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threshold.py
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from __future__ import print_function
import os, sys
import numpy
import matplotlib as mpl
import matplotlib.lines as lines
import matplotlib.patches as patches
import matplotlib.pyplot as plt
from PIL import Image
MACROWIDTH = 16
MACROHEIGHT = 16
def thresholdImage(image, threshold):
result = image.point(lambda x: 255 if x > threshold else 0)
return result
def normalizeImage(im, desiredMean, desiredVariance):
imarray = numpy.asarray(im)
mean = numpy.mean(imarray)
variance = numpy.var(imarray)
result = imarray
result.setflags(write=1)
for i in range(0, len(imarray - 1)):
for j in range(0, len(imarray[i]) - 1):
pixVal = numpy.floor((int(imarray[i][j][0]) + imarray[i][j][1] + imarray[i][j][2]) / 3)
if pixVal > mean:
newVal = desiredMean + numpy.sqrt((desiredVariance * (numpy.square(pixVal - mean))) / (variance))
toAppend = [newVal, newVal, newVal]
else:
newVal = desiredMean - numpy.sqrt((desiredVariance * (numpy.square(pixVal - mean))) / (variance))
toAppend = [newVal, newVal, newVal]
result[i][j] = toAppend
out = Image.fromarray(result)
im.paste(out)
return im
def binarizeMacroblock(im, xMacro, yMacro):
imarray = numpy.asarray(im)
macroBlockMean = 0
result = imarray
result.setflags(write=1)
for i in range(MACROWIDTH*xMacro, MACROWIDTH*xMacro + MACROWIDTH):
for j in range(MACROHEIGHT*yMacro, MACROHEIGHT*yMacro + MACROHEIGHT):
macroBlockMean += ((int(imarray[i][j][0]) + imarray[i][j][1] + imarray[i][j][2]) / 3)
macroBlockMean = macroBlockMean / (MACROWIDTH * MACROHEIGHT)
for i in range(MACROWIDTH*xMacro, MACROWIDTH*xMacro + MACROWIDTH):
for j in range(MACROHEIGHT*yMacro, MACROHEIGHT*yMacro + MACROHEIGHT):
if imarray[i][j][0] >= macroBlockMean:
result[i][j] = [255, 255, 255]
else:
result[i][j] = [0, 0, 0]
out = Image.fromarray(result)
im.paste(out)
return im
def verticalSobel(im, xMacro, yMacro):
imarray = numpy.asarray(im)
result = []
cellResult = 0
operator = [[1, 0, -1],[2, 0, -2],[1, 0, -1]]
for i in range(MACROWIDTH*xMacro + 1, MACROWIDTH*xMacro + MACROWIDTH-1):
result.append([])
for j in range(MACROHEIGHT*yMacro + 1, MACROHEIGHT*yMacro + MACROHEIGHT-1):
cellResult = 0
for k in range(-1, 2):
for l in range(-1, 2):
cellResult += imarray[i + k][j + l][0] * operator[k+1][l+1]
#cellResult = cellResult / 4
result[-1].append(cellResult)
return result
def horizontalSobel(im, xMacro, yMacro):
imarray = numpy.asarray(im)
result = []
cellResult = 0
operator = [[1, 2, 1],[0, 0, 0],[-1, -2, -1]]
for i in range(MACROWIDTH*xMacro + 1, MACROWIDTH*xMacro + MACROWIDTH-1):
result.append([])
for j in range(MACROHEIGHT*yMacro + 1, MACROHEIGHT*yMacro + MACROHEIGHT-1):
cellResult = 0
for k in range(-1, 2):
for l in range(-1, 2):
cellResult += imarray[i + k][j + l][0] * operator[k+1][l+1]
#cellResult = cellResult / 4
result[-1].append(cellResult)
return result
def ridgeOrientation(vSobel, hSobel):
result = []
macroResult = 0
verticalCells = 0
vx = 0
vy = 0
