def main(argv):
# load image into ndarray
if len(argv) > 1:
testimg = mpimg.imread(argv[1])
else:
testimg = mpimg.imread('/home/cilsat/Dropbox/kuliah/sem1/pp/portrait.jpeg')
# calc sorted 2-D representation of image
#imgs = util.sortimg(util.flattenimg(img))
# get count of unique colors
#uniq = util.getunique(imgs)
#print(uniq)
#pltshow(img)
# get and display histogram(s) of color(s)
#hist = util.gethistogram(img)
#plthist(hist)
# equalize image
#imgeq = util.equalize(img, hist)
#pltshow(imgeq)
# get and display background image with dynamic threshold 15
#background = util.getbackground(img, imgs, 15)
#pltshow(background)
# OCR
# training: only need to run ONCE for each font
# make sure you have an image containing all lower case letters, upper case letters, and digits in that order
"""
font = "dejavusans-alphanumeric"
fontimg = mpimg.imread('train/' + font + '.jpg')
util.train(fontimg, font)
# testing: this is only around 50% accurate for text of different font
print util.test(testimg, font)
"""
# License Plate recognition
"""
dataset = "plat"
util.gnb_train(dataset=dataset)
"""
# Face detection
h = testimg.shape[0]
w = testimg.shape[1]
face = testimg[int(0*h):int(0.7*h), int(0.15*w):int(0.45*w)]
pltshow(face)
imgf = util.mapImage(testimg, util.mapColor(face, 60))
pltshow(imgf)
imgt = util.thin(imgf, bg='dark')
pltshow(imgt)
obj = util.segment(imgt, minsize=0.5)[0]
print len(obj)
util.getobjimg(obj[0])
frame = np.array(obj[0])
x = frame[...,1]
y = frame[...,0]
h = y.ptp()
w = x.ptp()
x0 = x.min()
y0 = y.min()
mask = np.zeros(testimg.shape, dtype=np.uint8)
print mask.shape
def initUI(self):
"""
Main Window global parameters
"""
self.imgOriginal = np.array([])
self.mainWidth = 1280
self.mainHeight = 640
self.main = QLabel()
self.imgNpBefore = np.array([])
self.imgNpAfter = np.array([])
self.skin = mpimg.imread('res/skin.jpg')
grid = QGridLayout()
self.main.setLayout(grid)
self.mainBefore = QLabel('Before')
self.mainBefore.setAlignment(Qt.AlignHCenter | Qt.AlignVCenter)
self.mainBefore.setWordWrap(True)
self.mainBefore.setFont(QFont('Monospace', 10))
self.mainAfter = QLabel('After')
self.mainAfter.setAlignment(Qt.AlignHCenter | Qt.AlignVCenter)
self.mainAfter.setWordWrap(True)
self.mainAfter.setFont(QFont('Monospace', 10))
grid.addWidget(self.mainBefore, 0, 0)
grid.addWidget(self.mainAfter, 0, 1)
"""
Menu Bar
"""
# FILE MENU
openFile = QAction('Open', self)
openFile.setShortcut('Ctrl+O')
openFile.setStatusTip('Open new File')
openFile.triggered.connect(self.showDialog)
exitAction = QAction('Exit', self)
exitAction.setShortcut('Ctrl+Q')
exitAction.setStatusTip('Exit application')
exitAction.triggered.connect(self.close)
moveLeft = QAction('Move Left', self)
moveLeft.setShortcut('Ctrl+L')
moveLeft.triggered.connect(lambda: self.updateBefore(self.imgNpAfter))
# PROCESS MENU
equalizeMenu = QAction('Equalize', self)
equalizeMenu.triggered.connect(lambda: self.updateImgAfter(util.equalize(self.imgNpBefore)))
histogramMenu = QAction('Histogram', self)
#histogramMenu.triggered.connect(lambda: self.updateImgAfter(
grayscaleMenu = QAction('Grayscale', self)
grayscaleMenu.triggered.connect(lambda: self.updateImgAfter(util.getgrayscale(self.imgNpBefore)))
binarizeMenu = QAction('Binarize', self)
binarizeMenu.triggered.connect(lambda: self.updateImgAfter(util.otsu(self.imgNpBefore)))
gaussianMenu = QAction('Smooth', self)
gaussianMenu.triggered.connect(lambda: self.updateImgAfter(util.convolvefft(util.gaussian_filt(), util.getgrayscale(self.imgNpBefore))))
resizeMenu = QAction('Resize', self)
