def segmentImage(self, imageData, parameters, saveMaskImage=False):
# Requires: -imageData holds 0-1 values of color values (either RGB, HSV, or HLS)
# -parameters is three 2-tuple numbers between 0 - 1 that
# respectively represent (min, max) color ranges for foreground selection
# Effects: returns an RGB mask-list where
# (0, 0, 0) is background, (255, 255, 255) is foreground
if parameters.colorRanges == None:
print "No color range set!"
exit(1)
colorRanges = parameters.colorRanges
# check for bounds errors
for tuples in colorRanges:
if tuples[0] > tuples[1]:
print "Min > Max in parameter!"
exit(1)
for number in tuples:
if not (0 <= number <= 1):
print "Parameter not within 0 - 1 range!"
exit(1)
# xRange either R or H
# yRange either G or S
# zRange either B or V or L
xRange = colorRanges[0]
yRange = colorRanges[1]
zRange = colorRanges[2]
isForeground = lambda x: (
xRange[0] <= x[0] <= xRange[1] and yRange[0] <= x[1] <= yRange[1] and zRange[0] <= x[2] <= zRange[1]
)
# if flip bit off, act normally,
# else switch background and foreground
if parameters.flipBit == False:
foreground = (255, 255, 255)
background = (0, 0, 0)
else:
foreground = (0, 0, 0)
background = (255, 255, 255)
mask = [background] * len(imageData)
# mark foreground if in color range
for i in range(len(imageData)):
if isForeground(imageData[i]):
mask[i] = foreground
# save image if set true
if saveMaskImage == True:
dataToImage(mask, parameters.imageSize)
return mask
def segmentRegions(self, maskData, parameters, clearWhite = 0, countWhite = False, saveImage = False):
# Requires : -maskData is a (0, 0, 0) / (255, 255, 255)
# black / white rgb based list data set
# -parameters is of type Parameters
# -clearWhite is an integer, either 0 or 1
# -countWhite and saveImage is a bool
# Effects : -segments a black / white image into distinct
# black / white regions and attempts to fill
# smaller black regions for increased fit
# -if clearWhite set to 1, segmentRegions
# will remove any white that is
# not connected to the largest sized foreground object
# -if countWhite set to True, returns the count of
# distinct white / foreground regions bigger than 200 pixels
# -if saveImage set to False, segmentRegions does not
# save image of new filled image as it does by default
imageSize = parameters.imageSize
imageMat = imaging.dataToMatrix(maskData, imageSize)
# label matrix with "black"/"white" instead of rgb values for convenience
for row in range(imageSize[1]):
for col in range(imageSize[0]):
if imageMat[row][col] == (0, 0, 0):
imageMat[row][col] = "black"
else:
imageMat[row][col] = "white"
#list of distinct white / black regions with the count of pixels in each
blackRegions = []
whiteRegions = []
blackCount = 0
whiteCount = 0
# flood fill each pixel value with 'black' or 'white',
# and mark them with distinct values
for row in range(imageSize[1]):
for col in range(imageSize[0]):
if imageMat[row][col] == "black":
blackCount += 1
(count, border) = self.floodFill(imageMat, (col, row), blackCount, parameters)
blackRegions.append((count, border))
elif imageMat[row][col] == "white":
whiteCount -= 1
(count, border) = self.floodFill(imageMat, (col, row), whiteCount, parameters)
whiteRegions.append((count, border))
dataList = imaging.matrixToData(imageMat)
# clear out any foreground not connected to the largest piece
# of white foreground, executed after recursive call if clearWhite
# set to 1 by user
if clearWhite == 2:
maxWhiteMarked = (whiteRegions.index(max(whiteRegions)) + 1) * -1
for i in range(len(dataList)):
if dataList[i] == maxWhiteMarked:
dataList[i] = (255,) * 3
else:
dataList[i] = (0,) * 3
# any black that is adjacent to border is background
# otherwise surrounded by white, foreground
else:
for i in range(len(dataList)):
if dataList[i] > 0 and blackRegions[abs(dataList[i]) - 1][1] == False:
dataList[i] = (255,) * 3
elif dataList[i] > 0:
dataList[i] = (0,) * 3
else:
dataList[i] = (255,) * 3
# count white regions
if countWhite == True:
totalCount = 0
for i in range(len(whiteRegions)):
if whiteRegions[i][0] > 200:
totalCount += 1
print "Total foreground count is " + str(totalCount)
# if clear white is its non default value of 0, recursively call self
# to clear non connecting pieces
if clearWhite == 1:
