[x,y,1]
pxB = img[x,y,0]
pxRl = maskLogo[x,y,2]
pxGl = maskLogo[x,y,1]
pxBl = maskLogo[x,y,0]
if ((pxRl != 0) and (pxGl != 0) and (pxBl != 0)):
if ((pxR != 0) and (pxG != 0) and (pxB != 0)):
img[x,y] = maskLogo[x,y]
#############
#WRAPPER MASK
for x in range(0,height):
for y in range(0,width):
pxR = img[x,y,2]
pxG = img[x,y,1]
pxB = img[x,y,0]
if (isWrapper(pxR,pxG,pxB) or isSomethingElse(pxR,pxG,pxB)):
maskWrapper[x,y] = [255,255,255]
else:
maskWrapper[x,y] = [0,0,0]
maskWrapper = morpher.closeWithSquare(maskWrapper,2)
maskWrapper = morpher.dilateWithSquare(maskWrapper,2)
for x in range(0,height):
for y in range(0,width):
#print img
pxR = img[x,y,2]
pxG = img[x,y,1]
pxB = img[x,y,0]
pxRw = maskWrapper[x,y,2]
pxGw = maskWrapper[x,y,1]
pxBw = maskWrapper[x,y,0]
if ((pxRw != 0) and (pxGw != 0) and (pxBw != 0)):
if ((pxR != 0) and (pxG != 0) and (pxB != 0)):
img[x,y] = [0,0,0]
########################
#Final Pass for contours
temp = deepcopy(img)
tempgray = deepcopy(img)
tempgray = cv2.cvtColor(tempgray,cv2.COLOR_BGR2GRAY)
thresh = deepcopy(img)
for x in range(0,height):
for y in range(0,width):
pxR = img[x,y,2]
pxG = img[x,y,1]
pxB = img[x,y,0]
if ((pxR != 0) and (pxG != 0) and (pxB != 0)):
thresh[x,y] = [255,255,255]
thresh = cv2.cvtColor(thresh,cv2.COLOR_BGR2GRAY)
removing = True
removalpass = 1
while(removing):
contoured_img = deepcopy(thresh)
contoured_img, contours, hierarchy = cv2.findContours(contoured_img,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
removing = False
for j in range(0,len(contours)):
cnt = contours[j]
area = cv2.contourArea(cnt)
parent = hierarchy[0,j,3]
if ((area <= 3000)):
removing = True
if (removing == False):
break
removalpass = removalpass + 1
for i in range(0, len(contours)):
cnt = contours[i]
area = cv2.contourArea(cnt)
parent = hierarchy[0,i,3]
if ((area <= 3000) and (parent == -1)):
cv2.drawContours(thresh,cnt,-1,0,thickness=cv2.FILLED)
cv2.drawContours(img,cnt,-1,(0,0,0),thickness=cv2.FILLED)
if ((area <= 3000) and (parent != -1)):
thresh = basicFunctions.fillContourGray(thresh,cnt,255)
img = basicFunctions.fillContourColor(img,cnt,255,255,255)
if (debug):
plt.subplot(161), plt.imshow(original)
plt.subplot(162), plt.imshow(maskLogo)
plt.subplot(163), plt.imshow(maskWrapper)
plt.subplot(164), plt.imshow(temp)
plt.subplot(165), plt.imshow(thresh)
plt.subplot(166), plt.imshow(img)
plt.show()
return img, logoperc
