print " Wrapper: %s" % totalCandies[w][1]
print " Wrapper Conf: %s" % totalCandies[w][3]
print " Score: %s" % totalCandies[w][2]
displayname = "%s" % w
scorestring = "%s" % totalCandies[w][2]
wrapperscore = totalCandies[w][1]*100
wrapperstring = "%.3f" % wrapperscore
wrapperconf = "%.3f" % totalCandies[w][3]
x_start = (loc[0] - dist_full)*scale
x_end = (loc[0] + dist_full)*scale
y_start = (loc[1] - dist_full)*scale
y_end = (loc[1] + dist_full)*scale
cv2.putText(scaled_img,displayname,(int(x_start+1),int(y_start+25)),font,0.75,(0,255,255), 3, 8);
cv2.putText(scaled_img,scorestring,(int(x_start+1),int(y_end-1)),font,0.75,(0,255,0), 3, 8);
cv2.putText(scaled_img,wrapperstring,(int(x_end-75),int(y_start+25)),font,0.75,(255,255,0), 3, 8);
cv2.putText(scaled_img,wrapperconf,(int(x_end-75),int(y_end-1)),font,0.75,(255,0,255), 3, 8);
cv2.rectangle(scaled_img,(int(x_start),int(y_start)),(int(x_end),int(y_end)),(0,255,255),1)
cv2.imwrite("Test_Images/Output_Images/5_circled_image.jpg",scaled_img)
shrink, temp = ScaleImage.scale(scaled_img,1000)
showImage.showImage(shrink)
else:
print "No circles detected."
if __name__ == '__main__':
combined(sys.argv[1], sys.argv[2])
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
def findRed ( image ):
redimg = deepcopy(image)
whiteimg = deepcopy(image)
redscimg,scale = ScaleImage.scale(redimg, 500)
whitescimg,scale = ScaleImage.scale(whiteimg, 500)
#scimg = img
height, width, channels = redscimg.shape
RedSum = 0
GreenSum = 0
BlueSum = 0
for x in range(0,height):
for y in range(0,width):
pxR = redscimg[x,y,2]
pxG = redscimg[x,y,1]
pxB = redscimg[x,y,0]
RedSum = RedSum + pxR
GreenSum = GreenSum + pxG
BlueSum = BlueSum + pxB
#x += 1
avgRed = RedSum/(height*width/2)
avgGreen = GreenSum/(height*width/2)
avgBlue = BlueSum/(height*width/2)
print "Average RGB: (%s,%s,%s)" % (avgRed,avgGreen,avgBlue)
#cv2.namedWindow('image')
r = avgRed + 25
b = avgGreen - 50
g = avgBlue - 50
# create trackbars for color change
cv2.createTrackbar('R','image',0,255,nothing)
cv2.setTrackbarPos('R','image',r)
cv2.createTrackbar('B','image',0,255,nothing)
cv2.setTrackbarPos('B','image',b)
cv2.createTrackbar('G','image',0,255,nothing)
cv2.setTrackbarPos('G','image',g)
r = 0;
pastr = 0;
r = cv2.getTrackbarPos('R','image')
b = cv2.getTrackbarPos('B','image')
g = cv2.getTrackbarPos('G','image')
print "Red value: %s" % r
print "Green value: %s" % g
print "Blue value: %s" % b
## cv2.imshow('image',scimg)
##
## while(1):
## #cv2.imshow('image',scimg)
## k = cv2.waitKey(1) & 0xFF
## if k == 27:
## break
##
## pastr = r
## r = cv2.getTrackbarPos('R','image')
## b = cv2.getTrackbarPos('B','image')
## g = cv2.getTrackbarPos('G','image')
##
## if pastr != r:
## #cv2.imshow('image',scimg)
## #k = cv2.waitKey(1) & 0xFF
## #if k == 27:
## # break
## print "updated!"
##
## for x in range(0,height):
## for y in range(0,width):
## pxR = scimg[x,y,2]
## pxG = scimg[x,y,1]
## pxB = scimg[x,y,0]
##
## #if not REALLY white or not REALLY red
##
## #if ((pxR >= 200) & (pxB >= 200) & (pxG >= 200)):
## # reallyWhite = True
## #else:
## # reallyWhite = False
##
## #if ((pxR >= pxB + 75) & (pxR >= pxG +75) & (pxR >= 150)):
## # reallyRed = True
## #else:
## # reallyRed = False
##
## #if ~reallyWhite & ~reallyRed:
## # scimg[x,y] = [0,0,0]
##
## if ((~(pxR >= r)) | (~(pxG >= g)) | (~(pxB >= b))):
## if (~((pxR >= pxB + 75) & (pxR >= pxG +75) & (pxR >=150))):
## scimg[x,y] = [0,0,0]
##
## if ((pxR <= 175) & (pxB <= 175) & (pxG <= 175)):
## scimg[x,y] = [0,0,0]
##
## cv2.destroyAllWindows()
return r,g,b