macroWidth = len(vSobel)
macroHeight = len(vSobel[0])
macroInnerWidth = len(vSobel[0][0])
macroInnerHeight = len(vSobel[0][0][0])
for i in range(0, macroWidth):
result.append([])
for j in range(0, macroHeight):
vx = 0
vy = 0
count = 0
verticalCells = 0.0
macroResult = 0.0
for k in range (0, macroInnerWidth):
for l in range(0, macroInnerHeight):
#vx += 2 * (vSobel[i][j][k][l]) * (hSobel[i][j][k][l])
#vy += numpy.square(vSobel[i][j][k][l]) - numpy.square(hSobel[i][j][k][l])
if not (hSobel[i][j][k][l] == 0 and vSobel[i][j][k][l] == 0):
count += 1
if hSobel[i][j][k][l] != 0:
macroResult += (vSobel[i][j][k][l] / hSobel[i][j][k][l])
elif hSobel[i][j][k][l] == 0 and vSobel[i][j][k][l] != 0:
verticalCells += numpy.abs(vSobel[i][j][k][l]/125)
#if vx != 0:
#macroResult = numpy.rad2deg(0.5 * numpy.arctan(vy/vx))
#else:
#macroResult = 89
if macroResult > 0:
macroResult += (verticalCells * 1)
elif macroResult < 0:
macroResult += (verticalCells * -1)
if count > 0:
macroResult = macroResult / (count)
else:
macroResult = 0
macroResult = numpy.rad2deg(numpy.arctan(macroResult))
result[i].append(macroResult)
return result
def linearRegressionRidgeOrientation(im, xMacro, yMacro):
imarray = numpy.asarray(im)
blackRidgesX = []
blackRidgesY = []
whiteRidgesX = []
whiteRidgesY = []
whiteNum = 0
blackNum = 0
result = 0
for i in range(MACROWIDTH*xMacro + 1, MACROWIDTH*xMacro + MACROWIDTH-1):
for j in range(MACROHEIGHT*yMacro + 1, MACROHEIGHT*yMacro + MACROHEIGHT-1):
if imarray[i][j][0] == 0:
blackRidgesX.append(i % (16))
blackRidgesY.append(j % (16))
blackNum += 1
else:
whiteRidgesX.append(i % (16))
whiteRidgesY.append(j % (16))
whiteNum += 1
temp1 = [0]
temp2 = [0]
if len(blackRidgesX) > 0:
temp1 = numpy.polyfit(blackRidgesX, blackRidgesY, 1)
else:
temp1[0] = 0
if len(whiteRidgesX) > 0:
temp2 = numpy.polyfit(whiteRidgesX, whiteRidgesY, 1)
else:
temp2[0] = 0
result = (temp1[0])
result = numpy.arctan(result)
result = (numpy.rad2deg(result))
return result
def printRidgeOrientation(ridgeOrient):
result = []
for i in range(len(ridgeOrient)):
for j in range(len(ridgeOrient[0])):
m = -(numpy.tan(numpy.deg2rad(ridgeOrient[i][j])))
#print("SLOPE: ", m, "ANGLE: ", ridgeOrient[i][j])
x = (j*MACROWIDTH) + MACROWIDTH/2
y = (i*MACROHEIGHT) + MACROHEIGHT/2
b = (y) - (m * x)
d = MACROHEIGHT/2 - 1
xData = [0, 0]
xData[0] = ((((2 * x) + (2 * m * y) - (2 * m * b)) - numpy.sqrt(((-2 * x) - (2 * m * y) + (2 * m * b)) ** 2 - (4 * (1 + m**2) * (x**2 + y**2 + b**2 - d**2 - 2*b*y)))) / (2 * (1 + m**2)))
xData[1] = ((((2 * x) + (2 * m * y) - (2 * m * b)) + numpy.sqrt(((-2 * x) - (2 * m * y) + (2 * m * b)) ** 2 - (4 * (1 + m**2) * (x**2 + y**2 + b**2 - d**2 - 2*b*y)))) / (2 * (1 + m**2)))
yData = [0, 0]
yData[0] = (m * xData[0]) + b
yData[1] = (m * xData[1]) + b
temp = lines.Line2D(color = "red", xdata = xData, ydata = yData)
result.append(temp)
return result
def blackMacroBlock(im, xMacro, yMacro):
imarray = numpy.asarray(im)
result = imarray
result.setflags(write=1)
for i in range(MACROWIDTH*xMacro, MACROWIDTH*xMacro + MACROWIDTH):