self.segmentRegions(dataList, parameters, clearWhite = 2, saveImage = True)
# export image if saveImage true
# and clearWhite is either 0 or 2
if saveImage == True and clearWhite != 1:
imaging.dataToImage(dataList, parameters.imageSize)
def predict(folderName,
parameters,
segmentAlgo,
fitnessFunc,
plot=False,
exportImages=False):
# Requires: -parameters is of type Parameters
# -segmentAlgo is of type Segment
# -fitnessFunc is of type Fitness
# -folder is a string of a folder which contains two subfolders:
# -original which contains the original image
# -manualsegmentations which contains segmentations
# of the original images, the segmentations have the same
# name and are of .png format
# -plot and export is of type Bool
# Effects: -attempts to segment images inside of the speicied folder
# based on the single sample image / mask provided to the system
# -outputs a histogram plot, "plot-date-time.png", of the fitness
# distrubution of the images segmented if plot = True
# -saves masks of the segmented images if export = True
file_list = glob.glob(folderName + "original/" + "*")
prefixLen = len(folderName) + len("original/")
suffixLen = len(file_list[0].split(".")[-1]) + 1
fitnessList = []
iteration = 1
for fileName in file_list:
image = Image.open(fileName)
if parameters.colorSpace in ("hsv", "hls"):
imageData = colorSpaceConvert(list(image.getdata()),
parameters.colorSpace)
else:
imageData = colorSpaceConvert(list(image.getdata()),
parameters.colorSpace)
maskname = fileName[prefixLen:-suffixLen] + ".png"
mask = Image.open(folderName + "manualsegmentations/" + maskname)
idealMaskData = list(mask.getdata())
parameters.setImageSize(image.size)
maskData = segmentAlgo.segmentImage(imageData, parameters, True)
fit = fitnessFunc.findFitness(maskData, idealMaskData, parameters)
# save mask if export enabled
if exportImages == True:
dataToImage(maskData, image.size, "-" + str(iteration))
fitnessList.append(fit / float(image.size[0] * image.size[1]))
iteration += 1
time = datetime.datetime.now()
npFitnessList = numpy.array(fitnessList)
mu = npFitnessList.mean()
median = numpy.median(npFitnessList)
sigma = npFitnessList.std()
textstr = '$\overline{x}=%.2f$\n$\mathrm{median}=%.2f$\n$\sigma=%.2f$' % (
mu, median, sigma)
fig, ax = pylab.subplots(1)
pylab.hist(fitnessList, bins=10)
pylab.xlabel('Fitness ratio (Fitness / Image Size)')
pylab.ylabel('Number of Images')
pylab.title('Number of Images vs. Fitness Ratio')
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
pylab.text(0.78,
0.95,
textstr,
transform=ax.transAxes,
fontsize=14,
verticalalignment='top',
bbox=props)
timeString = "output-" + str(time.month) + '.' + str(time.day) + '.' + str(
time.year)
timeString += '-' + str(time.hour) + '.' + str(time.minute)
pylab.savefig("plotImages" + timeString + ".png")
def predict(folderName, parameters, segmentAlgo,
fitnessFunc, plot = False, exportImages = False):
# Requires: -parameters is of type Parameters
# -segmentAlgo is of type Segment
# -fitnessFunc is of type Fitness
# -folder is a string of a folder which contains two subfolders:
# -original which contains the original image
# -manualsegmentations which contains segmentations
# of the original images, the segmentations have the same
# name and are of .png format
# -plot and export is of type Bool
# Effects: -attempts to segment images inside of the speicied folder
# based on the single sample image / mask provided to the system
# -outputs a histogram plot, "plot-date-time.png", of the fitness
# distrubution of the images segmented if plot = True
# -saves masks of the segmented images if export = True
file_list = glob.glob(folderName + "original/" + "*")
prefixLen = len(folderName) + len("original/")
suffixLen = len(file_list[0].split(".")[-1]) + 1
fitnessList = []
iteration = 1
for fileName in file_list:
image = Image.open(fileName)
if parameters.colorSpace in ("hsv", "hls"):
imageData = colorSpaceConvert(list(image.getdata()), parameters.colorSpace)
else:
imageData = colorSpaceConvert(list(image.getdata()), parameters.colorSpace)
maskname = fileName[prefixLen:-suffixLen] + ".png"
mask = Image.open(folderName + "manualsegmentations/" + maskname)