resizeMenu.triggered.connect(lambda: self.updateImgAfter(util.downsample(self.imgNpBefore)))
segmentMenu = QAction('Segment', self)
segmentMenu.triggered.connect(lambda: self.updateImgAfter(util.showobj(util.downsample(self.imgNpBefore, target_height=480), util.segment(util.thin(util.otsu(util.downsample(self.imgNpBefore, target_height=480), bg='light'))), box=False)))
# EDGE DETECTION MENU
averageMenu = QAction('Average', self)
averageMenu.triggered.connect(lambda: self.updateImgAfter(util.degreezero(self.imgNpBefore, type="average")))
differenceMenu = QAction('Difference', self)
differenceMenu.triggered.connect(lambda: self.updateImgAfter(util.degreezero(self.imgNpBefore, type="difference")))
homogenMenu = QAction('Homogen', self)
homogenMenu.triggered.connect(lambda: self.updateImgAfter(util.degreezero(self.imgNpBefore, type="homogen")))
sobelMenu = QAction('Sobel', self)
sobelMenu.triggered.connect(lambda: self.updateImgAfter(util.degreeone(self.imgNpBefore, type="sobel")))
prewittMenu = QAction('Prewitt', self)
prewittMenu.triggered.connect(lambda: self.updateImgAfter(util.degreeone(self.imgNpBefore, type="prewitt")))
freichenMenu = QAction('Frei-Chen', self)
freichenMenu.triggered.connect(lambda: self.updateImgAfter(util.degreeone(self.imgNpBefore, type="freichen")))
kirschMenu = QAction('Kirsch', self)
kirschMenu.triggered.connect(lambda: self.updateImgAfter(util.degreetwo(self.imgNpBefore, type="kirsch")))
# FEATURE MENU
chaincodeMenu = QAction('Chain code', self)
chaincodeMenu.triggered.connect(lambda: self.updateTxtAfter(str([util.getdirection(chain[n][0], chain[n][1]) for chain in util.segment(util.thin(self.imgNpBefore), cc=True) for n in xrange(len(chain))])))
turncodeMenu = QAction('Turn code', self)
turncodeMenu.triggered.connect(lambda: self.updateTxtAfter(str([util.getturncode(cc) for cc in util.segment(util.thin(self.imgNpBefore, bg='light'), cc=False)])))
skeletonMenu = QAction('Zhang-Suen thinning', self)
skeletonMenu.triggered.connect(lambda:self.updateImgAfter(util.zhangsuen(util.binarize(self.imgNpBefore, bg='light'))))
skinMenu = QAction('Boundary detection', self)
skinMenu.triggered.connect(lambda:self.updateImgAfter(util.thin(self.imgNpBefore, bg='light')))
freemanMenu = QAction('Contour profile', self)
# RECOGNITION MENU
freemantrainfontMenu = QAction('Train Contour Font', self)
freemantrainfontMenu.triggered.connect(lambda: util.train(self.imgNpBefore, feats='zs', order='font', setname='font'))
freemantrainplatMenu = QAction('Train ZS Plate (GNB)', self)
freemantrainplatMenu.triggered.connect(lambda: util.train(self.imgNpBefore, feats='zs', order='plat', setname='plat'))
cctctrainfontMenu = QAction('Train CC + TC Font', self)
cctctrainplatMenu = QAction('Train CC + TC Plate', self)
freemantestfontMenu = QAction('Predict Contour Font', self)
freemantestfontMenu.triggered.connect(lambda: self.updateTxtAfter(util.test(self.imgNpBefore, feats='zs', order='font', setname='font')))
freemantestplatMenu = QAction('Predict Contour Plate', self)
freemantestplatMenu.triggered.connect(lambda:self.updateTxtAfter(util.test(self.imgNpBefore, feats='zs', order='plat', setname='plat')))
cctctestfontMenu = QAction('Predict CC + TC Font', self)
cctctestplatMenu = QAction('Predict CC + TC Plate', self)
facesMenu = QAction('Show faces', self)
facesMenu.triggered.connect(lambda: self.updateImgAfter(util.getFaces(self.imgNpBefore, self.skin, range=70)))
faceMenu = QAction('Show facial features', self)
faceMenu.triggered.connect(lambda: self.updateImgAfter(util.showobj(self.imgNpBefore, util.getFaceFeats(self.imgNpBefore, self.skin, range=100), color=False)))
