def applyKernel(inputImage, kernelImage):
height = len(inputImage)
width = len(inputImage[0])
kernelHeight = len(kernelImage)
kerelWidth = len(kernelImage[0])
kernelCentreY = int((kernelHeight - 1) / 2)
kernelCentreX = int((kerelWidth - 1) / 2)
# Create images to store the result
outputImage = createImageL(width, height)
for x, y in itertools.product(range(0, width), range(0, height)):
sumKernel = 0
sumKernelWeights = 0
for wx, wy in itertools.product(range(0, kerelWidth),
range(0, kernelHeight)):
posY = y + wy - kernelCentreY
posX = x + wx - kernelCentreX
if posY > -1 and posY < height and posX > -1 and posX < width:
sumKernel += float(inputImage[posY, posX]) * kernelImage[wy,
wx]
sumKernelWeights += kernelImage[wy, wx]
if sumKernelWeights > 0:
outputImage[y, x] = sumKernel / sumKernelWeights
return outputImage
def showShapeinImage(shape, centre, width, height):
segmentsImage = createImageL(width, height)
numPoints = len(shape[0])
for p in range(0, numPoints):
y, x = int(centre[0] + shape[0, p]), int(centre[1] + shape[1, p])
if x > 0 and y > 0 and x < width and y < height:
segmentsImage[y, x] = 255
showImageL(segmentsImage)
def thresholdImage(inputImage, threshold, binary = True):
height = len(inputImage)
width = len(inputImage[0])
# Create images to store the result
outputImage = createImageL(width, height)
# Set the pixels in the output image according to the accumulate histogram
for x,y in itertools.product(range(0, width), range(0, height)):
if inputImage[y,x] > threshold:
if binary:
outputImage[y,x] = 255
else:
outputImage[y,x] = inputImage[y,x]
else:
outputImage[y,x] = 0
return outputImage
pathToDir = Input image directory
imageName = Input image name
kernelSize = Size of the kernel
'''
pathToDir = "../../Images/Chapter3/Input/"
imageName = "Fence.png"
kernelSize = 5
# Read image into array
inputImage, width, height = imageReadL(pathToDir + imageName)
# Show input image
showImageL(inputImage)
# Create images to store the result
outputImage = createImageL(width, height)
# Apply filter
kernelCentre = int((kernelSize - 1) / 2)
for x,y in itertools.product(range(0, width), range(0, height)):
region = [ ]
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:
region.append(inputImage[posY,posX])
numPixels = len(region)
if numPixels > 0:
'''
Parameters:
pathToDir = Input image directory
imageName = Input image name
'''
pathToDir = "../../Images/Chapter3/Input/"
imageName = "Horse.png"
# Read image into array
inputImage, width, height = imageReadL(pathToDir + imageName)
# Show input image
showImageL(inputImage)
# Create image to store the normalization
outputNormalizedImage = createImageL(width, height)
# Maximum and range
maxVal, miniVal = imageMaxMin(inputImage)
brightRange = float(maxVal - miniVal)
# Set the pixels in the output image
for x, y in itertools.product(range(0, width), range(0, height)):
# Normalize the pixel value according to the range
outputNormalizedImage[y, x] = round(
(inputImage[y, x] - miniVal) * 255.0 / brightRange)
# Compute histogram
histogramNormalizedImage = computeHistogram(outputNormalizedImage)
from timeit import itertools
'''
Parameters:
pathToDir = Input image directory
imageName = Input image name
'''
pathToDir = "../../Images/Chapter2/Input/"
imageName = "Dandelion.png"
# Read image into array
inputImage, width, height = imageReadL(pathToDir + imageName)
# Shift the image
shiftDistance = int(width / 3);
shiftImage = createImageL(width, height)
for x,y in itertools.product(range(0, width), range(0, height)):
xShift = (x - shiftDistance) % width
shiftImage[y][x] = inputImage[y][xShift]
# Show images
showImageL(inputImage)
showImageL(shiftImage)
# Compute power and phase
powerImage, phaseImage = computePowerandPhase(inputImage)
powerShiftImage, phaseShiftImage = computePowerandPhase(shiftImage)
# Show power
powerImageLog = imageLogF(powerImage)
imageName = Input image name
kernelSize = Size of the kernel
'''
pathToDir = "../../Images/Chapter3/Input/"
imageName = "Logs.png"
kernelSize = 5
# Read image into array
inputImage, width, height = imageReadL(pathToDir + imageName)
# Show input image
showImageL(inputImage)
# Create Kernel
kernelCentre = int((kernelSize - 1) / 2)
kernelImage = createImageL(kernelSize, kernelSize)
# Set the pixels of a flat kernel
for x in range(0, kernelSize):
for y in range(0, kernelSize):
kernelImage[y, x] = 1
# Create images to store the result
outputImage = createImageL(width, height)
# Apply kernel
kernelCentre = int((kernelSize - 1) / 2)
for x, y in itertools.product(range(0, width), range(0, height)):
maxValue = 0
for wx, wy in itertools.product(range(0, kernelSize), range(0,
pathToDir = Input image directory
imageName = Input image name
intevalSize = Define the sawtooth fixed interval size
'''
pathToDir = "../../Images/Chapter3/Input/"
imageName = "Horse.png"
intevalSize = 64
# Read image into array
inputImage, width, height = imageReadL(pathToDir + imageName)
# Show input image
showImageL(inputImage)
# Create 3 images to store the result of 3 operators
outputSawtoothImage = createImageL(width, height)
outputLogarithmicImage = createImageL(width, height)
outputExponentialImage = createImageL(width, height)
# Set the pixels in the output image
for x, y in itertools.product(range(0, width), range(0, height)):
inputValue = int(inputImage[y, x])
# Set the pixels in the sawtooth image
pixelInInterval = inputValue % intevalSize
gain = float(pixelInInterval) / float(intevalSize)
outputSawtoothImage[y, x] = inputValue * gain
# Set the pixels in the Logarithmic
outputLogarithmicImage[y, x] = 20 * log(inputValue * 100.0)
sigma = 2
# Read image into array
inputImage, width, height = imageReadL(pathToDir + imageName)
# Show input image
showImageL(inputImage)
# Create Kernel
kernelLaplacian = createLaplacianKernel(kernelSize, sigma)
# Apply kernel
gaussianImage = applyKernelF(inputImage, kernelLaplacian)
# Zero-crossing detector
edges = createImageL(width, height)
kernelCentre = int((kernelSize - 1) / 2)
for x, y in itertools.product(range(1, width - 1), range(1, height - 1)):
quadrantValue = [0.0, 0.0, 0.0, 0.0]
for wx, wy in itertools.product(range(-1, 1), range(-1, 1)):
quadrantValue[0] += gaussianImage[y + wy, x + wx]
for wx, wy in itertools.product(range(-1, 1), range(0, 2)):
quadrantValue[1] += gaussianImage[y + wy, x + wx]
for wx, wy in itertools.product(range(0, 2), range(-1, 1)):
quadrantValue[2] += gaussianImage[y + wy, x + wx]
for wx, wy in itertools.product(range(0, 2), range(0, 2)):
quadrantValue[3] += gaussianImage[y + wy, x + wx]