for j in range(MACROHEIGHT*yMacro, MACROHEIGHT*yMacro + MACROHEIGHT):
result[i][j] = [0, 0, 0]
out = Image.fromarray(result)
im.paste(out)
return im
def printMacroBlock(im, xMacro, yMacro):
imarray = numpy.asarray(im)
for i in range(MACROWIDTH*xMacro, MACROWIDTH*xMacro + MACROWIDTH):
for j in range(MACROHEIGHT*yMacro, MACROHEIGHT*yMacro + MACROHEIGHT):
print(imarray[i][j])
def findSingularities(ridgeOrient, threshold):
result = []
for i in range(1, len(ridgeOrient)-1):
for j in range(1, len(ridgeOrient[0])-1):
tempTotal = 0.0
temp = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
temp[0] = ridgeOrient[i+1][j+1] - ridgeOrient[i+1][j ]
temp[1] = ridgeOrient[i ][j+1] - ridgeOrient[i+1][j+1]
temp[2] = ridgeOrient[i-1][j+1] - ridgeOrient[i ][j+1]
temp[3] = ridgeOrient[i-1][j ] - ridgeOrient[i-1][j+1]
temp[4] = ridgeOrient[i-1][j-1] - ridgeOrient[i-1][j ]
temp[5] = ridgeOrient[i ][j-1] - ridgeOrient[i-1][j-1]
temp[6] = ridgeOrient[i+1][j-1] - ridgeOrient[i ][j-1]
temp[7] = ridgeOrient[i+1][j ] - ridgeOrient[i+1][j-1]
for t in temp:
if t > 90:
t = t - 180
elif t <= -90:
t = t + 180
tempTotal += t
poincare = tempTotal/180
if poincare >= 1-threshold and poincare <= 1+threshold:
print ("LOOP FOUND")
result.append(patches.Circle((j*MACROHEIGHT + MACROHEIGHT/2, i*MACROWIDTH + MACROWIDTH/2), MACROHEIGHT/2, color="blue", fill=False, linewidth=2.0))
elif poincare >= 2-threshold and poincare <= 2+threshold:
print("WHORL FOUND")
result.append(patches.Circle((j*MACROHEIGHT + MACROHEIGHT/2, i*MACROWIDTH + MACROWIDTH/2), MACROHEIGHT/2, color="yellow", fill=False, linewidth=2.0))
elif poincare >= -1-threshold and poincare <= -1+threshold:
print("DELTA FOUND")
result.append(patches.Circle((j*MACROHEIGHT + MACROHEIGHT/2, i*MACROWIDTH + MACROWIDTH/2), MACROHEIGHT/2, color="green", fill=False, linewidth=2.0))
return result
for infile in sys.argv[1:]:
outfile = "test" + ".jpg"
im = Image.open(infile)
im.convert("L")
#im = normalizeImage(im, 100, 100)
#im = thresholdImage(im, 101)
(width, height) = im.size
macroWidth = int(numpy.floor(height/MACROWIDTH))
macroHeight = int(numpy.floor(width/MACROHEIGHT))
sobelVert = []
sobelHor = []
linearRegression = []
for i in range(0, macroWidth):
sobelVert.append([])
sobelHor.append([])
linearRegression.append([])
for j in range(0, macroHeight):
im = binarizeMacroblock(im, i, j)
sobelVert[i].append(verticalSobel(im, i, j))
sobelHor[i].append(horizontalSobel(im, i, j))
linearRegression[i].append(linearRegressionRidgeOrientation(im, i, j))
ridgeOr = ridgeOrientation(sobelVert, sobelHor)
#printMacroBlock(im, 15, 14)
#blackMacroBlock(im, 6, 7)
#blackMacroBlock(im, 7, 6)
#print(sobelVert[7][7])
#print(sobelHor[7][7])
fig, ax = plt.subplots()
ax.imshow(im)
#ridgeLines = printRidgeOrientation(linearRegression)
ridgeLines = printRidgeOrientation(ridgeOr)
singularityPoints = findSingularities(ridgeOr, 0.05)
for i in singularityPoints:
ax.add_patch(i)
for i in ridgeLines:
ax.add_line(i)
plt.show()
im.save(outfile)