def findLongestCentredSegmentinImage(imageName, gaussianKernelSize,
sobelKernelSize, upperT, lowerT):
# Read image into array and show
inputImage, width, height = imageReadL(imageName)
# Compute edges and find the segment in the image
magnitude, _ = applyCannyEdgeDetector(inputImage, gaussianKernelSize,
sobelKernelSize, upperT, lowerT)
mainSegmentAverage = findLongestSegment(magnitude)
# Compute centre
numPoints = len(mainSegmentAverage)
centre = [0, 0]
for p in range(0, numPoints):
centre[0] += (mainSegmentAverage[p])[0]
centre[1] += (mainSegmentAverage[p])[1]
centre[0] /= numPoints
centre[1] /= numPoints
# Respect to the center and convert to an image array
shape = createImageF(numPoints, 2)
for p in range(0, numPoints):
y, x = (mainSegmentAverage[p])[0], (mainSegmentAverage[p])[1]
shape[0, p] = y - centre[0]
shape[1, p] = x - centre[1]
return centre, shape, width, height
def findLongesSegmentinImage(imageName, gaussianKernelSize, sobelKernelSize,
upperT, lowerT):
# Read image into array and show
inputImage, width, height = imageReadL(imageName)
# Compute edges and find the segment in the image
magnitude, _ = applyCannyEdgeDetector(inputImage, gaussianKernelSize,
sobelKernelSize, upperT, lowerT)
mainSegmentAverage = findLongestSegment(magnitude)
# Convert to an image array
numPoints = len(mainSegmentAverage)
shape = createImageF(numPoints, 2)
for p in range(0, numPoints):
y, x = (mainSegmentAverage[p])[0], (mainSegmentAverage[p])[1]
shape[0, p] = y
shape[1, p] = x
return shape, width, height
sobelKernelSize = 3
upperT = 0.4
lowerT = 0.2
kernelSize = 9
k = .02
op = "H"
# Read image into array
inputImage, width, height = imageReadL(pathToDir + imageName)
# Show input image
showImageL(inputImage)
# We apply Canny to obtain the edges from the image
# but also need the results of the Sobel operator (Gradient)
magnitude, angle, mX, mY = applyCannyEdgeDetector(inputImage,
GaussianKernelSize, sobelKernelSize, upperT, lowerT, True) \
# The center of the kernel
kernelCentre = int((kernelSize - 1) / 2)
# Compute curvature
curvature = createImageF(width, height)
for x,y in itertools.product(range(0, width), range(0, height)):
# If it is an edge
if magnitude[y,x] > 0:
A, B, C = 0.0, 0.0, 0.0
for wx,wy in itertools.product(range(0, kernelSize), range(0, kernelSize)):
posY = y + wy - kernelCentre
posX = x + wx - kernelCentre
if posY > -1 and posY < height and posX > -1 and posX < width:
upperT = 0.3
lowerT = 0.2
axisRange = [20, 65]
angleRange = [0, 4]
segmentLenghtThreshod = 0.20
pairsPerPoint = 30
# Sample points distance. Higher the eccentricity higher the distance so points give accurate positions
axisRatio = float(axisRange[1]) / (2.0 * float(axisRange[0]))
deltaPointRange = [int(axisRatio * axisRange[0]), int(1.2 * axisRange[1])]
# Read image #49 20 0.03490658503988659 125 77 2
inputImage, width, height = imageReadL(pathToDir + imageName)
# Compute edges
magnitude, angle = applyCannyEdgeDetector(inputImage, gaussianKernelSize,
sobelKernelSize, upperT, lowerT)
showImageF(magnitude)
# Find segments
segmentsList = []
segmentsImage = createImageF(width, height)
maxSegmentLenght = 0
for x, y in itertools.product(range(0, width), range(0, height)):
if magnitude[y, x] != 0 and segmentsImage[y, x] == 0:
segment = []
segmentPoints = [(y, x)]
segmentsImage[y, x] = 255
while len(segmentPoints) > 0:
yc = (segmentPoints[0])[0]
xc = (segmentPoints[0])[1]
segment.append((yc, xc))
suppWindow = Size of the window used to find maxima
'''
pathToDir = "../../Images/Chapter8/Input/"
imageName = "Logs.png"
cannyKernelSize = 7
upperT = 0.5
lowerT = 0.1
windowDelta = 3
suppWindow = 5
# Read image into array and show
inputImage, width, height = imageReadL(pathToDir + imageName)
showImageL(inputImage)
# Compute edges
magnitude, angle = applyCannyEdgeDetector(inputImage, cannyKernelSize,
cannyKernelSize, upperT, lowerT)
showImageF(magnitude)
# Divide pixels into edge and region pixels
edgePixels = []
shapeImage = []
for x, y in itertools.product(range(0, width), range(0, height)):
if magnitude[y, x] > 0:
edgePixels.append((y, x))
shapeImage.append((y, x))
# Radial is the minimal distance to the edge
distanceImage = createImageF(width, height)
numEdges = len(edgePixels)
for x in range(0, width):
printProgress(x, width)