idealMaskData = list(mask.getdata())
parameters.setImageSize(image.size)
maskData = segmentAlgo.segmentImage(imageData, parameters, True)
fit = fitnessFunc.findFitness(maskData, idealMaskData, parameters)
# save mask if export enabled
if exportImages == True:
dataToImage(maskData, image.size, "-" + str(iteration))
fitnessList.append(fit / float(image.size[0] * image.size[1]))
iteration += 1
time = datetime.datetime.now()
npFitnessList = numpy.array(fitnessList)
mu = npFitnessList.mean()
median = numpy.median(npFitnessList)
sigma = npFitnessList.std()
textstr = '$\overline{x}=%.2f$\n$\mathrm{median}=%.2f$\n$\sigma=%.2f$'%(mu, median, sigma)
fig, ax = pylab.subplots(1)
pylab.hist(fitnessList, bins = 10)
pylab.xlabel('Fitness ratio (Fitness / Image Size)')
pylab.ylabel('Number of Images')
pylab.title('Number of Images vs. Fitness Ratio')
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
pylab.text(0.78, 0.95, textstr, transform=ax.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
timeString = "output-" + str(time.month) + '.' + str(time.day) + '.' + str(time.year)
timeString += '-' + str(time.hour) + '.' + str(time.minute)
pylab.savefig("plotImages" + timeString + ".png")
def segmentRegions(self,
maskData,
parameters,
clearWhite=0,
countWhite=False,
saveImage=False):
# Requires : -maskData is a (0, 0, 0) / (255, 255, 255)
# black / white rgb based list data set
# -parameters is of type Parameters
# -clearWhite is an integer, either 0 or 1
# -countWhite and saveImage is a bool
# Effects : -segments a black / white image into distinct
# black / white regions and attempts to fill
# smaller black regions for increased fit
# -if clearWhite set to 1, segmentRegions
# will remove any white that is
# not connected to the largest sized foreground object
# -if countWhite set to True, returns the count of
# distinct white / foreground regions bigger than 200 pixels
# -if saveImage set to False, segmentRegions does not
# save image of new filled image as it does by default
imageSize = parameters.imageSize
imageMat = imaging.dataToMatrix(maskData, imageSize)
# label matrix with "black"/"white" instead of rgb values for convenience
for row in range(imageSize[1]):
for col in range(imageSize[0]):
if imageMat[row][col] == (0, 0, 0):
imageMat[row][col] = "black"
else:
imageMat[row][col] = "white"
#list of distinct white / black regions with the count of pixels in each
blackRegions = []
whiteRegions = []
blackCount = 0
whiteCount = 0
# flood fill each pixel value with 'black' or 'white',
# and mark them with distinct values
for row in range(imageSize[1]):
for col in range(imageSize[0]):
if imageMat[row][col] == "black":
blackCount += 1
(count, border) = self.floodFill(imageMat, (col, row),
blackCount, parameters)
blackRegions.append((count, border))
elif imageMat[row][col] == "white":
whiteCount -= 1
(count, border) = self.floodFill(imageMat, (col, row),
whiteCount, parameters)
whiteRegions.append((count, border))
dataList = imaging.matrixToData(imageMat)
# clear out any foreground not connected to the largest piece
# of white foreground, executed after recursive call if clearWhite
# set to 1 by user
if clearWhite == 2:
maxWhiteMarked = (whiteRegions.index(max(whiteRegions)) + 1) * -1
for i in range(len(dataList)):
if dataList[i] == maxWhiteMarked:
dataList[i] = (255, ) * 3
else:
dataList[i] = (0, ) * 3
# any black that is adjacent to border is background
# otherwise surrounded by white, foreground
else:
for i in range(len(dataList)):
if dataList[i] > 0 and blackRegions[abs(dataList[i]) -
1][1] == False:
dataList[i] = (255, ) * 3
elif dataList[i] > 0:
dataList[i] = (0, ) * 3
else:
dataList[i] = (255, ) * 3
# count white regions
if countWhite == True:
totalCount = 0
for i in range(len(whiteRegions)):
if whiteRegions[i][0] > 200:
totalCount += 1
print "Total foreground count is " + str(totalCount)
# if clear white is its non default value of 0, recursively call self
# to clear non connecting pieces
if clearWhite == 1:
self.segmentRegions(dataList,
parameters,
clearWhite=2,
saveImage=True)
# export image if saveImage true
# and clearWhite is either 0 or 2
if saveImage == True and clearWhite != 1:
imaging.dataToImage(dataList, parameters.imageSize)