# MENU BAR
menubar = self.menuBar()
fileMenu = menubar.addMenu('&File')
fileMenu.addAction(openFile)
fileMenu.addAction(exitAction)
fileMenu.addAction(moveLeft)
processMenu = menubar.addMenu('&Preprocess')
#processMenu.addAction(histogramMenu)
processMenu.addAction(equalizeMenu)
processMenu.addAction(grayscaleMenu)
processMenu.addAction(binarizeMenu)
processMenu.addAction(gaussianMenu)
processMenu.addAction(resizeMenu)
processMenu.addAction(segmentMenu)
edgeMenu = menubar.addMenu('&Edge Detection')
edgeMenu.addAction(averageMenu)
edgeMenu.addAction(differenceMenu)
edgeMenu.addAction(homogenMenu)
edgeMenu.addAction(sobelMenu)
edgeMenu.addAction(prewittMenu)
edgeMenu.addAction(freichenMenu)
edgeMenu.addAction(kirschMenu)
featureMenu = menubar.addMenu('&Features')
featureMenu.addAction(chaincodeMenu)
featureMenu.addAction(turncodeMenu)
featureMenu.addAction(skeletonMenu)
featureMenu.addAction(skinMenu)
featureMenu.addAction(freemanMenu)
recogMenu = menubar.addMenu('&Recognition')
recogMenu.addAction(freemantrainfontMenu)
recogMenu.addAction(freemantrainplatMenu)
recogMenu.addAction(cctctrainfontMenu)
recogMenu.addAction(cctctrainplatMenu)
recogMenu.addAction(freemantestfontMenu)
recogMenu.addAction(freemantestplatMenu)
recogMenu.addAction(cctctestfontMenu)
recogMenu.addAction(cctctestplatMenu)
recogMenu.addAction(facesMenu)
recogMenu.addAction(faceMenu)
#recogMenu.addAction(
"""
Toolbar, Status Bar, Tooltip
"""
self.statusBar().showMessage('Ready')
QToolTip.setFont(QFont('SansSerif', 10))
#self.setToolTip('This is a <b>QWidget</b> widget')
"""
Displaying
"""
self.setGeometry(12, 30, self.mainWidth, self.mainHeight+80)
self.setWindowTitle('Pyxel')
self.setWindowIcon(QIcon('res/web.png'))
self.setCentralWidget(self.main)
self.main.setGeometry(QRect(0, 80, self.mainWidth, self.mainHeight))
#self.mainAfter.setGeometry(QRect(self.mainWidth/2, 80, self.mainWidth/2, self.mainHeight))
self.show()
def main(argv):
# load image into ndarray
if len(argv) > 1:
testimg = mpimg.imread(argv[1])
else:
testimg = mpimg.imread(
'/home/cilsat/Dropbox/kuliah/sem1/pp/portrait.jpeg')
# calc sorted 2-D representation of image
#imgs = util.sortimg(util.flattenimg(img))
# get count of unique colors
#uniq = util.getunique(imgs)
#print(uniq)
#pltshow(img)
# get and display histogram(s) of color(s)
#hist = util.gethistogram(img)
#plthist(hist)
# equalize image
#imgeq = util.equalize(img, hist)
#pltshow(imgeq)
# get and display background image with dynamic threshold 15
#background = util.getbackground(img, imgs, 15)
#pltshow(background)
# OCR
# training: only need to run ONCE for each font
# make sure you have an image containing all lower case letters, upper case letters, and digits in that order
"""
font = "dejavusans-alphanumeric"
fontimg = mpimg.imread('train/' + font + '.jpg')
util.train(fontimg, font)
# testing: this is only around 50% accurate for text of different font
print util.test(testimg, font)
"""
# License Plate recognition
"""
dataset = "plat"
util.gnb_train(dataset=dataset)
"""
# Face detection
h = testimg.shape[0]
w = testimg.shape[1]
face = testimg[int(0 * h):int(0.7 * h), int(0.15 * w):int(0.45 * w)]
pltshow(face)
imgf = util.mapImage(testimg, util.mapColor(face, 60))
pltshow(imgf)
imgt = util.thin(imgf, bg='dark')
pltshow(imgt)
obj = util.segment(imgt, minsize=0.5)[0]
print len(obj)
util.getobjimg(obj[0])
frame = np.array(obj[0])
x = frame[..., 1]
y = frame[..., 0]
h = y.ptp()
w = x.ptp()
x0 = x.min()
y0 = y.min()
mask = np.zeros(testimg.shape, dtype=np.uint8)
print mask.shape